Affiliations
Research Planning and Development, the Queen's Medical Center, Honolulu, Hawaii
Given name(s)
Sean M.
Family name
Thomas
Degrees
MD

Practice Patterns of Hospitalists and Community Physicians

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Comparison of practice patterns of hospitalists and community physicians in the care of patients with congestive heart failure

The use of hospitalists, physicians who specialize in inpatient care, has seen a rapid expansion over the last decade.1 Several studies have shown that with hospitalists there is a shorter length of stay (LOS) and decreased utilization of resources and that hospitalists play a positive role in medical education.24 However, only a few studies have examined the specific strategies employed by hospitalists to achieve improved efficiency and outcomes.

Congestive heart failure (CHF) is the most common diagnosis of hospitalized patients older than age 65, with more Medicare spending devoted to patients with CHF than to any other diagnosis‐related group (DRG).5, 6 Over the last 2 decades hospital discharges for congestive heart failure increased by 165%.7 In addition, the rate of hospital readmission of patients with CHF remains high: 2%, 20%, and 50% within 2 days, 1 month, and 6 months, respectively.8

Several previous studies have shown that patients cared for by hospitalists had improved clinical outcomes. Meltzer et al. found that 30‐day mortality of hospitalists' patients was lower than that of non‐hospitalists' patients, 4.2% versus 6.0%, respectively, in the second year of implementation of a hospitalist program.3 A study by Huddleston et al. showed a reduction of 11.8% in the rate of complications experienced by postsurgical orthopedic patients with the involvement of hospitalists in their care in conjunction with the surgeons.4

Many previous studies have pointed to improvements in economic outcomes such as LOS and costs for patients followed by hospitalists. Kulaga et al. showed that patients cared for by hospitalists had reductions of approximately 20% in LOS and 18% in total costs per case compared with those cared for by community‐based physicians.2 Meltzer et al. found a decrease in the average adjusted LOS of 0.49 days in the second year of implementation of a hospitalist program.3 Rifkin et al. found that patients with pneumonia cared for by hospitalists had a mean adjusted LOS of 5.6 days versus 6.5 days for those cared for by non‐hospitalists.9

Few previous studies have looked at specific practice patterns of hospitalists that result in improved efficiency and better outcomes. Rifkin et al., who found that patients with pneumonia cared for by hospitalists had a shorter LOS, suggested this finding was a result of the earlier recognition by hospitalists that patients were stable and more rapid conversion to oral antibiotics.9 Likewise, Stein et al. found that community‐acquired pneumonia patients treated by hospitalists had a shorter LOS than those treated by non‐hospitalists. However, they were unable to assess the differences in patient management that led to this result because of the design of the study.10

Lindenauer et al. compared quality‐of‐care indicators and resource utilization for patients with congestive heart failure treated by hospitalists and non‐hospitalist general internists. They found that patients under the care of hospitalists had a shorter LOS than those cared for by general internists but that the overall costs of care were similar between the groups.11 They compared the quality indicators developed by the Joint Commission on Accreditation of Healthcare Organizations in the Core Measures Initiative, but did not focus on patterns of practices of hospitalists and nonhospitalists. Moreover, they did not look at full‐time hospitalists but focused on physicians who spent at least 25% of their practice caring for inpatients.

We sought to identify distinct, quantifiable practices of full‐time hospitalists in the management of their patients with CHF. We hypothesized that hospitalists would adhere more closely to the current congestive heart failure guidelines and would utilize available resources more judiciously, leading to improved clinical and economic outcomes. To identify these practices, we compared utilization of well‐established therapeutic and diagnostic modalities such as use of ACE‐I, ARB, and beta‐blockers; ordering of chest x‐rays; measurement of brain natriuretic peptide (BNP); and use of medical subspecialty consultants. We also compared standard clinical and economic outcomes such as in‐hospital mortality, readmission rate, LOS, and costs per case between hospitalists and community‐based physicians.

METHODS

Design and Setting

The study was a retrospective chart review of 447 patients treated for CHF from July 1, 2003, through June 30, 2004, at the Queen's Medical Center, a 505‐bed community‐based teaching hospital in Honolulu, Hawaii, and the leading medical referral center in the Pacific Basin. All patients had been cared for by either a community‐based physician or a hospitalist. The community‐based physicians (referred to as non‐hospitalists from here on) were a diverse group of internists and subspecialists, in solo or group practice, who provided inpatient and ambulatory care. The non‐hospitalist group included 119 cardiologists (55%), 83 general internists (38%), and 3 family practitioners (1%), with the other 6% made up of clinicians in the medical oncology, pediatrics, pulmonary, radiation oncology, and thoracic/cardiovascular surgery subspecialties.

The hospitalist group comprised 10 full‐time internists employed by the hospital who provided care for patients only in the inpatient setting and 3 part‐time hospitalists who practiced in the ambulatory setting in addition to providing inpatient night coverage for the group. During the study period, 2 hospitalists left the group, and 2 hospitalists were hired. On average the length of involvement of a full‐time hospitalist in the study was 9 months. Permission to conduct this study was granted by the Queen's Medical Center Institutional Review Board.

Patient Population

Patients were included in the study if they were admitted to Queen's Medical Center during the 18‐month study period, were at least 18 years old, and were coded on discharge by the medical records department with a principal diagnosis of congestive heart failure (International Classification of Diseases, 9th Revision, codes 428, 428.1, 428.9, 402.01, 402.11, 402.91, 404.01, 404.11, and 404.91). Baseline characteristics of patients collected were age, sex, insurance status, comorbidities, and code status on admission. Comorbidities included coronary artery disease, diabetes mellitus (type 1 or 2), hypertension, chronic renal insufficiency (creatinine > 2 mg/dL), and chronic obstructive pulmonary disease (COPD). Patients were excluded if they had initially been admitted to the medical intensive care unit, required ventilatory support, had end‐stage renal disease requiring hemodialysis, or had an LOS greater than 14 days.

Data Collection

Medical records were reviewed by research nurses not directly involved with the hospitalist group. Training to ensure high‐level reliability of data collection was provided, and reliability was verified by the primary author (M.M.R.). The following data were collected: use of ACE‐I, ARB, and beta‐blockers on admission and discharge; use of intravenous and oral diuretics; time to switch to oral diuretic; rates of utilization of medical consultants, physical therapy, dietary consults, social work, and sodium and fluid restriction; and number of repeat chest radiographs, echocardiograms, and BNP measurements. These criteria were developed based on ACC/AHA 2005 guidelines for diagnosis and management of congestive heart failure in adults,11 several studies delineating the importance of initiating therapy in the inpatient setting, and the experience of the Cardiovascular Hospital Atherosclerosis Management Program (CHAMP) for patients with established coronary artery disease.1315 Data on medical resident involvement in patient care were collected for hospitalists and non‐hospitalists.

Additional outcomes included in‐hospital mortality, rate of acute renal failure, readmission rate, LOS, expense, revenue, and margin per case. Acute renal failure was defined as a doubling of the admission creatinine value. The rate of readmissiondefined as readmission to Queen's Medical Center for any reasonwas evaluated after 7, 14, and 30 days and was stratified further for readmissions for CHF. Expense was defined as costs directly related to patient care plus costs related to operating a hospital facility. Revenue was defined as the compensation the hospital expected to collect for service rendered adjusted for bad debt/charity care. Margin was defined as revenue minus expense.

Data Analysis

Descriptive statistics are reported for baseline patient characteristics (age, sex, insurance status, etc.), quality‐of‐care measures (ACE‐I, ARB, diuretic, and beta‐blocker use, time to oral diuretic, etc.), and outcome measures (readmission rate, in‐hospital mortality, LOS, cost data) using frequencies and proportions for categorical variables (eg, sex, ethnicity, insurance status), means and standard deviations (SDs) for continuous variables (age), and medians and interquartile ranges (Q1‐Q3) for skewed variables (eg, LOS, cost data). The patients cared for by hospitalists were compared with those cared for by non‐hospitalists using the chi‐square test or Fisher's exact test for categorical data and the Student t test for continuous data. All‐Payer Severity‐adjusted Diagnosis Related Groups (APS‐DRGs) were used to control for severity of patient illness. The severity of illness codes were taken from 3M APR Benchmarking software for DRGs adjusted for severity of illness and risk of mortality. 3M defined severity of illness as the extent of physiologic decompensation or organ system loss of function. Each diagnosis was assigned 1 of 4 severity levels: minor, moderate, major, or extreme. Kruskal‐Wallis analysis of covariance was used for LOS and cost outcomes, adjusting for age, insurance status, comorbidities, and severity of illness. Multivariate logistic regression was performed for binary outcomes (eg, ACE‐I, ARB, beta‐blocker use) to adjust for confounding variables. Statistical analysis was performed using SAS version 9 (SAS Institute Inc., Cary, NC). All tests were 2‐sided, and differences with a P value < .05 were considered significant.

RESULTS

Patient Characteristics

Table 1 shows the patient characteristic data. There were 447 admissions for congestive heart failure during the study period, 342 of which met study inclusion criteria. Hospitalists provided care for 126 of these patients and non‐hospitalists for 216 patients. Mean age of patients in the hospitalist and nonhospitalist groups was 63 and 73 years, respectively. There were significant differences in insurance status, with hospitalists more frequently caring for patients covered by Medicaid (26% vs. 7%; P < .001) and patients who were uninsured (6% vs. 1%; P = .04). Patients cared for by hospitalists had a lower incidence of coronary artery disease (42% vs. 59%; P = .003) and prior CHF (44% vs. 56%; P = .05). The hospitalists' patients were more likely to have a full resuscitation code status on admission; however, this difference did not reach statistical significance (90% vs. 81%; P = .07). There were no significant differences between patients cared for by hospitalists and non‐hospitalists in sex, ethnic background, other comorbidities, or house staff involvement.

Patient Characteristics by Physician Group
 Non‐hospitalist cases (%) (n = 216)Hospitalist cases (%) (n = 126)P value
  • HMSA, Hawaii Medical Service Association; CAD, coronary artery disease; DM, diabetes mellitus (type 1 or 2); HTN, hypertension; CRI, chronic renal insufficiency; COPD, chronic obstructive pulmonary disease; CHF, congestive heart failure.

Age (years, mean SD)73 1563 16< .001
Male sex124 (57)78 (62).41
Caucasian ethnicity41 (19)30 (24).29
Insurance status   
Medicare119 (55)58 (46).11
Medicaid/Quest16 (7)33 (26)< .001
HMSA68 (31)19 (15)< .001
Self‐pay3 (1)7 (6).04
Other10(5)9(7).33
Comorbidy   
CAD127 (59)53 (42).003
DM78 (36)53 (4).27
HTN139 (64)80 (63).87
CRI43 (20)28 (22).61
COPD30 (14)26 (21).10
Prior CHF120 (56)56 (44).05
Full code174 (81)113 (90).07
House staff involvement42 (19)20 (16).41

Practice Patterns and Resource Utilization

Practice patterns and resource utilization are shown in Table 2. Hospitalists used more ACE‐I/ARBs, with 86% of patients receiving these interventions within 24 hours of admission versus 72% of the patients of non‐hospitalists (adjusted P = .001). Hospitalists treated fewer patients with beta‐blockers on admission and on discharge and more patients with intravenous diuretics (90% vs. 73%; adjusted P = .001). The rate of beta‐blocker use did not change significantly after controlling for patients with COPD (data not shown).

Use of Therapeutic Modalities and Resource Utilization by Physician Group
 Non‐hospitalist cases (%) (n = 216)Hospitalist cases (%) (n = 126)P value*
  • P values after adjusting for age, insurance status, comorbidities, and severity.

ACE‐I/ARB within 24 hours155 (72)108 (86).001
Beta‐blocker within 24 hours119 (55)50 (40).004
ACE‐I/ARB at discharge147 (69)95 (75).24
Beta‐blocker at discharge116 (54)52 (41).03
Echocardiogram 1125 (58)81 (64).50
MD Consultants 235 (16)10 (8).01
Chest x‐ray 227 (13)5 (4).02
BNP 1128 (59)95 (75).005
BNP > 122 (10)7 (6).14
Physical therapy35 (16)17 (13).48
Dietary consult29 (13)19 (15).67
Social work62 (29)60 (48).003
Sodium restriction184 (85)102 (81).31
Fluid restriction47 (22)35 (28).21
IV diuretic158 (73)114 (90).001
Time to oral diuretic (days), median (Q1,Q3)1 (1, 3)1 (0, 2).30

Hospitalists were less likely to obtain 2 or more chest x‐rays (4% vs. 13%; adjusted P = .02) or to obtain 2 or more medical consultations (8% vs. 16%; adjusted P = .01). In addition, they obtained more initial measurements of BNP; however, there was a trend toward fewer repeat BNP measurements (6% vs. 10%; P = .14). There was a significantly higher rate of social work utilization by hospitalists than by nonhospitalists (48% vs. 29%; adjusted P = .003). There were no differences between the groups in the rates of obtaining echocardiograms, physical therapy, and dietary consults or in sodium and fluid restrictions.

Outcomes

Significant differences were noted in LOS and cost outcomes between hospitalists and non‐hospitalists after adjusting for age, insurance status, comorbidities, and severity of illness (Tables 3 and 4). Patients cared for by hospitalists had a shorter overall LOS than did patients cared for by non‐hospitalists (adjusted P = .002). A shorter LOS was noted for patients in the minor (median 3 vs. 5 days), moderate (median 4 vs. 5 days), and extreme (7 vs. 8 days) severity categories. Overall adjusted expense was significantly lower for the care of hospitalists' patients across all severity categories (P < .001). There was a trend toward lower adjusted revenue for patients of hospitalists than those of non‐hospitalist (P = .06). The adjusted profit margin did not significantly differ between the groups (P =.14).

Severity‐Adjusted LOS and Costs*
 SeverityNonhospitalist cases (n = 216)Hospitalist cases (n = 126)P value
  • LOS and cost data are presented as medians (Q1, Q3).

  • Kruskal‐Wallis analysis of covariance P value for hospitalist versus nonhospitalist cases, adjusting for age, insurance status, comorbidities, and severity.

Severity (%)Minor40 (19)30 (24).13
 Moderate99 (46)64 (51) 
 Major72 (33)27 (21) 
 Extreme5 (2)4 (3) 
LOS (days)Minor5 (3, 6)3 (2, 4).002
 Moderate5 (3, 7)4 (3, 6) 
 Major6 (4,10)6 (4, 10) 
 Extreme8 (2, 8)7 (6, 8) 
Expense ($)Minor5792 (4414, 6715)4164 (2401, 5499)< .001
 Moderate6953 (4273, 10,224)5951 (4301, 8621) 
 Major13,622 (8219, 28,553)10,519 (5249, 15,581) 
 Extreme18,908 (12913, 24,688)16,192 (6135, 26,147) 
Revenue ($)Minor7095 (6611, 7212)7116 (4160, 7218).06
 Moderate7118 (7025, 7215)6893 (3755, 7164) 
 Major9601 (6972, 16,668)6743 (4612, 7116) 
 Extreme11,019 (10,009, 24,897)9184 (5783, 13,931) 
Margin ($)Minor786 (162, 2997)2290 (409, 4768).14
 Moderate256 (1999, 3366)796 (2741, 1565) 
 Major2314 (7870, 1448)3499 (8818, 1008) 
 Extreme1263 (2904, 4012)6537 (15,617, 3050) 
Clinical Outcomes
 Nonhospitalist cases (%) (n = 216)Hospitalist cases (%) (n = 126)P value
  • P values after adjusting for age, insurance status, comorbidities, and severity.

Acute renal failure2 (1)0 (0)0.53
In‐hospital mortality9 (4)0 (0)0.03
Readmission for any reason53 (25)35 (28)0.52*
Readmission for CHF19 (9)18 (14)0.16*

In‐hospital mortality of patients treated by hospitalists was lower than that of non‐hospitalist‐treated patients (0% vs. 4%; P =.03). Rates of acute renal failure, overall readmissions and readmissions specifically for congestive heart failure did not differ significantly. Notably, severity of illness assessed by APS‐DRG did not differ between hospitalists' and nonhospitalists' patients (P = .13).

DISCUSSION

Practice Patterns

Our study identified specific practices that hospitalists use more than non‐hospitalists in the management of patients with CHF. These practices, which may have resulted in decreased LOS and lower costs, included higher use of ACE‐I/ARB within 24 hours of admission and of intravenous diuretics. We hypothesized that earlier and more aggressive use of ACE‐I/ARB contributed to after‐load reduction and alteration of cardiac remodeling5 and may have led to faster recovery and improved outcomes. Greater use of intravenous diuretics may signify that hospitalists have a more aggressive approach to managing exacerbations of acute congestive heart failure, which may also lead to faster recovery.

Hospitalists used fewer beta‐blockers on admission and at discharge. Reasons for this finding remain unclear; however, it may have been a result of the practice of avoiding beta‐blockers during exacerbations of acute CHF and the subsequent reliance on primary care providers to restart beta‐blockers after discharge. Lower use of beta‐blockers did not appear to have a negative impact on mortality or readmission rates.

Resource Utilization

Hospitalists used fewer serial chest x‐rays, more initial BNP measurements, and more social work consults, and there was a trend toward their using fewer repeat BNP measurements. The less frequent use of serial chest x‐rays may be a result of hospitalists being able to assess patients more frequently and to rely less on imaging. Higher rates of initial BNP measurement by hospitalists may reflect the ordering patterns of the emergency room physicians because most patients are admitted to the hospitalists via the emergency room. The trend toward fewer repeat BNP measurements by hospitalists may again reflect their ability to perform more frequent clinical assessments and to rely less on laboratory data. The higher rate of utilization of social workers by hospitalists is likely a reflection of a population in need of such interventions rather than the hospitalists having a lower threshold before requesting a social work consultation. There were no differences in the rates of obtaining echocardiograms, physical therapy, and dietary consults and of sodium and fluid restrictions.

Clinical Outcomes

Severity of illness assessed by APS‐DRG did not differ between the patients cared for by hospitalists and those care for by non‐hospitalists (P = .13) despite the hospitalists caring for a younger population. In‐hospital mortality of hospitalist‐treated patients was lower (0% vs. 4%), whereas the rates of readmission and renal failure did not differ between the 2 groups. A slight advantage in the mortality rate appears to be in agreement with prior findings3, 4; however, this may have been a result of the non‐hospitalists caring for an older patient population.

Economic Outcomes

The shorter LOS and lower overall costs of patients followed by hospitalists supports previous findings.2, 3, 10 The LOS in our study was found to be shorter for hospitalist‐treated patients whose illnesses were in the minor, moderate, and extreme severity categories by 40%, 20%, and 13%, respectively. The median expense per case was less across all severity categories, ranging from $1000 to $3100 for the patients followed by hospitalists compared with those followed by non‐hospitalists. There was a trend toward lower adjusted median revenue in all categories except for minor severity for hospitalists' patients (P = .06). The profit margin per case did not differ significantly between patients cared for by hospitalists and non‐hospitalists. The shorter LOS and lower expenses per case of patients under the care of hospitalists should have led to higher revenue and profit margin. However, our study showed lower revenue and no significant differences in profit margin, which may be explained by the fact that the hospitalists' patients had a worse insurance mix with a higher proportion of uninsured and Medicaid patients. It is also possible that non‐hospitalists, in particular, cardiologists, generate higher revenue by performing more procedures such as cardiac catheterizations, thus offsetting the costs.

As noted above, the analysis of LOS, expenses, revenue, and margin controlled for age, comorbidities, severity of illness, and insurance status (Table 3). The results were not significantly affected by adjusting for age, insurance status, and comorbidities after controlling for severity. The difference in age may in part be a result of older patients having established relationships with primary care physicians and being less likely to be admitted by hospitalists. It may also reflect the high prevalence of methamphetamine abuse, which has reached epidemic proportions in Hawaii, and methamphetamine‐induced cardiomyopathy in a younger population of patients followed by hospitalists. Further studies would be necessary to estimate the impact of drug‐induced congestive heart failure in these populations.

Although our study provided a detailed look at practice patterns of a coherent hospitalist group, it had several important limitations. It was a retrospective study conducted at a single institution, making the findings difficult to generalize to hospitalist practices nationwide. It included an unusually large number of non‐Caucasian patients, reflecting the demographics of the state of Hawaii. Data on contraindications to ACE‐I/ARB were not collected because the degree of renal dysfunction that would serve as a contraindication was difficult to define. The primary mode of adjustment was APS, which may have been a limiting factor in assessing severity of illness. The inability to follow patients' course after discharge limited collection of long‐term outcomes data.

In agreement with previous studies, we showed a decreased LOS and lower expenses per case of patients cared for by full‐time hospitalists while preserving quality of care and improving clinical outcomes. We identified specific practices of hospitalists in the management of patients with CHF that differ from those of non‐hospitalists. These practices include early use of ACE‐I/ARB, aggressive approach to diuresis, higher utilization of social work services, and decreased utilization of serial chest x‐rays, medical consultants, and serial BNP measurements. Our study was not designed to identify a direct causal relationship between hospitalist practices and improved outcomes; however, we believe it to be the first step in understanding practice patterns and the impact of the hospitalist movement.

References
  1. Williams MV,Huddleston J,Whitford K,Difrancesco L,Wilson M.Advances in hospital medicine: a review of key articles from the literature.Med Clin North Am.2002;86:797823.
  2. Kulaga ME,Charney P,O'Mahony SP, et al.The positive impact of initiation of hospitalist clinician educators.J Gen Intern Med.2004;19:293301.
  3. Meltzer D,Manning WG,Morrison J, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137:866874
  4. Huddelston JM,Hall Long K,Naessens JM, et al.Medical and surgical comanagement after elective hip and knee arthroplasty.Ann Intern Med.2004;141:2838.
  5. Lowery, SL,Massaro R,Yancy CW.Advances in the management of acute and chronic decompensated heart failure.Lippincotts Case Manag.2004;9:S1S15.
  6. Hunt SA,Baker DW,Chin MH,Cinquegrani , et al.ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult.Circulation.2001;104:29963007.
  7. American Heart Association.Heart disease and stroke statistics—2003 update.2003.
  8. Aghababian A.Acutely decompensated heart failure: opportunities to improve care and outcomes in the emergency department.Rev Cardiovasc Med.2002;3(suppl):S3S9.
  9. Rifkin WD,Conner D,Silver A,Eichorn A.Comparison of processes and outcomes of pneumonia care between hospitalists and community‐based primary care physicians.Mayo Clin Proc.2002;77:10531058.
  10. Stein MD,Hanson S,Tammaro D,Hanna L,Most AS.Economic effects of community versus hospital‐based faculty pneumonia care.J Gen Intern Med.1998;13:774777.
  11. Lindenauer PK,Chehabeddine R,Rekow P,Fitzgerald J,Benjamin EM.Quality of care for patients hospitalized with heart failure. Assessing the impact of hospitalists.Arch Intern Med.2002;162:12511256.
  12. Hunt SA,Abraham WT,Chin MH, et al.ACC/AHA 2005 guideline update for the diagnosis and management of chronic heart failure in adult.ACC/AHA Pract Guidel.2005:182
  13. Fonarow GC,Gheorghiade M,Abraham W.Importance of in‐hospital initiation of evidence‐based medical therapies for heart failure—a review.Am J Cardiol.2004;94:11551160.
  14. Fonarow GC.Role of in‐hospital initiation of carvedilol to improve treatment rates and clinical outcomes.Am J Cardiol.2004;93(suppl):77B81B.
  15. Fonarow GC,Gawlinski A.Rationale and design of the Cardiac Hospitalization Atherosclerosis Management Program at the University of California Los Angeles.Am J Cardiol.2000;85:10A17A.
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Journal of Hospital Medicine - 3(1)
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hospitalists, congestive heart failure, quality measures, resource utilization
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The use of hospitalists, physicians who specialize in inpatient care, has seen a rapid expansion over the last decade.1 Several studies have shown that with hospitalists there is a shorter length of stay (LOS) and decreased utilization of resources and that hospitalists play a positive role in medical education.24 However, only a few studies have examined the specific strategies employed by hospitalists to achieve improved efficiency and outcomes.

Congestive heart failure (CHF) is the most common diagnosis of hospitalized patients older than age 65, with more Medicare spending devoted to patients with CHF than to any other diagnosis‐related group (DRG).5, 6 Over the last 2 decades hospital discharges for congestive heart failure increased by 165%.7 In addition, the rate of hospital readmission of patients with CHF remains high: 2%, 20%, and 50% within 2 days, 1 month, and 6 months, respectively.8

Several previous studies have shown that patients cared for by hospitalists had improved clinical outcomes. Meltzer et al. found that 30‐day mortality of hospitalists' patients was lower than that of non‐hospitalists' patients, 4.2% versus 6.0%, respectively, in the second year of implementation of a hospitalist program.3 A study by Huddleston et al. showed a reduction of 11.8% in the rate of complications experienced by postsurgical orthopedic patients with the involvement of hospitalists in their care in conjunction with the surgeons.4

Many previous studies have pointed to improvements in economic outcomes such as LOS and costs for patients followed by hospitalists. Kulaga et al. showed that patients cared for by hospitalists had reductions of approximately 20% in LOS and 18% in total costs per case compared with those cared for by community‐based physicians.2 Meltzer et al. found a decrease in the average adjusted LOS of 0.49 days in the second year of implementation of a hospitalist program.3 Rifkin et al. found that patients with pneumonia cared for by hospitalists had a mean adjusted LOS of 5.6 days versus 6.5 days for those cared for by non‐hospitalists.9

Few previous studies have looked at specific practice patterns of hospitalists that result in improved efficiency and better outcomes. Rifkin et al., who found that patients with pneumonia cared for by hospitalists had a shorter LOS, suggested this finding was a result of the earlier recognition by hospitalists that patients were stable and more rapid conversion to oral antibiotics.9 Likewise, Stein et al. found that community‐acquired pneumonia patients treated by hospitalists had a shorter LOS than those treated by non‐hospitalists. However, they were unable to assess the differences in patient management that led to this result because of the design of the study.10

Lindenauer et al. compared quality‐of‐care indicators and resource utilization for patients with congestive heart failure treated by hospitalists and non‐hospitalist general internists. They found that patients under the care of hospitalists had a shorter LOS than those cared for by general internists but that the overall costs of care were similar between the groups.11 They compared the quality indicators developed by the Joint Commission on Accreditation of Healthcare Organizations in the Core Measures Initiative, but did not focus on patterns of practices of hospitalists and nonhospitalists. Moreover, they did not look at full‐time hospitalists but focused on physicians who spent at least 25% of their practice caring for inpatients.

We sought to identify distinct, quantifiable practices of full‐time hospitalists in the management of their patients with CHF. We hypothesized that hospitalists would adhere more closely to the current congestive heart failure guidelines and would utilize available resources more judiciously, leading to improved clinical and economic outcomes. To identify these practices, we compared utilization of well‐established therapeutic and diagnostic modalities such as use of ACE‐I, ARB, and beta‐blockers; ordering of chest x‐rays; measurement of brain natriuretic peptide (BNP); and use of medical subspecialty consultants. We also compared standard clinical and economic outcomes such as in‐hospital mortality, readmission rate, LOS, and costs per case between hospitalists and community‐based physicians.

METHODS

Design and Setting

The study was a retrospective chart review of 447 patients treated for CHF from July 1, 2003, through June 30, 2004, at the Queen's Medical Center, a 505‐bed community‐based teaching hospital in Honolulu, Hawaii, and the leading medical referral center in the Pacific Basin. All patients had been cared for by either a community‐based physician or a hospitalist. The community‐based physicians (referred to as non‐hospitalists from here on) were a diverse group of internists and subspecialists, in solo or group practice, who provided inpatient and ambulatory care. The non‐hospitalist group included 119 cardiologists (55%), 83 general internists (38%), and 3 family practitioners (1%), with the other 6% made up of clinicians in the medical oncology, pediatrics, pulmonary, radiation oncology, and thoracic/cardiovascular surgery subspecialties.

The hospitalist group comprised 10 full‐time internists employed by the hospital who provided care for patients only in the inpatient setting and 3 part‐time hospitalists who practiced in the ambulatory setting in addition to providing inpatient night coverage for the group. During the study period, 2 hospitalists left the group, and 2 hospitalists were hired. On average the length of involvement of a full‐time hospitalist in the study was 9 months. Permission to conduct this study was granted by the Queen's Medical Center Institutional Review Board.

Patient Population

Patients were included in the study if they were admitted to Queen's Medical Center during the 18‐month study period, were at least 18 years old, and were coded on discharge by the medical records department with a principal diagnosis of congestive heart failure (International Classification of Diseases, 9th Revision, codes 428, 428.1, 428.9, 402.01, 402.11, 402.91, 404.01, 404.11, and 404.91). Baseline characteristics of patients collected were age, sex, insurance status, comorbidities, and code status on admission. Comorbidities included coronary artery disease, diabetes mellitus (type 1 or 2), hypertension, chronic renal insufficiency (creatinine > 2 mg/dL), and chronic obstructive pulmonary disease (COPD). Patients were excluded if they had initially been admitted to the medical intensive care unit, required ventilatory support, had end‐stage renal disease requiring hemodialysis, or had an LOS greater than 14 days.

Data Collection

Medical records were reviewed by research nurses not directly involved with the hospitalist group. Training to ensure high‐level reliability of data collection was provided, and reliability was verified by the primary author (M.M.R.). The following data were collected: use of ACE‐I, ARB, and beta‐blockers on admission and discharge; use of intravenous and oral diuretics; time to switch to oral diuretic; rates of utilization of medical consultants, physical therapy, dietary consults, social work, and sodium and fluid restriction; and number of repeat chest radiographs, echocardiograms, and BNP measurements. These criteria were developed based on ACC/AHA 2005 guidelines for diagnosis and management of congestive heart failure in adults,11 several studies delineating the importance of initiating therapy in the inpatient setting, and the experience of the Cardiovascular Hospital Atherosclerosis Management Program (CHAMP) for patients with established coronary artery disease.1315 Data on medical resident involvement in patient care were collected for hospitalists and non‐hospitalists.

Additional outcomes included in‐hospital mortality, rate of acute renal failure, readmission rate, LOS, expense, revenue, and margin per case. Acute renal failure was defined as a doubling of the admission creatinine value. The rate of readmissiondefined as readmission to Queen's Medical Center for any reasonwas evaluated after 7, 14, and 30 days and was stratified further for readmissions for CHF. Expense was defined as costs directly related to patient care plus costs related to operating a hospital facility. Revenue was defined as the compensation the hospital expected to collect for service rendered adjusted for bad debt/charity care. Margin was defined as revenue minus expense.

Data Analysis

Descriptive statistics are reported for baseline patient characteristics (age, sex, insurance status, etc.), quality‐of‐care measures (ACE‐I, ARB, diuretic, and beta‐blocker use, time to oral diuretic, etc.), and outcome measures (readmission rate, in‐hospital mortality, LOS, cost data) using frequencies and proportions for categorical variables (eg, sex, ethnicity, insurance status), means and standard deviations (SDs) for continuous variables (age), and medians and interquartile ranges (Q1‐Q3) for skewed variables (eg, LOS, cost data). The patients cared for by hospitalists were compared with those cared for by non‐hospitalists using the chi‐square test or Fisher's exact test for categorical data and the Student t test for continuous data. All‐Payer Severity‐adjusted Diagnosis Related Groups (APS‐DRGs) were used to control for severity of patient illness. The severity of illness codes were taken from 3M APR Benchmarking software for DRGs adjusted for severity of illness and risk of mortality. 3M defined severity of illness as the extent of physiologic decompensation or organ system loss of function. Each diagnosis was assigned 1 of 4 severity levels: minor, moderate, major, or extreme. Kruskal‐Wallis analysis of covariance was used for LOS and cost outcomes, adjusting for age, insurance status, comorbidities, and severity of illness. Multivariate logistic regression was performed for binary outcomes (eg, ACE‐I, ARB, beta‐blocker use) to adjust for confounding variables. Statistical analysis was performed using SAS version 9 (SAS Institute Inc., Cary, NC). All tests were 2‐sided, and differences with a P value < .05 were considered significant.

RESULTS

Patient Characteristics

Table 1 shows the patient characteristic data. There were 447 admissions for congestive heart failure during the study period, 342 of which met study inclusion criteria. Hospitalists provided care for 126 of these patients and non‐hospitalists for 216 patients. Mean age of patients in the hospitalist and nonhospitalist groups was 63 and 73 years, respectively. There were significant differences in insurance status, with hospitalists more frequently caring for patients covered by Medicaid (26% vs. 7%; P < .001) and patients who were uninsured (6% vs. 1%; P = .04). Patients cared for by hospitalists had a lower incidence of coronary artery disease (42% vs. 59%; P = .003) and prior CHF (44% vs. 56%; P = .05). The hospitalists' patients were more likely to have a full resuscitation code status on admission; however, this difference did not reach statistical significance (90% vs. 81%; P = .07). There were no significant differences between patients cared for by hospitalists and non‐hospitalists in sex, ethnic background, other comorbidities, or house staff involvement.

Patient Characteristics by Physician Group
 Non‐hospitalist cases (%) (n = 216)Hospitalist cases (%) (n = 126)P value
  • HMSA, Hawaii Medical Service Association; CAD, coronary artery disease; DM, diabetes mellitus (type 1 or 2); HTN, hypertension; CRI, chronic renal insufficiency; COPD, chronic obstructive pulmonary disease; CHF, congestive heart failure.

Age (years, mean SD)73 1563 16< .001
Male sex124 (57)78 (62).41
Caucasian ethnicity41 (19)30 (24).29
Insurance status   
Medicare119 (55)58 (46).11
Medicaid/Quest16 (7)33 (26)< .001
HMSA68 (31)19 (15)< .001
Self‐pay3 (1)7 (6).04
Other10(5)9(7).33
Comorbidy   
CAD127 (59)53 (42).003
DM78 (36)53 (4).27
HTN139 (64)80 (63).87
CRI43 (20)28 (22).61
COPD30 (14)26 (21).10
Prior CHF120 (56)56 (44).05
Full code174 (81)113 (90).07
House staff involvement42 (19)20 (16).41

Practice Patterns and Resource Utilization

Practice patterns and resource utilization are shown in Table 2. Hospitalists used more ACE‐I/ARBs, with 86% of patients receiving these interventions within 24 hours of admission versus 72% of the patients of non‐hospitalists (adjusted P = .001). Hospitalists treated fewer patients with beta‐blockers on admission and on discharge and more patients with intravenous diuretics (90% vs. 73%; adjusted P = .001). The rate of beta‐blocker use did not change significantly after controlling for patients with COPD (data not shown).

Use of Therapeutic Modalities and Resource Utilization by Physician Group
 Non‐hospitalist cases (%) (n = 216)Hospitalist cases (%) (n = 126)P value*
  • P values after adjusting for age, insurance status, comorbidities, and severity.

ACE‐I/ARB within 24 hours155 (72)108 (86).001
Beta‐blocker within 24 hours119 (55)50 (40).004
ACE‐I/ARB at discharge147 (69)95 (75).24
Beta‐blocker at discharge116 (54)52 (41).03
Echocardiogram 1125 (58)81 (64).50
MD Consultants 235 (16)10 (8).01
Chest x‐ray 227 (13)5 (4).02
BNP 1128 (59)95 (75).005
BNP > 122 (10)7 (6).14
Physical therapy35 (16)17 (13).48
Dietary consult29 (13)19 (15).67
Social work62 (29)60 (48).003
Sodium restriction184 (85)102 (81).31
Fluid restriction47 (22)35 (28).21
IV diuretic158 (73)114 (90).001
Time to oral diuretic (days), median (Q1,Q3)1 (1, 3)1 (0, 2).30

Hospitalists were less likely to obtain 2 or more chest x‐rays (4% vs. 13%; adjusted P = .02) or to obtain 2 or more medical consultations (8% vs. 16%; adjusted P = .01). In addition, they obtained more initial measurements of BNP; however, there was a trend toward fewer repeat BNP measurements (6% vs. 10%; P = .14). There was a significantly higher rate of social work utilization by hospitalists than by nonhospitalists (48% vs. 29%; adjusted P = .003). There were no differences between the groups in the rates of obtaining echocardiograms, physical therapy, and dietary consults or in sodium and fluid restrictions.

Outcomes

Significant differences were noted in LOS and cost outcomes between hospitalists and non‐hospitalists after adjusting for age, insurance status, comorbidities, and severity of illness (Tables 3 and 4). Patients cared for by hospitalists had a shorter overall LOS than did patients cared for by non‐hospitalists (adjusted P = .002). A shorter LOS was noted for patients in the minor (median 3 vs. 5 days), moderate (median 4 vs. 5 days), and extreme (7 vs. 8 days) severity categories. Overall adjusted expense was significantly lower for the care of hospitalists' patients across all severity categories (P < .001). There was a trend toward lower adjusted revenue for patients of hospitalists than those of non‐hospitalist (P = .06). The adjusted profit margin did not significantly differ between the groups (P =.14).

Severity‐Adjusted LOS and Costs*
 SeverityNonhospitalist cases (n = 216)Hospitalist cases (n = 126)P value
  • LOS and cost data are presented as medians (Q1, Q3).

  • Kruskal‐Wallis analysis of covariance P value for hospitalist versus nonhospitalist cases, adjusting for age, insurance status, comorbidities, and severity.

Severity (%)Minor40 (19)30 (24).13
 Moderate99 (46)64 (51) 
 Major72 (33)27 (21) 
 Extreme5 (2)4 (3) 
LOS (days)Minor5 (3, 6)3 (2, 4).002
 Moderate5 (3, 7)4 (3, 6) 
 Major6 (4,10)6 (4, 10) 
 Extreme8 (2, 8)7 (6, 8) 
Expense ($)Minor5792 (4414, 6715)4164 (2401, 5499)< .001
 Moderate6953 (4273, 10,224)5951 (4301, 8621) 
 Major13,622 (8219, 28,553)10,519 (5249, 15,581) 
 Extreme18,908 (12913, 24,688)16,192 (6135, 26,147) 
Revenue ($)Minor7095 (6611, 7212)7116 (4160, 7218).06
 Moderate7118 (7025, 7215)6893 (3755, 7164) 
 Major9601 (6972, 16,668)6743 (4612, 7116) 
 Extreme11,019 (10,009, 24,897)9184 (5783, 13,931) 
Margin ($)Minor786 (162, 2997)2290 (409, 4768).14
 Moderate256 (1999, 3366)796 (2741, 1565) 
 Major2314 (7870, 1448)3499 (8818, 1008) 
 Extreme1263 (2904, 4012)6537 (15,617, 3050) 
Clinical Outcomes
 Nonhospitalist cases (%) (n = 216)Hospitalist cases (%) (n = 126)P value
  • P values after adjusting for age, insurance status, comorbidities, and severity.

Acute renal failure2 (1)0 (0)0.53
In‐hospital mortality9 (4)0 (0)0.03
Readmission for any reason53 (25)35 (28)0.52*
Readmission for CHF19 (9)18 (14)0.16*

In‐hospital mortality of patients treated by hospitalists was lower than that of non‐hospitalist‐treated patients (0% vs. 4%; P =.03). Rates of acute renal failure, overall readmissions and readmissions specifically for congestive heart failure did not differ significantly. Notably, severity of illness assessed by APS‐DRG did not differ between hospitalists' and nonhospitalists' patients (P = .13).

DISCUSSION

Practice Patterns

Our study identified specific practices that hospitalists use more than non‐hospitalists in the management of patients with CHF. These practices, which may have resulted in decreased LOS and lower costs, included higher use of ACE‐I/ARB within 24 hours of admission and of intravenous diuretics. We hypothesized that earlier and more aggressive use of ACE‐I/ARB contributed to after‐load reduction and alteration of cardiac remodeling5 and may have led to faster recovery and improved outcomes. Greater use of intravenous diuretics may signify that hospitalists have a more aggressive approach to managing exacerbations of acute congestive heart failure, which may also lead to faster recovery.

Hospitalists used fewer beta‐blockers on admission and at discharge. Reasons for this finding remain unclear; however, it may have been a result of the practice of avoiding beta‐blockers during exacerbations of acute CHF and the subsequent reliance on primary care providers to restart beta‐blockers after discharge. Lower use of beta‐blockers did not appear to have a negative impact on mortality or readmission rates.

Resource Utilization

Hospitalists used fewer serial chest x‐rays, more initial BNP measurements, and more social work consults, and there was a trend toward their using fewer repeat BNP measurements. The less frequent use of serial chest x‐rays may be a result of hospitalists being able to assess patients more frequently and to rely less on imaging. Higher rates of initial BNP measurement by hospitalists may reflect the ordering patterns of the emergency room physicians because most patients are admitted to the hospitalists via the emergency room. The trend toward fewer repeat BNP measurements by hospitalists may again reflect their ability to perform more frequent clinical assessments and to rely less on laboratory data. The higher rate of utilization of social workers by hospitalists is likely a reflection of a population in need of such interventions rather than the hospitalists having a lower threshold before requesting a social work consultation. There were no differences in the rates of obtaining echocardiograms, physical therapy, and dietary consults and of sodium and fluid restrictions.

Clinical Outcomes

Severity of illness assessed by APS‐DRG did not differ between the patients cared for by hospitalists and those care for by non‐hospitalists (P = .13) despite the hospitalists caring for a younger population. In‐hospital mortality of hospitalist‐treated patients was lower (0% vs. 4%), whereas the rates of readmission and renal failure did not differ between the 2 groups. A slight advantage in the mortality rate appears to be in agreement with prior findings3, 4; however, this may have been a result of the non‐hospitalists caring for an older patient population.

Economic Outcomes

The shorter LOS and lower overall costs of patients followed by hospitalists supports previous findings.2, 3, 10 The LOS in our study was found to be shorter for hospitalist‐treated patients whose illnesses were in the minor, moderate, and extreme severity categories by 40%, 20%, and 13%, respectively. The median expense per case was less across all severity categories, ranging from $1000 to $3100 for the patients followed by hospitalists compared with those followed by non‐hospitalists. There was a trend toward lower adjusted median revenue in all categories except for minor severity for hospitalists' patients (P = .06). The profit margin per case did not differ significantly between patients cared for by hospitalists and non‐hospitalists. The shorter LOS and lower expenses per case of patients under the care of hospitalists should have led to higher revenue and profit margin. However, our study showed lower revenue and no significant differences in profit margin, which may be explained by the fact that the hospitalists' patients had a worse insurance mix with a higher proportion of uninsured and Medicaid patients. It is also possible that non‐hospitalists, in particular, cardiologists, generate higher revenue by performing more procedures such as cardiac catheterizations, thus offsetting the costs.

As noted above, the analysis of LOS, expenses, revenue, and margin controlled for age, comorbidities, severity of illness, and insurance status (Table 3). The results were not significantly affected by adjusting for age, insurance status, and comorbidities after controlling for severity. The difference in age may in part be a result of older patients having established relationships with primary care physicians and being less likely to be admitted by hospitalists. It may also reflect the high prevalence of methamphetamine abuse, which has reached epidemic proportions in Hawaii, and methamphetamine‐induced cardiomyopathy in a younger population of patients followed by hospitalists. Further studies would be necessary to estimate the impact of drug‐induced congestive heart failure in these populations.

Although our study provided a detailed look at practice patterns of a coherent hospitalist group, it had several important limitations. It was a retrospective study conducted at a single institution, making the findings difficult to generalize to hospitalist practices nationwide. It included an unusually large number of non‐Caucasian patients, reflecting the demographics of the state of Hawaii. Data on contraindications to ACE‐I/ARB were not collected because the degree of renal dysfunction that would serve as a contraindication was difficult to define. The primary mode of adjustment was APS, which may have been a limiting factor in assessing severity of illness. The inability to follow patients' course after discharge limited collection of long‐term outcomes data.

In agreement with previous studies, we showed a decreased LOS and lower expenses per case of patients cared for by full‐time hospitalists while preserving quality of care and improving clinical outcomes. We identified specific practices of hospitalists in the management of patients with CHF that differ from those of non‐hospitalists. These practices include early use of ACE‐I/ARB, aggressive approach to diuresis, higher utilization of social work services, and decreased utilization of serial chest x‐rays, medical consultants, and serial BNP measurements. Our study was not designed to identify a direct causal relationship between hospitalist practices and improved outcomes; however, we believe it to be the first step in understanding practice patterns and the impact of the hospitalist movement.

The use of hospitalists, physicians who specialize in inpatient care, has seen a rapid expansion over the last decade.1 Several studies have shown that with hospitalists there is a shorter length of stay (LOS) and decreased utilization of resources and that hospitalists play a positive role in medical education.24 However, only a few studies have examined the specific strategies employed by hospitalists to achieve improved efficiency and outcomes.

Congestive heart failure (CHF) is the most common diagnosis of hospitalized patients older than age 65, with more Medicare spending devoted to patients with CHF than to any other diagnosis‐related group (DRG).5, 6 Over the last 2 decades hospital discharges for congestive heart failure increased by 165%.7 In addition, the rate of hospital readmission of patients with CHF remains high: 2%, 20%, and 50% within 2 days, 1 month, and 6 months, respectively.8

Several previous studies have shown that patients cared for by hospitalists had improved clinical outcomes. Meltzer et al. found that 30‐day mortality of hospitalists' patients was lower than that of non‐hospitalists' patients, 4.2% versus 6.0%, respectively, in the second year of implementation of a hospitalist program.3 A study by Huddleston et al. showed a reduction of 11.8% in the rate of complications experienced by postsurgical orthopedic patients with the involvement of hospitalists in their care in conjunction with the surgeons.4

Many previous studies have pointed to improvements in economic outcomes such as LOS and costs for patients followed by hospitalists. Kulaga et al. showed that patients cared for by hospitalists had reductions of approximately 20% in LOS and 18% in total costs per case compared with those cared for by community‐based physicians.2 Meltzer et al. found a decrease in the average adjusted LOS of 0.49 days in the second year of implementation of a hospitalist program.3 Rifkin et al. found that patients with pneumonia cared for by hospitalists had a mean adjusted LOS of 5.6 days versus 6.5 days for those cared for by non‐hospitalists.9

Few previous studies have looked at specific practice patterns of hospitalists that result in improved efficiency and better outcomes. Rifkin et al., who found that patients with pneumonia cared for by hospitalists had a shorter LOS, suggested this finding was a result of the earlier recognition by hospitalists that patients were stable and more rapid conversion to oral antibiotics.9 Likewise, Stein et al. found that community‐acquired pneumonia patients treated by hospitalists had a shorter LOS than those treated by non‐hospitalists. However, they were unable to assess the differences in patient management that led to this result because of the design of the study.10

Lindenauer et al. compared quality‐of‐care indicators and resource utilization for patients with congestive heart failure treated by hospitalists and non‐hospitalist general internists. They found that patients under the care of hospitalists had a shorter LOS than those cared for by general internists but that the overall costs of care were similar between the groups.11 They compared the quality indicators developed by the Joint Commission on Accreditation of Healthcare Organizations in the Core Measures Initiative, but did not focus on patterns of practices of hospitalists and nonhospitalists. Moreover, they did not look at full‐time hospitalists but focused on physicians who spent at least 25% of their practice caring for inpatients.

We sought to identify distinct, quantifiable practices of full‐time hospitalists in the management of their patients with CHF. We hypothesized that hospitalists would adhere more closely to the current congestive heart failure guidelines and would utilize available resources more judiciously, leading to improved clinical and economic outcomes. To identify these practices, we compared utilization of well‐established therapeutic and diagnostic modalities such as use of ACE‐I, ARB, and beta‐blockers; ordering of chest x‐rays; measurement of brain natriuretic peptide (BNP); and use of medical subspecialty consultants. We also compared standard clinical and economic outcomes such as in‐hospital mortality, readmission rate, LOS, and costs per case between hospitalists and community‐based physicians.

METHODS

Design and Setting

The study was a retrospective chart review of 447 patients treated for CHF from July 1, 2003, through June 30, 2004, at the Queen's Medical Center, a 505‐bed community‐based teaching hospital in Honolulu, Hawaii, and the leading medical referral center in the Pacific Basin. All patients had been cared for by either a community‐based physician or a hospitalist. The community‐based physicians (referred to as non‐hospitalists from here on) were a diverse group of internists and subspecialists, in solo or group practice, who provided inpatient and ambulatory care. The non‐hospitalist group included 119 cardiologists (55%), 83 general internists (38%), and 3 family practitioners (1%), with the other 6% made up of clinicians in the medical oncology, pediatrics, pulmonary, radiation oncology, and thoracic/cardiovascular surgery subspecialties.

The hospitalist group comprised 10 full‐time internists employed by the hospital who provided care for patients only in the inpatient setting and 3 part‐time hospitalists who practiced in the ambulatory setting in addition to providing inpatient night coverage for the group. During the study period, 2 hospitalists left the group, and 2 hospitalists were hired. On average the length of involvement of a full‐time hospitalist in the study was 9 months. Permission to conduct this study was granted by the Queen's Medical Center Institutional Review Board.

Patient Population

Patients were included in the study if they were admitted to Queen's Medical Center during the 18‐month study period, were at least 18 years old, and were coded on discharge by the medical records department with a principal diagnosis of congestive heart failure (International Classification of Diseases, 9th Revision, codes 428, 428.1, 428.9, 402.01, 402.11, 402.91, 404.01, 404.11, and 404.91). Baseline characteristics of patients collected were age, sex, insurance status, comorbidities, and code status on admission. Comorbidities included coronary artery disease, diabetes mellitus (type 1 or 2), hypertension, chronic renal insufficiency (creatinine > 2 mg/dL), and chronic obstructive pulmonary disease (COPD). Patients were excluded if they had initially been admitted to the medical intensive care unit, required ventilatory support, had end‐stage renal disease requiring hemodialysis, or had an LOS greater than 14 days.

Data Collection

Medical records were reviewed by research nurses not directly involved with the hospitalist group. Training to ensure high‐level reliability of data collection was provided, and reliability was verified by the primary author (M.M.R.). The following data were collected: use of ACE‐I, ARB, and beta‐blockers on admission and discharge; use of intravenous and oral diuretics; time to switch to oral diuretic; rates of utilization of medical consultants, physical therapy, dietary consults, social work, and sodium and fluid restriction; and number of repeat chest radiographs, echocardiograms, and BNP measurements. These criteria were developed based on ACC/AHA 2005 guidelines for diagnosis and management of congestive heart failure in adults,11 several studies delineating the importance of initiating therapy in the inpatient setting, and the experience of the Cardiovascular Hospital Atherosclerosis Management Program (CHAMP) for patients with established coronary artery disease.1315 Data on medical resident involvement in patient care were collected for hospitalists and non‐hospitalists.

Additional outcomes included in‐hospital mortality, rate of acute renal failure, readmission rate, LOS, expense, revenue, and margin per case. Acute renal failure was defined as a doubling of the admission creatinine value. The rate of readmissiondefined as readmission to Queen's Medical Center for any reasonwas evaluated after 7, 14, and 30 days and was stratified further for readmissions for CHF. Expense was defined as costs directly related to patient care plus costs related to operating a hospital facility. Revenue was defined as the compensation the hospital expected to collect for service rendered adjusted for bad debt/charity care. Margin was defined as revenue minus expense.

Data Analysis

Descriptive statistics are reported for baseline patient characteristics (age, sex, insurance status, etc.), quality‐of‐care measures (ACE‐I, ARB, diuretic, and beta‐blocker use, time to oral diuretic, etc.), and outcome measures (readmission rate, in‐hospital mortality, LOS, cost data) using frequencies and proportions for categorical variables (eg, sex, ethnicity, insurance status), means and standard deviations (SDs) for continuous variables (age), and medians and interquartile ranges (Q1‐Q3) for skewed variables (eg, LOS, cost data). The patients cared for by hospitalists were compared with those cared for by non‐hospitalists using the chi‐square test or Fisher's exact test for categorical data and the Student t test for continuous data. All‐Payer Severity‐adjusted Diagnosis Related Groups (APS‐DRGs) were used to control for severity of patient illness. The severity of illness codes were taken from 3M APR Benchmarking software for DRGs adjusted for severity of illness and risk of mortality. 3M defined severity of illness as the extent of physiologic decompensation or organ system loss of function. Each diagnosis was assigned 1 of 4 severity levels: minor, moderate, major, or extreme. Kruskal‐Wallis analysis of covariance was used for LOS and cost outcomes, adjusting for age, insurance status, comorbidities, and severity of illness. Multivariate logistic regression was performed for binary outcomes (eg, ACE‐I, ARB, beta‐blocker use) to adjust for confounding variables. Statistical analysis was performed using SAS version 9 (SAS Institute Inc., Cary, NC). All tests were 2‐sided, and differences with a P value < .05 were considered significant.

RESULTS

Patient Characteristics

Table 1 shows the patient characteristic data. There were 447 admissions for congestive heart failure during the study period, 342 of which met study inclusion criteria. Hospitalists provided care for 126 of these patients and non‐hospitalists for 216 patients. Mean age of patients in the hospitalist and nonhospitalist groups was 63 and 73 years, respectively. There were significant differences in insurance status, with hospitalists more frequently caring for patients covered by Medicaid (26% vs. 7%; P < .001) and patients who were uninsured (6% vs. 1%; P = .04). Patients cared for by hospitalists had a lower incidence of coronary artery disease (42% vs. 59%; P = .003) and prior CHF (44% vs. 56%; P = .05). The hospitalists' patients were more likely to have a full resuscitation code status on admission; however, this difference did not reach statistical significance (90% vs. 81%; P = .07). There were no significant differences between patients cared for by hospitalists and non‐hospitalists in sex, ethnic background, other comorbidities, or house staff involvement.

Patient Characteristics by Physician Group
 Non‐hospitalist cases (%) (n = 216)Hospitalist cases (%) (n = 126)P value
  • HMSA, Hawaii Medical Service Association; CAD, coronary artery disease; DM, diabetes mellitus (type 1 or 2); HTN, hypertension; CRI, chronic renal insufficiency; COPD, chronic obstructive pulmonary disease; CHF, congestive heart failure.

Age (years, mean SD)73 1563 16< .001
Male sex124 (57)78 (62).41
Caucasian ethnicity41 (19)30 (24).29
Insurance status   
Medicare119 (55)58 (46).11
Medicaid/Quest16 (7)33 (26)< .001
HMSA68 (31)19 (15)< .001
Self‐pay3 (1)7 (6).04
Other10(5)9(7).33
Comorbidy   
CAD127 (59)53 (42).003
DM78 (36)53 (4).27
HTN139 (64)80 (63).87
CRI43 (20)28 (22).61
COPD30 (14)26 (21).10
Prior CHF120 (56)56 (44).05
Full code174 (81)113 (90).07
House staff involvement42 (19)20 (16).41

Practice Patterns and Resource Utilization

Practice patterns and resource utilization are shown in Table 2. Hospitalists used more ACE‐I/ARBs, with 86% of patients receiving these interventions within 24 hours of admission versus 72% of the patients of non‐hospitalists (adjusted P = .001). Hospitalists treated fewer patients with beta‐blockers on admission and on discharge and more patients with intravenous diuretics (90% vs. 73%; adjusted P = .001). The rate of beta‐blocker use did not change significantly after controlling for patients with COPD (data not shown).

Use of Therapeutic Modalities and Resource Utilization by Physician Group
 Non‐hospitalist cases (%) (n = 216)Hospitalist cases (%) (n = 126)P value*
  • P values after adjusting for age, insurance status, comorbidities, and severity.

ACE‐I/ARB within 24 hours155 (72)108 (86).001
Beta‐blocker within 24 hours119 (55)50 (40).004
ACE‐I/ARB at discharge147 (69)95 (75).24
Beta‐blocker at discharge116 (54)52 (41).03
Echocardiogram 1125 (58)81 (64).50
MD Consultants 235 (16)10 (8).01
Chest x‐ray 227 (13)5 (4).02
BNP 1128 (59)95 (75).005
BNP > 122 (10)7 (6).14
Physical therapy35 (16)17 (13).48
Dietary consult29 (13)19 (15).67
Social work62 (29)60 (48).003
Sodium restriction184 (85)102 (81).31
Fluid restriction47 (22)35 (28).21
IV diuretic158 (73)114 (90).001
Time to oral diuretic (days), median (Q1,Q3)1 (1, 3)1 (0, 2).30

Hospitalists were less likely to obtain 2 or more chest x‐rays (4% vs. 13%; adjusted P = .02) or to obtain 2 or more medical consultations (8% vs. 16%; adjusted P = .01). In addition, they obtained more initial measurements of BNP; however, there was a trend toward fewer repeat BNP measurements (6% vs. 10%; P = .14). There was a significantly higher rate of social work utilization by hospitalists than by nonhospitalists (48% vs. 29%; adjusted P = .003). There were no differences between the groups in the rates of obtaining echocardiograms, physical therapy, and dietary consults or in sodium and fluid restrictions.

Outcomes

Significant differences were noted in LOS and cost outcomes between hospitalists and non‐hospitalists after adjusting for age, insurance status, comorbidities, and severity of illness (Tables 3 and 4). Patients cared for by hospitalists had a shorter overall LOS than did patients cared for by non‐hospitalists (adjusted P = .002). A shorter LOS was noted for patients in the minor (median 3 vs. 5 days), moderate (median 4 vs. 5 days), and extreme (7 vs. 8 days) severity categories. Overall adjusted expense was significantly lower for the care of hospitalists' patients across all severity categories (P < .001). There was a trend toward lower adjusted revenue for patients of hospitalists than those of non‐hospitalist (P = .06). The adjusted profit margin did not significantly differ between the groups (P =.14).

Severity‐Adjusted LOS and Costs*
 SeverityNonhospitalist cases (n = 216)Hospitalist cases (n = 126)P value
  • LOS and cost data are presented as medians (Q1, Q3).

  • Kruskal‐Wallis analysis of covariance P value for hospitalist versus nonhospitalist cases, adjusting for age, insurance status, comorbidities, and severity.

Severity (%)Minor40 (19)30 (24).13
 Moderate99 (46)64 (51) 
 Major72 (33)27 (21) 
 Extreme5 (2)4 (3) 
LOS (days)Minor5 (3, 6)3 (2, 4).002
 Moderate5 (3, 7)4 (3, 6) 
 Major6 (4,10)6 (4, 10) 
 Extreme8 (2, 8)7 (6, 8) 
Expense ($)Minor5792 (4414, 6715)4164 (2401, 5499)< .001
 Moderate6953 (4273, 10,224)5951 (4301, 8621) 
 Major13,622 (8219, 28,553)10,519 (5249, 15,581) 
 Extreme18,908 (12913, 24,688)16,192 (6135, 26,147) 
Revenue ($)Minor7095 (6611, 7212)7116 (4160, 7218).06
 Moderate7118 (7025, 7215)6893 (3755, 7164) 
 Major9601 (6972, 16,668)6743 (4612, 7116) 
 Extreme11,019 (10,009, 24,897)9184 (5783, 13,931) 
Margin ($)Minor786 (162, 2997)2290 (409, 4768).14
 Moderate256 (1999, 3366)796 (2741, 1565) 
 Major2314 (7870, 1448)3499 (8818, 1008) 
 Extreme1263 (2904, 4012)6537 (15,617, 3050) 
Clinical Outcomes
 Nonhospitalist cases (%) (n = 216)Hospitalist cases (%) (n = 126)P value
  • P values after adjusting for age, insurance status, comorbidities, and severity.

Acute renal failure2 (1)0 (0)0.53
In‐hospital mortality9 (4)0 (0)0.03
Readmission for any reason53 (25)35 (28)0.52*
Readmission for CHF19 (9)18 (14)0.16*

In‐hospital mortality of patients treated by hospitalists was lower than that of non‐hospitalist‐treated patients (0% vs. 4%; P =.03). Rates of acute renal failure, overall readmissions and readmissions specifically for congestive heart failure did not differ significantly. Notably, severity of illness assessed by APS‐DRG did not differ between hospitalists' and nonhospitalists' patients (P = .13).

DISCUSSION

Practice Patterns

Our study identified specific practices that hospitalists use more than non‐hospitalists in the management of patients with CHF. These practices, which may have resulted in decreased LOS and lower costs, included higher use of ACE‐I/ARB within 24 hours of admission and of intravenous diuretics. We hypothesized that earlier and more aggressive use of ACE‐I/ARB contributed to after‐load reduction and alteration of cardiac remodeling5 and may have led to faster recovery and improved outcomes. Greater use of intravenous diuretics may signify that hospitalists have a more aggressive approach to managing exacerbations of acute congestive heart failure, which may also lead to faster recovery.

Hospitalists used fewer beta‐blockers on admission and at discharge. Reasons for this finding remain unclear; however, it may have been a result of the practice of avoiding beta‐blockers during exacerbations of acute CHF and the subsequent reliance on primary care providers to restart beta‐blockers after discharge. Lower use of beta‐blockers did not appear to have a negative impact on mortality or readmission rates.

Resource Utilization

Hospitalists used fewer serial chest x‐rays, more initial BNP measurements, and more social work consults, and there was a trend toward their using fewer repeat BNP measurements. The less frequent use of serial chest x‐rays may be a result of hospitalists being able to assess patients more frequently and to rely less on imaging. Higher rates of initial BNP measurement by hospitalists may reflect the ordering patterns of the emergency room physicians because most patients are admitted to the hospitalists via the emergency room. The trend toward fewer repeat BNP measurements by hospitalists may again reflect their ability to perform more frequent clinical assessments and to rely less on laboratory data. The higher rate of utilization of social workers by hospitalists is likely a reflection of a population in need of such interventions rather than the hospitalists having a lower threshold before requesting a social work consultation. There were no differences in the rates of obtaining echocardiograms, physical therapy, and dietary consults and of sodium and fluid restrictions.

Clinical Outcomes

Severity of illness assessed by APS‐DRG did not differ between the patients cared for by hospitalists and those care for by non‐hospitalists (P = .13) despite the hospitalists caring for a younger population. In‐hospital mortality of hospitalist‐treated patients was lower (0% vs. 4%), whereas the rates of readmission and renal failure did not differ between the 2 groups. A slight advantage in the mortality rate appears to be in agreement with prior findings3, 4; however, this may have been a result of the non‐hospitalists caring for an older patient population.

Economic Outcomes

The shorter LOS and lower overall costs of patients followed by hospitalists supports previous findings.2, 3, 10 The LOS in our study was found to be shorter for hospitalist‐treated patients whose illnesses were in the minor, moderate, and extreme severity categories by 40%, 20%, and 13%, respectively. The median expense per case was less across all severity categories, ranging from $1000 to $3100 for the patients followed by hospitalists compared with those followed by non‐hospitalists. There was a trend toward lower adjusted median revenue in all categories except for minor severity for hospitalists' patients (P = .06). The profit margin per case did not differ significantly between patients cared for by hospitalists and non‐hospitalists. The shorter LOS and lower expenses per case of patients under the care of hospitalists should have led to higher revenue and profit margin. However, our study showed lower revenue and no significant differences in profit margin, which may be explained by the fact that the hospitalists' patients had a worse insurance mix with a higher proportion of uninsured and Medicaid patients. It is also possible that non‐hospitalists, in particular, cardiologists, generate higher revenue by performing more procedures such as cardiac catheterizations, thus offsetting the costs.

As noted above, the analysis of LOS, expenses, revenue, and margin controlled for age, comorbidities, severity of illness, and insurance status (Table 3). The results were not significantly affected by adjusting for age, insurance status, and comorbidities after controlling for severity. The difference in age may in part be a result of older patients having established relationships with primary care physicians and being less likely to be admitted by hospitalists. It may also reflect the high prevalence of methamphetamine abuse, which has reached epidemic proportions in Hawaii, and methamphetamine‐induced cardiomyopathy in a younger population of patients followed by hospitalists. Further studies would be necessary to estimate the impact of drug‐induced congestive heart failure in these populations.

Although our study provided a detailed look at practice patterns of a coherent hospitalist group, it had several important limitations. It was a retrospective study conducted at a single institution, making the findings difficult to generalize to hospitalist practices nationwide. It included an unusually large number of non‐Caucasian patients, reflecting the demographics of the state of Hawaii. Data on contraindications to ACE‐I/ARB were not collected because the degree of renal dysfunction that would serve as a contraindication was difficult to define. The primary mode of adjustment was APS, which may have been a limiting factor in assessing severity of illness. The inability to follow patients' course after discharge limited collection of long‐term outcomes data.

In agreement with previous studies, we showed a decreased LOS and lower expenses per case of patients cared for by full‐time hospitalists while preserving quality of care and improving clinical outcomes. We identified specific practices of hospitalists in the management of patients with CHF that differ from those of non‐hospitalists. These practices include early use of ACE‐I/ARB, aggressive approach to diuresis, higher utilization of social work services, and decreased utilization of serial chest x‐rays, medical consultants, and serial BNP measurements. Our study was not designed to identify a direct causal relationship between hospitalist practices and improved outcomes; however, we believe it to be the first step in understanding practice patterns and the impact of the hospitalist movement.

References
  1. Williams MV,Huddleston J,Whitford K,Difrancesco L,Wilson M.Advances in hospital medicine: a review of key articles from the literature.Med Clin North Am.2002;86:797823.
  2. Kulaga ME,Charney P,O'Mahony SP, et al.The positive impact of initiation of hospitalist clinician educators.J Gen Intern Med.2004;19:293301.
  3. Meltzer D,Manning WG,Morrison J, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137:866874
  4. Huddelston JM,Hall Long K,Naessens JM, et al.Medical and surgical comanagement after elective hip and knee arthroplasty.Ann Intern Med.2004;141:2838.
  5. Lowery, SL,Massaro R,Yancy CW.Advances in the management of acute and chronic decompensated heart failure.Lippincotts Case Manag.2004;9:S1S15.
  6. Hunt SA,Baker DW,Chin MH,Cinquegrani , et al.ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult.Circulation.2001;104:29963007.
  7. American Heart Association.Heart disease and stroke statistics—2003 update.2003.
  8. Aghababian A.Acutely decompensated heart failure: opportunities to improve care and outcomes in the emergency department.Rev Cardiovasc Med.2002;3(suppl):S3S9.
  9. Rifkin WD,Conner D,Silver A,Eichorn A.Comparison of processes and outcomes of pneumonia care between hospitalists and community‐based primary care physicians.Mayo Clin Proc.2002;77:10531058.
  10. Stein MD,Hanson S,Tammaro D,Hanna L,Most AS.Economic effects of community versus hospital‐based faculty pneumonia care.J Gen Intern Med.1998;13:774777.
  11. Lindenauer PK,Chehabeddine R,Rekow P,Fitzgerald J,Benjamin EM.Quality of care for patients hospitalized with heart failure. Assessing the impact of hospitalists.Arch Intern Med.2002;162:12511256.
  12. Hunt SA,Abraham WT,Chin MH, et al.ACC/AHA 2005 guideline update for the diagnosis and management of chronic heart failure in adult.ACC/AHA Pract Guidel.2005:182
  13. Fonarow GC,Gheorghiade M,Abraham W.Importance of in‐hospital initiation of evidence‐based medical therapies for heart failure—a review.Am J Cardiol.2004;94:11551160.
  14. Fonarow GC.Role of in‐hospital initiation of carvedilol to improve treatment rates and clinical outcomes.Am J Cardiol.2004;93(suppl):77B81B.
  15. Fonarow GC,Gawlinski A.Rationale and design of the Cardiac Hospitalization Atherosclerosis Management Program at the University of California Los Angeles.Am J Cardiol.2000;85:10A17A.
References
  1. Williams MV,Huddleston J,Whitford K,Difrancesco L,Wilson M.Advances in hospital medicine: a review of key articles from the literature.Med Clin North Am.2002;86:797823.
  2. Kulaga ME,Charney P,O'Mahony SP, et al.The positive impact of initiation of hospitalist clinician educators.J Gen Intern Med.2004;19:293301.
  3. Meltzer D,Manning WG,Morrison J, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137:866874
  4. Huddelston JM,Hall Long K,Naessens JM, et al.Medical and surgical comanagement after elective hip and knee arthroplasty.Ann Intern Med.2004;141:2838.
  5. Lowery, SL,Massaro R,Yancy CW.Advances in the management of acute and chronic decompensated heart failure.Lippincotts Case Manag.2004;9:S1S15.
  6. Hunt SA,Baker DW,Chin MH,Cinquegrani , et al.ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult.Circulation.2001;104:29963007.
  7. American Heart Association.Heart disease and stroke statistics—2003 update.2003.
  8. Aghababian A.Acutely decompensated heart failure: opportunities to improve care and outcomes in the emergency department.Rev Cardiovasc Med.2002;3(suppl):S3S9.
  9. Rifkin WD,Conner D,Silver A,Eichorn A.Comparison of processes and outcomes of pneumonia care between hospitalists and community‐based primary care physicians.Mayo Clin Proc.2002;77:10531058.
  10. Stein MD,Hanson S,Tammaro D,Hanna L,Most AS.Economic effects of community versus hospital‐based faculty pneumonia care.J Gen Intern Med.1998;13:774777.
  11. Lindenauer PK,Chehabeddine R,Rekow P,Fitzgerald J,Benjamin EM.Quality of care for patients hospitalized with heart failure. Assessing the impact of hospitalists.Arch Intern Med.2002;162:12511256.
  12. Hunt SA,Abraham WT,Chin MH, et al.ACC/AHA 2005 guideline update for the diagnosis and management of chronic heart failure in adult.ACC/AHA Pract Guidel.2005:182
  13. Fonarow GC,Gheorghiade M,Abraham W.Importance of in‐hospital initiation of evidence‐based medical therapies for heart failure—a review.Am J Cardiol.2004;94:11551160.
  14. Fonarow GC.Role of in‐hospital initiation of carvedilol to improve treatment rates and clinical outcomes.Am J Cardiol.2004;93(suppl):77B81B.
  15. Fonarow GC,Gawlinski A.Rationale and design of the Cardiac Hospitalization Atherosclerosis Management Program at the University of California Los Angeles.Am J Cardiol.2000;85:10A17A.
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Information Overload

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Information Overload

Physicians have struggled with the management of patient data for a long time. The struggle intensifies as we attempt to juggle increasingly large and complicated volumes of information during a 24-hour day. As the number and acuity of patients increase in parallel, our abilities to sift critical information and prioritize data are key.

Alarms or alerts to abnormal parameters are of limited benefit and may be counterproductive. The techniques of data display and information visualization hold great promise for revolutionizing how we manage this data overload. Other industries have realized such benefits, and healthcare (especially in the hospital) has good reason to want to catch up. In the meantime, before we can say management of clinical data in the hospital is optimal, there remains much work to do.

Physicians have struggled with the management of patient data for a long time. Such information used to be relatively simple—heart rate, respiratory rate, skin color and temperature, and so on. The limits of technology fundamentally limited what physicians could observe and record.

As our ability to gather information became more sophisticated, so did the data we could acquire. Still, the physician remained the primary collector, assessor, and interpreter of tests and their results. Individual physicians would spin urine and examine the sediment, perform blood smears, and even examine tissue samples for pathology. This was a manageable task for the physician because the number of tests was small, and the interpretation of results was fairly straightforward.

Today tests and the ways we can interpret them are both more numerous and more complicated. This has resulted in a significant issue for clinicians: How can we manage all of this information?

Too Much Data

The quantity of data available for the busy clinician is always increasing. This data explosion is happening for three reasons:

  1. Increased number of sophisticated tests. We test for more diseases, traits, and conditions than ever before. Example: Troponin I, T, and beta natriuretic peptide—all in widespread use today—were not available 10 years ago. Advanced genetic testing will continue this trend;
  2. Increased archival capability. The cost of data storage continues to decrease, making it inexpensive to archive data that might have been purged in the past; and
  3. Increased sophistication of data delivery methods. Computers and the networks that connect them are faster than ever. This allows for efficient transfer of data from the archive to the user. It also allows the user to access the data from a variety of geographic locations, including an outpatient office or home.

Patient care in the ICU provides a perfect example of the volumes of data that we generate in the course of clinical care. Monitors capture moment-by-moment readings of heart rate, blood pressure, respirations, oxygen saturation, temperature, electrocardiographic tracings, and more. In addition to capturing the patient’s physiologic signals, we also measure the interventions we perform on patients. We record intravenous fluid and medication rates, artificial ventilation parameters, and so on. A decade ago, East estimated the number of information categories in the ICU to have been in excess of 236.1 Certainly that number has only increased.

Increasingly Complicated Data

As the number of tests has increased, interpretation of the results has become more complex. In many institutions samples are obtained by highly qualified personnel—not the primary physician. Depending on the test, the sample may be sent to a lab (sometimes in a different area of the country) where another individual may perform the test. Finally, a trained observer reviews the results, may make an interpretation of those results, and then records that interpretation—together with the objective data—in the patient’s medical record. These data are then available for the physician to review.

 

 

A hospitalist is unlikely to collect the sample or run the test. How many of us centrifuge urine or examine blood smears? How many could read a complicated MRI or a PET scan? The busy clinical workflow coupled with the complexity of these tests demands that someone other than the primary caregiver interpret these tests. This also means that we are removed from the primary data and must rely on another practitioner’s interpretation. Even with this separation in the process, we are presented with more clinical data for each patient than ever before.

Too Much Information

Of course, the availability of these data is not without problems. An individual can review, assess, and act upon only so many data points.2 As the volume of data increases, so does the likelihood that a piece of important data will be missed. This setting can make things difficult for the busy hospitalist.

We need to quickly prioritize our time in the hospital. A first step in this process involves a quick review of physiologic studies that suggest levels of patient acuity. This information helps us to see the most critical patients first, and to identify other important issues that need to be addressed (although less urgently).

As more data are collected, this task gets more complicated. Certainly not all of the data collected are equally important for determining patient acuity. Given this, the volume of data contributes to the overall “noise” of the sample and—in some cases—the important data may be overlooked. Critical values (signals) become more likely to get lost in a sea of less important data (noise). More noise means more uncertainty and requires better evidence to make a clinical judgment.3

Information systems developers use various techniques to get around these problems. One way to manage the vast amount of information is to alert practitioners to outliers. Some have proposed that alarms may be the answer to our noise problem.

Alarms Don’t Work

Unfortunately, alerts and alarms can actually add to the noise, especially in ICU monitors. Anyone who has spent time in an ICU knows that alarms are constantly sounding. It has been estimated that false positive alarm rates range from 64%-87% in airway management situations.4

Alarms are often meaningless. Monitors can be so sensitive that they pick up background noise in their measurements, causing false alarms that increase the noise (both literally and figuratively). Anesthetists, recognizing that alarms are non-specific, frequently work without them.5 This is paralleled in the ICU because caregivers seem to ignore many alarms. (Most hospitalists who have spent time in the ICU can attest to this.) Specific problems with alarms include:

  • They seldom localize the problem;
  • They do not provide predictive information; and
  • The diagnostic process is still left to the practitioner.5

Alarms also vary in importance. An intravenous pump that sounds because the fluid bag is empty may not be as important as an apnea alarm on a mechanical ventilator. A single alarm may not be as concerning as multiple simultaneous alarms for a patient with low blood pressure, high heart rate, and apnea. The goal in these cases is to signal a problem and to transmit that signal appropriately. But until this can be done reliably, alarms do not seem to be the answer.

On the other hand, presenting all the data (rather than just the important data) to a clinician may obscure important elements. This can result in missed diagnosis, delayed treatment, or incorrect treatment. So we don’t want to overwhelm the hospitalist with all the data; we just need to highlight and present important data. But how?

 

 

How Can We Manage All This Information?

To tackle this problem, we can look to work that has been done in other fields, specifically human-computer interaction. Norman, in his discussion of user experience, describes the complexity curve in technology, with technologies starting off simply and growing more complex until they peak.6 At this point, they get simpler to use as the technology matures. He points to airplane instrumentation that peaked with the Concorde in the 1970s. Since then instruments have gotten much simpler, with cockpit automation and better displays and controls. This has made the user experience easier, more efficient, and more effective.

There is a parallel with Norman’s observations and our information management problem in medicine. Our display technologies (paper, computer screen) are actually quite mature and powerful. However, our ability to detect and measure physiologic data continues to rise and may be outpacing our display technologies. Are there techniques from our display technologies that can make this problem easier to deal with?

Here, it’s worth clarifying two terms: data display and information visualization. Data display is a method for arranging and presenting information in a way that is easily reviewed and assessed, such as tables and charts. Information visualization describes the manipulation of the data to make it more easily understood by humans. Specifically it has been described as “the process of transforming data, information and knowledge into visual form making use of humans’ natural visual capabilities.”7

Data Displays

The simplest way to present data is the data display. Data displays can be very simple (a paper report with a glucose value), or extremely complex. (Think of bus schedules or the stock price pages in a business newspaper.) A complex data display in the clinical arena (which doubles as a data collection tool) is the clinical flow sheet. Nurses use a combination of graphing (heart rate, blood pressure) and numerical entry (intravenous fluid rate, pain scale) to record data. The flow sheet is particularly useful in the ICU. A large amount of data can be scanned quickly and examined for trends and outliers.

An example of a useful clinical data display is Pocket Rounds, a paper clinical summary report developed at the Regenstrief Institute in Indianapolis, Ind. Pocket Rounds is a high-density display designed to present clinical information including allergies, lab results, vitals, imaging, and other diagnostic studies from inpatients on a single 8.5" by 11" page. It is printed in very small type that allows for two logical pages on one landscape-oriented sheet of paper. It is called Pocket Rounds because, when folded in half, the sheet of reports fits perfectly into a white coat pocket.

The strength of Pocket Rounds seems to be the richness of the content displayed all at once, allowing the user to focus on specific areas of the report by following visual formatting clues. Of course, a significant disadvantage of Pocket Rounds is that it is static, with data only as current as the time of printing. Both authors used Pocket Rounds during their training and wish it were a more widely available tool.

Powsner and Tufte proposed a much more sophisticated display of clinical data.8 Their display is really a hybrid of data display and visualization, as processing of the data points (normalization) improves the layout of the display. It is easy to examine the report and pick out important trends and outliers. Additionally, with some thought as to the arrangement of the data elements, different results are easy to compare (for instance, white count, gentamycin dosage, and serum creatinine.) Unfortunately, this display has not been tested to compare its effectiveness with that of any other display.

 

 

Lessons for Medicine from Information Visualization

Information visualization is an area of increasing research and development, both in the scientific and business communities. It is closely linked with data mining: a method for knowledge discovery from extremely large, complex data sets. The goals of information visualization specifically germane to medicine include aiding the “discovery of details and relations” and “supporting the recognition of relevant patterns.”9 These relations and patterns may offer new knowledge or understanding that the individual data points do not adequately convey.

Visualization has been important in medical imaging for some time; however, less attention has been given to analysis of numeric and time series data on an individual patient. One example that is widely used is the pediatric growth chart, where the height, weight, and head circumference are mapped to percentages and plotted on a normalized curve to assess the child’s development. This task goes beyond simple display, as data synthesis is used (conversion to a percentage of normal). There are two tasks being performed as well. The first is the initial assessment of the patient in relation to the rest of the population. The second is a longitudinal trajectory of growth, where the points should follow the same line (population percentile growth) even though the actual data points (height, weight, head circumference) changes.

There are numerous examples of information visualization across non-medical disciplines. Taken together, many of these insights can provide a framework for creating improved data displays for clinicians. However, these concepts have not been tested in the clinical setting to determine whether they will increase efficiency of routines, such as acuity ranking. Further, we may need to support hospitalists’ common tasks with separate approaches. The acuity ranking activity might be supported by a summary page showing key outliers and critical values for each patient. The rest of the report could show all abnormal data and (as needed) the details for closer review.

Unfortunately, there has not been much direction in solving this problem from a scientific standpoint. In a review of the literature on the presentation of medical data, Starren and Johnson noted that, “there is a paucity of methods for developing new presentations” in the medical setting.10 Further, they observe that clinical data displays are rarely evaluated quantitatively. Rather they are shown to users to assess acceptance. We need to alleviate that shortcoming.

Next Steps

We believe that a significant amount of research needs to be performed in this area. We also believe that this research should focus on hospital-based specialties—especially hospitalist medicine. Why? Because hospitalists are charged with quickly assessing lots of information on lots of patients, and anything we can do to make that process more efficient will result in better patient care and hopefully, happier hospitalists. So what are next steps?

We can break up the research agenda into two arms: what needs to be displayed, and how do we display it? Although it may seem intuitive, we think it is important to decide the what before the how because the content will really drive the improvements in care.

There has been some emphasis on determining what clinical data are important for physicians. Work on prediction algorithms and scores has led to some estimations of what numbers are important for determining patient acuity and severity. However, an accurate and dependable determination of who is sick, how sick, and who will get sicker is some time off.

For now, it would be helpful to know what data physicians want to see. This will vary by provider and may not always lead directly to a specific outcome, but it is a start. It would be helpful to identify the values that most clinicians would want to know most of the time: high or low white counts, decrease in hemoglobin, decrease in platelets, normalization of creatinine, or other. This would provide the basis for experimenting with how best to display these items.

 

 

We then could move on to explore how these data should be displayed. What should be presented as discrete numbers? What would be better to summarize graphically? How can we highlight important trends? A significant amount of work has been done in fields with so-called “knowledge workers”—professionals who need to review and act on large amounts of data. Work also has been done with other data-intensive professionals such as airplane pilots, air traffic controllers, and stockbrokers. We should be able to glean valuable insights into solutions from these investigations and use them to improve our data management problem.

Finally, these displays need to be prototyped and tested on the wards. Does the new display help make the hospitalist more efficient? Can they pick out the important data faster? Do they improve length of stay, morbidity and mortality, or patient satisfaction? It is this critical evaluation that is dearly lacking as we work to improve how hospitalists do their jobs.

Healthcare providers generally are capable, hard-working professionals with the best intentions. Inefficient, overwhelmed data management systems ultimately make us equally inefficient and overwhelmed providers. In an age when abundant scientific study and complex healthcare delivery systems are generating volumes of new information, we have a lot to learn about what to do with it all. TH

Dr. Thomas is a hospitalist and assistant medical director, Clinical Informatics, The Queen’s Medical Center, Honolulu, Hawaii. He’s also assistant professor and chief, Division of Medical Informatics, Department of Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa. Dr. Rosenman is senior associate consultant, Section of Hospital Medicine, Department of Internal Medicine, Mayo Clinic, and instructor of medicine, Mayo Clinic College of Medicine, Rochester, Minn.

References

  1. East TD, Morris AH, Wallace CJ, et al. A strategy for development of computerized critical care decision support systems. Int J Clin Monit Comput. 1992;8(4):263-269.
  2. Miller GA. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychol Rev. 1956;63(2):81-97.
  3. Morris AH, East TD, Wallace CJ, et al. Standardization of clinical decision making for the conduct of credible clinical research in complicated medical environments. Proc AMIA Annu Fall Symp. 1996;418-422.
  4. Xiao Y, Mackenzie CF, Spencer R, et al. Intrusiveness of auditory alarms during airway management. Anesthesiology. 1998 Sep;89(3AS):1224A.
  5. Lowe A, Jones RW, Harrison MJ. The graphical presentation of decision support in formation in an intelligent anaesthesia monitor. Artif Intell Med. 2001;22:173-191.
  6. Norman DA. The Invisible Computer. Cambridge, Mass.: The MIT Press; 1999.
  7. Gershon N, Eick SG, Card S. Information visualization. ACM Interactions. 1998;5(2):9-15.
  8. Powsner SM, Tufte ER. Graphical summary of patient status. Lancet. 1994; Aug 6:344(8919);386-389.
  9. Chittaro L. Information visualization and its application to medicine. Artif Intell Med. 2000;22:81-88.
  10. Starren J, Johnson SB. An object-oriented taxonomy of medical data presentations. J Am Med Inform Assoc. 2000 Jan;7(1):1-20.
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Physicians have struggled with the management of patient data for a long time. The struggle intensifies as we attempt to juggle increasingly large and complicated volumes of information during a 24-hour day. As the number and acuity of patients increase in parallel, our abilities to sift critical information and prioritize data are key.

Alarms or alerts to abnormal parameters are of limited benefit and may be counterproductive. The techniques of data display and information visualization hold great promise for revolutionizing how we manage this data overload. Other industries have realized such benefits, and healthcare (especially in the hospital) has good reason to want to catch up. In the meantime, before we can say management of clinical data in the hospital is optimal, there remains much work to do.

Physicians have struggled with the management of patient data for a long time. Such information used to be relatively simple—heart rate, respiratory rate, skin color and temperature, and so on. The limits of technology fundamentally limited what physicians could observe and record.

As our ability to gather information became more sophisticated, so did the data we could acquire. Still, the physician remained the primary collector, assessor, and interpreter of tests and their results. Individual physicians would spin urine and examine the sediment, perform blood smears, and even examine tissue samples for pathology. This was a manageable task for the physician because the number of tests was small, and the interpretation of results was fairly straightforward.

Today tests and the ways we can interpret them are both more numerous and more complicated. This has resulted in a significant issue for clinicians: How can we manage all of this information?

Too Much Data

The quantity of data available for the busy clinician is always increasing. This data explosion is happening for three reasons:

  1. Increased number of sophisticated tests. We test for more diseases, traits, and conditions than ever before. Example: Troponin I, T, and beta natriuretic peptide—all in widespread use today—were not available 10 years ago. Advanced genetic testing will continue this trend;
  2. Increased archival capability. The cost of data storage continues to decrease, making it inexpensive to archive data that might have been purged in the past; and
  3. Increased sophistication of data delivery methods. Computers and the networks that connect them are faster than ever. This allows for efficient transfer of data from the archive to the user. It also allows the user to access the data from a variety of geographic locations, including an outpatient office or home.

Patient care in the ICU provides a perfect example of the volumes of data that we generate in the course of clinical care. Monitors capture moment-by-moment readings of heart rate, blood pressure, respirations, oxygen saturation, temperature, electrocardiographic tracings, and more. In addition to capturing the patient’s physiologic signals, we also measure the interventions we perform on patients. We record intravenous fluid and medication rates, artificial ventilation parameters, and so on. A decade ago, East estimated the number of information categories in the ICU to have been in excess of 236.1 Certainly that number has only increased.

Increasingly Complicated Data

As the number of tests has increased, interpretation of the results has become more complex. In many institutions samples are obtained by highly qualified personnel—not the primary physician. Depending on the test, the sample may be sent to a lab (sometimes in a different area of the country) where another individual may perform the test. Finally, a trained observer reviews the results, may make an interpretation of those results, and then records that interpretation—together with the objective data—in the patient’s medical record. These data are then available for the physician to review.

 

 

A hospitalist is unlikely to collect the sample or run the test. How many of us centrifuge urine or examine blood smears? How many could read a complicated MRI or a PET scan? The busy clinical workflow coupled with the complexity of these tests demands that someone other than the primary caregiver interpret these tests. This also means that we are removed from the primary data and must rely on another practitioner’s interpretation. Even with this separation in the process, we are presented with more clinical data for each patient than ever before.

Too Much Information

Of course, the availability of these data is not without problems. An individual can review, assess, and act upon only so many data points.2 As the volume of data increases, so does the likelihood that a piece of important data will be missed. This setting can make things difficult for the busy hospitalist.

We need to quickly prioritize our time in the hospital. A first step in this process involves a quick review of physiologic studies that suggest levels of patient acuity. This information helps us to see the most critical patients first, and to identify other important issues that need to be addressed (although less urgently).

As more data are collected, this task gets more complicated. Certainly not all of the data collected are equally important for determining patient acuity. Given this, the volume of data contributes to the overall “noise” of the sample and—in some cases—the important data may be overlooked. Critical values (signals) become more likely to get lost in a sea of less important data (noise). More noise means more uncertainty and requires better evidence to make a clinical judgment.3

Information systems developers use various techniques to get around these problems. One way to manage the vast amount of information is to alert practitioners to outliers. Some have proposed that alarms may be the answer to our noise problem.

Alarms Don’t Work

Unfortunately, alerts and alarms can actually add to the noise, especially in ICU monitors. Anyone who has spent time in an ICU knows that alarms are constantly sounding. It has been estimated that false positive alarm rates range from 64%-87% in airway management situations.4

Alarms are often meaningless. Monitors can be so sensitive that they pick up background noise in their measurements, causing false alarms that increase the noise (both literally and figuratively). Anesthetists, recognizing that alarms are non-specific, frequently work without them.5 This is paralleled in the ICU because caregivers seem to ignore many alarms. (Most hospitalists who have spent time in the ICU can attest to this.) Specific problems with alarms include:

  • They seldom localize the problem;
  • They do not provide predictive information; and
  • The diagnostic process is still left to the practitioner.5

Alarms also vary in importance. An intravenous pump that sounds because the fluid bag is empty may not be as important as an apnea alarm on a mechanical ventilator. A single alarm may not be as concerning as multiple simultaneous alarms for a patient with low blood pressure, high heart rate, and apnea. The goal in these cases is to signal a problem and to transmit that signal appropriately. But until this can be done reliably, alarms do not seem to be the answer.

On the other hand, presenting all the data (rather than just the important data) to a clinician may obscure important elements. This can result in missed diagnosis, delayed treatment, or incorrect treatment. So we don’t want to overwhelm the hospitalist with all the data; we just need to highlight and present important data. But how?

 

 

How Can We Manage All This Information?

To tackle this problem, we can look to work that has been done in other fields, specifically human-computer interaction. Norman, in his discussion of user experience, describes the complexity curve in technology, with technologies starting off simply and growing more complex until they peak.6 At this point, they get simpler to use as the technology matures. He points to airplane instrumentation that peaked with the Concorde in the 1970s. Since then instruments have gotten much simpler, with cockpit automation and better displays and controls. This has made the user experience easier, more efficient, and more effective.

There is a parallel with Norman’s observations and our information management problem in medicine. Our display technologies (paper, computer screen) are actually quite mature and powerful. However, our ability to detect and measure physiologic data continues to rise and may be outpacing our display technologies. Are there techniques from our display technologies that can make this problem easier to deal with?

Here, it’s worth clarifying two terms: data display and information visualization. Data display is a method for arranging and presenting information in a way that is easily reviewed and assessed, such as tables and charts. Information visualization describes the manipulation of the data to make it more easily understood by humans. Specifically it has been described as “the process of transforming data, information and knowledge into visual form making use of humans’ natural visual capabilities.”7

Data Displays

The simplest way to present data is the data display. Data displays can be very simple (a paper report with a glucose value), or extremely complex. (Think of bus schedules or the stock price pages in a business newspaper.) A complex data display in the clinical arena (which doubles as a data collection tool) is the clinical flow sheet. Nurses use a combination of graphing (heart rate, blood pressure) and numerical entry (intravenous fluid rate, pain scale) to record data. The flow sheet is particularly useful in the ICU. A large amount of data can be scanned quickly and examined for trends and outliers.

An example of a useful clinical data display is Pocket Rounds, a paper clinical summary report developed at the Regenstrief Institute in Indianapolis, Ind. Pocket Rounds is a high-density display designed to present clinical information including allergies, lab results, vitals, imaging, and other diagnostic studies from inpatients on a single 8.5" by 11" page. It is printed in very small type that allows for two logical pages on one landscape-oriented sheet of paper. It is called Pocket Rounds because, when folded in half, the sheet of reports fits perfectly into a white coat pocket.

The strength of Pocket Rounds seems to be the richness of the content displayed all at once, allowing the user to focus on specific areas of the report by following visual formatting clues. Of course, a significant disadvantage of Pocket Rounds is that it is static, with data only as current as the time of printing. Both authors used Pocket Rounds during their training and wish it were a more widely available tool.

Powsner and Tufte proposed a much more sophisticated display of clinical data.8 Their display is really a hybrid of data display and visualization, as processing of the data points (normalization) improves the layout of the display. It is easy to examine the report and pick out important trends and outliers. Additionally, with some thought as to the arrangement of the data elements, different results are easy to compare (for instance, white count, gentamycin dosage, and serum creatinine.) Unfortunately, this display has not been tested to compare its effectiveness with that of any other display.

 

 

Lessons for Medicine from Information Visualization

Information visualization is an area of increasing research and development, both in the scientific and business communities. It is closely linked with data mining: a method for knowledge discovery from extremely large, complex data sets. The goals of information visualization specifically germane to medicine include aiding the “discovery of details and relations” and “supporting the recognition of relevant patterns.”9 These relations and patterns may offer new knowledge or understanding that the individual data points do not adequately convey.

Visualization has been important in medical imaging for some time; however, less attention has been given to analysis of numeric and time series data on an individual patient. One example that is widely used is the pediatric growth chart, where the height, weight, and head circumference are mapped to percentages and plotted on a normalized curve to assess the child’s development. This task goes beyond simple display, as data synthesis is used (conversion to a percentage of normal). There are two tasks being performed as well. The first is the initial assessment of the patient in relation to the rest of the population. The second is a longitudinal trajectory of growth, where the points should follow the same line (population percentile growth) even though the actual data points (height, weight, head circumference) changes.

There are numerous examples of information visualization across non-medical disciplines. Taken together, many of these insights can provide a framework for creating improved data displays for clinicians. However, these concepts have not been tested in the clinical setting to determine whether they will increase efficiency of routines, such as acuity ranking. Further, we may need to support hospitalists’ common tasks with separate approaches. The acuity ranking activity might be supported by a summary page showing key outliers and critical values for each patient. The rest of the report could show all abnormal data and (as needed) the details for closer review.

Unfortunately, there has not been much direction in solving this problem from a scientific standpoint. In a review of the literature on the presentation of medical data, Starren and Johnson noted that, “there is a paucity of methods for developing new presentations” in the medical setting.10 Further, they observe that clinical data displays are rarely evaluated quantitatively. Rather they are shown to users to assess acceptance. We need to alleviate that shortcoming.

Next Steps

We believe that a significant amount of research needs to be performed in this area. We also believe that this research should focus on hospital-based specialties—especially hospitalist medicine. Why? Because hospitalists are charged with quickly assessing lots of information on lots of patients, and anything we can do to make that process more efficient will result in better patient care and hopefully, happier hospitalists. So what are next steps?

We can break up the research agenda into two arms: what needs to be displayed, and how do we display it? Although it may seem intuitive, we think it is important to decide the what before the how because the content will really drive the improvements in care.

There has been some emphasis on determining what clinical data are important for physicians. Work on prediction algorithms and scores has led to some estimations of what numbers are important for determining patient acuity and severity. However, an accurate and dependable determination of who is sick, how sick, and who will get sicker is some time off.

For now, it would be helpful to know what data physicians want to see. This will vary by provider and may not always lead directly to a specific outcome, but it is a start. It would be helpful to identify the values that most clinicians would want to know most of the time: high or low white counts, decrease in hemoglobin, decrease in platelets, normalization of creatinine, or other. This would provide the basis for experimenting with how best to display these items.

 

 

We then could move on to explore how these data should be displayed. What should be presented as discrete numbers? What would be better to summarize graphically? How can we highlight important trends? A significant amount of work has been done in fields with so-called “knowledge workers”—professionals who need to review and act on large amounts of data. Work also has been done with other data-intensive professionals such as airplane pilots, air traffic controllers, and stockbrokers. We should be able to glean valuable insights into solutions from these investigations and use them to improve our data management problem.

Finally, these displays need to be prototyped and tested on the wards. Does the new display help make the hospitalist more efficient? Can they pick out the important data faster? Do they improve length of stay, morbidity and mortality, or patient satisfaction? It is this critical evaluation that is dearly lacking as we work to improve how hospitalists do their jobs.

Healthcare providers generally are capable, hard-working professionals with the best intentions. Inefficient, overwhelmed data management systems ultimately make us equally inefficient and overwhelmed providers. In an age when abundant scientific study and complex healthcare delivery systems are generating volumes of new information, we have a lot to learn about what to do with it all. TH

Dr. Thomas is a hospitalist and assistant medical director, Clinical Informatics, The Queen’s Medical Center, Honolulu, Hawaii. He’s also assistant professor and chief, Division of Medical Informatics, Department of Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa. Dr. Rosenman is senior associate consultant, Section of Hospital Medicine, Department of Internal Medicine, Mayo Clinic, and instructor of medicine, Mayo Clinic College of Medicine, Rochester, Minn.

References

  1. East TD, Morris AH, Wallace CJ, et al. A strategy for development of computerized critical care decision support systems. Int J Clin Monit Comput. 1992;8(4):263-269.
  2. Miller GA. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychol Rev. 1956;63(2):81-97.
  3. Morris AH, East TD, Wallace CJ, et al. Standardization of clinical decision making for the conduct of credible clinical research in complicated medical environments. Proc AMIA Annu Fall Symp. 1996;418-422.
  4. Xiao Y, Mackenzie CF, Spencer R, et al. Intrusiveness of auditory alarms during airway management. Anesthesiology. 1998 Sep;89(3AS):1224A.
  5. Lowe A, Jones RW, Harrison MJ. The graphical presentation of decision support in formation in an intelligent anaesthesia monitor. Artif Intell Med. 2001;22:173-191.
  6. Norman DA. The Invisible Computer. Cambridge, Mass.: The MIT Press; 1999.
  7. Gershon N, Eick SG, Card S. Information visualization. ACM Interactions. 1998;5(2):9-15.
  8. Powsner SM, Tufte ER. Graphical summary of patient status. Lancet. 1994; Aug 6:344(8919);386-389.
  9. Chittaro L. Information visualization and its application to medicine. Artif Intell Med. 2000;22:81-88.
  10. Starren J, Johnson SB. An object-oriented taxonomy of medical data presentations. J Am Med Inform Assoc. 2000 Jan;7(1):1-20.

Physicians have struggled with the management of patient data for a long time. The struggle intensifies as we attempt to juggle increasingly large and complicated volumes of information during a 24-hour day. As the number and acuity of patients increase in parallel, our abilities to sift critical information and prioritize data are key.

Alarms or alerts to abnormal parameters are of limited benefit and may be counterproductive. The techniques of data display and information visualization hold great promise for revolutionizing how we manage this data overload. Other industries have realized such benefits, and healthcare (especially in the hospital) has good reason to want to catch up. In the meantime, before we can say management of clinical data in the hospital is optimal, there remains much work to do.

Physicians have struggled with the management of patient data for a long time. Such information used to be relatively simple—heart rate, respiratory rate, skin color and temperature, and so on. The limits of technology fundamentally limited what physicians could observe and record.

As our ability to gather information became more sophisticated, so did the data we could acquire. Still, the physician remained the primary collector, assessor, and interpreter of tests and their results. Individual physicians would spin urine and examine the sediment, perform blood smears, and even examine tissue samples for pathology. This was a manageable task for the physician because the number of tests was small, and the interpretation of results was fairly straightforward.

Today tests and the ways we can interpret them are both more numerous and more complicated. This has resulted in a significant issue for clinicians: How can we manage all of this information?

Too Much Data

The quantity of data available for the busy clinician is always increasing. This data explosion is happening for three reasons:

  1. Increased number of sophisticated tests. We test for more diseases, traits, and conditions than ever before. Example: Troponin I, T, and beta natriuretic peptide—all in widespread use today—were not available 10 years ago. Advanced genetic testing will continue this trend;
  2. Increased archival capability. The cost of data storage continues to decrease, making it inexpensive to archive data that might have been purged in the past; and
  3. Increased sophistication of data delivery methods. Computers and the networks that connect them are faster than ever. This allows for efficient transfer of data from the archive to the user. It also allows the user to access the data from a variety of geographic locations, including an outpatient office or home.

Patient care in the ICU provides a perfect example of the volumes of data that we generate in the course of clinical care. Monitors capture moment-by-moment readings of heart rate, blood pressure, respirations, oxygen saturation, temperature, electrocardiographic tracings, and more. In addition to capturing the patient’s physiologic signals, we also measure the interventions we perform on patients. We record intravenous fluid and medication rates, artificial ventilation parameters, and so on. A decade ago, East estimated the number of information categories in the ICU to have been in excess of 236.1 Certainly that number has only increased.

Increasingly Complicated Data

As the number of tests has increased, interpretation of the results has become more complex. In many institutions samples are obtained by highly qualified personnel—not the primary physician. Depending on the test, the sample may be sent to a lab (sometimes in a different area of the country) where another individual may perform the test. Finally, a trained observer reviews the results, may make an interpretation of those results, and then records that interpretation—together with the objective data—in the patient’s medical record. These data are then available for the physician to review.

 

 

A hospitalist is unlikely to collect the sample or run the test. How many of us centrifuge urine or examine blood smears? How many could read a complicated MRI or a PET scan? The busy clinical workflow coupled with the complexity of these tests demands that someone other than the primary caregiver interpret these tests. This also means that we are removed from the primary data and must rely on another practitioner’s interpretation. Even with this separation in the process, we are presented with more clinical data for each patient than ever before.

Too Much Information

Of course, the availability of these data is not without problems. An individual can review, assess, and act upon only so many data points.2 As the volume of data increases, so does the likelihood that a piece of important data will be missed. This setting can make things difficult for the busy hospitalist.

We need to quickly prioritize our time in the hospital. A first step in this process involves a quick review of physiologic studies that suggest levels of patient acuity. This information helps us to see the most critical patients first, and to identify other important issues that need to be addressed (although less urgently).

As more data are collected, this task gets more complicated. Certainly not all of the data collected are equally important for determining patient acuity. Given this, the volume of data contributes to the overall “noise” of the sample and—in some cases—the important data may be overlooked. Critical values (signals) become more likely to get lost in a sea of less important data (noise). More noise means more uncertainty and requires better evidence to make a clinical judgment.3

Information systems developers use various techniques to get around these problems. One way to manage the vast amount of information is to alert practitioners to outliers. Some have proposed that alarms may be the answer to our noise problem.

Alarms Don’t Work

Unfortunately, alerts and alarms can actually add to the noise, especially in ICU monitors. Anyone who has spent time in an ICU knows that alarms are constantly sounding. It has been estimated that false positive alarm rates range from 64%-87% in airway management situations.4

Alarms are often meaningless. Monitors can be so sensitive that they pick up background noise in their measurements, causing false alarms that increase the noise (both literally and figuratively). Anesthetists, recognizing that alarms are non-specific, frequently work without them.5 This is paralleled in the ICU because caregivers seem to ignore many alarms. (Most hospitalists who have spent time in the ICU can attest to this.) Specific problems with alarms include:

  • They seldom localize the problem;
  • They do not provide predictive information; and
  • The diagnostic process is still left to the practitioner.5

Alarms also vary in importance. An intravenous pump that sounds because the fluid bag is empty may not be as important as an apnea alarm on a mechanical ventilator. A single alarm may not be as concerning as multiple simultaneous alarms for a patient with low blood pressure, high heart rate, and apnea. The goal in these cases is to signal a problem and to transmit that signal appropriately. But until this can be done reliably, alarms do not seem to be the answer.

On the other hand, presenting all the data (rather than just the important data) to a clinician may obscure important elements. This can result in missed diagnosis, delayed treatment, or incorrect treatment. So we don’t want to overwhelm the hospitalist with all the data; we just need to highlight and present important data. But how?

 

 

How Can We Manage All This Information?

To tackle this problem, we can look to work that has been done in other fields, specifically human-computer interaction. Norman, in his discussion of user experience, describes the complexity curve in technology, with technologies starting off simply and growing more complex until they peak.6 At this point, they get simpler to use as the technology matures. He points to airplane instrumentation that peaked with the Concorde in the 1970s. Since then instruments have gotten much simpler, with cockpit automation and better displays and controls. This has made the user experience easier, more efficient, and more effective.

There is a parallel with Norman’s observations and our information management problem in medicine. Our display technologies (paper, computer screen) are actually quite mature and powerful. However, our ability to detect and measure physiologic data continues to rise and may be outpacing our display technologies. Are there techniques from our display technologies that can make this problem easier to deal with?

Here, it’s worth clarifying two terms: data display and information visualization. Data display is a method for arranging and presenting information in a way that is easily reviewed and assessed, such as tables and charts. Information visualization describes the manipulation of the data to make it more easily understood by humans. Specifically it has been described as “the process of transforming data, information and knowledge into visual form making use of humans’ natural visual capabilities.”7

Data Displays

The simplest way to present data is the data display. Data displays can be very simple (a paper report with a glucose value), or extremely complex. (Think of bus schedules or the stock price pages in a business newspaper.) A complex data display in the clinical arena (which doubles as a data collection tool) is the clinical flow sheet. Nurses use a combination of graphing (heart rate, blood pressure) and numerical entry (intravenous fluid rate, pain scale) to record data. The flow sheet is particularly useful in the ICU. A large amount of data can be scanned quickly and examined for trends and outliers.

An example of a useful clinical data display is Pocket Rounds, a paper clinical summary report developed at the Regenstrief Institute in Indianapolis, Ind. Pocket Rounds is a high-density display designed to present clinical information including allergies, lab results, vitals, imaging, and other diagnostic studies from inpatients on a single 8.5" by 11" page. It is printed in very small type that allows for two logical pages on one landscape-oriented sheet of paper. It is called Pocket Rounds because, when folded in half, the sheet of reports fits perfectly into a white coat pocket.

The strength of Pocket Rounds seems to be the richness of the content displayed all at once, allowing the user to focus on specific areas of the report by following visual formatting clues. Of course, a significant disadvantage of Pocket Rounds is that it is static, with data only as current as the time of printing. Both authors used Pocket Rounds during their training and wish it were a more widely available tool.

Powsner and Tufte proposed a much more sophisticated display of clinical data.8 Their display is really a hybrid of data display and visualization, as processing of the data points (normalization) improves the layout of the display. It is easy to examine the report and pick out important trends and outliers. Additionally, with some thought as to the arrangement of the data elements, different results are easy to compare (for instance, white count, gentamycin dosage, and serum creatinine.) Unfortunately, this display has not been tested to compare its effectiveness with that of any other display.

 

 

Lessons for Medicine from Information Visualization

Information visualization is an area of increasing research and development, both in the scientific and business communities. It is closely linked with data mining: a method for knowledge discovery from extremely large, complex data sets. The goals of information visualization specifically germane to medicine include aiding the “discovery of details and relations” and “supporting the recognition of relevant patterns.”9 These relations and patterns may offer new knowledge or understanding that the individual data points do not adequately convey.

Visualization has been important in medical imaging for some time; however, less attention has been given to analysis of numeric and time series data on an individual patient. One example that is widely used is the pediatric growth chart, where the height, weight, and head circumference are mapped to percentages and plotted on a normalized curve to assess the child’s development. This task goes beyond simple display, as data synthesis is used (conversion to a percentage of normal). There are two tasks being performed as well. The first is the initial assessment of the patient in relation to the rest of the population. The second is a longitudinal trajectory of growth, where the points should follow the same line (population percentile growth) even though the actual data points (height, weight, head circumference) changes.

There are numerous examples of information visualization across non-medical disciplines. Taken together, many of these insights can provide a framework for creating improved data displays for clinicians. However, these concepts have not been tested in the clinical setting to determine whether they will increase efficiency of routines, such as acuity ranking. Further, we may need to support hospitalists’ common tasks with separate approaches. The acuity ranking activity might be supported by a summary page showing key outliers and critical values for each patient. The rest of the report could show all abnormal data and (as needed) the details for closer review.

Unfortunately, there has not been much direction in solving this problem from a scientific standpoint. In a review of the literature on the presentation of medical data, Starren and Johnson noted that, “there is a paucity of methods for developing new presentations” in the medical setting.10 Further, they observe that clinical data displays are rarely evaluated quantitatively. Rather they are shown to users to assess acceptance. We need to alleviate that shortcoming.

Next Steps

We believe that a significant amount of research needs to be performed in this area. We also believe that this research should focus on hospital-based specialties—especially hospitalist medicine. Why? Because hospitalists are charged with quickly assessing lots of information on lots of patients, and anything we can do to make that process more efficient will result in better patient care and hopefully, happier hospitalists. So what are next steps?

We can break up the research agenda into two arms: what needs to be displayed, and how do we display it? Although it may seem intuitive, we think it is important to decide the what before the how because the content will really drive the improvements in care.

There has been some emphasis on determining what clinical data are important for physicians. Work on prediction algorithms and scores has led to some estimations of what numbers are important for determining patient acuity and severity. However, an accurate and dependable determination of who is sick, how sick, and who will get sicker is some time off.

For now, it would be helpful to know what data physicians want to see. This will vary by provider and may not always lead directly to a specific outcome, but it is a start. It would be helpful to identify the values that most clinicians would want to know most of the time: high or low white counts, decrease in hemoglobin, decrease in platelets, normalization of creatinine, or other. This would provide the basis for experimenting with how best to display these items.

 

 

We then could move on to explore how these data should be displayed. What should be presented as discrete numbers? What would be better to summarize graphically? How can we highlight important trends? A significant amount of work has been done in fields with so-called “knowledge workers”—professionals who need to review and act on large amounts of data. Work also has been done with other data-intensive professionals such as airplane pilots, air traffic controllers, and stockbrokers. We should be able to glean valuable insights into solutions from these investigations and use them to improve our data management problem.

Finally, these displays need to be prototyped and tested on the wards. Does the new display help make the hospitalist more efficient? Can they pick out the important data faster? Do they improve length of stay, morbidity and mortality, or patient satisfaction? It is this critical evaluation that is dearly lacking as we work to improve how hospitalists do their jobs.

Healthcare providers generally are capable, hard-working professionals with the best intentions. Inefficient, overwhelmed data management systems ultimately make us equally inefficient and overwhelmed providers. In an age when abundant scientific study and complex healthcare delivery systems are generating volumes of new information, we have a lot to learn about what to do with it all. TH

Dr. Thomas is a hospitalist and assistant medical director, Clinical Informatics, The Queen’s Medical Center, Honolulu, Hawaii. He’s also assistant professor and chief, Division of Medical Informatics, Department of Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa. Dr. Rosenman is senior associate consultant, Section of Hospital Medicine, Department of Internal Medicine, Mayo Clinic, and instructor of medicine, Mayo Clinic College of Medicine, Rochester, Minn.

References

  1. East TD, Morris AH, Wallace CJ, et al. A strategy for development of computerized critical care decision support systems. Int J Clin Monit Comput. 1992;8(4):263-269.
  2. Miller GA. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychol Rev. 1956;63(2):81-97.
  3. Morris AH, East TD, Wallace CJ, et al. Standardization of clinical decision making for the conduct of credible clinical research in complicated medical environments. Proc AMIA Annu Fall Symp. 1996;418-422.
  4. Xiao Y, Mackenzie CF, Spencer R, et al. Intrusiveness of auditory alarms during airway management. Anesthesiology. 1998 Sep;89(3AS):1224A.
  5. Lowe A, Jones RW, Harrison MJ. The graphical presentation of decision support in formation in an intelligent anaesthesia monitor. Artif Intell Med. 2001;22:173-191.
  6. Norman DA. The Invisible Computer. Cambridge, Mass.: The MIT Press; 1999.
  7. Gershon N, Eick SG, Card S. Information visualization. ACM Interactions. 1998;5(2):9-15.
  8. Powsner SM, Tufte ER. Graphical summary of patient status. Lancet. 1994; Aug 6:344(8919);386-389.
  9. Chittaro L. Information visualization and its application to medicine. Artif Intell Med. 2000;22:81-88.
  10. Starren J, Johnson SB. An object-oriented taxonomy of medical data presentations. J Am Med Inform Assoc. 2000 Jan;7(1):1-20.
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