Affiliations
Department of Medicine, University of Illinois College of Medicine and OSF‐Saint Francis Medical Center, Peoria, Illinois
Given name(s)
Nancy L.
Family name
Novotny
Degrees
PhD

Perceptions of Hospital Discharge Software

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Patient and physician perceptions after software‐assisted hospital discharge: Cluster randomized trial

During the transition from inpatient to outpatient care, patients are vulnerable to adverse events.1 Poor communication between hospital personnel and either the patient or the outpatient primary care physician has been associated with preventable or ameliorable adverse events after discharge.1 Systematic reviews confirm that discharge communication is often delayed, inaccurate, or ineffective.2, 3

Discharge communication failures may occur if hospital processes rely on dictated discharge summaries.2 For several reasons, discharge summaries are inadequate for communication. Most patients complete their initial posthospital clinic visit before their primary care physician receives the discharge summary.4 For many patients, the discharge summary is unavailable for all posthospital visits.4 Discharge summaries often fail as communication because they are not generated or transmitted.4

Recommendations to improve discharge communication include the use of health information technology.2, 5 The benefits of computer‐generated discharge summaries include decreases in delivery time for discharge communications.2 The benefits of computerized physician order entry (CPOE) include reduction of medical errors.6 These theoretical benefits create a rationale for clinical trials to measure improvements after discharge software applications with CPOE.5

In an effort to improve discharge communication and clinically relevant outcomes, we performed a cluster‐randomized trial to assess the value of a discharge software application of CPOE. The clustered design followed recommendations from a systematic review of discharge interventions.3 We applied our research intervention at the physician level and measured outcomes at the patient level. Our objective was to assess the benefit of discharge software with CPOE vs. usual care when used to discharge patients at high risk for repeat admission. In a previous work, we reported that discharge software did not reduce rates of hospital readmission, emergency department visits, or postdischarge adverse events due to medical management.7 In the present article, we compare secondary outcomes after the research intervention: perceptions of the discharge from the perspectives of patients, primary care physicians, and hospital physicians.

Methods

The trial design was a cluster randomized, controlled trial. The setting was the postdischarge environment following index hospitalization at a 730‐bed, tertiary care, teaching hospital in central Illinois. The Peoria Institutional Review Board approved the protocol for human research.

Participants

We enrolled consenting hospital physicians and their patients between November 2004 and January 2007. The hospital physician defined the cluster. Patients discharged by the physician comprised the cluster. The eligibility criteria for hospital physicians required internal medicine resident or attending physicians with assignments to inpatient duties for at least 2 months during the 27‐month enrollment period. After achieving informed consent from physicians, research personnel screened all consecutive, adult inpatients who were discharged to home. Patient inclusion required a probability of repeat admission (Pra) equal to or greater than 0.40.8, 9 The purpose of the inclusion criterion was to enrich the sample with patients likely to benefit from interventions to improve discharge processes. Furthermore, hospital readmission was the primary endpoint of the study, as reported separately.7 The Pra came from a predictive model with scores for age, gender, prior hospitalizations, prior doctor visits, self‐rated health status, presence of informal caregiver in the home, and comorbid coronary heart disease and diabetes mellitus. Research coordinators calculated the Pra within 2 days before discharge from the index hospitalization.

Exclusion Criteria

We excluded patients previously enrolled in the study, candidates for hospice, and patients unable to participate in outcome ascertainment. Cognitive impairment was a conditional exclusion criterion for patients. We defined cognitive impairment as a score less than 9 on the 10‐point clock test.10 Patients with cognitive impairment participated only with consent from their legally authorized representative. We enrolled patients with cognitive impairment only if a proxy spent at least 3 hours daily with the patient and the proxy agreed to answer postdischarge interviews. If a patient's outpatient primary care physician treated the patient during the index hospitalization, then there was no perceived barrier in physician‐to‐physician communication and we excluded the patient.

Intervention

The research intervention was discharge software with CPOE. Detailed description of the software appeared previously.5 In summary, the CPOE software application facilitated communication at the time of hospital discharge to patients, retail pharmacists, and community physicians. The application had basic levels of clinical decision support, required fields, pick lists, standard drug doses, alerts, reminders, and online reference information. The software addressed deficiencies in the usual care discharge process reported globally and reviewed previously.5 For example, 1 deficiency occurred when inpatient physicians failed to warn outpatient physicians about diagnostic tests with results pending at discharge.11 Another deficiency was discharge medication error.12 The software prompted the discharging physician to enter pending tests, order tests after discharge, and perform medication reconciliation. On the day of discharge, hospital physicians used the software to automatically generate discharge documents and reconcile prescriptions for the patient, primary care physician, retail pharmacist, and the ward nurse. The discharge letter went to the outpatient practitioner via facsimile transmission plus a duplicate via U.S. mail.

The control intervention was the usual care, handwritten discharge process commonly used by hospitalists.2 Hospital physicians and ward nurses completed handwritten discharge forms on the day of discharge. The forms contained blanks for discharge diagnoses, discharge medications, medication instructions, postdischarge activities and restrictions, postdischarge diet, postdischarge diagnostic and therapeutic interventions, and appointments. Patients received handwritten copies of the forms, 1 page of which also included medication instructions and prescriptions. In a previous publication, we provided details about the usual care discharge process as well as the standard care available to all study patients regardless of intervention.5

Randomization

The hospital physician who completed the discharge process was the unit of randomization. Random allocation was to discharge software or usual care discharge process, with a randomization ratio of 1:1 and block size of 2. We concealed allocation with the following process. An investigator who was not involved with hospital physician recruitment generated the randomization sequence with a computerized random number generator. The randomization list was maintained in a secure location. Another investigator who was unaware of the next random assignment performed the hospital physician recruitment and informed consent. After confirming eligibility and obtaining informed consent from physicians, the blinded investigator requested the next random assignment from the custodian of the randomization list. Hospital physicians subsequently used their randomly assigned process when discharging their patients who enrolled in the study. After random allocation, it was not possible to conceal the test or control intervention from physicians or their patients.

Hospital physicians underwent training on the software or usual care discharge process; the details appeared previously.7 Physicians assigned to usual care did not receive training on the discharge software and were blocked from using the software. Patients were passive recipients of the research intervention performed by their discharging physician. Patients received the research intervention on the day of discharge of the index hospitalization.

Baseline Assessment

During the index hospitalization, trained data abstractors recorded baseline patient demographic data plus variables to calculate the Pra for probability for repeat admission. We recorded the availability of an informal caregiver in response to the question, Is there a friend, relative, or neighbor who would take care of you for a few days, if necessary? Data came from the patient or proxy for physical functioning, mental health,13 heart failure, and number of previous emergency department visits. Other data came from hospital records for chronic obstructive pulmonary disease, number of discharge medications, and length of stay for the index hospitalization.

Outcome Assessment

We assessed the patient's perception of the discharge with 2 validated survey instruments. One week after discharge, research personnel performed telephone interviews with patients or proxies. While following a script, interviewers instructed patients to avoid mentioning the discharge process. Interviewers read items from the B‐PREPARED questionnaire.14, 15 and the Satisfaction with Information About Medicines Scale (SIMS).16 The B‐PREPARED scale assessed 3 principal components of patient preparedness for discharge: self‐care information for medications and activities, equipment and services, and confidence. The scale demonstrated internal consistency, construct validity, and predictive validity. High scale values reflected high perceptions of discharge preparedness from the patient perspective.15 SIMS measured patient satisfaction with information about discharge medications. Validation studies revealed SIMS had internal consistency, test‐retest reliability, and criterion‐related validity.16 Interviewers recorded responses to calculate a total SIMS score. Patients with high total SIMS scores had high satisfaction. While assessing B‐PREPARED and SIMS, interviewers were blind to intervention assignment. We evaluated the adequacy of blinding by asking interviewers to guess the patient's intervention assignment.

We measured the quality of hospital discharge from the outpatient physician perspective. During the index hospitalization, patients designated an outpatient primary care practitioner to receive discharge reports and results of diagnostic tests. Ten days after discharge, research personnel mailed the Physician‐PREPARED questionnaire to the designated community practitioner.17 The sum of item responses comprised the Modified Physician‐PREPARED scale and demonstrated internal consistency and construct validity. The principal components of the Modified Physician‐PREPARED were timeliness of communication and adequacy of discharge plan/transmission. High scale values reflected high perceptions of discharge quality.17 Outpatient practitioners gave implied consent when they completed and returned questionnaires. We requested 1 questionnaire for each enrolled patient, so the outcome assessment was at the patient level. The assessment was not blinded because primary care physicians received the output of discharge software or usual care discharge.

We assessed the satisfaction of hospital physicians who used the discharge software and the usual care. After hospital physicians participated in the trial for 6 months, they rated their assigned discharge process on Likert scales. The first question was, On a scale of 1 to 10, indicate your satisfaction with your portion of the discharge process. The scale anchors were 1 for very dissatisfied and 10 for very satisfied. The second question was, On a scale of 1 to 10, indicate the effort to complete your portion of the discharge process. For the second question, the scale anchors were 1 for very difficult and 10 for very easy. It was not possible to mask the hospital physicians after they received their intervention assignment. Consequently, their outcome assessment was not blinded.

Statistical Methods

The cluster number and size were selected to maintain test significance level, 1‐sided alpha less than 0.05, and power greater than 80%. We previously published the assumptions and rationale for 35 hospital physician clusters per intervention and 9 patients per cluster.7 We did not perform separate sample size estimates for the secondary outcomes reported herein.

The statistical analyses employed SPSS PC (Version 15.0.1; SPSS, Inc., Chicago, IL). Statistical procedures for baseline variables were descriptive and included means and standard deviations (SDs) for interval variables and percentages for categorical variables. For all analyses, we employed the principle of intention‐to‐treat. We assumed patient or physician exposure to the intervention randomly assigned to the discharging physician. Analyses employed standard tests for normal distribution, homogeneity of variance, and linearity of relationships between independent and dependent variables. If assumptions failed, then we stratified variables or performed transformations. We accepted P < 0.05 as significant.

We tested hypotheses for patient‐level outcomes with generalized estimating equations (GEEs) that corrected for clustering by hospital physician. We employed GEEs because they provide unbiased estimates of standard errors for parameters even with incorrect specification of the intracluster dependence structure.18 Each patient‐level outcome was the dependent variable in a separate GEE. The intervention variable for each GEE was discharge software vs. usual care, handwritten discharge. The statistic of interest was the coefficient for the intervention variable. The null hypothesis was no difference between discharge software and usual care. The statistical definition of the null hypothesis was an intervention variable coefficient with a 95% confidence interval (CI) that included 0.

For analyses that were unaffected by the cluster assumption, we performed standard tests. The hypothesis for hospital physicians was significantly higher satisfaction for discharge software users and the inferential procedure was the t test. When we assessed the success of the study blinding, we assumed no association between true intervention allocation and guesses by outcome assessors. We used chi‐square for assessment of the blinding.

Results

We screened 127 physicians who were general internal medicine hospital physicians. Seventy physicians consented and received random allocation to discharge software or usual care. We excluded 57 physicians for reasons shown in the trial flow diagram (Figure 1). We approached 6884 patients during their index hospitalization. After excluding 6253 ineligible patients, we enrolled 631 willing patients (Supplementary Appendix). As depicted in Figure 1, the most common reason for ineligibility occurred for patients with Pra score <0.40 (2168/6253 exclusions; 34.7%). We followed 631 patients who received the discharge intervention (Figure 1). There was no differential dropout between the interventions. Protocol deviations were rare, 0.5% (3/631). Three patients erroneously received usual care discharge from physicians assigned to discharge software. All 631 patients were included in the intention‐to‐treat analysis. The baseline characteristics of the randomly assigned hospital physicians and their patients are in Table 1. Most of the hospital physicians were residents in the first year of postgraduate training.

Figure 1
Trial flow diagram.
Baseline Characteristics for Each Intervention at the Hospital Physician Cluster Level and Individual Patient Level
 Discharge SoftwareUsual Care
  • Abbreviation: Pra, probability of repeat admission; SD, standard deviation.

  • Missing data for 1 or 2 subjects.

Hospital physician characteristics, n (%)n = 35n = 35
Postgraduate year 118 (51.4)23 (65.7)
Postgraduate years 2‐410 (28.6)7 (20.0)
Attending physician7 (20.0)5 (14.3)
Patient characteristics, n (%)n = 316n = 315
Gender, male136 (43.0)147 (46.7)
Age, years  
18‐4468 (21.5)95 (30.2)
45‐5479 (25.0)76 (24.1)
55‐6486 (27.2)74 (23.5)
65‐9883 (26.3)70 (22.2)
Self‐rated health status  
Poor82 (25.9)108 (34.3)
Fair169 (53.5)147 (46.7)
Good54 (17.1)46 (14.6)
Very good10 (3.2)11 (3.5)
Excellent1 (0.3)3 (1.0)
Diabetes mellitus172 (54.4)177 (56.2)
Chronic obstructive pulmonary disease  
None259 (82.0)257 (81.6)
Without oral steroid or home oxygen28 (8.9)26 (8.3)
With chronic oral steroid10 (3.2)8 (2.5)
With home oxygen oral steroid19 (6.0)24 (7.6)
Coronary heart disease133 (42.1)120 (38.1)
Heart failure80 (25.3)67 (21.3)
Physical Functioning from SF‐36  
Lowest third128 (40.5)121 (38.4)
Upper two‐thirds188 (59.5)194 (61.6)
Mental Health from SF‐36  
Lowest one‐third113 (35.8)117 (37.1)*
Upper two‐thirds203 (64.2)197 (62.5)*
Emergency department visits during 6 months before index admission  
0 or 1194 (61.4)168 (53.3)
2 or more122 (38.6)147 (46.7)
Mean (SD)  
Number of discharge medications10.5 (4.8)9.9 (5.1)
Index hospital length of stay, days3.9 (3.5)3.5 (3.5)
Pra0.486 (0.072)0.495 (0.076)

We assessed the patient's perception of discharge preparedness. One week after discharge, research personnel interviewed 92.4% (292/316) of patients in the discharge software group and 92.4% (291/315) in the usual care group. The mean (SD) B‐PREPARED scores for discharge preparedness were 17.7 (4.1) in the discharge software group and 17.2 (4.0) in the usual care group. In the generalized estimating equation that accounted for potential clustering within hospital physicians, the parameter estimate for the intervention variable coefficient was small but significant (P = 0.042; Table 2). Patients in the discharge software group had slightly better perceptions of their discharge preparedness.

Perceptions of Patients and Their Outpatient Primary Care Physicians for 316 Patients Assigned to Discharge Software Intervention vs. 315 Patients Assigned to Usual Care
Outcome VariableDischarge Software [mean (SD)]Usual Care [mean (SD)]Parameter Estimate Without Cluster Correction (95% CI)P ValueParameter Estimate with Cluster Correction (95% CI)P Value
  • NOTE: Parameter estimates are intervention variable coefficients in generalized estimating equations for outcome variables. Parameter estimates from generalized estimating equations appear with and without correction for clustering by hospital physician: 34 physicians assigned to discharge software and 35 assigned to usual care.

  • Abbreviations: CI, confidence interval; SD, standard deviation; SIMS, Satisfaction with Information About Medicines Scale.

  • Outcome variable transformation was square root (23 B‐PREPARED value).

  • Outcome variable transformation was square root (25 Modified Physician‐PREPARED value).

Patient perception of discharge preparedness (B‐PREPARED)17.7 (4.1)17.2 (4.0)0.147* (0.006‐0.288)0.0400.147* (0.005‐0.289)0.042
Patient satisfaction with medication information score (SIMS)12.3 (4.8)12.1 (4.6)0.212 (0.978‐0.554)0.5870.212 (0.937‐0.513)0.567
Outpatient physician perception (Modified Physician‐PREPARED)17.2 (3.8)16.5 (3.9)0.133 (0.012‐0.254)0.0310.133 (0.015‐0.251)0.027

Another outcome was the patient's satisfaction with information about discharge medications (Table 2). One week after discharge, mean (SD) SIMS scores for satisfaction were 12.3 (4.8) in the discharge software group and 12.1 (4.6) in the usual care group. The generalized estimating equation revealed an insignificant coefficient for the intervention variable (P = 0.567; Table 2).

We assessed the outpatient physician perception of the discharge with a questionnaire sent 10 days after discharge. We received 496 out of 631 questionnaires (78.6%) from outpatient practitioners and the median response time was 19 days after the date of discharge. The practitioner specialty was internal medicine for 38.9% (193/496), family medicine for 27.2% (135/496), medicine‐pediatrics for 27.0% (134/496), advance practice nurse for 4.4% (22/496), other physician specialties for 2.0% (10/496), and physician assistant for 0.4% (2/496). We excluded 18 questionnaires from analysis because outpatient practitioners failed to answer 2 or more items in the Modified Physician‐PREPARED scale. When we compared baseline characteristics for patients who had complete questionnaires vs. patients with nonrespondent or excluded questionnaires, we found no significant differences (data available upon request). Among the discharge software group, 72.2% (228/316) of patients had complete questionnaires from their outpatient physicians. The response rate with complete questionnaires was 79.4% (250/315) of patients assigned to usual care. On the Modified Physician‐PREPARED scale, the mean (SD) scores were 17.2 (3.8) for the discharge software group and 16.5 (3.9) for the usual care group. The parameter estimate from the generalized estimating equation was significant (P = 0.027; Table 2). Outpatient physicians had slightly better perception of discharge quality for patients assigned to discharge software.

In the questionnaire sent to outpatient practitioners, we requested additional information about discharge communication. When asked about timeliness, outpatient physicians perceived no faster communication with the discharge software (Table 3). We asked about the media for discharge information exchange. It was uncommon for community physicians to receive discharge information via electronic mail (Table 3). Outpatient physicians acknowledged receipt of a minority of facsimile transmissions with no significant difference between discharge software vs. usual care (Table 3). Investigators documented facsimile transmission of the output from the discharge software to outpatient practitioners. Transmission occurred on the first business day after discharge. Despite the documentation of all facsimile transmissions, only 23.4% of patients assigned to discharge software had community practitioners who acknowledged receipt.

Answers from Outpatient Physicians About Their Receipt of Discharge Information About Their Patients Assigned to Discharge Software or Usual Care
 Discharge Software (n = 316) [n (%)]Usual Care (n = 315) [n (%)]
  • The text of the item in the questionnaire was, Have you received adequate information about this patient's discharge health status? How did you receive this information? (Check all that apply).

Question: How soon after discharge did you receive any information (in any form) relating to this patient's hospital admission and discharge plans?
Within 1‐2 days72 (22.8)55 (17.5)
Within 1 week105 (33.2)125 (39.7)
Longer than 1 week36 (11.4)41 (13.0)
Not received20 (6.3)26 (8.3)
Other4 (1.3)7 (2.2)
Question: How did you receive discharge health status information? (Check all that apply)*
Written/typed letter106 (33.5)89 (28.3)
Telephone call82 (25.9)67 (21.3)
Fax (facsimile transmission)74 (23.4)90 (28.6)
Electronic mail8 (2.5)23 (7.3)
Other15 (4.7)15 (4.8)

In exploratory analyses, we evaluated the effect of hospital physician level of training. We wondered if discharging physician experience or seniority affected perceptions of patients or primary care physicians. We entered level of training as a covariate in generalized estimating equations. When patient perception of discharge preparedness (B‐PREPARED) was the dependent variable, then physician level of training had a nonsignificant coefficient (P > 0.219). Likewise, physician level of training was nonsignificant in models of patient satisfaction with medication information, SIMS (P > 0.068), and outpatient physician perception, Modified Physician‐PREPARED (P > 0.177). We concluded that physician level of training had no influence on the patient‐level outcomes assessed in our study.

We compared the satisfaction of hospital physicians who used the discharge software and the usual care discharge. The proportions of hospital physicians who returned questionnaires were 85.7% (30/35) in the discharge software group and 97% (34/35) in the usual care group. After using their assigned discharge process for at least 6 months, discharge software users had mean (SD) satisfaction 7.4 (1.4) vs. 7.9 (1.4) for usual care physicians (difference = 0.5; 95% CI = 0.2‐1.3; P = 0.129). The effort for discharge software users was more difficult than the effort for usual care (mean [SD] effort = 6.5 [1.9] vs. 7.9 [2.1], respectively). The mean difference in effort was significant (difference = 1.4; 95% CI = 0.3‐2.4; P = 0.011). We reviewed free‐text comments on hospital physician questionnaires. The common theme was software users spent more time to complete discharges. We did not perform time‐motion assessments so we cannot confirm or refute these impressions. Even though hospital physicians found the discharge software significantly more difficult, they did not report a significant decrease in their satisfaction between the 2 discharge interventions.

The cluster design of our trial assumed variance in outcomes measured at the patient level. We predicted some variance attributable to clustering by hospital physician. After the trial, we calculated the intracluster correlation coefficients for B‐PREPARED, SIMS, and Modified Physician‐PREPARED. For all of these outcome variables, the intracluster correlation coefficients were negligible. We also evaluated generalized estimating equations with and without correction for hospital physician cluster. We confirmed the negligible cluster effect on CIs for intervention coefficients (Table 2).

We evaluated the adequacy of the blind for outcome assessors who interviewed patients for B‐PREPARED and SIMS. The guesses of outcomes assessors were unrelated to true intervention assignment (P = 0.253). We interpreted the blind as adequate.

Discussion

We performed a cluster‐randomized clinical trial to measure the effects of discharge software vs. usual care discharge. The discharge software incorporated the ASTM (American Society for Testing and Material) Continuity of Care Record (CCR) standards.19 The CCR is a patient health summary standard with widespread support from medical and specialty organizations. The rationale for the CCR was the need for continuity of care from 1 provider or practitioner to any other practitioner. Our discharge software had the same rationale as the CCR and included a subset of the clinical content specified by the CCR. Like the CCR, our discharge software produced concise reports, and emphasized a brief, postdischarge, care plan. Since we included clinical data elements recommended by the CCR, we hypothesized our discharge software would produce clinically relevant improvements.

Our discharge software also implemented elements of high‐quality discharge planning and communication endorsed by the Society of Hospital Medicine.20 For example, the discharge software produced a legible, typed, discharge plan for the patient or caregiver with medication instructions, follow‐up tests, studies, and appointments. The discharge software generated a discharge summary for the outpatient primary care physician and other clinicians who provided postdischarge care. The summary included discharge diagnoses, key findings and test results, follow‐up appointments, pending diagnostic tests, documentation of patient education, reconciled medication list, and contact information for the hospital physician. The discharge software compiled data for purposes of benchmarking, measurement, and continuous quality improvement. We thought our implementation of discharge software would lead to improved outcomes.

Despite our deployment of recommended strategies, we detected only small increases in patient perceptions of discharge preparedness. We do not know if small changes in B‐PREPARED values were clinically important. We found no improvement in patient satisfaction with medication information. Our results are consistent with systematic reviews that revealed limited benefit of interventions other than discharge planning with postdischarge support.21 Since our discharge software was added to robust discharge planning and support, we possibly had limited ability to detect benefit unless the intervention had a large effect size.

Our discharge software caused a small increase in positive perception reported by outpatient physicians. Small changes in the Modified Physician‐PREPARED had uncertain clinical relevance. Potential delays imposed by our distribution method may have contributed to our findings. Output from our discharge software went to community physicians via facsimile transmission with backup copies via standard U.S. mail. Our distribution system responded to several realities. Most community physicians in our area had no access to interoperable electronic medical records or secured e‐mail. In addition, electronic transmittal of prescriptions was not commonplace. Our discharge intervention did not control the flow of information inside the offices of outpatient physicians. We did not know if our facsimile transmissions joined piles of unread laboratory and imaging reports on the desks of busy primary care physicians. Despite the limited technology available to community physicians, they perceived communication generated by the software to be an improvement over the handwritten process. Our results support previous studies in which physicians preferred computer‐generated discharge summaries and summaries in standardized formats.2224

One of the limitations of our trial design was the unmasked intervention. Hospital physicians assigned to usual care might have improved their handwritten and verbal discharge communication after observation of their colleagues assigned to discharge software. This phenomenon is encountered in unmasked trials and is called contamination. We attempted to minimize contamination when we blocked usual care physicians from access to the discharge software. However, we could not eliminate cross‐talk among unmasked hospital physicians who worked together in close proximity during 27 months of patient enrollment. Some contamination was inevitable. When contamination occurred, there was bias toward the null (increased type II error).

Another limitation was the large proportion of hospital physicians in the first year of postgraduate training. There was a potential for variance from multilevel clusters with patient‐level outcomes clustered within first‐year hospital physicians who were clustered within teams supervised by senior resident or attending physicians. Our results argued against hierarchical clusters because intracluster correlation coefficients were negligible. Furthermore, our exploratory analysis suggested physician training level had no influence on patient outcomes measured in our study. We speculate the highly structured discharge process for both usual care and software minimized variance attributable to physician training level.

The research intervention in our trial was a stand‐alone software application. The discharge software did not integrate with the hospital electronic medical record. Consequently, hospital physician users had to reenter patient demographic data and prescription data that already existed in the electronic record. Data reentry probably caused hospital physicians to attribute greater effort to the discharge software.

In our study, hospital physicians incorporated discharge software with CPOE into their clinical workflow without deterioration in their satisfaction. Our experience may inform the decisions of hospital personnel who design health information systems. When designing discharge functions, developers should consider medication reconciliation and the standards of the CCR.19 Modules within the discharge software would likely be more efficient with prepopulated data from the electronic record. Then users could shift their work from data entry to data verification and possibly mitigate their perceived effort. Software developers may wish to explore options for data transmission to community physicians: secure e‐mail, automated fax servers, and direct digital file transfer. Future studies should test the acceptability of discharge functions incorporated within electronic health records with robust clinical decision support.

Our results apply to a population of adults of all ages with high risk for readmission. The results may not generalize to children, surgical patients, or people with low risk for readmission. All of the patients in our study were discharged to home. The exclusion of other discharge destinations helped us to enroll a homogenous cohort. However, the exclusion criteria did not allow us to generalize our results to patients discharged to nursing homes, inpatient rehabilitation units, or other acute care facilities. We designed the intervention to apply to the hospitalist model, in which responsibility for patient care transitions to a different physician after discharge. The results of our study do not apply when the inpatient and outpatient physician are the same. Since we enrolled general internal medicine hospital physicians, our results may not generalize to care provided by other specialists.

Conclusions

A discharge software application with CPOE improved perceptions of the hospital discharge process for patients and their outpatient physicians. When compared to the handwritten discharge process, the improvements were small in magnitude. Hospital physicians who used the discharge software reported more effort but otherwise no decrement in their satisfaction with the discharge process.

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References
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Journal of Hospital Medicine - 4(6)
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356-363
Legacy Keywords
continuity of patient care, electronic discharge summary, health care surveys, hospital information systems, hospitalists, medical records systems–computerized, medication reconciliation, patient care transitions, patient discharge, patient satisfaction
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During the transition from inpatient to outpatient care, patients are vulnerable to adverse events.1 Poor communication between hospital personnel and either the patient or the outpatient primary care physician has been associated with preventable or ameliorable adverse events after discharge.1 Systematic reviews confirm that discharge communication is often delayed, inaccurate, or ineffective.2, 3

Discharge communication failures may occur if hospital processes rely on dictated discharge summaries.2 For several reasons, discharge summaries are inadequate for communication. Most patients complete their initial posthospital clinic visit before their primary care physician receives the discharge summary.4 For many patients, the discharge summary is unavailable for all posthospital visits.4 Discharge summaries often fail as communication because they are not generated or transmitted.4

Recommendations to improve discharge communication include the use of health information technology.2, 5 The benefits of computer‐generated discharge summaries include decreases in delivery time for discharge communications.2 The benefits of computerized physician order entry (CPOE) include reduction of medical errors.6 These theoretical benefits create a rationale for clinical trials to measure improvements after discharge software applications with CPOE.5

In an effort to improve discharge communication and clinically relevant outcomes, we performed a cluster‐randomized trial to assess the value of a discharge software application of CPOE. The clustered design followed recommendations from a systematic review of discharge interventions.3 We applied our research intervention at the physician level and measured outcomes at the patient level. Our objective was to assess the benefit of discharge software with CPOE vs. usual care when used to discharge patients at high risk for repeat admission. In a previous work, we reported that discharge software did not reduce rates of hospital readmission, emergency department visits, or postdischarge adverse events due to medical management.7 In the present article, we compare secondary outcomes after the research intervention: perceptions of the discharge from the perspectives of patients, primary care physicians, and hospital physicians.

Methods

The trial design was a cluster randomized, controlled trial. The setting was the postdischarge environment following index hospitalization at a 730‐bed, tertiary care, teaching hospital in central Illinois. The Peoria Institutional Review Board approved the protocol for human research.

Participants

We enrolled consenting hospital physicians and their patients between November 2004 and January 2007. The hospital physician defined the cluster. Patients discharged by the physician comprised the cluster. The eligibility criteria for hospital physicians required internal medicine resident or attending physicians with assignments to inpatient duties for at least 2 months during the 27‐month enrollment period. After achieving informed consent from physicians, research personnel screened all consecutive, adult inpatients who were discharged to home. Patient inclusion required a probability of repeat admission (Pra) equal to or greater than 0.40.8, 9 The purpose of the inclusion criterion was to enrich the sample with patients likely to benefit from interventions to improve discharge processes. Furthermore, hospital readmission was the primary endpoint of the study, as reported separately.7 The Pra came from a predictive model with scores for age, gender, prior hospitalizations, prior doctor visits, self‐rated health status, presence of informal caregiver in the home, and comorbid coronary heart disease and diabetes mellitus. Research coordinators calculated the Pra within 2 days before discharge from the index hospitalization.

Exclusion Criteria

We excluded patients previously enrolled in the study, candidates for hospice, and patients unable to participate in outcome ascertainment. Cognitive impairment was a conditional exclusion criterion for patients. We defined cognitive impairment as a score less than 9 on the 10‐point clock test.10 Patients with cognitive impairment participated only with consent from their legally authorized representative. We enrolled patients with cognitive impairment only if a proxy spent at least 3 hours daily with the patient and the proxy agreed to answer postdischarge interviews. If a patient's outpatient primary care physician treated the patient during the index hospitalization, then there was no perceived barrier in physician‐to‐physician communication and we excluded the patient.

Intervention

The research intervention was discharge software with CPOE. Detailed description of the software appeared previously.5 In summary, the CPOE software application facilitated communication at the time of hospital discharge to patients, retail pharmacists, and community physicians. The application had basic levels of clinical decision support, required fields, pick lists, standard drug doses, alerts, reminders, and online reference information. The software addressed deficiencies in the usual care discharge process reported globally and reviewed previously.5 For example, 1 deficiency occurred when inpatient physicians failed to warn outpatient physicians about diagnostic tests with results pending at discharge.11 Another deficiency was discharge medication error.12 The software prompted the discharging physician to enter pending tests, order tests after discharge, and perform medication reconciliation. On the day of discharge, hospital physicians used the software to automatically generate discharge documents and reconcile prescriptions for the patient, primary care physician, retail pharmacist, and the ward nurse. The discharge letter went to the outpatient practitioner via facsimile transmission plus a duplicate via U.S. mail.

The control intervention was the usual care, handwritten discharge process commonly used by hospitalists.2 Hospital physicians and ward nurses completed handwritten discharge forms on the day of discharge. The forms contained blanks for discharge diagnoses, discharge medications, medication instructions, postdischarge activities and restrictions, postdischarge diet, postdischarge diagnostic and therapeutic interventions, and appointments. Patients received handwritten copies of the forms, 1 page of which also included medication instructions and prescriptions. In a previous publication, we provided details about the usual care discharge process as well as the standard care available to all study patients regardless of intervention.5

Randomization

The hospital physician who completed the discharge process was the unit of randomization. Random allocation was to discharge software or usual care discharge process, with a randomization ratio of 1:1 and block size of 2. We concealed allocation with the following process. An investigator who was not involved with hospital physician recruitment generated the randomization sequence with a computerized random number generator. The randomization list was maintained in a secure location. Another investigator who was unaware of the next random assignment performed the hospital physician recruitment and informed consent. After confirming eligibility and obtaining informed consent from physicians, the blinded investigator requested the next random assignment from the custodian of the randomization list. Hospital physicians subsequently used their randomly assigned process when discharging their patients who enrolled in the study. After random allocation, it was not possible to conceal the test or control intervention from physicians or their patients.

Hospital physicians underwent training on the software or usual care discharge process; the details appeared previously.7 Physicians assigned to usual care did not receive training on the discharge software and were blocked from using the software. Patients were passive recipients of the research intervention performed by their discharging physician. Patients received the research intervention on the day of discharge of the index hospitalization.

Baseline Assessment

During the index hospitalization, trained data abstractors recorded baseline patient demographic data plus variables to calculate the Pra for probability for repeat admission. We recorded the availability of an informal caregiver in response to the question, Is there a friend, relative, or neighbor who would take care of you for a few days, if necessary? Data came from the patient or proxy for physical functioning, mental health,13 heart failure, and number of previous emergency department visits. Other data came from hospital records for chronic obstructive pulmonary disease, number of discharge medications, and length of stay for the index hospitalization.

Outcome Assessment

We assessed the patient's perception of the discharge with 2 validated survey instruments. One week after discharge, research personnel performed telephone interviews with patients or proxies. While following a script, interviewers instructed patients to avoid mentioning the discharge process. Interviewers read items from the B‐PREPARED questionnaire.14, 15 and the Satisfaction with Information About Medicines Scale (SIMS).16 The B‐PREPARED scale assessed 3 principal components of patient preparedness for discharge: self‐care information for medications and activities, equipment and services, and confidence. The scale demonstrated internal consistency, construct validity, and predictive validity. High scale values reflected high perceptions of discharge preparedness from the patient perspective.15 SIMS measured patient satisfaction with information about discharge medications. Validation studies revealed SIMS had internal consistency, test‐retest reliability, and criterion‐related validity.16 Interviewers recorded responses to calculate a total SIMS score. Patients with high total SIMS scores had high satisfaction. While assessing B‐PREPARED and SIMS, interviewers were blind to intervention assignment. We evaluated the adequacy of blinding by asking interviewers to guess the patient's intervention assignment.

We measured the quality of hospital discharge from the outpatient physician perspective. During the index hospitalization, patients designated an outpatient primary care practitioner to receive discharge reports and results of diagnostic tests. Ten days after discharge, research personnel mailed the Physician‐PREPARED questionnaire to the designated community practitioner.17 The sum of item responses comprised the Modified Physician‐PREPARED scale and demonstrated internal consistency and construct validity. The principal components of the Modified Physician‐PREPARED were timeliness of communication and adequacy of discharge plan/transmission. High scale values reflected high perceptions of discharge quality.17 Outpatient practitioners gave implied consent when they completed and returned questionnaires. We requested 1 questionnaire for each enrolled patient, so the outcome assessment was at the patient level. The assessment was not blinded because primary care physicians received the output of discharge software or usual care discharge.

We assessed the satisfaction of hospital physicians who used the discharge software and the usual care. After hospital physicians participated in the trial for 6 months, they rated their assigned discharge process on Likert scales. The first question was, On a scale of 1 to 10, indicate your satisfaction with your portion of the discharge process. The scale anchors were 1 for very dissatisfied and 10 for very satisfied. The second question was, On a scale of 1 to 10, indicate the effort to complete your portion of the discharge process. For the second question, the scale anchors were 1 for very difficult and 10 for very easy. It was not possible to mask the hospital physicians after they received their intervention assignment. Consequently, their outcome assessment was not blinded.

Statistical Methods

The cluster number and size were selected to maintain test significance level, 1‐sided alpha less than 0.05, and power greater than 80%. We previously published the assumptions and rationale for 35 hospital physician clusters per intervention and 9 patients per cluster.7 We did not perform separate sample size estimates for the secondary outcomes reported herein.

The statistical analyses employed SPSS PC (Version 15.0.1; SPSS, Inc., Chicago, IL). Statistical procedures for baseline variables were descriptive and included means and standard deviations (SDs) for interval variables and percentages for categorical variables. For all analyses, we employed the principle of intention‐to‐treat. We assumed patient or physician exposure to the intervention randomly assigned to the discharging physician. Analyses employed standard tests for normal distribution, homogeneity of variance, and linearity of relationships between independent and dependent variables. If assumptions failed, then we stratified variables or performed transformations. We accepted P < 0.05 as significant.

We tested hypotheses for patient‐level outcomes with generalized estimating equations (GEEs) that corrected for clustering by hospital physician. We employed GEEs because they provide unbiased estimates of standard errors for parameters even with incorrect specification of the intracluster dependence structure.18 Each patient‐level outcome was the dependent variable in a separate GEE. The intervention variable for each GEE was discharge software vs. usual care, handwritten discharge. The statistic of interest was the coefficient for the intervention variable. The null hypothesis was no difference between discharge software and usual care. The statistical definition of the null hypothesis was an intervention variable coefficient with a 95% confidence interval (CI) that included 0.

For analyses that were unaffected by the cluster assumption, we performed standard tests. The hypothesis for hospital physicians was significantly higher satisfaction for discharge software users and the inferential procedure was the t test. When we assessed the success of the study blinding, we assumed no association between true intervention allocation and guesses by outcome assessors. We used chi‐square for assessment of the blinding.

Results

We screened 127 physicians who were general internal medicine hospital physicians. Seventy physicians consented and received random allocation to discharge software or usual care. We excluded 57 physicians for reasons shown in the trial flow diagram (Figure 1). We approached 6884 patients during their index hospitalization. After excluding 6253 ineligible patients, we enrolled 631 willing patients (Supplementary Appendix). As depicted in Figure 1, the most common reason for ineligibility occurred for patients with Pra score <0.40 (2168/6253 exclusions; 34.7%). We followed 631 patients who received the discharge intervention (Figure 1). There was no differential dropout between the interventions. Protocol deviations were rare, 0.5% (3/631). Three patients erroneously received usual care discharge from physicians assigned to discharge software. All 631 patients were included in the intention‐to‐treat analysis. The baseline characteristics of the randomly assigned hospital physicians and their patients are in Table 1. Most of the hospital physicians were residents in the first year of postgraduate training.

Figure 1
Trial flow diagram.
Baseline Characteristics for Each Intervention at the Hospital Physician Cluster Level and Individual Patient Level
 Discharge SoftwareUsual Care
  • Abbreviation: Pra, probability of repeat admission; SD, standard deviation.

  • Missing data for 1 or 2 subjects.

Hospital physician characteristics, n (%)n = 35n = 35
Postgraduate year 118 (51.4)23 (65.7)
Postgraduate years 2‐410 (28.6)7 (20.0)
Attending physician7 (20.0)5 (14.3)
Patient characteristics, n (%)n = 316n = 315
Gender, male136 (43.0)147 (46.7)
Age, years  
18‐4468 (21.5)95 (30.2)
45‐5479 (25.0)76 (24.1)
55‐6486 (27.2)74 (23.5)
65‐9883 (26.3)70 (22.2)
Self‐rated health status  
Poor82 (25.9)108 (34.3)
Fair169 (53.5)147 (46.7)
Good54 (17.1)46 (14.6)
Very good10 (3.2)11 (3.5)
Excellent1 (0.3)3 (1.0)
Diabetes mellitus172 (54.4)177 (56.2)
Chronic obstructive pulmonary disease  
None259 (82.0)257 (81.6)
Without oral steroid or home oxygen28 (8.9)26 (8.3)
With chronic oral steroid10 (3.2)8 (2.5)
With home oxygen oral steroid19 (6.0)24 (7.6)
Coronary heart disease133 (42.1)120 (38.1)
Heart failure80 (25.3)67 (21.3)
Physical Functioning from SF‐36  
Lowest third128 (40.5)121 (38.4)
Upper two‐thirds188 (59.5)194 (61.6)
Mental Health from SF‐36  
Lowest one‐third113 (35.8)117 (37.1)*
Upper two‐thirds203 (64.2)197 (62.5)*
Emergency department visits during 6 months before index admission  
0 or 1194 (61.4)168 (53.3)
2 or more122 (38.6)147 (46.7)
Mean (SD)  
Number of discharge medications10.5 (4.8)9.9 (5.1)
Index hospital length of stay, days3.9 (3.5)3.5 (3.5)
Pra0.486 (0.072)0.495 (0.076)

We assessed the patient's perception of discharge preparedness. One week after discharge, research personnel interviewed 92.4% (292/316) of patients in the discharge software group and 92.4% (291/315) in the usual care group. The mean (SD) B‐PREPARED scores for discharge preparedness were 17.7 (4.1) in the discharge software group and 17.2 (4.0) in the usual care group. In the generalized estimating equation that accounted for potential clustering within hospital physicians, the parameter estimate for the intervention variable coefficient was small but significant (P = 0.042; Table 2). Patients in the discharge software group had slightly better perceptions of their discharge preparedness.

Perceptions of Patients and Their Outpatient Primary Care Physicians for 316 Patients Assigned to Discharge Software Intervention vs. 315 Patients Assigned to Usual Care
Outcome VariableDischarge Software [mean (SD)]Usual Care [mean (SD)]Parameter Estimate Without Cluster Correction (95% CI)P ValueParameter Estimate with Cluster Correction (95% CI)P Value
  • NOTE: Parameter estimates are intervention variable coefficients in generalized estimating equations for outcome variables. Parameter estimates from generalized estimating equations appear with and without correction for clustering by hospital physician: 34 physicians assigned to discharge software and 35 assigned to usual care.

  • Abbreviations: CI, confidence interval; SD, standard deviation; SIMS, Satisfaction with Information About Medicines Scale.

  • Outcome variable transformation was square root (23 B‐PREPARED value).

  • Outcome variable transformation was square root (25 Modified Physician‐PREPARED value).

Patient perception of discharge preparedness (B‐PREPARED)17.7 (4.1)17.2 (4.0)0.147* (0.006‐0.288)0.0400.147* (0.005‐0.289)0.042
Patient satisfaction with medication information score (SIMS)12.3 (4.8)12.1 (4.6)0.212 (0.978‐0.554)0.5870.212 (0.937‐0.513)0.567
Outpatient physician perception (Modified Physician‐PREPARED)17.2 (3.8)16.5 (3.9)0.133 (0.012‐0.254)0.0310.133 (0.015‐0.251)0.027

Another outcome was the patient's satisfaction with information about discharge medications (Table 2). One week after discharge, mean (SD) SIMS scores for satisfaction were 12.3 (4.8) in the discharge software group and 12.1 (4.6) in the usual care group. The generalized estimating equation revealed an insignificant coefficient for the intervention variable (P = 0.567; Table 2).

We assessed the outpatient physician perception of the discharge with a questionnaire sent 10 days after discharge. We received 496 out of 631 questionnaires (78.6%) from outpatient practitioners and the median response time was 19 days after the date of discharge. The practitioner specialty was internal medicine for 38.9% (193/496), family medicine for 27.2% (135/496), medicine‐pediatrics for 27.0% (134/496), advance practice nurse for 4.4% (22/496), other physician specialties for 2.0% (10/496), and physician assistant for 0.4% (2/496). We excluded 18 questionnaires from analysis because outpatient practitioners failed to answer 2 or more items in the Modified Physician‐PREPARED scale. When we compared baseline characteristics for patients who had complete questionnaires vs. patients with nonrespondent or excluded questionnaires, we found no significant differences (data available upon request). Among the discharge software group, 72.2% (228/316) of patients had complete questionnaires from their outpatient physicians. The response rate with complete questionnaires was 79.4% (250/315) of patients assigned to usual care. On the Modified Physician‐PREPARED scale, the mean (SD) scores were 17.2 (3.8) for the discharge software group and 16.5 (3.9) for the usual care group. The parameter estimate from the generalized estimating equation was significant (P = 0.027; Table 2). Outpatient physicians had slightly better perception of discharge quality for patients assigned to discharge software.

In the questionnaire sent to outpatient practitioners, we requested additional information about discharge communication. When asked about timeliness, outpatient physicians perceived no faster communication with the discharge software (Table 3). We asked about the media for discharge information exchange. It was uncommon for community physicians to receive discharge information via electronic mail (Table 3). Outpatient physicians acknowledged receipt of a minority of facsimile transmissions with no significant difference between discharge software vs. usual care (Table 3). Investigators documented facsimile transmission of the output from the discharge software to outpatient practitioners. Transmission occurred on the first business day after discharge. Despite the documentation of all facsimile transmissions, only 23.4% of patients assigned to discharge software had community practitioners who acknowledged receipt.

Answers from Outpatient Physicians About Their Receipt of Discharge Information About Their Patients Assigned to Discharge Software or Usual Care
 Discharge Software (n = 316) [n (%)]Usual Care (n = 315) [n (%)]
  • The text of the item in the questionnaire was, Have you received adequate information about this patient's discharge health status? How did you receive this information? (Check all that apply).

Question: How soon after discharge did you receive any information (in any form) relating to this patient's hospital admission and discharge plans?
Within 1‐2 days72 (22.8)55 (17.5)
Within 1 week105 (33.2)125 (39.7)
Longer than 1 week36 (11.4)41 (13.0)
Not received20 (6.3)26 (8.3)
Other4 (1.3)7 (2.2)
Question: How did you receive discharge health status information? (Check all that apply)*
Written/typed letter106 (33.5)89 (28.3)
Telephone call82 (25.9)67 (21.3)
Fax (facsimile transmission)74 (23.4)90 (28.6)
Electronic mail8 (2.5)23 (7.3)
Other15 (4.7)15 (4.8)

In exploratory analyses, we evaluated the effect of hospital physician level of training. We wondered if discharging physician experience or seniority affected perceptions of patients or primary care physicians. We entered level of training as a covariate in generalized estimating equations. When patient perception of discharge preparedness (B‐PREPARED) was the dependent variable, then physician level of training had a nonsignificant coefficient (P > 0.219). Likewise, physician level of training was nonsignificant in models of patient satisfaction with medication information, SIMS (P > 0.068), and outpatient physician perception, Modified Physician‐PREPARED (P > 0.177). We concluded that physician level of training had no influence on the patient‐level outcomes assessed in our study.

We compared the satisfaction of hospital physicians who used the discharge software and the usual care discharge. The proportions of hospital physicians who returned questionnaires were 85.7% (30/35) in the discharge software group and 97% (34/35) in the usual care group. After using their assigned discharge process for at least 6 months, discharge software users had mean (SD) satisfaction 7.4 (1.4) vs. 7.9 (1.4) for usual care physicians (difference = 0.5; 95% CI = 0.2‐1.3; P = 0.129). The effort for discharge software users was more difficult than the effort for usual care (mean [SD] effort = 6.5 [1.9] vs. 7.9 [2.1], respectively). The mean difference in effort was significant (difference = 1.4; 95% CI = 0.3‐2.4; P = 0.011). We reviewed free‐text comments on hospital physician questionnaires. The common theme was software users spent more time to complete discharges. We did not perform time‐motion assessments so we cannot confirm or refute these impressions. Even though hospital physicians found the discharge software significantly more difficult, they did not report a significant decrease in their satisfaction between the 2 discharge interventions.

The cluster design of our trial assumed variance in outcomes measured at the patient level. We predicted some variance attributable to clustering by hospital physician. After the trial, we calculated the intracluster correlation coefficients for B‐PREPARED, SIMS, and Modified Physician‐PREPARED. For all of these outcome variables, the intracluster correlation coefficients were negligible. We also evaluated generalized estimating equations with and without correction for hospital physician cluster. We confirmed the negligible cluster effect on CIs for intervention coefficients (Table 2).

We evaluated the adequacy of the blind for outcome assessors who interviewed patients for B‐PREPARED and SIMS. The guesses of outcomes assessors were unrelated to true intervention assignment (P = 0.253). We interpreted the blind as adequate.

Discussion

We performed a cluster‐randomized clinical trial to measure the effects of discharge software vs. usual care discharge. The discharge software incorporated the ASTM (American Society for Testing and Material) Continuity of Care Record (CCR) standards.19 The CCR is a patient health summary standard with widespread support from medical and specialty organizations. The rationale for the CCR was the need for continuity of care from 1 provider or practitioner to any other practitioner. Our discharge software had the same rationale as the CCR and included a subset of the clinical content specified by the CCR. Like the CCR, our discharge software produced concise reports, and emphasized a brief, postdischarge, care plan. Since we included clinical data elements recommended by the CCR, we hypothesized our discharge software would produce clinically relevant improvements.

Our discharge software also implemented elements of high‐quality discharge planning and communication endorsed by the Society of Hospital Medicine.20 For example, the discharge software produced a legible, typed, discharge plan for the patient or caregiver with medication instructions, follow‐up tests, studies, and appointments. The discharge software generated a discharge summary for the outpatient primary care physician and other clinicians who provided postdischarge care. The summary included discharge diagnoses, key findings and test results, follow‐up appointments, pending diagnostic tests, documentation of patient education, reconciled medication list, and contact information for the hospital physician. The discharge software compiled data for purposes of benchmarking, measurement, and continuous quality improvement. We thought our implementation of discharge software would lead to improved outcomes.

Despite our deployment of recommended strategies, we detected only small increases in patient perceptions of discharge preparedness. We do not know if small changes in B‐PREPARED values were clinically important. We found no improvement in patient satisfaction with medication information. Our results are consistent with systematic reviews that revealed limited benefit of interventions other than discharge planning with postdischarge support.21 Since our discharge software was added to robust discharge planning and support, we possibly had limited ability to detect benefit unless the intervention had a large effect size.

Our discharge software caused a small increase in positive perception reported by outpatient physicians. Small changes in the Modified Physician‐PREPARED had uncertain clinical relevance. Potential delays imposed by our distribution method may have contributed to our findings. Output from our discharge software went to community physicians via facsimile transmission with backup copies via standard U.S. mail. Our distribution system responded to several realities. Most community physicians in our area had no access to interoperable electronic medical records or secured e‐mail. In addition, electronic transmittal of prescriptions was not commonplace. Our discharge intervention did not control the flow of information inside the offices of outpatient physicians. We did not know if our facsimile transmissions joined piles of unread laboratory and imaging reports on the desks of busy primary care physicians. Despite the limited technology available to community physicians, they perceived communication generated by the software to be an improvement over the handwritten process. Our results support previous studies in which physicians preferred computer‐generated discharge summaries and summaries in standardized formats.2224

One of the limitations of our trial design was the unmasked intervention. Hospital physicians assigned to usual care might have improved their handwritten and verbal discharge communication after observation of their colleagues assigned to discharge software. This phenomenon is encountered in unmasked trials and is called contamination. We attempted to minimize contamination when we blocked usual care physicians from access to the discharge software. However, we could not eliminate cross‐talk among unmasked hospital physicians who worked together in close proximity during 27 months of patient enrollment. Some contamination was inevitable. When contamination occurred, there was bias toward the null (increased type II error).

Another limitation was the large proportion of hospital physicians in the first year of postgraduate training. There was a potential for variance from multilevel clusters with patient‐level outcomes clustered within first‐year hospital physicians who were clustered within teams supervised by senior resident or attending physicians. Our results argued against hierarchical clusters because intracluster correlation coefficients were negligible. Furthermore, our exploratory analysis suggested physician training level had no influence on patient outcomes measured in our study. We speculate the highly structured discharge process for both usual care and software minimized variance attributable to physician training level.

The research intervention in our trial was a stand‐alone software application. The discharge software did not integrate with the hospital electronic medical record. Consequently, hospital physician users had to reenter patient demographic data and prescription data that already existed in the electronic record. Data reentry probably caused hospital physicians to attribute greater effort to the discharge software.

In our study, hospital physicians incorporated discharge software with CPOE into their clinical workflow without deterioration in their satisfaction. Our experience may inform the decisions of hospital personnel who design health information systems. When designing discharge functions, developers should consider medication reconciliation and the standards of the CCR.19 Modules within the discharge software would likely be more efficient with prepopulated data from the electronic record. Then users could shift their work from data entry to data verification and possibly mitigate their perceived effort. Software developers may wish to explore options for data transmission to community physicians: secure e‐mail, automated fax servers, and direct digital file transfer. Future studies should test the acceptability of discharge functions incorporated within electronic health records with robust clinical decision support.

Our results apply to a population of adults of all ages with high risk for readmission. The results may not generalize to children, surgical patients, or people with low risk for readmission. All of the patients in our study were discharged to home. The exclusion of other discharge destinations helped us to enroll a homogenous cohort. However, the exclusion criteria did not allow us to generalize our results to patients discharged to nursing homes, inpatient rehabilitation units, or other acute care facilities. We designed the intervention to apply to the hospitalist model, in which responsibility for patient care transitions to a different physician after discharge. The results of our study do not apply when the inpatient and outpatient physician are the same. Since we enrolled general internal medicine hospital physicians, our results may not generalize to care provided by other specialists.

Conclusions

A discharge software application with CPOE improved perceptions of the hospital discharge process for patients and their outpatient physicians. When compared to the handwritten discharge process, the improvements were small in magnitude. Hospital physicians who used the discharge software reported more effort but otherwise no decrement in their satisfaction with the discharge process.

During the transition from inpatient to outpatient care, patients are vulnerable to adverse events.1 Poor communication between hospital personnel and either the patient or the outpatient primary care physician has been associated with preventable or ameliorable adverse events after discharge.1 Systematic reviews confirm that discharge communication is often delayed, inaccurate, or ineffective.2, 3

Discharge communication failures may occur if hospital processes rely on dictated discharge summaries.2 For several reasons, discharge summaries are inadequate for communication. Most patients complete their initial posthospital clinic visit before their primary care physician receives the discharge summary.4 For many patients, the discharge summary is unavailable for all posthospital visits.4 Discharge summaries often fail as communication because they are not generated or transmitted.4

Recommendations to improve discharge communication include the use of health information technology.2, 5 The benefits of computer‐generated discharge summaries include decreases in delivery time for discharge communications.2 The benefits of computerized physician order entry (CPOE) include reduction of medical errors.6 These theoretical benefits create a rationale for clinical trials to measure improvements after discharge software applications with CPOE.5

In an effort to improve discharge communication and clinically relevant outcomes, we performed a cluster‐randomized trial to assess the value of a discharge software application of CPOE. The clustered design followed recommendations from a systematic review of discharge interventions.3 We applied our research intervention at the physician level and measured outcomes at the patient level. Our objective was to assess the benefit of discharge software with CPOE vs. usual care when used to discharge patients at high risk for repeat admission. In a previous work, we reported that discharge software did not reduce rates of hospital readmission, emergency department visits, or postdischarge adverse events due to medical management.7 In the present article, we compare secondary outcomes after the research intervention: perceptions of the discharge from the perspectives of patients, primary care physicians, and hospital physicians.

Methods

The trial design was a cluster randomized, controlled trial. The setting was the postdischarge environment following index hospitalization at a 730‐bed, tertiary care, teaching hospital in central Illinois. The Peoria Institutional Review Board approved the protocol for human research.

Participants

We enrolled consenting hospital physicians and their patients between November 2004 and January 2007. The hospital physician defined the cluster. Patients discharged by the physician comprised the cluster. The eligibility criteria for hospital physicians required internal medicine resident or attending physicians with assignments to inpatient duties for at least 2 months during the 27‐month enrollment period. After achieving informed consent from physicians, research personnel screened all consecutive, adult inpatients who were discharged to home. Patient inclusion required a probability of repeat admission (Pra) equal to or greater than 0.40.8, 9 The purpose of the inclusion criterion was to enrich the sample with patients likely to benefit from interventions to improve discharge processes. Furthermore, hospital readmission was the primary endpoint of the study, as reported separately.7 The Pra came from a predictive model with scores for age, gender, prior hospitalizations, prior doctor visits, self‐rated health status, presence of informal caregiver in the home, and comorbid coronary heart disease and diabetes mellitus. Research coordinators calculated the Pra within 2 days before discharge from the index hospitalization.

Exclusion Criteria

We excluded patients previously enrolled in the study, candidates for hospice, and patients unable to participate in outcome ascertainment. Cognitive impairment was a conditional exclusion criterion for patients. We defined cognitive impairment as a score less than 9 on the 10‐point clock test.10 Patients with cognitive impairment participated only with consent from their legally authorized representative. We enrolled patients with cognitive impairment only if a proxy spent at least 3 hours daily with the patient and the proxy agreed to answer postdischarge interviews. If a patient's outpatient primary care physician treated the patient during the index hospitalization, then there was no perceived barrier in physician‐to‐physician communication and we excluded the patient.

Intervention

The research intervention was discharge software with CPOE. Detailed description of the software appeared previously.5 In summary, the CPOE software application facilitated communication at the time of hospital discharge to patients, retail pharmacists, and community physicians. The application had basic levels of clinical decision support, required fields, pick lists, standard drug doses, alerts, reminders, and online reference information. The software addressed deficiencies in the usual care discharge process reported globally and reviewed previously.5 For example, 1 deficiency occurred when inpatient physicians failed to warn outpatient physicians about diagnostic tests with results pending at discharge.11 Another deficiency was discharge medication error.12 The software prompted the discharging physician to enter pending tests, order tests after discharge, and perform medication reconciliation. On the day of discharge, hospital physicians used the software to automatically generate discharge documents and reconcile prescriptions for the patient, primary care physician, retail pharmacist, and the ward nurse. The discharge letter went to the outpatient practitioner via facsimile transmission plus a duplicate via U.S. mail.

The control intervention was the usual care, handwritten discharge process commonly used by hospitalists.2 Hospital physicians and ward nurses completed handwritten discharge forms on the day of discharge. The forms contained blanks for discharge diagnoses, discharge medications, medication instructions, postdischarge activities and restrictions, postdischarge diet, postdischarge diagnostic and therapeutic interventions, and appointments. Patients received handwritten copies of the forms, 1 page of which also included medication instructions and prescriptions. In a previous publication, we provided details about the usual care discharge process as well as the standard care available to all study patients regardless of intervention.5

Randomization

The hospital physician who completed the discharge process was the unit of randomization. Random allocation was to discharge software or usual care discharge process, with a randomization ratio of 1:1 and block size of 2. We concealed allocation with the following process. An investigator who was not involved with hospital physician recruitment generated the randomization sequence with a computerized random number generator. The randomization list was maintained in a secure location. Another investigator who was unaware of the next random assignment performed the hospital physician recruitment and informed consent. After confirming eligibility and obtaining informed consent from physicians, the blinded investigator requested the next random assignment from the custodian of the randomization list. Hospital physicians subsequently used their randomly assigned process when discharging their patients who enrolled in the study. After random allocation, it was not possible to conceal the test or control intervention from physicians or their patients.

Hospital physicians underwent training on the software or usual care discharge process; the details appeared previously.7 Physicians assigned to usual care did not receive training on the discharge software and were blocked from using the software. Patients were passive recipients of the research intervention performed by their discharging physician. Patients received the research intervention on the day of discharge of the index hospitalization.

Baseline Assessment

During the index hospitalization, trained data abstractors recorded baseline patient demographic data plus variables to calculate the Pra for probability for repeat admission. We recorded the availability of an informal caregiver in response to the question, Is there a friend, relative, or neighbor who would take care of you for a few days, if necessary? Data came from the patient or proxy for physical functioning, mental health,13 heart failure, and number of previous emergency department visits. Other data came from hospital records for chronic obstructive pulmonary disease, number of discharge medications, and length of stay for the index hospitalization.

Outcome Assessment

We assessed the patient's perception of the discharge with 2 validated survey instruments. One week after discharge, research personnel performed telephone interviews with patients or proxies. While following a script, interviewers instructed patients to avoid mentioning the discharge process. Interviewers read items from the B‐PREPARED questionnaire.14, 15 and the Satisfaction with Information About Medicines Scale (SIMS).16 The B‐PREPARED scale assessed 3 principal components of patient preparedness for discharge: self‐care information for medications and activities, equipment and services, and confidence. The scale demonstrated internal consistency, construct validity, and predictive validity. High scale values reflected high perceptions of discharge preparedness from the patient perspective.15 SIMS measured patient satisfaction with information about discharge medications. Validation studies revealed SIMS had internal consistency, test‐retest reliability, and criterion‐related validity.16 Interviewers recorded responses to calculate a total SIMS score. Patients with high total SIMS scores had high satisfaction. While assessing B‐PREPARED and SIMS, interviewers were blind to intervention assignment. We evaluated the adequacy of blinding by asking interviewers to guess the patient's intervention assignment.

We measured the quality of hospital discharge from the outpatient physician perspective. During the index hospitalization, patients designated an outpatient primary care practitioner to receive discharge reports and results of diagnostic tests. Ten days after discharge, research personnel mailed the Physician‐PREPARED questionnaire to the designated community practitioner.17 The sum of item responses comprised the Modified Physician‐PREPARED scale and demonstrated internal consistency and construct validity. The principal components of the Modified Physician‐PREPARED were timeliness of communication and adequacy of discharge plan/transmission. High scale values reflected high perceptions of discharge quality.17 Outpatient practitioners gave implied consent when they completed and returned questionnaires. We requested 1 questionnaire for each enrolled patient, so the outcome assessment was at the patient level. The assessment was not blinded because primary care physicians received the output of discharge software or usual care discharge.

We assessed the satisfaction of hospital physicians who used the discharge software and the usual care. After hospital physicians participated in the trial for 6 months, they rated their assigned discharge process on Likert scales. The first question was, On a scale of 1 to 10, indicate your satisfaction with your portion of the discharge process. The scale anchors were 1 for very dissatisfied and 10 for very satisfied. The second question was, On a scale of 1 to 10, indicate the effort to complete your portion of the discharge process. For the second question, the scale anchors were 1 for very difficult and 10 for very easy. It was not possible to mask the hospital physicians after they received their intervention assignment. Consequently, their outcome assessment was not blinded.

Statistical Methods

The cluster number and size were selected to maintain test significance level, 1‐sided alpha less than 0.05, and power greater than 80%. We previously published the assumptions and rationale for 35 hospital physician clusters per intervention and 9 patients per cluster.7 We did not perform separate sample size estimates for the secondary outcomes reported herein.

The statistical analyses employed SPSS PC (Version 15.0.1; SPSS, Inc., Chicago, IL). Statistical procedures for baseline variables were descriptive and included means and standard deviations (SDs) for interval variables and percentages for categorical variables. For all analyses, we employed the principle of intention‐to‐treat. We assumed patient or physician exposure to the intervention randomly assigned to the discharging physician. Analyses employed standard tests for normal distribution, homogeneity of variance, and linearity of relationships between independent and dependent variables. If assumptions failed, then we stratified variables or performed transformations. We accepted P < 0.05 as significant.

We tested hypotheses for patient‐level outcomes with generalized estimating equations (GEEs) that corrected for clustering by hospital physician. We employed GEEs because they provide unbiased estimates of standard errors for parameters even with incorrect specification of the intracluster dependence structure.18 Each patient‐level outcome was the dependent variable in a separate GEE. The intervention variable for each GEE was discharge software vs. usual care, handwritten discharge. The statistic of interest was the coefficient for the intervention variable. The null hypothesis was no difference between discharge software and usual care. The statistical definition of the null hypothesis was an intervention variable coefficient with a 95% confidence interval (CI) that included 0.

For analyses that were unaffected by the cluster assumption, we performed standard tests. The hypothesis for hospital physicians was significantly higher satisfaction for discharge software users and the inferential procedure was the t test. When we assessed the success of the study blinding, we assumed no association between true intervention allocation and guesses by outcome assessors. We used chi‐square for assessment of the blinding.

Results

We screened 127 physicians who were general internal medicine hospital physicians. Seventy physicians consented and received random allocation to discharge software or usual care. We excluded 57 physicians for reasons shown in the trial flow diagram (Figure 1). We approached 6884 patients during their index hospitalization. After excluding 6253 ineligible patients, we enrolled 631 willing patients (Supplementary Appendix). As depicted in Figure 1, the most common reason for ineligibility occurred for patients with Pra score <0.40 (2168/6253 exclusions; 34.7%). We followed 631 patients who received the discharge intervention (Figure 1). There was no differential dropout between the interventions. Protocol deviations were rare, 0.5% (3/631). Three patients erroneously received usual care discharge from physicians assigned to discharge software. All 631 patients were included in the intention‐to‐treat analysis. The baseline characteristics of the randomly assigned hospital physicians and their patients are in Table 1. Most of the hospital physicians were residents in the first year of postgraduate training.

Figure 1
Trial flow diagram.
Baseline Characteristics for Each Intervention at the Hospital Physician Cluster Level and Individual Patient Level
 Discharge SoftwareUsual Care
  • Abbreviation: Pra, probability of repeat admission; SD, standard deviation.

  • Missing data for 1 or 2 subjects.

Hospital physician characteristics, n (%)n = 35n = 35
Postgraduate year 118 (51.4)23 (65.7)
Postgraduate years 2‐410 (28.6)7 (20.0)
Attending physician7 (20.0)5 (14.3)
Patient characteristics, n (%)n = 316n = 315
Gender, male136 (43.0)147 (46.7)
Age, years  
18‐4468 (21.5)95 (30.2)
45‐5479 (25.0)76 (24.1)
55‐6486 (27.2)74 (23.5)
65‐9883 (26.3)70 (22.2)
Self‐rated health status  
Poor82 (25.9)108 (34.3)
Fair169 (53.5)147 (46.7)
Good54 (17.1)46 (14.6)
Very good10 (3.2)11 (3.5)
Excellent1 (0.3)3 (1.0)
Diabetes mellitus172 (54.4)177 (56.2)
Chronic obstructive pulmonary disease  
None259 (82.0)257 (81.6)
Without oral steroid or home oxygen28 (8.9)26 (8.3)
With chronic oral steroid10 (3.2)8 (2.5)
With home oxygen oral steroid19 (6.0)24 (7.6)
Coronary heart disease133 (42.1)120 (38.1)
Heart failure80 (25.3)67 (21.3)
Physical Functioning from SF‐36  
Lowest third128 (40.5)121 (38.4)
Upper two‐thirds188 (59.5)194 (61.6)
Mental Health from SF‐36  
Lowest one‐third113 (35.8)117 (37.1)*
Upper two‐thirds203 (64.2)197 (62.5)*
Emergency department visits during 6 months before index admission  
0 or 1194 (61.4)168 (53.3)
2 or more122 (38.6)147 (46.7)
Mean (SD)  
Number of discharge medications10.5 (4.8)9.9 (5.1)
Index hospital length of stay, days3.9 (3.5)3.5 (3.5)
Pra0.486 (0.072)0.495 (0.076)

We assessed the patient's perception of discharge preparedness. One week after discharge, research personnel interviewed 92.4% (292/316) of patients in the discharge software group and 92.4% (291/315) in the usual care group. The mean (SD) B‐PREPARED scores for discharge preparedness were 17.7 (4.1) in the discharge software group and 17.2 (4.0) in the usual care group. In the generalized estimating equation that accounted for potential clustering within hospital physicians, the parameter estimate for the intervention variable coefficient was small but significant (P = 0.042; Table 2). Patients in the discharge software group had slightly better perceptions of their discharge preparedness.

Perceptions of Patients and Their Outpatient Primary Care Physicians for 316 Patients Assigned to Discharge Software Intervention vs. 315 Patients Assigned to Usual Care
Outcome VariableDischarge Software [mean (SD)]Usual Care [mean (SD)]Parameter Estimate Without Cluster Correction (95% CI)P ValueParameter Estimate with Cluster Correction (95% CI)P Value
  • NOTE: Parameter estimates are intervention variable coefficients in generalized estimating equations for outcome variables. Parameter estimates from generalized estimating equations appear with and without correction for clustering by hospital physician: 34 physicians assigned to discharge software and 35 assigned to usual care.

  • Abbreviations: CI, confidence interval; SD, standard deviation; SIMS, Satisfaction with Information About Medicines Scale.

  • Outcome variable transformation was square root (23 B‐PREPARED value).

  • Outcome variable transformation was square root (25 Modified Physician‐PREPARED value).

Patient perception of discharge preparedness (B‐PREPARED)17.7 (4.1)17.2 (4.0)0.147* (0.006‐0.288)0.0400.147* (0.005‐0.289)0.042
Patient satisfaction with medication information score (SIMS)12.3 (4.8)12.1 (4.6)0.212 (0.978‐0.554)0.5870.212 (0.937‐0.513)0.567
Outpatient physician perception (Modified Physician‐PREPARED)17.2 (3.8)16.5 (3.9)0.133 (0.012‐0.254)0.0310.133 (0.015‐0.251)0.027

Another outcome was the patient's satisfaction with information about discharge medications (Table 2). One week after discharge, mean (SD) SIMS scores for satisfaction were 12.3 (4.8) in the discharge software group and 12.1 (4.6) in the usual care group. The generalized estimating equation revealed an insignificant coefficient for the intervention variable (P = 0.567; Table 2).

We assessed the outpatient physician perception of the discharge with a questionnaire sent 10 days after discharge. We received 496 out of 631 questionnaires (78.6%) from outpatient practitioners and the median response time was 19 days after the date of discharge. The practitioner specialty was internal medicine for 38.9% (193/496), family medicine for 27.2% (135/496), medicine‐pediatrics for 27.0% (134/496), advance practice nurse for 4.4% (22/496), other physician specialties for 2.0% (10/496), and physician assistant for 0.4% (2/496). We excluded 18 questionnaires from analysis because outpatient practitioners failed to answer 2 or more items in the Modified Physician‐PREPARED scale. When we compared baseline characteristics for patients who had complete questionnaires vs. patients with nonrespondent or excluded questionnaires, we found no significant differences (data available upon request). Among the discharge software group, 72.2% (228/316) of patients had complete questionnaires from their outpatient physicians. The response rate with complete questionnaires was 79.4% (250/315) of patients assigned to usual care. On the Modified Physician‐PREPARED scale, the mean (SD) scores were 17.2 (3.8) for the discharge software group and 16.5 (3.9) for the usual care group. The parameter estimate from the generalized estimating equation was significant (P = 0.027; Table 2). Outpatient physicians had slightly better perception of discharge quality for patients assigned to discharge software.

In the questionnaire sent to outpatient practitioners, we requested additional information about discharge communication. When asked about timeliness, outpatient physicians perceived no faster communication with the discharge software (Table 3). We asked about the media for discharge information exchange. It was uncommon for community physicians to receive discharge information via electronic mail (Table 3). Outpatient physicians acknowledged receipt of a minority of facsimile transmissions with no significant difference between discharge software vs. usual care (Table 3). Investigators documented facsimile transmission of the output from the discharge software to outpatient practitioners. Transmission occurred on the first business day after discharge. Despite the documentation of all facsimile transmissions, only 23.4% of patients assigned to discharge software had community practitioners who acknowledged receipt.

Answers from Outpatient Physicians About Their Receipt of Discharge Information About Their Patients Assigned to Discharge Software or Usual Care
 Discharge Software (n = 316) [n (%)]Usual Care (n = 315) [n (%)]
  • The text of the item in the questionnaire was, Have you received adequate information about this patient's discharge health status? How did you receive this information? (Check all that apply).

Question: How soon after discharge did you receive any information (in any form) relating to this patient's hospital admission and discharge plans?
Within 1‐2 days72 (22.8)55 (17.5)
Within 1 week105 (33.2)125 (39.7)
Longer than 1 week36 (11.4)41 (13.0)
Not received20 (6.3)26 (8.3)
Other4 (1.3)7 (2.2)
Question: How did you receive discharge health status information? (Check all that apply)*
Written/typed letter106 (33.5)89 (28.3)
Telephone call82 (25.9)67 (21.3)
Fax (facsimile transmission)74 (23.4)90 (28.6)
Electronic mail8 (2.5)23 (7.3)
Other15 (4.7)15 (4.8)

In exploratory analyses, we evaluated the effect of hospital physician level of training. We wondered if discharging physician experience or seniority affected perceptions of patients or primary care physicians. We entered level of training as a covariate in generalized estimating equations. When patient perception of discharge preparedness (B‐PREPARED) was the dependent variable, then physician level of training had a nonsignificant coefficient (P > 0.219). Likewise, physician level of training was nonsignificant in models of patient satisfaction with medication information, SIMS (P > 0.068), and outpatient physician perception, Modified Physician‐PREPARED (P > 0.177). We concluded that physician level of training had no influence on the patient‐level outcomes assessed in our study.

We compared the satisfaction of hospital physicians who used the discharge software and the usual care discharge. The proportions of hospital physicians who returned questionnaires were 85.7% (30/35) in the discharge software group and 97% (34/35) in the usual care group. After using their assigned discharge process for at least 6 months, discharge software users had mean (SD) satisfaction 7.4 (1.4) vs. 7.9 (1.4) for usual care physicians (difference = 0.5; 95% CI = 0.2‐1.3; P = 0.129). The effort for discharge software users was more difficult than the effort for usual care (mean [SD] effort = 6.5 [1.9] vs. 7.9 [2.1], respectively). The mean difference in effort was significant (difference = 1.4; 95% CI = 0.3‐2.4; P = 0.011). We reviewed free‐text comments on hospital physician questionnaires. The common theme was software users spent more time to complete discharges. We did not perform time‐motion assessments so we cannot confirm or refute these impressions. Even though hospital physicians found the discharge software significantly more difficult, they did not report a significant decrease in their satisfaction between the 2 discharge interventions.

The cluster design of our trial assumed variance in outcomes measured at the patient level. We predicted some variance attributable to clustering by hospital physician. After the trial, we calculated the intracluster correlation coefficients for B‐PREPARED, SIMS, and Modified Physician‐PREPARED. For all of these outcome variables, the intracluster correlation coefficients were negligible. We also evaluated generalized estimating equations with and without correction for hospital physician cluster. We confirmed the negligible cluster effect on CIs for intervention coefficients (Table 2).

We evaluated the adequacy of the blind for outcome assessors who interviewed patients for B‐PREPARED and SIMS. The guesses of outcomes assessors were unrelated to true intervention assignment (P = 0.253). We interpreted the blind as adequate.

Discussion

We performed a cluster‐randomized clinical trial to measure the effects of discharge software vs. usual care discharge. The discharge software incorporated the ASTM (American Society for Testing and Material) Continuity of Care Record (CCR) standards.19 The CCR is a patient health summary standard with widespread support from medical and specialty organizations. The rationale for the CCR was the need for continuity of care from 1 provider or practitioner to any other practitioner. Our discharge software had the same rationale as the CCR and included a subset of the clinical content specified by the CCR. Like the CCR, our discharge software produced concise reports, and emphasized a brief, postdischarge, care plan. Since we included clinical data elements recommended by the CCR, we hypothesized our discharge software would produce clinically relevant improvements.

Our discharge software also implemented elements of high‐quality discharge planning and communication endorsed by the Society of Hospital Medicine.20 For example, the discharge software produced a legible, typed, discharge plan for the patient or caregiver with medication instructions, follow‐up tests, studies, and appointments. The discharge software generated a discharge summary for the outpatient primary care physician and other clinicians who provided postdischarge care. The summary included discharge diagnoses, key findings and test results, follow‐up appointments, pending diagnostic tests, documentation of patient education, reconciled medication list, and contact information for the hospital physician. The discharge software compiled data for purposes of benchmarking, measurement, and continuous quality improvement. We thought our implementation of discharge software would lead to improved outcomes.

Despite our deployment of recommended strategies, we detected only small increases in patient perceptions of discharge preparedness. We do not know if small changes in B‐PREPARED values were clinically important. We found no improvement in patient satisfaction with medication information. Our results are consistent with systematic reviews that revealed limited benefit of interventions other than discharge planning with postdischarge support.21 Since our discharge software was added to robust discharge planning and support, we possibly had limited ability to detect benefit unless the intervention had a large effect size.

Our discharge software caused a small increase in positive perception reported by outpatient physicians. Small changes in the Modified Physician‐PREPARED had uncertain clinical relevance. Potential delays imposed by our distribution method may have contributed to our findings. Output from our discharge software went to community physicians via facsimile transmission with backup copies via standard U.S. mail. Our distribution system responded to several realities. Most community physicians in our area had no access to interoperable electronic medical records or secured e‐mail. In addition, electronic transmittal of prescriptions was not commonplace. Our discharge intervention did not control the flow of information inside the offices of outpatient physicians. We did not know if our facsimile transmissions joined piles of unread laboratory and imaging reports on the desks of busy primary care physicians. Despite the limited technology available to community physicians, they perceived communication generated by the software to be an improvement over the handwritten process. Our results support previous studies in which physicians preferred computer‐generated discharge summaries and summaries in standardized formats.2224

One of the limitations of our trial design was the unmasked intervention. Hospital physicians assigned to usual care might have improved their handwritten and verbal discharge communication after observation of their colleagues assigned to discharge software. This phenomenon is encountered in unmasked trials and is called contamination. We attempted to minimize contamination when we blocked usual care physicians from access to the discharge software. However, we could not eliminate cross‐talk among unmasked hospital physicians who worked together in close proximity during 27 months of patient enrollment. Some contamination was inevitable. When contamination occurred, there was bias toward the null (increased type II error).

Another limitation was the large proportion of hospital physicians in the first year of postgraduate training. There was a potential for variance from multilevel clusters with patient‐level outcomes clustered within first‐year hospital physicians who were clustered within teams supervised by senior resident or attending physicians. Our results argued against hierarchical clusters because intracluster correlation coefficients were negligible. Furthermore, our exploratory analysis suggested physician training level had no influence on patient outcomes measured in our study. We speculate the highly structured discharge process for both usual care and software minimized variance attributable to physician training level.

The research intervention in our trial was a stand‐alone software application. The discharge software did not integrate with the hospital electronic medical record. Consequently, hospital physician users had to reenter patient demographic data and prescription data that already existed in the electronic record. Data reentry probably caused hospital physicians to attribute greater effort to the discharge software.

In our study, hospital physicians incorporated discharge software with CPOE into their clinical workflow without deterioration in their satisfaction. Our experience may inform the decisions of hospital personnel who design health information systems. When designing discharge functions, developers should consider medication reconciliation and the standards of the CCR.19 Modules within the discharge software would likely be more efficient with prepopulated data from the electronic record. Then users could shift their work from data entry to data verification and possibly mitigate their perceived effort. Software developers may wish to explore options for data transmission to community physicians: secure e‐mail, automated fax servers, and direct digital file transfer. Future studies should test the acceptability of discharge functions incorporated within electronic health records with robust clinical decision support.

Our results apply to a population of adults of all ages with high risk for readmission. The results may not generalize to children, surgical patients, or people with low risk for readmission. All of the patients in our study were discharged to home. The exclusion of other discharge destinations helped us to enroll a homogenous cohort. However, the exclusion criteria did not allow us to generalize our results to patients discharged to nursing homes, inpatient rehabilitation units, or other acute care facilities. We designed the intervention to apply to the hospitalist model, in which responsibility for patient care transitions to a different physician after discharge. The results of our study do not apply when the inpatient and outpatient physician are the same. Since we enrolled general internal medicine hospital physicians, our results may not generalize to care provided by other specialists.

Conclusions

A discharge software application with CPOE improved perceptions of the hospital discharge process for patients and their outpatient physicians. When compared to the handwritten discharge process, the improvements were small in magnitude. Hospital physicians who used the discharge software reported more effort but otherwise no decrement in their satisfaction with the discharge process.

References
  1. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138:161167.
  2. Kripalani S,LeFevre F,Phillips CO,Williams MV,Basaviah P,Baker DW.Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care.JAMA.2007;297:831841.
  3. Shepperd S,Parkes J,McClaren J,Phillips C.Discharge planning from hospital to home.Cochrane Database Syst Rev.2004;(1):CD000313.
  4. van Walraven C,Seth R,Laupacis A.Dissemination of discharge summaries. Not reaching follow‐up physicians.Can Fam Physician.2002;48:737742.
  5. Nace GS,Graumlich JF,Aldag JC.Software design to facilitate information transfer at hospital discharge.Inform Prim Care.2006;14:109119.
  6. Kuperman GJ,Gibson RF.Computer physician order entry: benefits, costs, and issues.Ann Intern Med.2003;139:3139.
  7. Graumlich JF,Novotny NL,Nace GS, et al.Patient readmissions, emergency visits, and adverse events after software‐assisted discharge from hospital: cluster randomized trial.J Hosp Med.2009; DOI: 10.1002/jhm.459. PMID: 19479782.
  8. Pacala JT,Boult C,Boult L.Predictive validity of a questionnaire that identifies older persons at risk for hospital admission.J Am Geriatr Soc.1995;43:374377.
  9. Novotny NL,Anderson MA.Prediction of early readmission in medical inpatients using the Probability of Repeated Admission instrument.Nurs Res.2008;57:406415.
  10. Manos PJ,Wu R.The ten point clock test: a quick screen and grading method for cognitive impairment in medical and surgical patients.Int J Psychiatry Med.1994;24:229244.
  11. Roy CL,Poon EG,Karson AS, et al.Patient safety concerns arising from test results that return after hospital discharge.Ann Intern Med.2005;143:121128.
  12. Schnipper JL,Kirwin JL,Cotugno MC, et al.Role of pharmacist counseling in preventing adverse drug events after hospitalization.Arch Intern Med.2006;166:565571.
  13. Ware JE.SF‐36 health survey update.Spine.2000;25:31303139.
  14. Grimmer K,Moss J.The development, validity and application of a new instrument to assess the quality of discharge planning activities from the community perspective.Int J Qual Health Care.2001;13:109116.
  15. Graumlich JF,Novotny NL,Aldag JC.Brief scale measuring patient preparedness for hospital discharge to home: psychometric properties.J Hosp Med.2008;3:446454.
  16. Horne R,Hankins M,Jenkins R.The Satisfaction with Information about Medicines Scale (SIMS): a new measurement tool for audit and research.Qual Health Care.2001;10:135140.
  17. Graumlich JF,Grimmer‐Somers K,Aldag JC.Discharge planning scale: community physicians' perspective.J Hosp Med.2008;3:455464.
  18. Ghisletta P,Spini D.An introduction to generalized estimating equations and an application to assess selectivity effects in a longitudinal study on very old individuals.J Educ Behav Stat.2004;29:421437. Available at: http://jeb.sagepub.com/cgi/content/abstract/29/4/421. Accessed June 2009.
  19. ASTM. E2369‐05 Standard Specification for Continuity of Care Record (CCR). Available at: http://www.astm.org/Standards/E2369.htm. Accessed June2009.
  20. Halasyamani L,Kripalani S,Coleman E, et al.Transition of care for hospitalized elderly patients—development of a discharge checklist for hospitalists.J Hosp Med.2006;1:354360.
  21. Mistiaen P,Francke AL,Poot E.Interventions aimed at reducing problems in adult patients discharged from hospital to home: a systematic meta‐review.BMC Health Serv Res.2007;7:47.
  22. Archbold RA,Laji K,Suliman A,Ranjadayalan K,Hemingway H,Timmis AD.Evaluation of a computer‐generated discharge summary for patients with acute coronary syndromes.Br J Gen Pract.1998;48:11631164.
  23. van Walraven C,Duke SM,Weinberg AL,Wells PS.Standardized or narrative discharge summaries. Which do family physicians prefer?Can Fam Physician.1998;44:6269.
  24. van Walraven C,Laupacis A,Seth R,Wells G.Dictated versus database‐generated discharge summaries: a randomized clinical trial.CMAJ.1999;160:319326.
References
  1. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138:161167.
  2. Kripalani S,LeFevre F,Phillips CO,Williams MV,Basaviah P,Baker DW.Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care.JAMA.2007;297:831841.
  3. Shepperd S,Parkes J,McClaren J,Phillips C.Discharge planning from hospital to home.Cochrane Database Syst Rev.2004;(1):CD000313.
  4. van Walraven C,Seth R,Laupacis A.Dissemination of discharge summaries. Not reaching follow‐up physicians.Can Fam Physician.2002;48:737742.
  5. Nace GS,Graumlich JF,Aldag JC.Software design to facilitate information transfer at hospital discharge.Inform Prim Care.2006;14:109119.
  6. Kuperman GJ,Gibson RF.Computer physician order entry: benefits, costs, and issues.Ann Intern Med.2003;139:3139.
  7. Graumlich JF,Novotny NL,Nace GS, et al.Patient readmissions, emergency visits, and adverse events after software‐assisted discharge from hospital: cluster randomized trial.J Hosp Med.2009; DOI: 10.1002/jhm.459. PMID: 19479782.
  8. Pacala JT,Boult C,Boult L.Predictive validity of a questionnaire that identifies older persons at risk for hospital admission.J Am Geriatr Soc.1995;43:374377.
  9. Novotny NL,Anderson MA.Prediction of early readmission in medical inpatients using the Probability of Repeated Admission instrument.Nurs Res.2008;57:406415.
  10. Manos PJ,Wu R.The ten point clock test: a quick screen and grading method for cognitive impairment in medical and surgical patients.Int J Psychiatry Med.1994;24:229244.
  11. Roy CL,Poon EG,Karson AS, et al.Patient safety concerns arising from test results that return after hospital discharge.Ann Intern Med.2005;143:121128.
  12. Schnipper JL,Kirwin JL,Cotugno MC, et al.Role of pharmacist counseling in preventing adverse drug events after hospitalization.Arch Intern Med.2006;166:565571.
  13. Ware JE.SF‐36 health survey update.Spine.2000;25:31303139.
  14. Grimmer K,Moss J.The development, validity and application of a new instrument to assess the quality of discharge planning activities from the community perspective.Int J Qual Health Care.2001;13:109116.
  15. Graumlich JF,Novotny NL,Aldag JC.Brief scale measuring patient preparedness for hospital discharge to home: psychometric properties.J Hosp Med.2008;3:446454.
  16. Horne R,Hankins M,Jenkins R.The Satisfaction with Information about Medicines Scale (SIMS): a new measurement tool for audit and research.Qual Health Care.2001;10:135140.
  17. Graumlich JF,Grimmer‐Somers K,Aldag JC.Discharge planning scale: community physicians' perspective.J Hosp Med.2008;3:455464.
  18. Ghisletta P,Spini D.An introduction to generalized estimating equations and an application to assess selectivity effects in a longitudinal study on very old individuals.J Educ Behav Stat.2004;29:421437. Available at: http://jeb.sagepub.com/cgi/content/abstract/29/4/421. Accessed June 2009.
  19. ASTM. E2369‐05 Standard Specification for Continuity of Care Record (CCR). Available at: http://www.astm.org/Standards/E2369.htm. Accessed June2009.
  20. Halasyamani L,Kripalani S,Coleman E, et al.Transition of care for hospitalized elderly patients—development of a discharge checklist for hospitalists.J Hosp Med.2006;1:354360.
  21. Mistiaen P,Francke AL,Poot E.Interventions aimed at reducing problems in adult patients discharged from hospital to home: a systematic meta‐review.BMC Health Serv Res.2007;7:47.
  22. Archbold RA,Laji K,Suliman A,Ranjadayalan K,Hemingway H,Timmis AD.Evaluation of a computer‐generated discharge summary for patients with acute coronary syndromes.Br J Gen Pract.1998;48:11631164.
  23. van Walraven C,Duke SM,Weinberg AL,Wells PS.Standardized or narrative discharge summaries. Which do family physicians prefer?Can Fam Physician.1998;44:6269.
  24. van Walraven C,Laupacis A,Seth R,Wells G.Dictated versus database‐generated discharge summaries: a randomized clinical trial.CMAJ.1999;160:319326.
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Journal of Hospital Medicine - 4(6)
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Journal of Hospital Medicine - 4(6)
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Patient and physician perceptions after software‐assisted hospital discharge: Cluster randomized trial
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Patient and physician perceptions after software‐assisted hospital discharge: Cluster randomized trial
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continuity of patient care, electronic discharge summary, health care surveys, hospital information systems, hospitalists, medical records systems–computerized, medication reconciliation, patient care transitions, patient discharge, patient satisfaction
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continuity of patient care, electronic discharge summary, health care surveys, hospital information systems, hospitalists, medical records systems–computerized, medication reconciliation, patient care transitions, patient discharge, patient satisfaction
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Discharge Software and Readmissions

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Patient readmissions, emergency visits, and adverse events after software‐assisted discharge from hospital: Cluster randomized trial

Adverse events occur to patients after their discharge from acute care hospitals.1, 2 Most of these injuries are adverse drug events, procedure‐related events, nosocomial infections, or falls.1 Postdischarge adverse events are associated with several days of symptoms, nonpermanent disability, emergency department visits, or hospital readmission.1, 3 When adverse events are preventable or ameliorable, the most common root cause is poor communication between hospital personnel and either the patient or the outpatient primary care physician.1 In addition, there may be deficits in discharge processes related to assessment and communication of unresolved problems.1 Systematic reviews have shown that discharge communication is an inefficient and error‐prone process.46

One potential solution to poor discharge communication is health information technology.7 An example of technology is discharge software with a computerized physician order entry (CPOE) system. By definition, a CPOE system is a computer‐based system that automates direct entry of orders by physicians and ensures standardized, legible, and complete orders.8 The benefits of CPOE have been tested in other inpatient settings.8, 9 It is logical to consider software applications with CPOE for discharge interventions.7

Several mechanisms explain the potential benefit of discharge software with CPOE.7 Applications with CPOE decrease medication errors.8, 10 Software with decision support could prompt physicians to enter posthospitalization appointment dates and orders for preventive services.11, 12 Discharge software could facilitate medication reconciliation and generate patient instructions and information.4, 1315 The potential benefits of discharge software with CPOE provide a rationale for clinical trials to measure benefits.

Previous studies addressed discharge applications of health information technology. Observational studies recorded outcomes such as physician satisfaction.16, 17 Prior randomized clinical trials measured quality and timeliness of discharge summaries.18 However, these previous trials did not assess clinically relevant outcomes like readmissions, emergency department visits, or adverse events. We performed a cluster‐randomized trial to assess the value of a discharge software application of CPOE. The rationale for our clustered design complied with recommendations from a systematic review of discharge interventions.5 Our objective was to assess the benefit of discharge software with CPOE when used to discharge patients at high risk for repeat admission. After the intervention, we compared the rates of hospital readmission, emergency department visits, and postdischarge adverse events due to medical management.

Methods

The trial design was a cluster randomized, controlled trial with blinded outcome assessment. Follow‐up occurred until 6 months after discharge from index hospitalization at a 730‐bed, tertiary care, teaching hospital in central Illinois. The Peoria Institutional Review Board approved the protocol for human research.

Participants

The cluster definition was the hospital physician. Patients discharged by the physician comprised the cluster. Hospital physicians and patients were enrolled between November 2004 and January 2007. Internal medicine resident or attending physicians were eligible. We excluded hospital physicians if their assignments to inpatient duties were less than 2 months during the 27‐month enrollment period. The rationale for the physician exclusion was a consequence of the patient enrollment rate of 3 to 5 patients per physician per month. Physicians with brief assignments could not achieve the goal of 9 or more patients per cluster. After physicians gave informed consent to screen their patients, trained research coordinators applied inclusion and exclusion criteria and obtained informed consent from patients. Research personnel identified all consecutive, unique, adult inpatients who were discharged to home. Patient inclusion required a probability of repeat admission (Pra) score 0.40.19, 20 The Pra score came from a logistic model of age, gender, prior hospitalizations, prior doctor visits, self‐rated health status, presence of informal caregiver in the home, and comorbid coronary heart disease and diabetes mellitus. Research coordinators calculated the Pra within 2 days before discharge from the index hospitalization. Other details about exclusion criteria have been published.21 If a patient's outpatient primary care physician treated the patient during the index hospitalization, then there was no perceived barrier in physician‐to‐physician communication and we excluded the patient.

Intervention

The research intervention was a CPOE software application that facilitated communication at the time of hospital discharge to patients, retail pharmacists, and community physicians. Details about the discharge software appeared in a previous publication.7 Software features included required fields, pick lists, standard drug doses, alerts, reminders, and online reference information. The software prompted the discharging physician to enter pending tests and order tests after discharge. Hospital physicians used the software on the day of discharge and automatically generated 4 discharge documents. The first document was a personalized letter to the outpatient physician with discharge diagnoses, reconciled medication list, diet and activity instructions, patient education materials provided, and follow‐up appointments and studies. Second, the software printed legible prescriptions along with specific information for the dispensing pharmacist about changes and deletions in the patient's previous regimen. Third, the software created patient instructions with addresses and telephone numbers for follow‐up appointments and tests. Fourth, the software printed a legible discharge order including all of the aforementioned information.

The control intervention was the usual care discharge process as described previously.7 Hospital physicians and ward nurses completed handwritten discharge forms on the day of discharge. The forms contained blanks for discharge diagnoses, discharge medications, medication instructions, postdischarge activities and restrictions, postdischarge diet, postdischarge diagnostic and therapeutic interventions, and appointments. Patients received handwritten copies of the forms, 1 page of which also included medication instructions and prescriptions. A previous publication gave details about the standard care available to all patients regardless of intervention.7

Randomization

The unit of randomization was the hospital physician who performed the discharge process. Random allocation was to discharge software or usual care discharge process. The randomization ratio was 1:1, the block size was 2, and there was no stratification or matching. There was concealed allocation and details are available from the investigators. Hospital physicians subsequently used their randomly assigned process when discharging their patients who enrolled in the study. After random allocation, it was not possible to conceal the test or control intervention from physicians or their patients. Likewise, it was not possible to conceal the outcome ascertainment, including readmission, from the hospital physicians.

All hospital physicians received training on the usual care discharge process. Physicians assigned to discharge software completed additional training via multimedia demonstration with 1‐on‐1 coaching as needed. Physicians assigned to usual care did not receive training on the discharge software and were blocked from using the software. After informed consent, patients were passive recipients of the research intervention performed by their discharging physician. Patients received the research intervention on the day of discharge from the index hospitalization.

The baseline assessment of patient characteristics occurred during the index hospitalization. Trained data abstractors recorded patient demographic data plus variables to calculate the Pra score for probability for repeat admission. We recorded additional variables because of their possible association with readmission.15, 2229 Data came from the patient or proxy for physical functioning and mental health (SF‐36, Version 2; Medical Outcomes Trust, Boston, MA). Other data for predictor variables came from interviews or hospital records.

Outcome Assessment

The primary study outcome was the proportion of patients readmitted at least once within 6 months after the index hospitalization. Readmission was for any reason and included observation and full admission status. Secondary outcomes were emergency department visits that did not result in hospital admission. Outcome assessment occurred at the patient level. We obtained data for readmissions and emergency department visits from 6 hospitals in central Illinois where study patients were likely to seek care. We validated readmissions and emergency department visits via patient/proxy telephone interviews that occurred 6 months after index hospital discharge. Interviewers were blind to intervention assignment. We evaluated the adequacy of the blind and asked interviewers to guess the patient's intervention assignment.

Another secondary outcome was the proportion of patients who experienced an adverse event related to medical management within 1 month after discharge. For adverse event ascertainment, we employed the process of Forster et al.1, 2 Within 20 to 40 days after discharge, an internal medicine physician performed telephone interviews with the patient or proxy. The interviewer recorded symptoms, drug information, other treatment, hospital readmissions, and emergency department visits. Another physician compiled case summaries from interview data and information abstracted from the electronic medical record, including dictated discharge summaries from the index hospitalization and postdischarge emergency department visits, diagnostic test results, and readmission reports. Two additional internal medicine physicians adjudicated each case summary separately. We counted adverse events only when adjudicators agreed that medical management probably or definitely caused the event. The initial rating by each adjudicator revealed moderate‐to‐good agreement (Kappa = 0.52).30 When initial adjudications were discordant, then adjudicators met and resolved all discrepancies. The adjudicators also scored the severity of the adverse event. The severity scale options were serious laboratory abnormality only, 1 day of symptoms, several days of symptoms, nonpermanent disability, permanent disability, or death. The adjudicators also scored the adverse event as preventable (yes/no), ameliorable (yes/no), and recorded system problems associated with preventable and ameliorable adverse events.1 For adverse drug events, the adjudicators recorded preventability categories defined by previous investigators.31 We designed the adverse event outcome ascertainment as a blinded process. We evaluated the success of the blind and asked adjudicators to guess the patient's intervention assignment.

Sample Size

The sample size analysis employed several assumptions regarding the proportion of readmitted patients. The estimated readmission rate after usual care was 37%.24, 3236 The minimum clinically relevant difference in readmission rates was 13%, an empirical boundary for quantitative significance.37 Estimates for intracluster correlation were not available when we designed the trial. We projected intracluster correlations with low, medium, and high values. The cluster number and size were selected to maintain test significance level, 1‐sided alpha, <0.05 and power >80%. The sample size assumed no interim analysis. The initial sample size estimates were 11 physician clusters per intervention with 25 patients per cluster. During the first 2 months of patient recruitment, we observed that we could not consistently achieve clusters with 25 patients. We recalculated the sample size. Using the same assumptions, we found we could achieve similar test significance and power with 35 physician clusters per intervention and 9 patients per cluster. The sample size calculator was nQuery (Statistical Solutions, Saugus, MA).

Statistical Methods

Analyses were performed with SPSS PC (Version 15.0.1; SPSS Inc, Chicago, IL). Using descriptive statistics, we reported baseline variables as means and standard deviations (SD) for interval variables, and percentages for categorical variables. For outcome variables, we utilized the principle of intention‐to‐treat and assumed patient exposure to the intervention randomly assigned to their discharging physician. We inspected scatter plots and correlations for all variables to test assumptions regarding normal distribution, homogeneity of variance, and linearity of relationships between independent and dependent variables. When assumptions failed, we stratified variables (median or thirds) or performed transformations to satisfy assumptions. For patient‐level outcome variables, we calculated intracluster correlation coefficients. The assessment of the blind was unaffected by the cluster assumption so we used the chi‐square procedure. For analysis of time to event, we used Kaplan‐Meier plots.

The primary hypothesis was a significant decrease in the primary readmission outcome for patients assigned to discharge software. We tested the primary hypothesis with generalized estimating equations that corrected for clustering by hospital physician and adjusted for covariates that predicted readmission. The intervention variable was discharge software versus usual care handwritten discharge. We reported parameter estimates of the intervention variable coefficient and Wald 95% confidence interval (95% CI) with and without correction for cluster. For the secondary, patient‐level outcomes, we performed similar analyses with generalized estimating equations that corrected for clustering by hospital physician.

During covariate analysis, we screened all baseline variables for their correlation with readmission. The variable with the highest correlation and P value <0.05 entered initially in the general estimating equation. After initial variable entry, we evaluated subsequent variables with partial correlations that controlled for variables entered previously. At each iterative step, we entered into the model the variable with the highest partial correlation and P value <0.05.

In exploratory analyses, we examined intervention group differences within strata defined by covariates that predicted readmission. We used generalized estimating equations and adjusted for the other covariates that predicted readmission.

Results

We screened 127 physicians who were general internal medicine hospital physicians. Seventy physicians consented and received random allocation to discharge software or usual care. The physician characteristics appear in Table 1. Most of the hospital physicians were interns in the first year of postgraduate training (58.6%; 41/70). We excluded 57 physicians for reasons shown in the trial flow diagram (Figure 1). The most common reason for hospital physician exclusion applied to resident physicians in their last months of training before graduation or emergency department residents temporarily assigned to internal medicine training. We approached 6,884 patients during their index hospitalization. After excluding 6,253 ineligible patients, we enrolled and followed 631 patients who received the discharge intervention (Figure 1). During 6 months of follow‐up, a small proportion of patients died (3%; 20/631). Hospital records were available for deceased patients and they were included in the analysis. A small proportion (6%; 41/631) of patients withdrew consent or left the trial for other reasons during 6 months. There was no differential dropout between the interventions. Protocol deviations were rare (0.5%; 3/631). Three patients erroneously received usual care discharge from physicians assigned to discharge software. All 631 patients were included in the intention‐to‐treat analysis. The baseline characteristics of the randomly‐assigned hospital physicians and their patients are in Table 1.

Figure 1
Trial flow diagram for hospital physicians and patients.
Baseline Characteristics for Each Intervention at the Hospital Physician Cluster Level and Individual Patient Level
 Discharge SoftwareUsual Care
  • Abbreviation: SD, standard deviation.

  • Missing data for 1 or 2 subjects.

Hospital physician characteristics, n (%)(n = 35)(n = 35)
Postgraduate year 118 (51.4)23 (65.7)
Postgraduate years 2‐410 (28.6)7 (20.0)
Attending physician7 (20.0)5 (14.3)
Patient characteristics(n = 316)(n = 315)
Gender, female, n (%)180 (57.0)168 (53.3)
Age, years, n (%)  
18‐4468 (21.5)95 (30.2)
45‐5479 (25.0)76 (24.1)
55‐6486 (27.2)74 (23.5)
65‐9883 (26.3)70 (22.2)
Race, n (%)  
Caucasian239 (75.6)229 (72.7)
Black72 (22.8)85 (27.0)
Other5 (1.6)1 (0.3)
Self‐rated health status, n (%)  
Poor82 (25.9)108 (34.3)
Fair169 (53.5)147 (46.7)
Good54 (17.1)46 (14.6)
Very good10 (3.2)11 (3.5)
Excellent1 (0.3)3 (1.0)
Diabetes mellitus, n (%)172 (54.4)177 (56.2)
Chronic obstructive pulmonary disease, n (%)  
None259 (82.0)257 (81.6)
Without oral steroid or home oxygen28 (8.9)26 (8.3)
With chronic oral steroid10 (3.2)8 (2.5)
With home oxygen oral steroid19 (6.0)24 (7.6)
Coronary heart disease, n (%)133 (42.1)120 (38.1)
Heart failure, n (%)80 (25.3)67 (21.3)
Informal caregiver available, yes, n (%)313 (99.1)313 (99.4)
Taking loop diuretic, n (%)110 (34.8)88 (27.9)
Physical functioning from SF‐36, n (%)  
Lowest third128 (40.5)121 (38.4)
Upper two‐thirds188 (59.5)194 (61.6)
Mental health from SF‐36, n (%)  
Lowest third113 (35.8)117 (37.1)*
Upper two‐thirds203 (64.2)197 (62.5)*
Hospital admissions during year prior to index admission, n (%)  
0 or 1247 (78.2)224 (71.1)
2 or more69 (21.8)91 (28.9)
Emergency department visits during 6 months before index admission, n (%)  
0 or 1194 (61.4)168 (53.3)
2 or more122 (38.6)147 (46.7)
Outpatient doctor or clinic visits during year prior to index admission  
0 to 497 (30.7%)77 (24.4%)
5 to 868 (21.5%)81 (25.7%)
9 to 1282 (25.9%)84 (26.7%)
13 or more69 (21.8%)73 (23.2%)
Insurance or payor  
Medicare, age less than 65 years18 (5.7%)13 (4.1%)
Medicare, age 65 years and older56 (17.7%)40 (12.7%)
Medicaid, age less than 65 years98 (31.0%)130 (41.3%)
Medicaid, age 65 years and older17 (5.4%)20 (6.3%)
Commercial or veteran85 (26.9%)61 (19.4%)
Self‐pay42 (13.3%)51 (16.2%)
Religious participation  
Never159 (50.3%)164 (52.1%)
1‐24 times per year55 (17.4%)51 (16.2%)
1‐7 times per week102 (32.3%)100 (31.7%)
Volunteer activity, 1 or more hour/month31 (9.8%)39 (12.4%)
Employment status  
Not working229 (72.5%)233 (74.4%)*
Part‐time (<37.5 hours/week)30 (9.5%)25 (8.0%)*
Full‐time (at least 37.5 hour/week)57 (18.0%)55 (17.6%)*
Number of discharge medications, mean (SD)10.5 (4.8)9.9 (5.1)
Severity of illness, mean (SD)1.8 (1.2)1.6 (1.3)
Charlson‐Deyo comorbidity, mean (SD)1.7 (1.4)1.6 (1.9)
Index hospital length of stay, days, mean (SD)3.9 (3.5)3.5 (3.5)
Blood urea nitrogen, mean (SD)17.9 (12.9)19.1 (12.9)
Probability of repeat admission, Pra, mean (SD)0.486 (0.072)0.495 (0.076)

We asked outpatient physicians about their receipt of discharge communication from hospital physicians. The text of the question was, How soon after discharge did you receive any information (in any form) relating to this patient's hospital admission and discharge plans? We mailed the question 10 days after discharge to outpatient physicians designated by patients enrolled in the study. Among patients in the discharge software group, 75.0% (237/316) of their outpatient physicians responded to the question. The response rate was 80.6% (254/315) from physicians who followed patients in the usual care group. Respondents from the discharge software group said within 1‐2 days or within a week for 56.0% (177/316) of patients. Respondents from the usual care group said within 1‐2 days or within a week for 57.1% (180/315) of patients. The difference between the intervention groups, 1.1% (95% CI, 9.2% to 6.9%), was not significant.

The primary, prespecified, outcome of the study was the proportion of patients with at least 1 readmission to the hospital. After intervention with discharge software versus usual care, there was no significant difference in readmission rates (Table 2) or time to first readmission (Figure 2). We screened all baseline variables in Table 1 and sought predictors of readmission to employ in adjusted models. For example, we evaluated physician level of training because we wondered if experience or seniority affected readmission when hospital physicians used the discharge software or usual care discharge. The candidate variable, physician level of training, did not correlate with readmission (rho = 0.066; P = 0.100), so it was dropped from subsequent analyses. After screening all variables in Table 1, we found 4 independent predictors of readmission: previous hospitalizations, previous emergency department visits, heart failure, and physical function. Generalized estimating equations for readmission that adjusted for predictor variables confirmed a negligible parameter estimate for the discharge intervention variable coefficient (Table 2).

Figure 2
Kaplan‐Meier curves for first readmission after index hospital discharge for patients assigned to receive discharge software versus usual care discharge. Solid line represents patients assigned to discharge software. Dotted line represents patients assigned to usual care discharge.
Outcomes for 316 Patients Assigned to Discharge Software and 315 Patients Assigned to Usual Care Intervention
OutcomeDischarge Software, n (%)Usual Care, n (%)Parameter Estimate Without Cluster Correction Intervention Coefficient (95% CI)P ValueParameter Estimate With Cluster Correction Intervention Coefficient (95% CI)P Value
  • NOTE: Parameter estimates are intervention coefficients from generalized estimating equations for outcome variables. Parameter estimates from generalized estimating equations appear with and without correction for clustering by hospital physician: 34 physicians assigned to discharge software and 35 assigned to usual care.

  • Abbreviation: 95% CI, Wald 95% confidence interval.

  • Generalized estimating equations adjusted for previous hospitalizations, previous emergency department visits, heart failure, and physical function.

Readmitted within 6 months117 (37.0%)119 (37.8%)0.005* (0.076, 0.067)0.8970.005* (0.074, 0.065)0.894
Emergency department visit within 6 months112 (35.4%)128 (40.6%)0.052 (0.128, 0.024)0.1790.052 (0.115, 0.011)0.108
Adverse event within 1 month23 (7.3%)23 (7.3%)0.003 (0.037, 0.043)0.8860.003 (0.037, 0.043)0.884

We evaluated emergency department visits that were unrelated to readmission as secondary, prespecified, outcomes. The results were similar to readmission results. While the proportion of patients with at least 1 emergency department visit was lower for the discharge software intervention, the difference with usual care was not significant (Table 2). There was no significant difference between interventions for time to first emergency department visit (Figure 3).

Figure 3
Kaplan‐Meier curves for first emergency department visit after index hospital discharge for patients assigned to receive discharge software versus usual care discharge. Solid line represents patients assigned to discharge software. Dotted line represents patients assigned to usual care discharge.

Postdischarge adverse events were secondary, prespecified, outcomes. Data for adverse event adjudication were available for 98% (309/316) of discharge software patients and 97% (307/315) of usual care patients. Within 1 month after discharge, 46 patients had adverse events probably or definitely related to medical management. Two patients had 2 events and 1 patient had 3 events. For analysis, we randomly selected 1 event per patient. When comparing patients assigned to discharge software versus usual care, there were no differences in adverse events related to medical management (Table 2). Most of the events were possible adverse drug events (74%; 34/46). The adverse event severity was several days of symptoms or nonpermanent disability for 76% (35/46) of the adverse events. Adjudicators rated 26% (12/46) of the adverse events as preventable and 46% (21/46) as ameliorable. The absolute numbers of events were small. There were no differences between discharge software and usual care patients within adverse event strata defined by type, severity, preventable, or ameliorable (Table 3). For most of the patients with adverse events, the adjudicators could not identify a system problem or preventability category (Table 3). When a deficiency was evident, there was no pattern to suggest a significant difference between discharge software patients versus usual care patients.

Number of Adverse Events Related to Medical Management Within 1 Month After Discharge
 Discharge Software (n)Usual Care Discharge (n)
  • Abbreviation: ADE, adverse drug event.

At least 1 adverse event2323
Preventable adverse event75
Ameliorable adverse event912
Adverse event severity  
Serious laboratory abnormality only or 1 day of symptoms55
Several days of symptoms or nonpermanent disability1817
Permanent disability or death01
Adverse event by type  
Possible adverse drug event1717
Procedure‐related injury21
Therapeutic error44
Diagnostic error01
System problems associated with preventable or ameliorable adverse events  
Inadequate patient education regarding the medical condition or its treatment01
Poor communication between patient and physician21
Poor communication between hospital and community physicians00
Inadequate monitoring of the patient's illness after discharge06
Inadequate monitoring of the patient's treatment after discharge26
No emergency contact number given to patient to call about problems00
Patient with problems getting prescribed medications immediately10
Inadequate home services00
Delayed follow‐up care03
Premature hospital discharge12
Adverse drug event (ADE) preventability categories  
Drug involved in the ADE inappropriate for the clinical condition24
Dose, route, or frequency inappropriate for age, weight, creatinine clearance, or disease12
Failure to obtain required lab tests and/or drug levels12
Prior history of an adverse event or allergy to the drug12
Drug‐drug interaction involved in the ADE20
Toxic serum drug level documented00
Noncompliance involved in the ADE01

When we designed the trial, we assumed variance in outcomes measured at the patient level. We predicted some variance attributable to clustering by hospital physician. After the trial, we calculated the intracluster correlation coefficients for readmissions, emergency department visits, and adverse events. For all of these outcomes, the intracluster correlation coefficients were negligible. We also evaluated generalized estimating equations with and without correction for hospital physician cluster. We confirmed the negligible cluster effect on confidence intervals for intervention coefficients (Table 2).

We performed an exploratory stratified analysis. We evaluated the intervention effect on readmission within subgroups defined by covariates that predicted readmission (Table 4). When the intervention groups were compared within baseline categories of previous hospitalizations, previous emergency department visits, heart failure, and physical functioning, there was a consistent pattern with no differential effect by intervention assignment. None of the intervention coefficients were statistically significant (Table 4).

Patients Readmitted At Least Once Within 6 Months by Subgroup
SubgroupDischarge Software Readmitted n/n (%)Usual Care Readmitted n/n (%)Adjusted Parameter Estimate Intervention Coefficient (95% CI)
  • NOTE: Intervention was discharge software or usual care. Adjusted parameter estimates are intervention coefficients from generalized estimating equations.

  • Abbreviation: 95% CI, Wald 95% confidence interval.

  • Adjusted for previous emergency department visits, heart failure, and physical functioning.

  • Adjusted for previous hospital admissions, heart failure, and physical functioning.

  • Adjusted for previous hospital admissions, previous emergency department visits, and physical functioning.

  • Adjusted for previous hospital admissions, previous emergency department visits, and heart failure.

Hospital admissions during year prior to index admission   
0 or 177/247 (31.2)73/224 (32.6)0.025 (0.095, 0.045)*
2 or more40/69 (58.0)46/91 (50.5)0.059 (0.090, 0.208)*
Emergency department visits during 6 months before index admission   
0 or 164/194 (33.0)45/168 (26.8)0.033 (0.047, 0.113)
2 or more53/122 (43.4)74/147 (50.3)0.071 (0.188, 0.046)
Heart failure   
Present40/80 (50.0)36/67 (53.7)0.024 (0.224, 0.177)
Absent77/236 (32.6)83/248 (33.5)0.000 (0.076, 0.075)
Physical functioning from SF‐36   
Lowest third55/128 (43.0)59/121 (48.8)0.032 (0.161, 0.096)
Upper two‐thirds62/188 (33.0)60/194 (30.9)0.012 (0.071, 0.095)

Assessment of the Success of the Blind

We evaluated the adequacy of the blind for outcome assessors who interviewed patients or adjudicated adverse events. The guesses of outcomes assessors were unrelated to true intervention assignment (all P values >0.097). We interpreted the blind as adequate for outcome assessors who recorded readmissions, emergency department visits, and adverse events.

Discussion

We performed a cluster‐randomized clinical trial to measure the effects of discharge software versus usual care handwritten discharge. The discharge software with CPOE implemented elements of high‐quality discharge planning and communication endorsed by the National Quality Forum and systematic reviews.6, 38 Despite theoretical benefits, our discharge software intervention did not reduce readmissions or emergency department visits. What were potential explanations for our results? We assumed an association between postdischarge adverse events and readmissions or emergency department visits.1 Our failure to reduce adverse events might explain the failure to reduce readmissions or emergency department visits. Another potential explanation was related to adverse drug events. Other investigators showed most postdischarge adverse events were adverse drug events and our data confirmed previous studies.1, 2 Medication reconciliation at discharge was a potential mechanism for adverse drug event reduction.14 Medication reconciliation was the standard at the study hospital, so it was unethical to deny reconciliation to patients assigned to either intervention.39 Required medication reconciliation in both groups, by its known effect on preventable adverse drug events, might have reduced the event rates in both groups.14 This possibility is supported by the low rate of adverse events observed in our study compared with other studies.1 We speculate that the low background rate of adverse events at the study hospital may have minimized events in both the discharge software and usual care groups and prevented detection of software benefits, if present.39

One limitation of our study may have been the discharge software. The automated decision support in our software lacked features that might have improved outcomes. For example, the software did not generate a list of diagnostic test results that were pending at the time of discharge. Our software relied on prompts to the physician user that did not specify which tests were pending. The software did not perform error checks on the discharge orders to warn physicians about drug‐drug interactions, therapeutic duplications, or missing items (eg, immunizations, drugs, education). The absence of these software enhancements made our discharge process vulnerable to the lapses and slips of the physician user. Whether or not such enhancements affect clinically relevant outcomes remains a testable hypothesis for future studies.

Another limitation of our study was the outpatient physician response. Discharge software did not increase the proportion of outpatient physicians who said they received communication within 7 days after hospital discharge. Our intervention addressed the sending partner but not the receiving partner in the communication dyad. Our discharge software was not designed to change information flow within the outpatient physician office. We do not know if discharge communication arrived and remained unnoticed until the patient called or visited the outpatient clinic. Future studies of discharge communication should consider a closed loop design to assure receipt and comprehension.

When we designed our study, we expected at least some variance between patient clusters attributable to the physician who performed the discharge. Our analysis of intracluster correlation revealed negligible variance. We speculate the highly‐standardized discharge process implemented by discharge software and usual care at our hospital resulted in minimal variance. Future studies of discharge interventions may consider designs that avoid cluster randomization.

In conclusion, a discharge software application of CPOE did not affect readmissions, emergency department visits, or adverse events after discharge.

Acknowledgements

The authors thank Howard S. Cohen, MD, for his review of the trial protocol and the manuscript.

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  19. Pacala JT,Boult C,Boult L.Predictive validity of a questionnaire that identifies older persons at risk for hospital admission.J Am Geriatr Soc.1995;43(4):374377.
  20. Pacala JT,Boult C,Reed RL,Aliberti E.Predictive validity of the Pra instrument among older recipients of managed care.J Am Geriatr Soc.1997;45(5):614617.
  21. Graumlich JF,Novotny NL,Aldag JC.Brief scale measuring patient preparedness for hospital discharge to home: psychometric properties.J Hosp Med.2008;3(6):446454.
  22. Ware JE.SF‐36 health survey update.Spine.2000;25(24):31303139.
  23. Reuben DB,Keeler E,Seeman TE,Sewall A,Hirsch SH,Guralnik JM.Development of a method to identify seniors at high risk for high hospital utilization.Med Care.2002;40(9):782793.
  24. Naylor MD,Brooten D,Campbell R, et al.Comprehensive discharge planning and home follow‐up of hospitalized elders: arandomized clinical trial.JAMA.1999;281(7):613620.
  25. Smith DM,Giobbie‐Hurder A,Weinberger M, et al.Predicting non‐elective hospital readmissions: a multi‐site study. Department of Veterans Affairs Cooperative Study Group on Primary Care and Readmissions.J Clin Epidemiol.2000;53:11131118.
  26. Corrigan JM,Martin JB.Identification of factors associated with hospital readmission and development of a predictive model.Health Serv Res.1992;27(1):81101.
  27. Romano PS,Chan BK.Risk‐adjusting acute myocardial infarction mortality: are APR‐DRGs the right tool?Health Serv Res.2000;34(7):14691489.
  28. Deyo RA,Cherkin DC,Ciol MA.Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases.J Clin Epidemiol.1992;45(6):613619.
  29. Shelton P,Sager MA,Schraeder C.The community assessment risk screen (CARS): identifying elderly persons at risk for hospitalization or emergency department visit.Am J Manag Care.2000;6(8):925933.
  30. Sackett DL,Haynes RB,Guyatt GH,Tugwell P.Clinical Epidemiology: A Basic Science for Clinical Medicine.2nd ed.Boston:Little, Brown;1991.
  31. Winterstein AG,Hatton RC,Gonzalez‐Rothi R,Johns TE,Segal R.Identifying clinically significant preventable adverse drug events through a hospital's database of adverse drug reaction reports.Am J Health Syst Pharm.2002;59(18):17421749.
  32. Nazareth I,Burton A,Shulman S,Smith P,Haines A,Timberal H.A pharmacy discharge plan for hospitalized elderly patients—a randomized controlled trial.Age Ageing.2001;30(1):3340.
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Issue
Journal of Hospital Medicine - 4(7)
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E11-E19
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continuity of patient care, electronic discharge summary, health care surveys, hospital information systems, hospitalists, medical records systems–computerized, medication reconciliation, patient care transitions, patient discharge, patient satisfaction
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Adverse events occur to patients after their discharge from acute care hospitals.1, 2 Most of these injuries are adverse drug events, procedure‐related events, nosocomial infections, or falls.1 Postdischarge adverse events are associated with several days of symptoms, nonpermanent disability, emergency department visits, or hospital readmission.1, 3 When adverse events are preventable or ameliorable, the most common root cause is poor communication between hospital personnel and either the patient or the outpatient primary care physician.1 In addition, there may be deficits in discharge processes related to assessment and communication of unresolved problems.1 Systematic reviews have shown that discharge communication is an inefficient and error‐prone process.46

One potential solution to poor discharge communication is health information technology.7 An example of technology is discharge software with a computerized physician order entry (CPOE) system. By definition, a CPOE system is a computer‐based system that automates direct entry of orders by physicians and ensures standardized, legible, and complete orders.8 The benefits of CPOE have been tested in other inpatient settings.8, 9 It is logical to consider software applications with CPOE for discharge interventions.7

Several mechanisms explain the potential benefit of discharge software with CPOE.7 Applications with CPOE decrease medication errors.8, 10 Software with decision support could prompt physicians to enter posthospitalization appointment dates and orders for preventive services.11, 12 Discharge software could facilitate medication reconciliation and generate patient instructions and information.4, 1315 The potential benefits of discharge software with CPOE provide a rationale for clinical trials to measure benefits.

Previous studies addressed discharge applications of health information technology. Observational studies recorded outcomes such as physician satisfaction.16, 17 Prior randomized clinical trials measured quality and timeliness of discharge summaries.18 However, these previous trials did not assess clinically relevant outcomes like readmissions, emergency department visits, or adverse events. We performed a cluster‐randomized trial to assess the value of a discharge software application of CPOE. The rationale for our clustered design complied with recommendations from a systematic review of discharge interventions.5 Our objective was to assess the benefit of discharge software with CPOE when used to discharge patients at high risk for repeat admission. After the intervention, we compared the rates of hospital readmission, emergency department visits, and postdischarge adverse events due to medical management.

Methods

The trial design was a cluster randomized, controlled trial with blinded outcome assessment. Follow‐up occurred until 6 months after discharge from index hospitalization at a 730‐bed, tertiary care, teaching hospital in central Illinois. The Peoria Institutional Review Board approved the protocol for human research.

Participants

The cluster definition was the hospital physician. Patients discharged by the physician comprised the cluster. Hospital physicians and patients were enrolled between November 2004 and January 2007. Internal medicine resident or attending physicians were eligible. We excluded hospital physicians if their assignments to inpatient duties were less than 2 months during the 27‐month enrollment period. The rationale for the physician exclusion was a consequence of the patient enrollment rate of 3 to 5 patients per physician per month. Physicians with brief assignments could not achieve the goal of 9 or more patients per cluster. After physicians gave informed consent to screen their patients, trained research coordinators applied inclusion and exclusion criteria and obtained informed consent from patients. Research personnel identified all consecutive, unique, adult inpatients who were discharged to home. Patient inclusion required a probability of repeat admission (Pra) score 0.40.19, 20 The Pra score came from a logistic model of age, gender, prior hospitalizations, prior doctor visits, self‐rated health status, presence of informal caregiver in the home, and comorbid coronary heart disease and diabetes mellitus. Research coordinators calculated the Pra within 2 days before discharge from the index hospitalization. Other details about exclusion criteria have been published.21 If a patient's outpatient primary care physician treated the patient during the index hospitalization, then there was no perceived barrier in physician‐to‐physician communication and we excluded the patient.

Intervention

The research intervention was a CPOE software application that facilitated communication at the time of hospital discharge to patients, retail pharmacists, and community physicians. Details about the discharge software appeared in a previous publication.7 Software features included required fields, pick lists, standard drug doses, alerts, reminders, and online reference information. The software prompted the discharging physician to enter pending tests and order tests after discharge. Hospital physicians used the software on the day of discharge and automatically generated 4 discharge documents. The first document was a personalized letter to the outpatient physician with discharge diagnoses, reconciled medication list, diet and activity instructions, patient education materials provided, and follow‐up appointments and studies. Second, the software printed legible prescriptions along with specific information for the dispensing pharmacist about changes and deletions in the patient's previous regimen. Third, the software created patient instructions with addresses and telephone numbers for follow‐up appointments and tests. Fourth, the software printed a legible discharge order including all of the aforementioned information.

The control intervention was the usual care discharge process as described previously.7 Hospital physicians and ward nurses completed handwritten discharge forms on the day of discharge. The forms contained blanks for discharge diagnoses, discharge medications, medication instructions, postdischarge activities and restrictions, postdischarge diet, postdischarge diagnostic and therapeutic interventions, and appointments. Patients received handwritten copies of the forms, 1 page of which also included medication instructions and prescriptions. A previous publication gave details about the standard care available to all patients regardless of intervention.7

Randomization

The unit of randomization was the hospital physician who performed the discharge process. Random allocation was to discharge software or usual care discharge process. The randomization ratio was 1:1, the block size was 2, and there was no stratification or matching. There was concealed allocation and details are available from the investigators. Hospital physicians subsequently used their randomly assigned process when discharging their patients who enrolled in the study. After random allocation, it was not possible to conceal the test or control intervention from physicians or their patients. Likewise, it was not possible to conceal the outcome ascertainment, including readmission, from the hospital physicians.

All hospital physicians received training on the usual care discharge process. Physicians assigned to discharge software completed additional training via multimedia demonstration with 1‐on‐1 coaching as needed. Physicians assigned to usual care did not receive training on the discharge software and were blocked from using the software. After informed consent, patients were passive recipients of the research intervention performed by their discharging physician. Patients received the research intervention on the day of discharge from the index hospitalization.

The baseline assessment of patient characteristics occurred during the index hospitalization. Trained data abstractors recorded patient demographic data plus variables to calculate the Pra score for probability for repeat admission. We recorded additional variables because of their possible association with readmission.15, 2229 Data came from the patient or proxy for physical functioning and mental health (SF‐36, Version 2; Medical Outcomes Trust, Boston, MA). Other data for predictor variables came from interviews or hospital records.

Outcome Assessment

The primary study outcome was the proportion of patients readmitted at least once within 6 months after the index hospitalization. Readmission was for any reason and included observation and full admission status. Secondary outcomes were emergency department visits that did not result in hospital admission. Outcome assessment occurred at the patient level. We obtained data for readmissions and emergency department visits from 6 hospitals in central Illinois where study patients were likely to seek care. We validated readmissions and emergency department visits via patient/proxy telephone interviews that occurred 6 months after index hospital discharge. Interviewers were blind to intervention assignment. We evaluated the adequacy of the blind and asked interviewers to guess the patient's intervention assignment.

Another secondary outcome was the proportion of patients who experienced an adverse event related to medical management within 1 month after discharge. For adverse event ascertainment, we employed the process of Forster et al.1, 2 Within 20 to 40 days after discharge, an internal medicine physician performed telephone interviews with the patient or proxy. The interviewer recorded symptoms, drug information, other treatment, hospital readmissions, and emergency department visits. Another physician compiled case summaries from interview data and information abstracted from the electronic medical record, including dictated discharge summaries from the index hospitalization and postdischarge emergency department visits, diagnostic test results, and readmission reports. Two additional internal medicine physicians adjudicated each case summary separately. We counted adverse events only when adjudicators agreed that medical management probably or definitely caused the event. The initial rating by each adjudicator revealed moderate‐to‐good agreement (Kappa = 0.52).30 When initial adjudications were discordant, then adjudicators met and resolved all discrepancies. The adjudicators also scored the severity of the adverse event. The severity scale options were serious laboratory abnormality only, 1 day of symptoms, several days of symptoms, nonpermanent disability, permanent disability, or death. The adjudicators also scored the adverse event as preventable (yes/no), ameliorable (yes/no), and recorded system problems associated with preventable and ameliorable adverse events.1 For adverse drug events, the adjudicators recorded preventability categories defined by previous investigators.31 We designed the adverse event outcome ascertainment as a blinded process. We evaluated the success of the blind and asked adjudicators to guess the patient's intervention assignment.

Sample Size

The sample size analysis employed several assumptions regarding the proportion of readmitted patients. The estimated readmission rate after usual care was 37%.24, 3236 The minimum clinically relevant difference in readmission rates was 13%, an empirical boundary for quantitative significance.37 Estimates for intracluster correlation were not available when we designed the trial. We projected intracluster correlations with low, medium, and high values. The cluster number and size were selected to maintain test significance level, 1‐sided alpha, <0.05 and power >80%. The sample size assumed no interim analysis. The initial sample size estimates were 11 physician clusters per intervention with 25 patients per cluster. During the first 2 months of patient recruitment, we observed that we could not consistently achieve clusters with 25 patients. We recalculated the sample size. Using the same assumptions, we found we could achieve similar test significance and power with 35 physician clusters per intervention and 9 patients per cluster. The sample size calculator was nQuery (Statistical Solutions, Saugus, MA).

Statistical Methods

Analyses were performed with SPSS PC (Version 15.0.1; SPSS Inc, Chicago, IL). Using descriptive statistics, we reported baseline variables as means and standard deviations (SD) for interval variables, and percentages for categorical variables. For outcome variables, we utilized the principle of intention‐to‐treat and assumed patient exposure to the intervention randomly assigned to their discharging physician. We inspected scatter plots and correlations for all variables to test assumptions regarding normal distribution, homogeneity of variance, and linearity of relationships between independent and dependent variables. When assumptions failed, we stratified variables (median or thirds) or performed transformations to satisfy assumptions. For patient‐level outcome variables, we calculated intracluster correlation coefficients. The assessment of the blind was unaffected by the cluster assumption so we used the chi‐square procedure. For analysis of time to event, we used Kaplan‐Meier plots.

The primary hypothesis was a significant decrease in the primary readmission outcome for patients assigned to discharge software. We tested the primary hypothesis with generalized estimating equations that corrected for clustering by hospital physician and adjusted for covariates that predicted readmission. The intervention variable was discharge software versus usual care handwritten discharge. We reported parameter estimates of the intervention variable coefficient and Wald 95% confidence interval (95% CI) with and without correction for cluster. For the secondary, patient‐level outcomes, we performed similar analyses with generalized estimating equations that corrected for clustering by hospital physician.

During covariate analysis, we screened all baseline variables for their correlation with readmission. The variable with the highest correlation and P value <0.05 entered initially in the general estimating equation. After initial variable entry, we evaluated subsequent variables with partial correlations that controlled for variables entered previously. At each iterative step, we entered into the model the variable with the highest partial correlation and P value <0.05.

In exploratory analyses, we examined intervention group differences within strata defined by covariates that predicted readmission. We used generalized estimating equations and adjusted for the other covariates that predicted readmission.

Results

We screened 127 physicians who were general internal medicine hospital physicians. Seventy physicians consented and received random allocation to discharge software or usual care. The physician characteristics appear in Table 1. Most of the hospital physicians were interns in the first year of postgraduate training (58.6%; 41/70). We excluded 57 physicians for reasons shown in the trial flow diagram (Figure 1). The most common reason for hospital physician exclusion applied to resident physicians in their last months of training before graduation or emergency department residents temporarily assigned to internal medicine training. We approached 6,884 patients during their index hospitalization. After excluding 6,253 ineligible patients, we enrolled and followed 631 patients who received the discharge intervention (Figure 1). During 6 months of follow‐up, a small proportion of patients died (3%; 20/631). Hospital records were available for deceased patients and they were included in the analysis. A small proportion (6%; 41/631) of patients withdrew consent or left the trial for other reasons during 6 months. There was no differential dropout between the interventions. Protocol deviations were rare (0.5%; 3/631). Three patients erroneously received usual care discharge from physicians assigned to discharge software. All 631 patients were included in the intention‐to‐treat analysis. The baseline characteristics of the randomly‐assigned hospital physicians and their patients are in Table 1.

Figure 1
Trial flow diagram for hospital physicians and patients.
Baseline Characteristics for Each Intervention at the Hospital Physician Cluster Level and Individual Patient Level
 Discharge SoftwareUsual Care
  • Abbreviation: SD, standard deviation.

  • Missing data for 1 or 2 subjects.

Hospital physician characteristics, n (%)(n = 35)(n = 35)
Postgraduate year 118 (51.4)23 (65.7)
Postgraduate years 2‐410 (28.6)7 (20.0)
Attending physician7 (20.0)5 (14.3)
Patient characteristics(n = 316)(n = 315)
Gender, female, n (%)180 (57.0)168 (53.3)
Age, years, n (%)  
18‐4468 (21.5)95 (30.2)
45‐5479 (25.0)76 (24.1)
55‐6486 (27.2)74 (23.5)
65‐9883 (26.3)70 (22.2)
Race, n (%)  
Caucasian239 (75.6)229 (72.7)
Black72 (22.8)85 (27.0)
Other5 (1.6)1 (0.3)
Self‐rated health status, n (%)  
Poor82 (25.9)108 (34.3)
Fair169 (53.5)147 (46.7)
Good54 (17.1)46 (14.6)
Very good10 (3.2)11 (3.5)
Excellent1 (0.3)3 (1.0)
Diabetes mellitus, n (%)172 (54.4)177 (56.2)
Chronic obstructive pulmonary disease, n (%)  
None259 (82.0)257 (81.6)
Without oral steroid or home oxygen28 (8.9)26 (8.3)
With chronic oral steroid10 (3.2)8 (2.5)
With home oxygen oral steroid19 (6.0)24 (7.6)
Coronary heart disease, n (%)133 (42.1)120 (38.1)
Heart failure, n (%)80 (25.3)67 (21.3)
Informal caregiver available, yes, n (%)313 (99.1)313 (99.4)
Taking loop diuretic, n (%)110 (34.8)88 (27.9)
Physical functioning from SF‐36, n (%)  
Lowest third128 (40.5)121 (38.4)
Upper two‐thirds188 (59.5)194 (61.6)
Mental health from SF‐36, n (%)  
Lowest third113 (35.8)117 (37.1)*
Upper two‐thirds203 (64.2)197 (62.5)*
Hospital admissions during year prior to index admission, n (%)  
0 or 1247 (78.2)224 (71.1)
2 or more69 (21.8)91 (28.9)
Emergency department visits during 6 months before index admission, n (%)  
0 or 1194 (61.4)168 (53.3)
2 or more122 (38.6)147 (46.7)
Outpatient doctor or clinic visits during year prior to index admission  
0 to 497 (30.7%)77 (24.4%)
5 to 868 (21.5%)81 (25.7%)
9 to 1282 (25.9%)84 (26.7%)
13 or more69 (21.8%)73 (23.2%)
Insurance or payor  
Medicare, age less than 65 years18 (5.7%)13 (4.1%)
Medicare, age 65 years and older56 (17.7%)40 (12.7%)
Medicaid, age less than 65 years98 (31.0%)130 (41.3%)
Medicaid, age 65 years and older17 (5.4%)20 (6.3%)
Commercial or veteran85 (26.9%)61 (19.4%)
Self‐pay42 (13.3%)51 (16.2%)
Religious participation  
Never159 (50.3%)164 (52.1%)
1‐24 times per year55 (17.4%)51 (16.2%)
1‐7 times per week102 (32.3%)100 (31.7%)
Volunteer activity, 1 or more hour/month31 (9.8%)39 (12.4%)
Employment status  
Not working229 (72.5%)233 (74.4%)*
Part‐time (<37.5 hours/week)30 (9.5%)25 (8.0%)*
Full‐time (at least 37.5 hour/week)57 (18.0%)55 (17.6%)*
Number of discharge medications, mean (SD)10.5 (4.8)9.9 (5.1)
Severity of illness, mean (SD)1.8 (1.2)1.6 (1.3)
Charlson‐Deyo comorbidity, mean (SD)1.7 (1.4)1.6 (1.9)
Index hospital length of stay, days, mean (SD)3.9 (3.5)3.5 (3.5)
Blood urea nitrogen, mean (SD)17.9 (12.9)19.1 (12.9)
Probability of repeat admission, Pra, mean (SD)0.486 (0.072)0.495 (0.076)

We asked outpatient physicians about their receipt of discharge communication from hospital physicians. The text of the question was, How soon after discharge did you receive any information (in any form) relating to this patient's hospital admission and discharge plans? We mailed the question 10 days after discharge to outpatient physicians designated by patients enrolled in the study. Among patients in the discharge software group, 75.0% (237/316) of their outpatient physicians responded to the question. The response rate was 80.6% (254/315) from physicians who followed patients in the usual care group. Respondents from the discharge software group said within 1‐2 days or within a week for 56.0% (177/316) of patients. Respondents from the usual care group said within 1‐2 days or within a week for 57.1% (180/315) of patients. The difference between the intervention groups, 1.1% (95% CI, 9.2% to 6.9%), was not significant.

The primary, prespecified, outcome of the study was the proportion of patients with at least 1 readmission to the hospital. After intervention with discharge software versus usual care, there was no significant difference in readmission rates (Table 2) or time to first readmission (Figure 2). We screened all baseline variables in Table 1 and sought predictors of readmission to employ in adjusted models. For example, we evaluated physician level of training because we wondered if experience or seniority affected readmission when hospital physicians used the discharge software or usual care discharge. The candidate variable, physician level of training, did not correlate with readmission (rho = 0.066; P = 0.100), so it was dropped from subsequent analyses. After screening all variables in Table 1, we found 4 independent predictors of readmission: previous hospitalizations, previous emergency department visits, heart failure, and physical function. Generalized estimating equations for readmission that adjusted for predictor variables confirmed a negligible parameter estimate for the discharge intervention variable coefficient (Table 2).

Figure 2
Kaplan‐Meier curves for first readmission after index hospital discharge for patients assigned to receive discharge software versus usual care discharge. Solid line represents patients assigned to discharge software. Dotted line represents patients assigned to usual care discharge.
Outcomes for 316 Patients Assigned to Discharge Software and 315 Patients Assigned to Usual Care Intervention
OutcomeDischarge Software, n (%)Usual Care, n (%)Parameter Estimate Without Cluster Correction Intervention Coefficient (95% CI)P ValueParameter Estimate With Cluster Correction Intervention Coefficient (95% CI)P Value
  • NOTE: Parameter estimates are intervention coefficients from generalized estimating equations for outcome variables. Parameter estimates from generalized estimating equations appear with and without correction for clustering by hospital physician: 34 physicians assigned to discharge software and 35 assigned to usual care.

  • Abbreviation: 95% CI, Wald 95% confidence interval.

  • Generalized estimating equations adjusted for previous hospitalizations, previous emergency department visits, heart failure, and physical function.

Readmitted within 6 months117 (37.0%)119 (37.8%)0.005* (0.076, 0.067)0.8970.005* (0.074, 0.065)0.894
Emergency department visit within 6 months112 (35.4%)128 (40.6%)0.052 (0.128, 0.024)0.1790.052 (0.115, 0.011)0.108
Adverse event within 1 month23 (7.3%)23 (7.3%)0.003 (0.037, 0.043)0.8860.003 (0.037, 0.043)0.884

We evaluated emergency department visits that were unrelated to readmission as secondary, prespecified, outcomes. The results were similar to readmission results. While the proportion of patients with at least 1 emergency department visit was lower for the discharge software intervention, the difference with usual care was not significant (Table 2). There was no significant difference between interventions for time to first emergency department visit (Figure 3).

Figure 3
Kaplan‐Meier curves for first emergency department visit after index hospital discharge for patients assigned to receive discharge software versus usual care discharge. Solid line represents patients assigned to discharge software. Dotted line represents patients assigned to usual care discharge.

Postdischarge adverse events were secondary, prespecified, outcomes. Data for adverse event adjudication were available for 98% (309/316) of discharge software patients and 97% (307/315) of usual care patients. Within 1 month after discharge, 46 patients had adverse events probably or definitely related to medical management. Two patients had 2 events and 1 patient had 3 events. For analysis, we randomly selected 1 event per patient. When comparing patients assigned to discharge software versus usual care, there were no differences in adverse events related to medical management (Table 2). Most of the events were possible adverse drug events (74%; 34/46). The adverse event severity was several days of symptoms or nonpermanent disability for 76% (35/46) of the adverse events. Adjudicators rated 26% (12/46) of the adverse events as preventable and 46% (21/46) as ameliorable. The absolute numbers of events were small. There were no differences between discharge software and usual care patients within adverse event strata defined by type, severity, preventable, or ameliorable (Table 3). For most of the patients with adverse events, the adjudicators could not identify a system problem or preventability category (Table 3). When a deficiency was evident, there was no pattern to suggest a significant difference between discharge software patients versus usual care patients.

Number of Adverse Events Related to Medical Management Within 1 Month After Discharge
 Discharge Software (n)Usual Care Discharge (n)
  • Abbreviation: ADE, adverse drug event.

At least 1 adverse event2323
Preventable adverse event75
Ameliorable adverse event912
Adverse event severity  
Serious laboratory abnormality only or 1 day of symptoms55
Several days of symptoms or nonpermanent disability1817
Permanent disability or death01
Adverse event by type  
Possible adverse drug event1717
Procedure‐related injury21
Therapeutic error44
Diagnostic error01
System problems associated with preventable or ameliorable adverse events  
Inadequate patient education regarding the medical condition or its treatment01
Poor communication between patient and physician21
Poor communication between hospital and community physicians00
Inadequate monitoring of the patient's illness after discharge06
Inadequate monitoring of the patient's treatment after discharge26
No emergency contact number given to patient to call about problems00
Patient with problems getting prescribed medications immediately10
Inadequate home services00
Delayed follow‐up care03
Premature hospital discharge12
Adverse drug event (ADE) preventability categories  
Drug involved in the ADE inappropriate for the clinical condition24
Dose, route, or frequency inappropriate for age, weight, creatinine clearance, or disease12
Failure to obtain required lab tests and/or drug levels12
Prior history of an adverse event or allergy to the drug12
Drug‐drug interaction involved in the ADE20
Toxic serum drug level documented00
Noncompliance involved in the ADE01

When we designed the trial, we assumed variance in outcomes measured at the patient level. We predicted some variance attributable to clustering by hospital physician. After the trial, we calculated the intracluster correlation coefficients for readmissions, emergency department visits, and adverse events. For all of these outcomes, the intracluster correlation coefficients were negligible. We also evaluated generalized estimating equations with and without correction for hospital physician cluster. We confirmed the negligible cluster effect on confidence intervals for intervention coefficients (Table 2).

We performed an exploratory stratified analysis. We evaluated the intervention effect on readmission within subgroups defined by covariates that predicted readmission (Table 4). When the intervention groups were compared within baseline categories of previous hospitalizations, previous emergency department visits, heart failure, and physical functioning, there was a consistent pattern with no differential effect by intervention assignment. None of the intervention coefficients were statistically significant (Table 4).

Patients Readmitted At Least Once Within 6 Months by Subgroup
SubgroupDischarge Software Readmitted n/n (%)Usual Care Readmitted n/n (%)Adjusted Parameter Estimate Intervention Coefficient (95% CI)
  • NOTE: Intervention was discharge software or usual care. Adjusted parameter estimates are intervention coefficients from generalized estimating equations.

  • Abbreviation: 95% CI, Wald 95% confidence interval.

  • Adjusted for previous emergency department visits, heart failure, and physical functioning.

  • Adjusted for previous hospital admissions, heart failure, and physical functioning.

  • Adjusted for previous hospital admissions, previous emergency department visits, and physical functioning.

  • Adjusted for previous hospital admissions, previous emergency department visits, and heart failure.

Hospital admissions during year prior to index admission   
0 or 177/247 (31.2)73/224 (32.6)0.025 (0.095, 0.045)*
2 or more40/69 (58.0)46/91 (50.5)0.059 (0.090, 0.208)*
Emergency department visits during 6 months before index admission   
0 or 164/194 (33.0)45/168 (26.8)0.033 (0.047, 0.113)
2 or more53/122 (43.4)74/147 (50.3)0.071 (0.188, 0.046)
Heart failure   
Present40/80 (50.0)36/67 (53.7)0.024 (0.224, 0.177)
Absent77/236 (32.6)83/248 (33.5)0.000 (0.076, 0.075)
Physical functioning from SF‐36   
Lowest third55/128 (43.0)59/121 (48.8)0.032 (0.161, 0.096)
Upper two‐thirds62/188 (33.0)60/194 (30.9)0.012 (0.071, 0.095)

Assessment of the Success of the Blind

We evaluated the adequacy of the blind for outcome assessors who interviewed patients or adjudicated adverse events. The guesses of outcomes assessors were unrelated to true intervention assignment (all P values >0.097). We interpreted the blind as adequate for outcome assessors who recorded readmissions, emergency department visits, and adverse events.

Discussion

We performed a cluster‐randomized clinical trial to measure the effects of discharge software versus usual care handwritten discharge. The discharge software with CPOE implemented elements of high‐quality discharge planning and communication endorsed by the National Quality Forum and systematic reviews.6, 38 Despite theoretical benefits, our discharge software intervention did not reduce readmissions or emergency department visits. What were potential explanations for our results? We assumed an association between postdischarge adverse events and readmissions or emergency department visits.1 Our failure to reduce adverse events might explain the failure to reduce readmissions or emergency department visits. Another potential explanation was related to adverse drug events. Other investigators showed most postdischarge adverse events were adverse drug events and our data confirmed previous studies.1, 2 Medication reconciliation at discharge was a potential mechanism for adverse drug event reduction.14 Medication reconciliation was the standard at the study hospital, so it was unethical to deny reconciliation to patients assigned to either intervention.39 Required medication reconciliation in both groups, by its known effect on preventable adverse drug events, might have reduced the event rates in both groups.14 This possibility is supported by the low rate of adverse events observed in our study compared with other studies.1 We speculate that the low background rate of adverse events at the study hospital may have minimized events in both the discharge software and usual care groups and prevented detection of software benefits, if present.39

One limitation of our study may have been the discharge software. The automated decision support in our software lacked features that might have improved outcomes. For example, the software did not generate a list of diagnostic test results that were pending at the time of discharge. Our software relied on prompts to the physician user that did not specify which tests were pending. The software did not perform error checks on the discharge orders to warn physicians about drug‐drug interactions, therapeutic duplications, or missing items (eg, immunizations, drugs, education). The absence of these software enhancements made our discharge process vulnerable to the lapses and slips of the physician user. Whether or not such enhancements affect clinically relevant outcomes remains a testable hypothesis for future studies.

Another limitation of our study was the outpatient physician response. Discharge software did not increase the proportion of outpatient physicians who said they received communication within 7 days after hospital discharge. Our intervention addressed the sending partner but not the receiving partner in the communication dyad. Our discharge software was not designed to change information flow within the outpatient physician office. We do not know if discharge communication arrived and remained unnoticed until the patient called or visited the outpatient clinic. Future studies of discharge communication should consider a closed loop design to assure receipt and comprehension.

When we designed our study, we expected at least some variance between patient clusters attributable to the physician who performed the discharge. Our analysis of intracluster correlation revealed negligible variance. We speculate the highly‐standardized discharge process implemented by discharge software and usual care at our hospital resulted in minimal variance. Future studies of discharge interventions may consider designs that avoid cluster randomization.

In conclusion, a discharge software application of CPOE did not affect readmissions, emergency department visits, or adverse events after discharge.

Acknowledgements

The authors thank Howard S. Cohen, MD, for his review of the trial protocol and the manuscript.

Adverse events occur to patients after their discharge from acute care hospitals.1, 2 Most of these injuries are adverse drug events, procedure‐related events, nosocomial infections, or falls.1 Postdischarge adverse events are associated with several days of symptoms, nonpermanent disability, emergency department visits, or hospital readmission.1, 3 When adverse events are preventable or ameliorable, the most common root cause is poor communication between hospital personnel and either the patient or the outpatient primary care physician.1 In addition, there may be deficits in discharge processes related to assessment and communication of unresolved problems.1 Systematic reviews have shown that discharge communication is an inefficient and error‐prone process.46

One potential solution to poor discharge communication is health information technology.7 An example of technology is discharge software with a computerized physician order entry (CPOE) system. By definition, a CPOE system is a computer‐based system that automates direct entry of orders by physicians and ensures standardized, legible, and complete orders.8 The benefits of CPOE have been tested in other inpatient settings.8, 9 It is logical to consider software applications with CPOE for discharge interventions.7

Several mechanisms explain the potential benefit of discharge software with CPOE.7 Applications with CPOE decrease medication errors.8, 10 Software with decision support could prompt physicians to enter posthospitalization appointment dates and orders for preventive services.11, 12 Discharge software could facilitate medication reconciliation and generate patient instructions and information.4, 1315 The potential benefits of discharge software with CPOE provide a rationale for clinical trials to measure benefits.

Previous studies addressed discharge applications of health information technology. Observational studies recorded outcomes such as physician satisfaction.16, 17 Prior randomized clinical trials measured quality and timeliness of discharge summaries.18 However, these previous trials did not assess clinically relevant outcomes like readmissions, emergency department visits, or adverse events. We performed a cluster‐randomized trial to assess the value of a discharge software application of CPOE. The rationale for our clustered design complied with recommendations from a systematic review of discharge interventions.5 Our objective was to assess the benefit of discharge software with CPOE when used to discharge patients at high risk for repeat admission. After the intervention, we compared the rates of hospital readmission, emergency department visits, and postdischarge adverse events due to medical management.

Methods

The trial design was a cluster randomized, controlled trial with blinded outcome assessment. Follow‐up occurred until 6 months after discharge from index hospitalization at a 730‐bed, tertiary care, teaching hospital in central Illinois. The Peoria Institutional Review Board approved the protocol for human research.

Participants

The cluster definition was the hospital physician. Patients discharged by the physician comprised the cluster. Hospital physicians and patients were enrolled between November 2004 and January 2007. Internal medicine resident or attending physicians were eligible. We excluded hospital physicians if their assignments to inpatient duties were less than 2 months during the 27‐month enrollment period. The rationale for the physician exclusion was a consequence of the patient enrollment rate of 3 to 5 patients per physician per month. Physicians with brief assignments could not achieve the goal of 9 or more patients per cluster. After physicians gave informed consent to screen their patients, trained research coordinators applied inclusion and exclusion criteria and obtained informed consent from patients. Research personnel identified all consecutive, unique, adult inpatients who were discharged to home. Patient inclusion required a probability of repeat admission (Pra) score 0.40.19, 20 The Pra score came from a logistic model of age, gender, prior hospitalizations, prior doctor visits, self‐rated health status, presence of informal caregiver in the home, and comorbid coronary heart disease and diabetes mellitus. Research coordinators calculated the Pra within 2 days before discharge from the index hospitalization. Other details about exclusion criteria have been published.21 If a patient's outpatient primary care physician treated the patient during the index hospitalization, then there was no perceived barrier in physician‐to‐physician communication and we excluded the patient.

Intervention

The research intervention was a CPOE software application that facilitated communication at the time of hospital discharge to patients, retail pharmacists, and community physicians. Details about the discharge software appeared in a previous publication.7 Software features included required fields, pick lists, standard drug doses, alerts, reminders, and online reference information. The software prompted the discharging physician to enter pending tests and order tests after discharge. Hospital physicians used the software on the day of discharge and automatically generated 4 discharge documents. The first document was a personalized letter to the outpatient physician with discharge diagnoses, reconciled medication list, diet and activity instructions, patient education materials provided, and follow‐up appointments and studies. Second, the software printed legible prescriptions along with specific information for the dispensing pharmacist about changes and deletions in the patient's previous regimen. Third, the software created patient instructions with addresses and telephone numbers for follow‐up appointments and tests. Fourth, the software printed a legible discharge order including all of the aforementioned information.

The control intervention was the usual care discharge process as described previously.7 Hospital physicians and ward nurses completed handwritten discharge forms on the day of discharge. The forms contained blanks for discharge diagnoses, discharge medications, medication instructions, postdischarge activities and restrictions, postdischarge diet, postdischarge diagnostic and therapeutic interventions, and appointments. Patients received handwritten copies of the forms, 1 page of which also included medication instructions and prescriptions. A previous publication gave details about the standard care available to all patients regardless of intervention.7

Randomization

The unit of randomization was the hospital physician who performed the discharge process. Random allocation was to discharge software or usual care discharge process. The randomization ratio was 1:1, the block size was 2, and there was no stratification or matching. There was concealed allocation and details are available from the investigators. Hospital physicians subsequently used their randomly assigned process when discharging their patients who enrolled in the study. After random allocation, it was not possible to conceal the test or control intervention from physicians or their patients. Likewise, it was not possible to conceal the outcome ascertainment, including readmission, from the hospital physicians.

All hospital physicians received training on the usual care discharge process. Physicians assigned to discharge software completed additional training via multimedia demonstration with 1‐on‐1 coaching as needed. Physicians assigned to usual care did not receive training on the discharge software and were blocked from using the software. After informed consent, patients were passive recipients of the research intervention performed by their discharging physician. Patients received the research intervention on the day of discharge from the index hospitalization.

The baseline assessment of patient characteristics occurred during the index hospitalization. Trained data abstractors recorded patient demographic data plus variables to calculate the Pra score for probability for repeat admission. We recorded additional variables because of their possible association with readmission.15, 2229 Data came from the patient or proxy for physical functioning and mental health (SF‐36, Version 2; Medical Outcomes Trust, Boston, MA). Other data for predictor variables came from interviews or hospital records.

Outcome Assessment

The primary study outcome was the proportion of patients readmitted at least once within 6 months after the index hospitalization. Readmission was for any reason and included observation and full admission status. Secondary outcomes were emergency department visits that did not result in hospital admission. Outcome assessment occurred at the patient level. We obtained data for readmissions and emergency department visits from 6 hospitals in central Illinois where study patients were likely to seek care. We validated readmissions and emergency department visits via patient/proxy telephone interviews that occurred 6 months after index hospital discharge. Interviewers were blind to intervention assignment. We evaluated the adequacy of the blind and asked interviewers to guess the patient's intervention assignment.

Another secondary outcome was the proportion of patients who experienced an adverse event related to medical management within 1 month after discharge. For adverse event ascertainment, we employed the process of Forster et al.1, 2 Within 20 to 40 days after discharge, an internal medicine physician performed telephone interviews with the patient or proxy. The interviewer recorded symptoms, drug information, other treatment, hospital readmissions, and emergency department visits. Another physician compiled case summaries from interview data and information abstracted from the electronic medical record, including dictated discharge summaries from the index hospitalization and postdischarge emergency department visits, diagnostic test results, and readmission reports. Two additional internal medicine physicians adjudicated each case summary separately. We counted adverse events only when adjudicators agreed that medical management probably or definitely caused the event. The initial rating by each adjudicator revealed moderate‐to‐good agreement (Kappa = 0.52).30 When initial adjudications were discordant, then adjudicators met and resolved all discrepancies. The adjudicators also scored the severity of the adverse event. The severity scale options were serious laboratory abnormality only, 1 day of symptoms, several days of symptoms, nonpermanent disability, permanent disability, or death. The adjudicators also scored the adverse event as preventable (yes/no), ameliorable (yes/no), and recorded system problems associated with preventable and ameliorable adverse events.1 For adverse drug events, the adjudicators recorded preventability categories defined by previous investigators.31 We designed the adverse event outcome ascertainment as a blinded process. We evaluated the success of the blind and asked adjudicators to guess the patient's intervention assignment.

Sample Size

The sample size analysis employed several assumptions regarding the proportion of readmitted patients. The estimated readmission rate after usual care was 37%.24, 3236 The minimum clinically relevant difference in readmission rates was 13%, an empirical boundary for quantitative significance.37 Estimates for intracluster correlation were not available when we designed the trial. We projected intracluster correlations with low, medium, and high values. The cluster number and size were selected to maintain test significance level, 1‐sided alpha, <0.05 and power >80%. The sample size assumed no interim analysis. The initial sample size estimates were 11 physician clusters per intervention with 25 patients per cluster. During the first 2 months of patient recruitment, we observed that we could not consistently achieve clusters with 25 patients. We recalculated the sample size. Using the same assumptions, we found we could achieve similar test significance and power with 35 physician clusters per intervention and 9 patients per cluster. The sample size calculator was nQuery (Statistical Solutions, Saugus, MA).

Statistical Methods

Analyses were performed with SPSS PC (Version 15.0.1; SPSS Inc, Chicago, IL). Using descriptive statistics, we reported baseline variables as means and standard deviations (SD) for interval variables, and percentages for categorical variables. For outcome variables, we utilized the principle of intention‐to‐treat and assumed patient exposure to the intervention randomly assigned to their discharging physician. We inspected scatter plots and correlations for all variables to test assumptions regarding normal distribution, homogeneity of variance, and linearity of relationships between independent and dependent variables. When assumptions failed, we stratified variables (median or thirds) or performed transformations to satisfy assumptions. For patient‐level outcome variables, we calculated intracluster correlation coefficients. The assessment of the blind was unaffected by the cluster assumption so we used the chi‐square procedure. For analysis of time to event, we used Kaplan‐Meier plots.

The primary hypothesis was a significant decrease in the primary readmission outcome for patients assigned to discharge software. We tested the primary hypothesis with generalized estimating equations that corrected for clustering by hospital physician and adjusted for covariates that predicted readmission. The intervention variable was discharge software versus usual care handwritten discharge. We reported parameter estimates of the intervention variable coefficient and Wald 95% confidence interval (95% CI) with and without correction for cluster. For the secondary, patient‐level outcomes, we performed similar analyses with generalized estimating equations that corrected for clustering by hospital physician.

During covariate analysis, we screened all baseline variables for their correlation with readmission. The variable with the highest correlation and P value <0.05 entered initially in the general estimating equation. After initial variable entry, we evaluated subsequent variables with partial correlations that controlled for variables entered previously. At each iterative step, we entered into the model the variable with the highest partial correlation and P value <0.05.

In exploratory analyses, we examined intervention group differences within strata defined by covariates that predicted readmission. We used generalized estimating equations and adjusted for the other covariates that predicted readmission.

Results

We screened 127 physicians who were general internal medicine hospital physicians. Seventy physicians consented and received random allocation to discharge software or usual care. The physician characteristics appear in Table 1. Most of the hospital physicians were interns in the first year of postgraduate training (58.6%; 41/70). We excluded 57 physicians for reasons shown in the trial flow diagram (Figure 1). The most common reason for hospital physician exclusion applied to resident physicians in their last months of training before graduation or emergency department residents temporarily assigned to internal medicine training. We approached 6,884 patients during their index hospitalization. After excluding 6,253 ineligible patients, we enrolled and followed 631 patients who received the discharge intervention (Figure 1). During 6 months of follow‐up, a small proportion of patients died (3%; 20/631). Hospital records were available for deceased patients and they were included in the analysis. A small proportion (6%; 41/631) of patients withdrew consent or left the trial for other reasons during 6 months. There was no differential dropout between the interventions. Protocol deviations were rare (0.5%; 3/631). Three patients erroneously received usual care discharge from physicians assigned to discharge software. All 631 patients were included in the intention‐to‐treat analysis. The baseline characteristics of the randomly‐assigned hospital physicians and their patients are in Table 1.

Figure 1
Trial flow diagram for hospital physicians and patients.
Baseline Characteristics for Each Intervention at the Hospital Physician Cluster Level and Individual Patient Level
 Discharge SoftwareUsual Care
  • Abbreviation: SD, standard deviation.

  • Missing data for 1 or 2 subjects.

Hospital physician characteristics, n (%)(n = 35)(n = 35)
Postgraduate year 118 (51.4)23 (65.7)
Postgraduate years 2‐410 (28.6)7 (20.0)
Attending physician7 (20.0)5 (14.3)
Patient characteristics(n = 316)(n = 315)
Gender, female, n (%)180 (57.0)168 (53.3)
Age, years, n (%)  
18‐4468 (21.5)95 (30.2)
45‐5479 (25.0)76 (24.1)
55‐6486 (27.2)74 (23.5)
65‐9883 (26.3)70 (22.2)
Race, n (%)  
Caucasian239 (75.6)229 (72.7)
Black72 (22.8)85 (27.0)
Other5 (1.6)1 (0.3)
Self‐rated health status, n (%)  
Poor82 (25.9)108 (34.3)
Fair169 (53.5)147 (46.7)
Good54 (17.1)46 (14.6)
Very good10 (3.2)11 (3.5)
Excellent1 (0.3)3 (1.0)
Diabetes mellitus, n (%)172 (54.4)177 (56.2)
Chronic obstructive pulmonary disease, n (%)  
None259 (82.0)257 (81.6)
Without oral steroid or home oxygen28 (8.9)26 (8.3)
With chronic oral steroid10 (3.2)8 (2.5)
With home oxygen oral steroid19 (6.0)24 (7.6)
Coronary heart disease, n (%)133 (42.1)120 (38.1)
Heart failure, n (%)80 (25.3)67 (21.3)
Informal caregiver available, yes, n (%)313 (99.1)313 (99.4)
Taking loop diuretic, n (%)110 (34.8)88 (27.9)
Physical functioning from SF‐36, n (%)  
Lowest third128 (40.5)121 (38.4)
Upper two‐thirds188 (59.5)194 (61.6)
Mental health from SF‐36, n (%)  
Lowest third113 (35.8)117 (37.1)*
Upper two‐thirds203 (64.2)197 (62.5)*
Hospital admissions during year prior to index admission, n (%)  
0 or 1247 (78.2)224 (71.1)
2 or more69 (21.8)91 (28.9)
Emergency department visits during 6 months before index admission, n (%)  
0 or 1194 (61.4)168 (53.3)
2 or more122 (38.6)147 (46.7)
Outpatient doctor or clinic visits during year prior to index admission  
0 to 497 (30.7%)77 (24.4%)
5 to 868 (21.5%)81 (25.7%)
9 to 1282 (25.9%)84 (26.7%)
13 or more69 (21.8%)73 (23.2%)
Insurance or payor  
Medicare, age less than 65 years18 (5.7%)13 (4.1%)
Medicare, age 65 years and older56 (17.7%)40 (12.7%)
Medicaid, age less than 65 years98 (31.0%)130 (41.3%)
Medicaid, age 65 years and older17 (5.4%)20 (6.3%)
Commercial or veteran85 (26.9%)61 (19.4%)
Self‐pay42 (13.3%)51 (16.2%)
Religious participation  
Never159 (50.3%)164 (52.1%)
1‐24 times per year55 (17.4%)51 (16.2%)
1‐7 times per week102 (32.3%)100 (31.7%)
Volunteer activity, 1 or more hour/month31 (9.8%)39 (12.4%)
Employment status  
Not working229 (72.5%)233 (74.4%)*
Part‐time (<37.5 hours/week)30 (9.5%)25 (8.0%)*
Full‐time (at least 37.5 hour/week)57 (18.0%)55 (17.6%)*
Number of discharge medications, mean (SD)10.5 (4.8)9.9 (5.1)
Severity of illness, mean (SD)1.8 (1.2)1.6 (1.3)
Charlson‐Deyo comorbidity, mean (SD)1.7 (1.4)1.6 (1.9)
Index hospital length of stay, days, mean (SD)3.9 (3.5)3.5 (3.5)
Blood urea nitrogen, mean (SD)17.9 (12.9)19.1 (12.9)
Probability of repeat admission, Pra, mean (SD)0.486 (0.072)0.495 (0.076)

We asked outpatient physicians about their receipt of discharge communication from hospital physicians. The text of the question was, How soon after discharge did you receive any information (in any form) relating to this patient's hospital admission and discharge plans? We mailed the question 10 days after discharge to outpatient physicians designated by patients enrolled in the study. Among patients in the discharge software group, 75.0% (237/316) of their outpatient physicians responded to the question. The response rate was 80.6% (254/315) from physicians who followed patients in the usual care group. Respondents from the discharge software group said within 1‐2 days or within a week for 56.0% (177/316) of patients. Respondents from the usual care group said within 1‐2 days or within a week for 57.1% (180/315) of patients. The difference between the intervention groups, 1.1% (95% CI, 9.2% to 6.9%), was not significant.

The primary, prespecified, outcome of the study was the proportion of patients with at least 1 readmission to the hospital. After intervention with discharge software versus usual care, there was no significant difference in readmission rates (Table 2) or time to first readmission (Figure 2). We screened all baseline variables in Table 1 and sought predictors of readmission to employ in adjusted models. For example, we evaluated physician level of training because we wondered if experience or seniority affected readmission when hospital physicians used the discharge software or usual care discharge. The candidate variable, physician level of training, did not correlate with readmission (rho = 0.066; P = 0.100), so it was dropped from subsequent analyses. After screening all variables in Table 1, we found 4 independent predictors of readmission: previous hospitalizations, previous emergency department visits, heart failure, and physical function. Generalized estimating equations for readmission that adjusted for predictor variables confirmed a negligible parameter estimate for the discharge intervention variable coefficient (Table 2).

Figure 2
Kaplan‐Meier curves for first readmission after index hospital discharge for patients assigned to receive discharge software versus usual care discharge. Solid line represents patients assigned to discharge software. Dotted line represents patients assigned to usual care discharge.
Outcomes for 316 Patients Assigned to Discharge Software and 315 Patients Assigned to Usual Care Intervention
OutcomeDischarge Software, n (%)Usual Care, n (%)Parameter Estimate Without Cluster Correction Intervention Coefficient (95% CI)P ValueParameter Estimate With Cluster Correction Intervention Coefficient (95% CI)P Value
  • NOTE: Parameter estimates are intervention coefficients from generalized estimating equations for outcome variables. Parameter estimates from generalized estimating equations appear with and without correction for clustering by hospital physician: 34 physicians assigned to discharge software and 35 assigned to usual care.

  • Abbreviation: 95% CI, Wald 95% confidence interval.

  • Generalized estimating equations adjusted for previous hospitalizations, previous emergency department visits, heart failure, and physical function.

Readmitted within 6 months117 (37.0%)119 (37.8%)0.005* (0.076, 0.067)0.8970.005* (0.074, 0.065)0.894
Emergency department visit within 6 months112 (35.4%)128 (40.6%)0.052 (0.128, 0.024)0.1790.052 (0.115, 0.011)0.108
Adverse event within 1 month23 (7.3%)23 (7.3%)0.003 (0.037, 0.043)0.8860.003 (0.037, 0.043)0.884

We evaluated emergency department visits that were unrelated to readmission as secondary, prespecified, outcomes. The results were similar to readmission results. While the proportion of patients with at least 1 emergency department visit was lower for the discharge software intervention, the difference with usual care was not significant (Table 2). There was no significant difference between interventions for time to first emergency department visit (Figure 3).

Figure 3
Kaplan‐Meier curves for first emergency department visit after index hospital discharge for patients assigned to receive discharge software versus usual care discharge. Solid line represents patients assigned to discharge software. Dotted line represents patients assigned to usual care discharge.

Postdischarge adverse events were secondary, prespecified, outcomes. Data for adverse event adjudication were available for 98% (309/316) of discharge software patients and 97% (307/315) of usual care patients. Within 1 month after discharge, 46 patients had adverse events probably or definitely related to medical management. Two patients had 2 events and 1 patient had 3 events. For analysis, we randomly selected 1 event per patient. When comparing patients assigned to discharge software versus usual care, there were no differences in adverse events related to medical management (Table 2). Most of the events were possible adverse drug events (74%; 34/46). The adverse event severity was several days of symptoms or nonpermanent disability for 76% (35/46) of the adverse events. Adjudicators rated 26% (12/46) of the adverse events as preventable and 46% (21/46) as ameliorable. The absolute numbers of events were small. There were no differences between discharge software and usual care patients within adverse event strata defined by type, severity, preventable, or ameliorable (Table 3). For most of the patients with adverse events, the adjudicators could not identify a system problem or preventability category (Table 3). When a deficiency was evident, there was no pattern to suggest a significant difference between discharge software patients versus usual care patients.

Number of Adverse Events Related to Medical Management Within 1 Month After Discharge
 Discharge Software (n)Usual Care Discharge (n)
  • Abbreviation: ADE, adverse drug event.

At least 1 adverse event2323
Preventable adverse event75
Ameliorable adverse event912
Adverse event severity  
Serious laboratory abnormality only or 1 day of symptoms55
Several days of symptoms or nonpermanent disability1817
Permanent disability or death01
Adverse event by type  
Possible adverse drug event1717
Procedure‐related injury21
Therapeutic error44
Diagnostic error01
System problems associated with preventable or ameliorable adverse events  
Inadequate patient education regarding the medical condition or its treatment01
Poor communication between patient and physician21
Poor communication between hospital and community physicians00
Inadequate monitoring of the patient's illness after discharge06
Inadequate monitoring of the patient's treatment after discharge26
No emergency contact number given to patient to call about problems00
Patient with problems getting prescribed medications immediately10
Inadequate home services00
Delayed follow‐up care03
Premature hospital discharge12
Adverse drug event (ADE) preventability categories  
Drug involved in the ADE inappropriate for the clinical condition24
Dose, route, or frequency inappropriate for age, weight, creatinine clearance, or disease12
Failure to obtain required lab tests and/or drug levels12
Prior history of an adverse event or allergy to the drug12
Drug‐drug interaction involved in the ADE20
Toxic serum drug level documented00
Noncompliance involved in the ADE01

When we designed the trial, we assumed variance in outcomes measured at the patient level. We predicted some variance attributable to clustering by hospital physician. After the trial, we calculated the intracluster correlation coefficients for readmissions, emergency department visits, and adverse events. For all of these outcomes, the intracluster correlation coefficients were negligible. We also evaluated generalized estimating equations with and without correction for hospital physician cluster. We confirmed the negligible cluster effect on confidence intervals for intervention coefficients (Table 2).

We performed an exploratory stratified analysis. We evaluated the intervention effect on readmission within subgroups defined by covariates that predicted readmission (Table 4). When the intervention groups were compared within baseline categories of previous hospitalizations, previous emergency department visits, heart failure, and physical functioning, there was a consistent pattern with no differential effect by intervention assignment. None of the intervention coefficients were statistically significant (Table 4).

Patients Readmitted At Least Once Within 6 Months by Subgroup
SubgroupDischarge Software Readmitted n/n (%)Usual Care Readmitted n/n (%)Adjusted Parameter Estimate Intervention Coefficient (95% CI)
  • NOTE: Intervention was discharge software or usual care. Adjusted parameter estimates are intervention coefficients from generalized estimating equations.

  • Abbreviation: 95% CI, Wald 95% confidence interval.

  • Adjusted for previous emergency department visits, heart failure, and physical functioning.

  • Adjusted for previous hospital admissions, heart failure, and physical functioning.

  • Adjusted for previous hospital admissions, previous emergency department visits, and physical functioning.

  • Adjusted for previous hospital admissions, previous emergency department visits, and heart failure.

Hospital admissions during year prior to index admission   
0 or 177/247 (31.2)73/224 (32.6)0.025 (0.095, 0.045)*
2 or more40/69 (58.0)46/91 (50.5)0.059 (0.090, 0.208)*
Emergency department visits during 6 months before index admission   
0 or 164/194 (33.0)45/168 (26.8)0.033 (0.047, 0.113)
2 or more53/122 (43.4)74/147 (50.3)0.071 (0.188, 0.046)
Heart failure   
Present40/80 (50.0)36/67 (53.7)0.024 (0.224, 0.177)
Absent77/236 (32.6)83/248 (33.5)0.000 (0.076, 0.075)
Physical functioning from SF‐36   
Lowest third55/128 (43.0)59/121 (48.8)0.032 (0.161, 0.096)
Upper two‐thirds62/188 (33.0)60/194 (30.9)0.012 (0.071, 0.095)

Assessment of the Success of the Blind

We evaluated the adequacy of the blind for outcome assessors who interviewed patients or adjudicated adverse events. The guesses of outcomes assessors were unrelated to true intervention assignment (all P values >0.097). We interpreted the blind as adequate for outcome assessors who recorded readmissions, emergency department visits, and adverse events.

Discussion

We performed a cluster‐randomized clinical trial to measure the effects of discharge software versus usual care handwritten discharge. The discharge software with CPOE implemented elements of high‐quality discharge planning and communication endorsed by the National Quality Forum and systematic reviews.6, 38 Despite theoretical benefits, our discharge software intervention did not reduce readmissions or emergency department visits. What were potential explanations for our results? We assumed an association between postdischarge adverse events and readmissions or emergency department visits.1 Our failure to reduce adverse events might explain the failure to reduce readmissions or emergency department visits. Another potential explanation was related to adverse drug events. Other investigators showed most postdischarge adverse events were adverse drug events and our data confirmed previous studies.1, 2 Medication reconciliation at discharge was a potential mechanism for adverse drug event reduction.14 Medication reconciliation was the standard at the study hospital, so it was unethical to deny reconciliation to patients assigned to either intervention.39 Required medication reconciliation in both groups, by its known effect on preventable adverse drug events, might have reduced the event rates in both groups.14 This possibility is supported by the low rate of adverse events observed in our study compared with other studies.1 We speculate that the low background rate of adverse events at the study hospital may have minimized events in both the discharge software and usual care groups and prevented detection of software benefits, if present.39

One limitation of our study may have been the discharge software. The automated decision support in our software lacked features that might have improved outcomes. For example, the software did not generate a list of diagnostic test results that were pending at the time of discharge. Our software relied on prompts to the physician user that did not specify which tests were pending. The software did not perform error checks on the discharge orders to warn physicians about drug‐drug interactions, therapeutic duplications, or missing items (eg, immunizations, drugs, education). The absence of these software enhancements made our discharge process vulnerable to the lapses and slips of the physician user. Whether or not such enhancements affect clinically relevant outcomes remains a testable hypothesis for future studies.

Another limitation of our study was the outpatient physician response. Discharge software did not increase the proportion of outpatient physicians who said they received communication within 7 days after hospital discharge. Our intervention addressed the sending partner but not the receiving partner in the communication dyad. Our discharge software was not designed to change information flow within the outpatient physician office. We do not know if discharge communication arrived and remained unnoticed until the patient called or visited the outpatient clinic. Future studies of discharge communication should consider a closed loop design to assure receipt and comprehension.

When we designed our study, we expected at least some variance between patient clusters attributable to the physician who performed the discharge. Our analysis of intracluster correlation revealed negligible variance. We speculate the highly‐standardized discharge process implemented by discharge software and usual care at our hospital resulted in minimal variance. Future studies of discharge interventions may consider designs that avoid cluster randomization.

In conclusion, a discharge software application of CPOE did not affect readmissions, emergency department visits, or adverse events after discharge.

Acknowledgements

The authors thank Howard S. Cohen, MD, for his review of the trial protocol and the manuscript.

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  5. Shepperd S,Parkes J,McClaren J,Phillips C.Discharge planning from hospital to home.Cochrane Database Syst Rev.2004;(1):CD000313.
  6. Kripalani S,LeFevre F,Phillips CO,Williams MV,Basaviah P,Baker DW.Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care.JAMA.2007;297(8):831841.
  7. Nace GS,Graumlich JF,Aldag JC.Software design to facilitate information transfer at hospital discharge.Inform Prim Care.2006;14(2):109119.
  8. Kaushal R,Shojania KG,Bates DW.Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review.Arch Intern Med.2003;163(12):14091416.
  9. Kuperman GJ,Gibson RF.Computer physician order entry: benefits, costs, and issues.Ann Intern Med.2003;139(1):3139.
  10. Chaudhry B,Wang J,Wu S, et al.Systematic review: impact of health information technology on quality, efficiency, and costs of medical care.Ann Intern Med.2006;144(10):742752.
  11. Kiefe CI,Heudebert G,Box JB,Farmer RM,Michael M,Clancy CM.Compliance with post‐hospitalization follow‐up visits: rationing by inconvenience?Ethn Dis.1999;9(3):387395.
  12. Dexter PR,Perkins S,Overhage JM,Maharry K,Kohler RB,McDonald CJ.A computerized reminder system to increase the use of preventive care for hospitalized patients.N Engl J Med.2001;345(13):965970.
  13. Paquette‐Lamontagne N,McLean WM,Besse L,Cusson J.Evaluation of a new integrated discharge prescription form.Ann Pharmacother.2001;35(7‐8):953958.
  14. Schnipper JL,Kirwin JL,Cotugno MC, et al.Role of pharmacist counseling in preventing adverse drug events after hospitalization.Arch Intern Med.2006;166(5):565571.
  15. Marcantonio ER,McKean S,Goldfinger M,Kleefield S,Yurkofsky M,Brennan TA.Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan.Am J Med.1999;107(1):1317.
  16. Sands DZ,Safran C.Closing the loop of patient care—a clinical trial of a computerized discharge medication program.Proc Annu Symp Comput Appl Med Care.1994:841845.
  17. O'Connell EM,Teich JM,Pedraza LA,Thomas D.A comprehensive inpatient discharge system.Proc AMIA Annu Fall Symp.1996:699703.
  18. Agency for Healthcare Research and Quality. Making health care safer: a critical analysis of patient safety practices, subchapter 42.3. Discharge summaries and follow‐up. Available at: http://www.ahrq.gov/clinic/ptsafety/chap42b. htm#42.3. Accessed January 2009.
  19. Pacala JT,Boult C,Boult L.Predictive validity of a questionnaire that identifies older persons at risk for hospital admission.J Am Geriatr Soc.1995;43(4):374377.
  20. Pacala JT,Boult C,Reed RL,Aliberti E.Predictive validity of the Pra instrument among older recipients of managed care.J Am Geriatr Soc.1997;45(5):614617.
  21. Graumlich JF,Novotny NL,Aldag JC.Brief scale measuring patient preparedness for hospital discharge to home: psychometric properties.J Hosp Med.2008;3(6):446454.
  22. Ware JE.SF‐36 health survey update.Spine.2000;25(24):31303139.
  23. Reuben DB,Keeler E,Seeman TE,Sewall A,Hirsch SH,Guralnik JM.Development of a method to identify seniors at high risk for high hospital utilization.Med Care.2002;40(9):782793.
  24. Naylor MD,Brooten D,Campbell R, et al.Comprehensive discharge planning and home follow‐up of hospitalized elders: arandomized clinical trial.JAMA.1999;281(7):613620.
  25. Smith DM,Giobbie‐Hurder A,Weinberger M, et al.Predicting non‐elective hospital readmissions: a multi‐site study. Department of Veterans Affairs Cooperative Study Group on Primary Care and Readmissions.J Clin Epidemiol.2000;53:11131118.
  26. Corrigan JM,Martin JB.Identification of factors associated with hospital readmission and development of a predictive model.Health Serv Res.1992;27(1):81101.
  27. Romano PS,Chan BK.Risk‐adjusting acute myocardial infarction mortality: are APR‐DRGs the right tool?Health Serv Res.2000;34(7):14691489.
  28. Deyo RA,Cherkin DC,Ciol MA.Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases.J Clin Epidemiol.1992;45(6):613619.
  29. Shelton P,Sager MA,Schraeder C.The community assessment risk screen (CARS): identifying elderly persons at risk for hospitalization or emergency department visit.Am J Manag Care.2000;6(8):925933.
  30. Sackett DL,Haynes RB,Guyatt GH,Tugwell P.Clinical Epidemiology: A Basic Science for Clinical Medicine.2nd ed.Boston:Little, Brown;1991.
  31. Winterstein AG,Hatton RC,Gonzalez‐Rothi R,Johns TE,Segal R.Identifying clinically significant preventable adverse drug events through a hospital's database of adverse drug reaction reports.Am J Health Syst Pharm.2002;59(18):17421749.
  32. Nazareth I,Burton A,Shulman S,Smith P,Haines A,Timberal H.A pharmacy discharge plan for hospitalized elderly patients—a randomized controlled trial.Age Ageing.2001;30(1):3340.
  33. McInnes E,Mira M,Atkin N,Kennedy P,Cullen J.Can GP input into discharge planning result in better outcomes for the frail aged: results from a randomized controlled trial.Fam Pract.1999;16(3):289293.
  34. Phillips CO,Wright SM,Kern DE,Singa RM,Shepperd S,Rubin HR.Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta‐analysis.JAMA.2004;291(11):13581367.
  35. Andersen HE,Schultz‐Larsen K,Kreiner S,Forchhammer BH,Eriksen K,Brown A.Can readmission after stroke be prevented? Results of a randomized clinical study: a postdischarge follow‐up service for stroke survivors.Stroke.2000;31(5):10381045.
  36. Weinberger M,Oddone EZ,Henderson WG.Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission.N Engl J Med.1996;334(22):1441147.
  37. Burnand B,Kernan WN,Feinstein AR.Indexes and boundaries for “quantitative significance” in statistical decisions.J Clin Epidemiol.1990;43(12):12731284.
  38. National Quality Forum. Safe Practices for Better Healthcare 2006 Update, A Consensus Report, Safe Practice 11: Discharge Systems. Available at: http://qualityforum.org/pdf/reports/safe_practices/txsppublic.pdf. Accessed January 2009.
  39. Whittington J,Cohen H.OSF Healthcare's journey in patient safety.Qual Manag Health Care.2004;13(1):5359.
References
  1. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138(3):161167.
  2. Forster AJ,Clark HD,Menard A, et al.Adverse events among medical patients after discharge from hospital.CMAJ.2004;170(3):345349.
  3. Epstein K,Juarez E,Loya K,Gorman MJ,Singer A.Frequency of new or worsening symptoms in the posthospitalization period.J Hosp Med.2007;2(2):5868.
  4. Johnson A,Sandford J,Tyndall J.Written and verbal information versus verbal information only for patients being discharged from acute hospital settings to home.Cochrane Database Syst Rev.2003;(4):CD003716.
  5. Shepperd S,Parkes J,McClaren J,Phillips C.Discharge planning from hospital to home.Cochrane Database Syst Rev.2004;(1):CD000313.
  6. Kripalani S,LeFevre F,Phillips CO,Williams MV,Basaviah P,Baker DW.Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care.JAMA.2007;297(8):831841.
  7. Nace GS,Graumlich JF,Aldag JC.Software design to facilitate information transfer at hospital discharge.Inform Prim Care.2006;14(2):109119.
  8. Kaushal R,Shojania KG,Bates DW.Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review.Arch Intern Med.2003;163(12):14091416.
  9. Kuperman GJ,Gibson RF.Computer physician order entry: benefits, costs, and issues.Ann Intern Med.2003;139(1):3139.
  10. Chaudhry B,Wang J,Wu S, et al.Systematic review: impact of health information technology on quality, efficiency, and costs of medical care.Ann Intern Med.2006;144(10):742752.
  11. Kiefe CI,Heudebert G,Box JB,Farmer RM,Michael M,Clancy CM.Compliance with post‐hospitalization follow‐up visits: rationing by inconvenience?Ethn Dis.1999;9(3):387395.
  12. Dexter PR,Perkins S,Overhage JM,Maharry K,Kohler RB,McDonald CJ.A computerized reminder system to increase the use of preventive care for hospitalized patients.N Engl J Med.2001;345(13):965970.
  13. Paquette‐Lamontagne N,McLean WM,Besse L,Cusson J.Evaluation of a new integrated discharge prescription form.Ann Pharmacother.2001;35(7‐8):953958.
  14. Schnipper JL,Kirwin JL,Cotugno MC, et al.Role of pharmacist counseling in preventing adverse drug events after hospitalization.Arch Intern Med.2006;166(5):565571.
  15. Marcantonio ER,McKean S,Goldfinger M,Kleefield S,Yurkofsky M,Brennan TA.Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan.Am J Med.1999;107(1):1317.
  16. Sands DZ,Safran C.Closing the loop of patient care—a clinical trial of a computerized discharge medication program.Proc Annu Symp Comput Appl Med Care.1994:841845.
  17. O'Connell EM,Teich JM,Pedraza LA,Thomas D.A comprehensive inpatient discharge system.Proc AMIA Annu Fall Symp.1996:699703.
  18. Agency for Healthcare Research and Quality. Making health care safer: a critical analysis of patient safety practices, subchapter 42.3. Discharge summaries and follow‐up. Available at: http://www.ahrq.gov/clinic/ptsafety/chap42b. htm#42.3. Accessed January 2009.
  19. Pacala JT,Boult C,Boult L.Predictive validity of a questionnaire that identifies older persons at risk for hospital admission.J Am Geriatr Soc.1995;43(4):374377.
  20. Pacala JT,Boult C,Reed RL,Aliberti E.Predictive validity of the Pra instrument among older recipients of managed care.J Am Geriatr Soc.1997;45(5):614617.
  21. Graumlich JF,Novotny NL,Aldag JC.Brief scale measuring patient preparedness for hospital discharge to home: psychometric properties.J Hosp Med.2008;3(6):446454.
  22. Ware JE.SF‐36 health survey update.Spine.2000;25(24):31303139.
  23. Reuben DB,Keeler E,Seeman TE,Sewall A,Hirsch SH,Guralnik JM.Development of a method to identify seniors at high risk for high hospital utilization.Med Care.2002;40(9):782793.
  24. Naylor MD,Brooten D,Campbell R, et al.Comprehensive discharge planning and home follow‐up of hospitalized elders: arandomized clinical trial.JAMA.1999;281(7):613620.
  25. Smith DM,Giobbie‐Hurder A,Weinberger M, et al.Predicting non‐elective hospital readmissions: a multi‐site study. Department of Veterans Affairs Cooperative Study Group on Primary Care and Readmissions.J Clin Epidemiol.2000;53:11131118.
  26. Corrigan JM,Martin JB.Identification of factors associated with hospital readmission and development of a predictive model.Health Serv Res.1992;27(1):81101.
  27. Romano PS,Chan BK.Risk‐adjusting acute myocardial infarction mortality: are APR‐DRGs the right tool?Health Serv Res.2000;34(7):14691489.
  28. Deyo RA,Cherkin DC,Ciol MA.Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases.J Clin Epidemiol.1992;45(6):613619.
  29. Shelton P,Sager MA,Schraeder C.The community assessment risk screen (CARS): identifying elderly persons at risk for hospitalization or emergency department visit.Am J Manag Care.2000;6(8):925933.
  30. Sackett DL,Haynes RB,Guyatt GH,Tugwell P.Clinical Epidemiology: A Basic Science for Clinical Medicine.2nd ed.Boston:Little, Brown;1991.
  31. Winterstein AG,Hatton RC,Gonzalez‐Rothi R,Johns TE,Segal R.Identifying clinically significant preventable adverse drug events through a hospital's database of adverse drug reaction reports.Am J Health Syst Pharm.2002;59(18):17421749.
  32. Nazareth I,Burton A,Shulman S,Smith P,Haines A,Timberal H.A pharmacy discharge plan for hospitalized elderly patients—a randomized controlled trial.Age Ageing.2001;30(1):3340.
  33. McInnes E,Mira M,Atkin N,Kennedy P,Cullen J.Can GP input into discharge planning result in better outcomes for the frail aged: results from a randomized controlled trial.Fam Pract.1999;16(3):289293.
  34. Phillips CO,Wright SM,Kern DE,Singa RM,Shepperd S,Rubin HR.Comprehensive discharge planning with postdischarge support for older patients with congestive heart failure: a meta‐analysis.JAMA.2004;291(11):13581367.
  35. Andersen HE,Schultz‐Larsen K,Kreiner S,Forchhammer BH,Eriksen K,Brown A.Can readmission after stroke be prevented? Results of a randomized clinical study: a postdischarge follow‐up service for stroke survivors.Stroke.2000;31(5):10381045.
  36. Weinberger M,Oddone EZ,Henderson WG.Does increased access to primary care reduce hospital readmissions? Veterans Affairs Cooperative Study Group on Primary Care and Hospital Readmission.N Engl J Med.1996;334(22):1441147.
  37. Burnand B,Kernan WN,Feinstein AR.Indexes and boundaries for “quantitative significance” in statistical decisions.J Clin Epidemiol.1990;43(12):12731284.
  38. National Quality Forum. Safe Practices for Better Healthcare 2006 Update, A Consensus Report, Safe Practice 11: Discharge Systems. Available at: http://qualityforum.org/pdf/reports/safe_practices/txsppublic.pdf. Accessed January 2009.
  39. Whittington J,Cohen H.OSF Healthcare's journey in patient safety.Qual Manag Health Care.2004;13(1):5359.
Issue
Journal of Hospital Medicine - 4(7)
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Journal of Hospital Medicine - 4(7)
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E11-E19
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E11-E19
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Patient readmissions, emergency visits, and adverse events after software‐assisted discharge from hospital: Cluster randomized trial
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Patient readmissions, emergency visits, and adverse events after software‐assisted discharge from hospital: Cluster randomized trial
Legacy Keywords
continuity of patient care, electronic discharge summary, health care surveys, hospital information systems, hospitalists, medical records systems–computerized, medication reconciliation, patient care transitions, patient discharge, patient satisfaction
Legacy Keywords
continuity of patient care, electronic discharge summary, health care surveys, hospital information systems, hospitalists, medical records systems–computerized, medication reconciliation, patient care transitions, patient discharge, patient satisfaction
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