Sunil Kripalani, MD, MSc, SFHM

Unintentional medication discrepancies in the hospital setting are common and contribute to adverse drug events, resulting in patient harm.¹ Discrepancies can be resolved by implementing high-quality medication reconciliation, but there are insufficient data to guide hospitals as to which interventions are most effective at improving medication reconciliation processes and reducing harm.² We recently reported that implementation of a best practices toolkit reduced total medication discrepancies in the Multi-Center Medication Reconciliation Quality Improvement Study (MARQUIS).³ This report describes the effect of individual toolkit components on rates of medication discrepancies with the potential for patient harm.

Detailed descriptions of the intervention toolkit and study design of MARQUIS are published.^4,5 Briefly, MARQUIS was a pragmatic, mentored, quality improvement (QI) study in which five hospitals in the United States implemented interventions from a best practices toolkit to improve medication reconciliation on noncritical care medical and surgical units from September 2011 to July 2014. We used a mentored implementation approach, in which each site identified the leaders of their local quality improvement team (ie, mentees) who received mentorship from a trained physician with QI and medication safety experience.⁶ Mentors conducted monthly calls with their mentees and two site visits. Sites adapted and implemented one or more components from the MARQUIS toolkit, a compilation of evidence-based best practices in medication reconciliation.^5,7

The primary outcome was unintentional medication discrepancies in admission and discharge orders with the potential for causing harm, as previously described.⁴ Trained study pharmacists at each site took “gold standard” medication histories on a random sample of up to 22 patients per month. These medications were then compared with admission and discharge medication orders, and all unintentional discrepancies were identified. The discrepancies were then adjudicated by physicians blinded to the treatment arm, who confirmed whether discrepancies were unintentional and carried the potential for patient harm.

We employed a modification of a stepped wedge methodology to measure the incremental effect of implementing nine different intervention components, introduced at different sites over the course of the study, on the number of potentially harmful discrepancies per patient. These analyses were restricted to the postimplementation period on hospital units that implemented at least one intervention. All interventions conducted at each site were categorized by component, including dates of implementation. Each intervention component could be applied more than once per site (eg, when involving a new group of providers) or implemented on a new hospital unit or service, in which case, all dates were included in the analysis. We conducted a multivariable Poisson regression (with time divided into months) adjusted for patient factors, season, and site, with the number of potentially harmful discrepancies as the dependent variable, and the total number of gold standard medications as a model offset. The model was designed to analyze changes in the y-intercept each time an intervention component was either implemented or spread and assumed the change in the y-intercept was the same for each of these events for any given component. The model also assumes that combinations of interventions had independent additive effects.

Across the five participating sites, 1,648 patients were enrolled from September 2011 to July 2014. This number included 613 patients during the preimplementation period and 1,035 patients during the postimplementation period, of which 791 were on intervention units and comprised the study population. Table 1 displays the intervention components implemented by site. Sites implemented between one and seven components. The most frequently implemented intervention component was training existing staff to take the best possible medication histories (BPMHs), implemented at four sites. The regression results are displayed in Table 2. Three interventions were associated with significant decreases in potentially harmful discrepancy rates: (1) clearly defining roles and responsibilities and communicating this with clinical staff (hazard ratio [HR] 0.53, 95% CI: 0.32–0.87); (2) training existing staff to perform discharge medication reconciliation and patient counseling (HR 0.64, 95% CI: 0.46–0.89); and (3) hiring additional staff to perform discharge medication reconciliation and patient counseling (HR 0.48, 95% CI: 0.31–0.77). Two interventions were associated with significant increases in potentially harmful discrepancy rates: training existing staff to take BPMHs (HR 1.38, 95% CI: 1.21–1.57) and implementing a new electronic health record (EHR; HR 2.21, 95% CI: 1.64–2.97).

We noted that three intervention components were associated with decreased rates of unintentional medication discrepancies with potential for harm, whereas two were associated with increased rates. The components with a beneficial effect were not surprising. A prior qualitative study demonstrated the confusion related to clinicians’ roles and responsibilities during medication reconciliation; therefore, clear delineations should reduce rework and improve the medication reconciliation process.⁸ Other studies have shown the benefits of pharmacist involvement in the inpatient setting, particularly in reducing errors at discharge.⁹ However, we did not anticipate that training staff to take BPMHs would be detrimental. Possible reasons for this finding that are based on direct observations by mentors at site visits or noted during monthly calls include (1) training personnel on this task without certification of competency may not sufficiently improve their skills, leading instead to diffusion of responsibility; (2) training personnel without sufficient time to perform the task well (eg, frontline nurses with many other responsibilities) may be counterproductive compared with training a few personnel with time dedicated to this task; and (3) training existing personnel in history-taking may have been used to delay the necessary hiring of more staff to take BPMHs. Future studies could address several of these shortcomings in both the design and implementation of medication history-training intervention components.

Several reasons may explain the association we found between implementing a new EHR and increased rates of discrepancies. Based on mentors’ experiences, we suspect it is because sitewide EHR implementation requires significant resources, time, and effort. Therefore, sitewide EHR implementation pulls attention away from a focus on medication safety. Most large vendor EHRs have design flaws in their medication reconciliation modules, with the overarching problem being that their systems are not designed for an interdisciplinary team approach to medication reconciliation (unpublished material). In addition, problems may also exist with the local implementation of these modules and the way they are used by clinicians (eg, bypassing critical steps in the medication reconciliation process that lead to new medication errors). We have updated the MARQUIS toolkit to include pros and cons of EHR software and ideal features and functions of medication reconciliation information technology. We should note that this finding contrasts with previous studies that showed beneficial effects of dedicated medication reconciliation applications, which used proprietary technology, often combined with process redesign, in a focused QI effort.^10-13 These findings suggest the need for improvements in the design, local customization, and use of medication reconciliation modules in vendor EHRs.

Our study has several limitations. We conducted an on-treatment analysis, which may be confounded by characteristics of sites that chose to implement different intervention components; however, we adjusted for sites in the analysis. Some results are based on a limited number of sites implementing an intervention component (eg, defining roles and responsibilities). Although this was a longitudinal study, and we adjusted for seasonal effects, it is possible that temporal trends and cointerventions confounded our results. The adjudication of discrepancies for the potential for harm was somewhat subjective, although we used a rigorous process to ensure the reliability of adjudication, as in prior studies.^3,14 As in the main analysis of the MARQUIS study, this analysis did not measure intervention fidelity.

Based on these analyses and the literature base, we recommend that hospitals focus first on hiring and training dedicated staff (usually pharmacists) to assist with medication reconciliation at discharge.⁷ Hospitals should also be aware of potential increases in medication discrepancies when implementing a large vendor EHR across their institution. Further work is needed on the best ways to mitigate these adverse effects, at both the design and local site levels. Finally, the effect of medication history training on discrepancies warrants further study.

Disclosures

SK has served as a consultant to Verustat, a remote health monitoring company. All other authors have no disclosures or conflicts of interests.

Funding

This study was supported by the Agency for Healthcare Research and Quality (grant number: R18 HS019598). JLS has received funding from (1) Mallinckrodt Pharmaceuticals for an investigator-initiated study of opioid-related adverse drug events in postsurgical patients; (2) Horizon Blue Cross Blue Shield for an honorarium and travel expenses for workshop on medication reconciliation; (3) Island Peer Review Organization for honorarium and travel expenses for workshop on medication reconciliation; and, (4) Portola Pharmaceuticals for investigator-initiated study of inpatients who decline subcutaneous medications for venous thromboembolism prophylaxis. ASM was funded by a VA HSR&D Career Development Award (12-168).

Trial Registration

ClinicalTrials.gov NCT01337063

Unintentional medication discrepancies in the hospital setting are common and contribute to adverse drug events, resulting in patient harm.¹ Discrepancies can be resolved by implementing high-quality medication reconciliation, but there are insufficient data to guide hospitals as to which interventions are most effective at improving medication reconciliation processes and reducing harm.² We recently reported that implementation of a best practices toolkit reduced total medication discrepancies in the Multi-Center Medication Reconciliation Quality Improvement Study (MARQUIS).³ This report describes the effect of individual toolkit components on rates of medication discrepancies with the potential for patient harm.

Detailed descriptions of the intervention toolkit and study design of MARQUIS are published.^4,5 Briefly, MARQUIS was a pragmatic, mentored, quality improvement (QI) study in which five hospitals in the United States implemented interventions from a best practices toolkit to improve medication reconciliation on noncritical care medical and surgical units from September 2011 to July 2014. We used a mentored implementation approach, in which each site identified the leaders of their local quality improvement team (ie, mentees) who received mentorship from a trained physician with QI and medication safety experience.⁶ Mentors conducted monthly calls with their mentees and two site visits. Sites adapted and implemented one or more components from the MARQUIS toolkit, a compilation of evidence-based best practices in medication reconciliation.^5,7

The primary outcome was unintentional medication discrepancies in admission and discharge orders with the potential for causing harm, as previously described.⁴ Trained study pharmacists at each site took “gold standard” medication histories on a random sample of up to 22 patients per month. These medications were then compared with admission and discharge medication orders, and all unintentional discrepancies were identified. The discrepancies were then adjudicated by physicians blinded to the treatment arm, who confirmed whether discrepancies were unintentional and carried the potential for patient harm.

We employed a modification of a stepped wedge methodology to measure the incremental effect of implementing nine different intervention components, introduced at different sites over the course of the study, on the number of potentially harmful discrepancies per patient. These analyses were restricted to the postimplementation period on hospital units that implemented at least one intervention. All interventions conducted at each site were categorized by component, including dates of implementation. Each intervention component could be applied more than once per site (eg, when involving a new group of providers) or implemented on a new hospital unit or service, in which case, all dates were included in the analysis. We conducted a multivariable Poisson regression (with time divided into months) adjusted for patient factors, season, and site, with the number of potentially harmful discrepancies as the dependent variable, and the total number of gold standard medications as a model offset. The model was designed to analyze changes in the y-intercept each time an intervention component was either implemented or spread and assumed the change in the y-intercept was the same for each of these events for any given component. The model also assumes that combinations of interventions had independent additive effects.

Across the five participating sites, 1,648 patients were enrolled from September 2011 to July 2014. This number included 613 patients during the preimplementation period and 1,035 patients during the postimplementation period, of which 791 were on intervention units and comprised the study population. Table 1 displays the intervention components implemented by site. Sites implemented between one and seven components. The most frequently implemented intervention component was training existing staff to take the best possible medication histories (BPMHs), implemented at four sites. The regression results are displayed in Table 2. Three interventions were associated with significant decreases in potentially harmful discrepancy rates: (1) clearly defining roles and responsibilities and communicating this with clinical staff (hazard ratio [HR] 0.53, 95% CI: 0.32–0.87); (2) training existing staff to perform discharge medication reconciliation and patient counseling (HR 0.64, 95% CI: 0.46–0.89); and (3) hiring additional staff to perform discharge medication reconciliation and patient counseling (HR 0.48, 95% CI: 0.31–0.77). Two interventions were associated with significant increases in potentially harmful discrepancy rates: training existing staff to take BPMHs (HR 1.38, 95% CI: 1.21–1.57) and implementing a new electronic health record (EHR; HR 2.21, 95% CI: 1.64–2.97).

We noted that three intervention components were associated with decreased rates of unintentional medication discrepancies with potential for harm, whereas two were associated with increased rates. The components with a beneficial effect were not surprising. A prior qualitative study demonstrated the confusion related to clinicians’ roles and responsibilities during medication reconciliation; therefore, clear delineations should reduce rework and improve the medication reconciliation process.⁸ Other studies have shown the benefits of pharmacist involvement in the inpatient setting, particularly in reducing errors at discharge.⁹ However, we did not anticipate that training staff to take BPMHs would be detrimental. Possible reasons for this finding that are based on direct observations by mentors at site visits or noted during monthly calls include (1) training personnel on this task without certification of competency may not sufficiently improve their skills, leading instead to diffusion of responsibility; (2) training personnel without sufficient time to perform the task well (eg, frontline nurses with many other responsibilities) may be counterproductive compared with training a few personnel with time dedicated to this task; and (3) training existing personnel in history-taking may have been used to delay the necessary hiring of more staff to take BPMHs. Future studies could address several of these shortcomings in both the design and implementation of medication history-training intervention components.

Several reasons may explain the association we found between implementing a new EHR and increased rates of discrepancies. Based on mentors’ experiences, we suspect it is because sitewide EHR implementation requires significant resources, time, and effort. Therefore, sitewide EHR implementation pulls attention away from a focus on medication safety. Most large vendor EHRs have design flaws in their medication reconciliation modules, with the overarching problem being that their systems are not designed for an interdisciplinary team approach to medication reconciliation (unpublished material). In addition, problems may also exist with the local implementation of these modules and the way they are used by clinicians (eg, bypassing critical steps in the medication reconciliation process that lead to new medication errors). We have updated the MARQUIS toolkit to include pros and cons of EHR software and ideal features and functions of medication reconciliation information technology. We should note that this finding contrasts with previous studies that showed beneficial effects of dedicated medication reconciliation applications, which used proprietary technology, often combined with process redesign, in a focused QI effort.^10-13 These findings suggest the need for improvements in the design, local customization, and use of medication reconciliation modules in vendor EHRs.

Our study has several limitations. We conducted an on-treatment analysis, which may be confounded by characteristics of sites that chose to implement different intervention components; however, we adjusted for sites in the analysis. Some results are based on a limited number of sites implementing an intervention component (eg, defining roles and responsibilities). Although this was a longitudinal study, and we adjusted for seasonal effects, it is possible that temporal trends and cointerventions confounded our results. The adjudication of discrepancies for the potential for harm was somewhat subjective, although we used a rigorous process to ensure the reliability of adjudication, as in prior studies.^3,14 As in the main analysis of the MARQUIS study, this analysis did not measure intervention fidelity.

Based on these analyses and the literature base, we recommend that hospitals focus first on hiring and training dedicated staff (usually pharmacists) to assist with medication reconciliation at discharge.⁷ Hospitals should also be aware of potential increases in medication discrepancies when implementing a large vendor EHR across their institution. Further work is needed on the best ways to mitigate these adverse effects, at both the design and local site levels. Finally, the effect of medication history training on discrepancies warrants further study.

Disclosures

SK has served as a consultant to Verustat, a remote health monitoring company. All other authors have no disclosures or conflicts of interests.

Funding

This study was supported by the Agency for Healthcare Research and Quality (grant number: R18 HS019598). JLS has received funding from (1) Mallinckrodt Pharmaceuticals for an investigator-initiated study of opioid-related adverse drug events in postsurgical patients; (2) Horizon Blue Cross Blue Shield for an honorarium and travel expenses for workshop on medication reconciliation; (3) Island Peer Review Organization for honorarium and travel expenses for workshop on medication reconciliation; and, (4) Portola Pharmaceuticals for investigator-initiated study of inpatients who decline subcutaneous medications for venous thromboembolism prophylaxis. ASM was funded by a VA HSR&D Career Development Award (12-168).

Trial Registration

ClinicalTrials.gov NCT01337063

Unintentional medication discrepancies in the hospital setting are common and contribute to adverse drug events, resulting in patient harm.¹ Discrepancies can be resolved by implementing high-quality medication reconciliation, but there are insufficient data to guide hospitals as to which interventions are most effective at improving medication reconciliation processes and reducing harm.² We recently reported that implementation of a best practices toolkit reduced total medication discrepancies in the Multi-Center Medication Reconciliation Quality Improvement Study (MARQUIS).³ This report describes the effect of individual toolkit components on rates of medication discrepancies with the potential for patient harm.

Detailed descriptions of the intervention toolkit and study design of MARQUIS are published.^4,5 Briefly, MARQUIS was a pragmatic, mentored, quality improvement (QI) study in which five hospitals in the United States implemented interventions from a best practices toolkit to improve medication reconciliation on noncritical care medical and surgical units from September 2011 to July 2014. We used a mentored implementation approach, in which each site identified the leaders of their local quality improvement team (ie, mentees) who received mentorship from a trained physician with QI and medication safety experience.⁶ Mentors conducted monthly calls with their mentees and two site visits. Sites adapted and implemented one or more components from the MARQUIS toolkit, a compilation of evidence-based best practices in medication reconciliation.^5,7

The primary outcome was unintentional medication discrepancies in admission and discharge orders with the potential for causing harm, as previously described.⁴ Trained study pharmacists at each site took “gold standard” medication histories on a random sample of up to 22 patients per month. These medications were then compared with admission and discharge medication orders, and all unintentional discrepancies were identified. The discrepancies were then adjudicated by physicians blinded to the treatment arm, who confirmed whether discrepancies were unintentional and carried the potential for patient harm.

We employed a modification of a stepped wedge methodology to measure the incremental effect of implementing nine different intervention components, introduced at different sites over the course of the study, on the number of potentially harmful discrepancies per patient. These analyses were restricted to the postimplementation period on hospital units that implemented at least one intervention. All interventions conducted at each site were categorized by component, including dates of implementation. Each intervention component could be applied more than once per site (eg, when involving a new group of providers) or implemented on a new hospital unit or service, in which case, all dates were included in the analysis. We conducted a multivariable Poisson regression (with time divided into months) adjusted for patient factors, season, and site, with the number of potentially harmful discrepancies as the dependent variable, and the total number of gold standard medications as a model offset. The model was designed to analyze changes in the y-intercept each time an intervention component was either implemented or spread and assumed the change in the y-intercept was the same for each of these events for any given component. The model also assumes that combinations of interventions had independent additive effects.

Across the five participating sites, 1,648 patients were enrolled from September 2011 to July 2014. This number included 613 patients during the preimplementation period and 1,035 patients during the postimplementation period, of which 791 were on intervention units and comprised the study population. Table 1 displays the intervention components implemented by site. Sites implemented between one and seven components. The most frequently implemented intervention component was training existing staff to take the best possible medication histories (BPMHs), implemented at four sites. The regression results are displayed in Table 2. Three interventions were associated with significant decreases in potentially harmful discrepancy rates: (1) clearly defining roles and responsibilities and communicating this with clinical staff (hazard ratio [HR] 0.53, 95% CI: 0.32–0.87); (2) training existing staff to perform discharge medication reconciliation and patient counseling (HR 0.64, 95% CI: 0.46–0.89); and (3) hiring additional staff to perform discharge medication reconciliation and patient counseling (HR 0.48, 95% CI: 0.31–0.77). Two interventions were associated with significant increases in potentially harmful discrepancy rates: training existing staff to take BPMHs (HR 1.38, 95% CI: 1.21–1.57) and implementing a new electronic health record (EHR; HR 2.21, 95% CI: 1.64–2.97).

We noted that three intervention components were associated with decreased rates of unintentional medication discrepancies with potential for harm, whereas two were associated with increased rates. The components with a beneficial effect were not surprising. A prior qualitative study demonstrated the confusion related to clinicians’ roles and responsibilities during medication reconciliation; therefore, clear delineations should reduce rework and improve the medication reconciliation process.⁸ Other studies have shown the benefits of pharmacist involvement in the inpatient setting, particularly in reducing errors at discharge.⁹ However, we did not anticipate that training staff to take BPMHs would be detrimental. Possible reasons for this finding that are based on direct observations by mentors at site visits or noted during monthly calls include (1) training personnel on this task without certification of competency may not sufficiently improve their skills, leading instead to diffusion of responsibility; (2) training personnel without sufficient time to perform the task well (eg, frontline nurses with many other responsibilities) may be counterproductive compared with training a few personnel with time dedicated to this task; and (3) training existing personnel in history-taking may have been used to delay the necessary hiring of more staff to take BPMHs. Future studies could address several of these shortcomings in both the design and implementation of medication history-training intervention components.

Several reasons may explain the association we found between implementing a new EHR and increased rates of discrepancies. Based on mentors’ experiences, we suspect it is because sitewide EHR implementation requires significant resources, time, and effort. Therefore, sitewide EHR implementation pulls attention away from a focus on medication safety. Most large vendor EHRs have design flaws in their medication reconciliation modules, with the overarching problem being that their systems are not designed for an interdisciplinary team approach to medication reconciliation (unpublished material). In addition, problems may also exist with the local implementation of these modules and the way they are used by clinicians (eg, bypassing critical steps in the medication reconciliation process that lead to new medication errors). We have updated the MARQUIS toolkit to include pros and cons of EHR software and ideal features and functions of medication reconciliation information technology. We should note that this finding contrasts with previous studies that showed beneficial effects of dedicated medication reconciliation applications, which used proprietary technology, often combined with process redesign, in a focused QI effort.^10-13 These findings suggest the need for improvements in the design, local customization, and use of medication reconciliation modules in vendor EHRs.

Our study has several limitations. We conducted an on-treatment analysis, which may be confounded by characteristics of sites that chose to implement different intervention components; however, we adjusted for sites in the analysis. Some results are based on a limited number of sites implementing an intervention component (eg, defining roles and responsibilities). Although this was a longitudinal study, and we adjusted for seasonal effects, it is possible that temporal trends and cointerventions confounded our results. The adjudication of discrepancies for the potential for harm was somewhat subjective, although we used a rigorous process to ensure the reliability of adjudication, as in prior studies.^3,14 As in the main analysis of the MARQUIS study, this analysis did not measure intervention fidelity.

Based on these analyses and the literature base, we recommend that hospitals focus first on hiring and training dedicated staff (usually pharmacists) to assist with medication reconciliation at discharge.⁷ Hospitals should also be aware of potential increases in medication discrepancies when implementing a large vendor EHR across their institution. Further work is needed on the best ways to mitigate these adverse effects, at both the design and local site levels. Finally, the effect of medication history training on discrepancies warrants further study.

Disclosures

SK has served as a consultant to Verustat, a remote health monitoring company. All other authors have no disclosures or conflicts of interests.

Funding

This study was supported by the Agency for Healthcare Research and Quality (grant number: R18 HS019598). JLS has received funding from (1) Mallinckrodt Pharmaceuticals for an investigator-initiated study of opioid-related adverse drug events in postsurgical patients; (2) Horizon Blue Cross Blue Shield for an honorarium and travel expenses for workshop on medication reconciliation; (3) Island Peer Review Organization for honorarium and travel expenses for workshop on medication reconciliation; and, (4) Portola Pharmaceuticals for investigator-initiated study of inpatients who decline subcutaneous medications for venous thromboembolism prophylaxis. ASM was funded by a VA HSR&D Career Development Award (12-168).

Trial Registration

ClinicalTrials.gov NCT01337063

Most studies to identify risk factors for readmission among patients with heart failure (HF) have focused on demographic and clinical characteristics.^1,2 Although easy to extract from administrative databases, this approach fails to capture the complex psychosocial and cognitive factors that influence the ability of HF patients to manage their disease in the postdischarge period, as depicted in the framework by Meyers et al.³ (2014). To date, studies have found low health literacy, decreased social support, and cognitive impairment to be associated with health behaviors and outcomes among HF patients, including decreased self-care,⁴ low HF-specific knowledge,⁵ medication nonadherence,⁶ hospitalizations,⁷ and mortality.^8-10 Less, however, is known about the effect of numeracy on HF outcomes, such as 30-day readmission.

Numeracy, or quantitative literacy, refers to the ability to access, understand, and apply numerical data to health-related decisions.¹¹ It is estimated that 110 million people in the United States have limited numeracy skills.¹² Low numeracy is a risk factor for poor glycemic control among patients with diabetes,¹³ medication adherence in HIV/AIDS,¹⁴ and worse blood pressure control in hypertensives.¹⁵ Much like these conditions, HF requires that patients understand, use, and act on numerical information. Maintaining a low-salt diet, monitoring weight, adjusting diuretic doses, and measuring blood pressure are tasks that HF patients are asked to perform on a daily or near-daily basis. These tasks are particularly important in the posthospitalization period and could be complicated by medication changes, which might create additional challenges for patients with inadequate numeracy. Additionally, cognitive impairment, which is a highly prevalent comorbid condition among adults with HF,^16,17 might impose additional barriers for those with inadequate numeracy who do not have adequate social support. However, to date, numeracy in the context of HF has not been well described.

Herein, we examined the effects of numeracy, alongside health literacy and cognition, on 30-day readmission risk among patients hospitalized for acute decompensated HF (ADHF).

Study Design

The Vanderbilt Inpatient Cohort Study (VICS) is a prospective observational study of patients admitted with cardiovascular disease to Vanderbilt University Medical Center (VUMC), an academic tertiary care hospital. VICS was designed to investigate the impact of social determinants of health on postdischarge health outcomes. A detailed description of the study rationale, design, and methods is described elsewhere.³

Briefly, participants completed a baseline interview while hospitalized, and follow-up phone calls were conducted within 1 week of discharge, at 30 days, and at 90 days. At 30 and 90 days postdischarge, healthcare utilization was ascertained by review of medical records and patient report. Clinical data about the index hospitalization were also abstracted. The Vanderbilt University Institutional Review Board approved the study.

Study Population

Patients hospitalized from 2011 to 2015 with a likely diagnosis of acute coronary syndrome and/or ADHF, as determined by a physician’s review of the medical record, were identified as potentially eligible. Research assistants assessed these patients for the presence of the following exclusion criteria: less than 18 years of age, non-English speaking, unstable psychiatric illness, a low likelihood of follow-up (eg, no reliable telephone number), on hospice, or otherwise too ill to complete an interview. Additionally, those with severe cognitive impairment, as assessed from the medical record (such as seeing a note describing dementia), and those with delirium, as assessed by the brief confusion assessment method, were excluded from enrollment in the study.^18,19 Those who died before discharge or during the 30-day follow-up period were excluded. For this analysis, we restricted our sample to only include participants who were hospitalized for ADHF.

Outcome Measure: 30-Day Readmission

The main outcome was all-cause readmission to any hospital within 30 days of discharge, as determined by patient interview, review of electronic medical records from VUMC, and review of outside hospital records.

Main Exposures: Numeracy, Health Literacy, and Cognitive Impairment

Numeracy was assessed with a 3-item version of the Subjective Numeracy Scale (SNS-3), which quantifies the patients perceived quantitative abilities.²⁰ Other authors have shown that the SNS-3 has a correlation coefficient of 0.88 with the full-length SNS-8 and a Cronbach’s alpha of 0.78.^20-22 The SNS-3 is reported as the mean on a scale from 1 to 6, with higher scores reflecting higher numeracy.

Subjective health literacy was assessed by using the 3-item Brief Health Literacy Screen (BHLS).²³ Scores range from 3 to 15, with higher scores reflecting higher literacy. Objective health literacy was assessed with the short form of the Test of Functional Health Literacy in Adults (sTOFHLA).^24,25 Scores may be categorized as inadequate (0-16), marginal (17-22), or adequate (23-36).

We assessed cognition by using the 10-item Short Portable Mental Status Questionnaire (SPMSQ).²⁶ The SPMSQ, which describes a person’s capacity for memory, structured thought, and orientation, has been validated and has demonstrated good reliability and validity.²⁷ Scores of 0 were considered to reflect intact cognition, and scores of 1 or more were considered to reflect any cognitive impairment, a scoring approach employed by other authors.²⁸ We used this approach, rather than the traditional scoring system developed by Pfeiffer et al.²⁶ (1975), because it would be the most sensitive to detect any cognitive impairment in the VICS cohort, which excluded those with severe cognition impairment, dementia, and delirium.

Covariates

During the hospitalization, participants completed an in-person interviewer-administered baseline assessment composed of demographic information, including age, self-reported race (white and nonwhite), educational attainment, home status (married, not married and living with someone, not married and living alone), and household income.

Clinical and diagnostic characteristics abstracted from the medical record included a medical history of HF, HF subtype (classified by left ventricular ejection fraction [LVEF]), coronary artery disease, chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM), and comorbidity burden as summarized by the van Walraven-Elixhauser score.^29,30 Depressive symptoms were assessed during the 2 weeks prior to the hospitalization by using the first 8 items of the Patient Health Questionnaire.³¹ Scores ranged from 0 to 24, with higher scores reflecting more severe depressive symptoms. Laboratory values included estimated glomerular filtration rate (eGFR), hemoglobin (g/dl), sodium (mg/L), and brain natriuretic peptide (BNP) (pg/ml) from the last laboratory draw before discharge. Smoking status was also assessed (current and former/nonsmokers).

Hospitalization characteristics included length of stay in days, number of prior admissions in the last year, and transfer to the intensive care unit during the index admission.

Statistical Analysis

Descriptive statistics were used to summarize patient characteristics. The Kruskal-Wallis test and the Pearson χ² test were used to determine the association between patient characteristics and levels of numeracy, literacy, and cognition separately. The unadjusted relationship between patient characteristics and 30-day readmission was assessed by using Wilcoxon rank sums tests for continuous variables and Pearson χ² tests for categorical variables. In addition, a correlation matrix was performed to assess the correlations between numeracy, health literacy, and cognition (supplementary Figure 1).

To examine the association between numeracy, health literacy, and cognition and 30-day readmissions, a series of multivariable Poisson (log-linear) regression models were fit.³² Like other studies, numeracy, health literacy, and cognition were examined as categorical and continuous measures in models.³³ Each model was modified with a sandwich estimator for robust standard errors. Log-linear models were chosen over logistic regression models for ease of interpretation because (exponentiated) parameters correspond to risk ratios (RRs) as opposed to odds ratios. Furthermore, the fitting challenges associated with log-linear models when predicted probabilities are near 0 or 1 were not present in these analyses. Redundancy analyses were conducted to ensure that independent variables were not highly correlated with a linear combination of the other independent variables. To avoid case-wise deletion of records with missing covariates, we employed multiple imputation with 10 imputation samples by using predictive mean matching.^34,35 All analyses were conducted in R version 3.1.2 (The R Foundation, Vienna, Austria).³⁶

Overall, 883 patients were included in this analysis (supplementary Figure 2). Of the 883 participants, 46% were female and 76% were white (Table 1). Their median age was 60 years (interdecile range [IDR] 39-78) and the median educational attainment was 13.5 years (IDR 11-18).

This is the first study to examine the effect of numeracy alongside literacy and cognition on 30-day readmission risk among patients hospitalized with ADHF. Overall, we found that 33.9% of participants had inadequate numeracy skills, and 24.6% had inadequate or marginal health literacy. In unadjusted and adjusted models, numeracy was not associated with 30-day readmission. Although (objective) low health literacy was associated with 30-day readmission in unadjusted models, it was not in adjusted models. Additionally, though 53% of participants had any cognitive impairment, readmission did not differ significantly by this factor. Taken together, these findings suggest that other factors may be greater determinants of 30-day readmissions among patients hospitalized for ADHF.

Only 1 other study has examined the effect of numeracy on readmission risk among patients hospitalized for HF. In this multicenter prospective study, McNaughton et al.³⁷ found low numeracy to be associated with higher odds of recidivism to the emergency department (ED) or hospital within 30 days. Our findings may differ from theirs for a few reasons. First, their study had a significantly higher percentage of individuals with low numeracy (55%) compared with ours (33.9%). This may be because they did not exclude individuals with severe cognitive impairment, and their patient population was of lower socioeconomic status (SES) than ours. Low SES is associated with higher 30-day readmissions among HF patients^1,10 throughout the literature, and low numeracy is associated with low SES in other diseases.^13,38,39 Finally, they studied recidivism, which was defined as any unplanned return to the ED or hospital within 30 days of the index ED visit for acute HF. We only focused on 30-day readmissions, which also may explain why our results differed.

We found that health literacy was not associated with 30-day readmissions, which is consistent with the literature. Although an association between health literacy and mortality exists among adults with HF, several studies have not found an association between health literacy and 30- and 90-day readmission among adults hospitalized for HF.^8,9,40 Although we found an association between objective health literacy and 30-day readmission in unadjusted analyses, we did not find one in the multivariable model. This, along with our numeracy finding, suggests that numeracy and literacy may not be driving the 30-day readmission risk among patients hospitalized with ADHF.

We examined cognition alongside numeracy and literacy because it is a prevalent condition among HF patients and because it is associated with adverse outcomes among patients with HF, including readmission.^41,42 Studies have shown that HF preferentially affects certain cognitive domains,⁴³ some of which are vital to HF self-care activities. We found that 53% of patients had any cognitive impairment, which is consistent with the literature of adults hospitalized for ADHF.^44,45 Cognitive impairment was not, however, associated with 30-day readmissions. There may be a couple reasons for this. First, we measured cognitive impairment with the SPMSQ, which, although widely used and well-validated, does not assess executive function, the domain most commonly affected in HF patients with cognitive impairment.⁴⁶ Second, patients with severe cognitive impairment and those with delirium were excluded from this study, which may have limited our ability to detect differences in readmission by this factor.

As in prior studies, we found that a history of DM and more hospitalizations in the prior year were independently associated with 30-day readmissions in fully adjusted models. Like other studies, in adjusted models, we found that LVEF and a history of HF were not independently associated with 30-day readmission.^47-49 This, however, is not surprising because recent studies have shown that, although HF patients are at risk for multiple hospitalizations, early readmission after a hospitalization for ADHF specifically is often because of reasons unrelated to HF or a non-cardiovascular cause in general.^50,51

Although a negative study, several important themes emerged. First, while we were able to assess numeracy, health literacy, and cognition, none of these measures were HF-specific. It is possible that we did not see an effect on readmission because our instruments failed to assess domains specific to HF, such as monitoring weight changes, following a low-salt diet, and interpreting blood pressure. Currently, however, no HF-specific objective numeracy measure exists. With respect to health literacy, only 1 HF-specific measure exists,⁵² although it was only recently developed and validated. Second, while numeracy may not be a driving influence of all-cause 30-day readmissions, it may be associated with other health behaviors and quality metrics that we did not examine here, such as self-care, medication adherence, and HF-specific readmissions. Third, it is likely that the progression of HF itself, as well as the clinical management of patients following discharge, contribute significantly to 30-day readmissions. Increased attention to predischarge processes for HF patients occurred at VUMC during the study period; close follow-up and evidence-directed therapies may have mitigated some of the expected associations. Finally, we were not able to assess numeracy of participants’ primary caregivers who may help patients at home, especially postdischarge. Though a number of studies have examined the role of family caregivers in the management of HF,^53,54 none have examined numeracy levels of caregivers in the context of HF, and this may be worth doing in future studies.

Overall, our study has several strengths. The size of the cohort is large and there were high response rates during the follow-up period. Unlike other HF readmission studies, VICS accounts for readmissions to outside hospitals. Approximately 35% of all hospitalizations in VICS are to outside facilities. Thus, the ascertainment of readmissions to hospitals other than Vanderbilt is more comprehensive than if readmissions to VUMC were only considered. We were able to include a number of clinical comorbidities, laboratory and diagnostic tests from the index admission, and hospitalization characteristics in our analyses. Finally, we performed additional analyses to investigate the correlation between numeracy, literacy, and cognition; ultimately, we found that the majority of these correlations were weak, which supports our ability to study them simultaneously among VICS participants.

Nonetheless, we note some limitations. Although we captured readmissions to outside hospitals, the study took place at a single referral center in Tennessee. Though patients were diverse in age and comorbidities, they were mostly white and of higher SES. Finally, we used home status as a proxy for social support, which may underestimate the support that home care workers provide.

In conclusion, in this prospective longitudinal study of adults hospitalized with ADHF, inadequate numeracy was present in more than a third of patients, and low health literacy was present in roughly a quarter of patients. Neither numeracy nor health literacy, however, were associated with 30-day readmissions in adjusted analyses. Any cognitive impairment, although present in roughly one-half of patients, was not associated with 30-day readmission either. Our findings suggest that other influences may play a more dominant role in determining 30-day readmission rates in patients hospitalized for ADHF than inadequate numeracy, low health literacy, or cognitive impairment as assessed here.

Acknowledgments

This research was supported by the National Heart, Lung, and Blood Institute (R01 HL109388) and in part by the National Center for Advancing Translational Sciences (UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the National Institutes of Health. The authors’ funding sources did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication. Dr. Sterling is supported by T32HS000066 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Dr. Mixon has a VA Health Services Research and Development Service Career Development Award at the Tennessee Valley Healthcare System, Department of Veterans Affairs (CDA 12-168). This material was presented at the Society of General Internal Medicine Annual Meeting on April 20, 2017, in Washington, DC.

Disclosure

Dr. Kripalani reports personal fees from Verustat, personal fees from SAI Interactive, and equity from Bioscape Digital, all outside of the submitted work. Dr. Rothman and Dr. Wallston report personal fees from EdLogics outside of the submitted work. All of the other authors have nothing to disclose

Most studies to identify risk factors for readmission among patients with heart failure (HF) have focused on demographic and clinical characteristics.^1,2 Although easy to extract from administrative databases, this approach fails to capture the complex psychosocial and cognitive factors that influence the ability of HF patients to manage their disease in the postdischarge period, as depicted in the framework by Meyers et al.³ (2014). To date, studies have found low health literacy, decreased social support, and cognitive impairment to be associated with health behaviors and outcomes among HF patients, including decreased self-care,⁴ low HF-specific knowledge,⁵ medication nonadherence,⁶ hospitalizations,⁷ and mortality.^8-10 Less, however, is known about the effect of numeracy on HF outcomes, such as 30-day readmission.

Numeracy, or quantitative literacy, refers to the ability to access, understand, and apply numerical data to health-related decisions.¹¹ It is estimated that 110 million people in the United States have limited numeracy skills.¹² Low numeracy is a risk factor for poor glycemic control among patients with diabetes,¹³ medication adherence in HIV/AIDS,¹⁴ and worse blood pressure control in hypertensives.¹⁵ Much like these conditions, HF requires that patients understand, use, and act on numerical information. Maintaining a low-salt diet, monitoring weight, adjusting diuretic doses, and measuring blood pressure are tasks that HF patients are asked to perform on a daily or near-daily basis. These tasks are particularly important in the posthospitalization period and could be complicated by medication changes, which might create additional challenges for patients with inadequate numeracy. Additionally, cognitive impairment, which is a highly prevalent comorbid condition among adults with HF,^16,17 might impose additional barriers for those with inadequate numeracy who do not have adequate social support. However, to date, numeracy in the context of HF has not been well described.

Herein, we examined the effects of numeracy, alongside health literacy and cognition, on 30-day readmission risk among patients hospitalized for acute decompensated HF (ADHF).

Study Design

The Vanderbilt Inpatient Cohort Study (VICS) is a prospective observational study of patients admitted with cardiovascular disease to Vanderbilt University Medical Center (VUMC), an academic tertiary care hospital. VICS was designed to investigate the impact of social determinants of health on postdischarge health outcomes. A detailed description of the study rationale, design, and methods is described elsewhere.³

Briefly, participants completed a baseline interview while hospitalized, and follow-up phone calls were conducted within 1 week of discharge, at 30 days, and at 90 days. At 30 and 90 days postdischarge, healthcare utilization was ascertained by review of medical records and patient report. Clinical data about the index hospitalization were also abstracted. The Vanderbilt University Institutional Review Board approved the study.

Study Population

Patients hospitalized from 2011 to 2015 with a likely diagnosis of acute coronary syndrome and/or ADHF, as determined by a physician’s review of the medical record, were identified as potentially eligible. Research assistants assessed these patients for the presence of the following exclusion criteria: less than 18 years of age, non-English speaking, unstable psychiatric illness, a low likelihood of follow-up (eg, no reliable telephone number), on hospice, or otherwise too ill to complete an interview. Additionally, those with severe cognitive impairment, as assessed from the medical record (such as seeing a note describing dementia), and those with delirium, as assessed by the brief confusion assessment method, were excluded from enrollment in the study.^18,19 Those who died before discharge or during the 30-day follow-up period were excluded. For this analysis, we restricted our sample to only include participants who were hospitalized for ADHF.

Outcome Measure: 30-Day Readmission

The main outcome was all-cause readmission to any hospital within 30 days of discharge, as determined by patient interview, review of electronic medical records from VUMC, and review of outside hospital records.

Main Exposures: Numeracy, Health Literacy, and Cognitive Impairment

Numeracy was assessed with a 3-item version of the Subjective Numeracy Scale (SNS-3), which quantifies the patients perceived quantitative abilities.²⁰ Other authors have shown that the SNS-3 has a correlation coefficient of 0.88 with the full-length SNS-8 and a Cronbach’s alpha of 0.78.^20-22 The SNS-3 is reported as the mean on a scale from 1 to 6, with higher scores reflecting higher numeracy.

Subjective health literacy was assessed by using the 3-item Brief Health Literacy Screen (BHLS).²³ Scores range from 3 to 15, with higher scores reflecting higher literacy. Objective health literacy was assessed with the short form of the Test of Functional Health Literacy in Adults (sTOFHLA).^24,25 Scores may be categorized as inadequate (0-16), marginal (17-22), or adequate (23-36).

We assessed cognition by using the 10-item Short Portable Mental Status Questionnaire (SPMSQ).²⁶ The SPMSQ, which describes a person’s capacity for memory, structured thought, and orientation, has been validated and has demonstrated good reliability and validity.²⁷ Scores of 0 were considered to reflect intact cognition, and scores of 1 or more were considered to reflect any cognitive impairment, a scoring approach employed by other authors.²⁸ We used this approach, rather than the traditional scoring system developed by Pfeiffer et al.²⁶ (1975), because it would be the most sensitive to detect any cognitive impairment in the VICS cohort, which excluded those with severe cognition impairment, dementia, and delirium.

Covariates

During the hospitalization, participants completed an in-person interviewer-administered baseline assessment composed of demographic information, including age, self-reported race (white and nonwhite), educational attainment, home status (married, not married and living with someone, not married and living alone), and household income.

Clinical and diagnostic characteristics abstracted from the medical record included a medical history of HF, HF subtype (classified by left ventricular ejection fraction [LVEF]), coronary artery disease, chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM), and comorbidity burden as summarized by the van Walraven-Elixhauser score.^29,30 Depressive symptoms were assessed during the 2 weeks prior to the hospitalization by using the first 8 items of the Patient Health Questionnaire.³¹ Scores ranged from 0 to 24, with higher scores reflecting more severe depressive symptoms. Laboratory values included estimated glomerular filtration rate (eGFR), hemoglobin (g/dl), sodium (mg/L), and brain natriuretic peptide (BNP) (pg/ml) from the last laboratory draw before discharge. Smoking status was also assessed (current and former/nonsmokers).

Hospitalization characteristics included length of stay in days, number of prior admissions in the last year, and transfer to the intensive care unit during the index admission.

Statistical Analysis

Descriptive statistics were used to summarize patient characteristics. The Kruskal-Wallis test and the Pearson χ² test were used to determine the association between patient characteristics and levels of numeracy, literacy, and cognition separately. The unadjusted relationship between patient characteristics and 30-day readmission was assessed by using Wilcoxon rank sums tests for continuous variables and Pearson χ² tests for categorical variables. In addition, a correlation matrix was performed to assess the correlations between numeracy, health literacy, and cognition (supplementary Figure 1).

To examine the association between numeracy, health literacy, and cognition and 30-day readmissions, a series of multivariable Poisson (log-linear) regression models were fit.³² Like other studies, numeracy, health literacy, and cognition were examined as categorical and continuous measures in models.³³ Each model was modified with a sandwich estimator for robust standard errors. Log-linear models were chosen over logistic regression models for ease of interpretation because (exponentiated) parameters correspond to risk ratios (RRs) as opposed to odds ratios. Furthermore, the fitting challenges associated with log-linear models when predicted probabilities are near 0 or 1 were not present in these analyses. Redundancy analyses were conducted to ensure that independent variables were not highly correlated with a linear combination of the other independent variables. To avoid case-wise deletion of records with missing covariates, we employed multiple imputation with 10 imputation samples by using predictive mean matching.^34,35 All analyses were conducted in R version 3.1.2 (The R Foundation, Vienna, Austria).³⁶

Overall, 883 patients were included in this analysis (supplementary Figure 2). Of the 883 participants, 46% were female and 76% were white (Table 1). Their median age was 60 years (interdecile range [IDR] 39-78) and the median educational attainment was 13.5 years (IDR 11-18).

This is the first study to examine the effect of numeracy alongside literacy and cognition on 30-day readmission risk among patients hospitalized with ADHF. Overall, we found that 33.9% of participants had inadequate numeracy skills, and 24.6% had inadequate or marginal health literacy. In unadjusted and adjusted models, numeracy was not associated with 30-day readmission. Although (objective) low health literacy was associated with 30-day readmission in unadjusted models, it was not in adjusted models. Additionally, though 53% of participants had any cognitive impairment, readmission did not differ significantly by this factor. Taken together, these findings suggest that other factors may be greater determinants of 30-day readmissions among patients hospitalized for ADHF.

Only 1 other study has examined the effect of numeracy on readmission risk among patients hospitalized for HF. In this multicenter prospective study, McNaughton et al.³⁷ found low numeracy to be associated with higher odds of recidivism to the emergency department (ED) or hospital within 30 days. Our findings may differ from theirs for a few reasons. First, their study had a significantly higher percentage of individuals with low numeracy (55%) compared with ours (33.9%). This may be because they did not exclude individuals with severe cognitive impairment, and their patient population was of lower socioeconomic status (SES) than ours. Low SES is associated with higher 30-day readmissions among HF patients^1,10 throughout the literature, and low numeracy is associated with low SES in other diseases.^13,38,39 Finally, they studied recidivism, which was defined as any unplanned return to the ED or hospital within 30 days of the index ED visit for acute HF. We only focused on 30-day readmissions, which also may explain why our results differed.

We found that health literacy was not associated with 30-day readmissions, which is consistent with the literature. Although an association between health literacy and mortality exists among adults with HF, several studies have not found an association between health literacy and 30- and 90-day readmission among adults hospitalized for HF.^8,9,40 Although we found an association between objective health literacy and 30-day readmission in unadjusted analyses, we did not find one in the multivariable model. This, along with our numeracy finding, suggests that numeracy and literacy may not be driving the 30-day readmission risk among patients hospitalized with ADHF.

We examined cognition alongside numeracy and literacy because it is a prevalent condition among HF patients and because it is associated with adverse outcomes among patients with HF, including readmission.^41,42 Studies have shown that HF preferentially affects certain cognitive domains,⁴³ some of which are vital to HF self-care activities. We found that 53% of patients had any cognitive impairment, which is consistent with the literature of adults hospitalized for ADHF.^44,45 Cognitive impairment was not, however, associated with 30-day readmissions. There may be a couple reasons for this. First, we measured cognitive impairment with the SPMSQ, which, although widely used and well-validated, does not assess executive function, the domain most commonly affected in HF patients with cognitive impairment.⁴⁶ Second, patients with severe cognitive impairment and those with delirium were excluded from this study, which may have limited our ability to detect differences in readmission by this factor.

As in prior studies, we found that a history of DM and more hospitalizations in the prior year were independently associated with 30-day readmissions in fully adjusted models. Like other studies, in adjusted models, we found that LVEF and a history of HF were not independently associated with 30-day readmission.^47-49 This, however, is not surprising because recent studies have shown that, although HF patients are at risk for multiple hospitalizations, early readmission after a hospitalization for ADHF specifically is often because of reasons unrelated to HF or a non-cardiovascular cause in general.^50,51

Although a negative study, several important themes emerged. First, while we were able to assess numeracy, health literacy, and cognition, none of these measures were HF-specific. It is possible that we did not see an effect on readmission because our instruments failed to assess domains specific to HF, such as monitoring weight changes, following a low-salt diet, and interpreting blood pressure. Currently, however, no HF-specific objective numeracy measure exists. With respect to health literacy, only 1 HF-specific measure exists,⁵² although it was only recently developed and validated. Second, while numeracy may not be a driving influence of all-cause 30-day readmissions, it may be associated with other health behaviors and quality metrics that we did not examine here, such as self-care, medication adherence, and HF-specific readmissions. Third, it is likely that the progression of HF itself, as well as the clinical management of patients following discharge, contribute significantly to 30-day readmissions. Increased attention to predischarge processes for HF patients occurred at VUMC during the study period; close follow-up and evidence-directed therapies may have mitigated some of the expected associations. Finally, we were not able to assess numeracy of participants’ primary caregivers who may help patients at home, especially postdischarge. Though a number of studies have examined the role of family caregivers in the management of HF,^53,54 none have examined numeracy levels of caregivers in the context of HF, and this may be worth doing in future studies.

Overall, our study has several strengths. The size of the cohort is large and there were high response rates during the follow-up period. Unlike other HF readmission studies, VICS accounts for readmissions to outside hospitals. Approximately 35% of all hospitalizations in VICS are to outside facilities. Thus, the ascertainment of readmissions to hospitals other than Vanderbilt is more comprehensive than if readmissions to VUMC were only considered. We were able to include a number of clinical comorbidities, laboratory and diagnostic tests from the index admission, and hospitalization characteristics in our analyses. Finally, we performed additional analyses to investigate the correlation between numeracy, literacy, and cognition; ultimately, we found that the majority of these correlations were weak, which supports our ability to study them simultaneously among VICS participants.

Nonetheless, we note some limitations. Although we captured readmissions to outside hospitals, the study took place at a single referral center in Tennessee. Though patients were diverse in age and comorbidities, they were mostly white and of higher SES. Finally, we used home status as a proxy for social support, which may underestimate the support that home care workers provide.

In conclusion, in this prospective longitudinal study of adults hospitalized with ADHF, inadequate numeracy was present in more than a third of patients, and low health literacy was present in roughly a quarter of patients. Neither numeracy nor health literacy, however, were associated with 30-day readmissions in adjusted analyses. Any cognitive impairment, although present in roughly one-half of patients, was not associated with 30-day readmission either. Our findings suggest that other influences may play a more dominant role in determining 30-day readmission rates in patients hospitalized for ADHF than inadequate numeracy, low health literacy, or cognitive impairment as assessed here.

Acknowledgments

This research was supported by the National Heart, Lung, and Blood Institute (R01 HL109388) and in part by the National Center for Advancing Translational Sciences (UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the National Institutes of Health. The authors’ funding sources did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication. Dr. Sterling is supported by T32HS000066 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Dr. Mixon has a VA Health Services Research and Development Service Career Development Award at the Tennessee Valley Healthcare System, Department of Veterans Affairs (CDA 12-168). This material was presented at the Society of General Internal Medicine Annual Meeting on April 20, 2017, in Washington, DC.

Disclosure

Dr. Kripalani reports personal fees from Verustat, personal fees from SAI Interactive, and equity from Bioscape Digital, all outside of the submitted work. Dr. Rothman and Dr. Wallston report personal fees from EdLogics outside of the submitted work. All of the other authors have nothing to disclose

Most studies to identify risk factors for readmission among patients with heart failure (HF) have focused on demographic and clinical characteristics.^1,2 Although easy to extract from administrative databases, this approach fails to capture the complex psychosocial and cognitive factors that influence the ability of HF patients to manage their disease in the postdischarge period, as depicted in the framework by Meyers et al.³ (2014). To date, studies have found low health literacy, decreased social support, and cognitive impairment to be associated with health behaviors and outcomes among HF patients, including decreased self-care,⁴ low HF-specific knowledge,⁵ medication nonadherence,⁶ hospitalizations,⁷ and mortality.^8-10 Less, however, is known about the effect of numeracy on HF outcomes, such as 30-day readmission.

Numeracy, or quantitative literacy, refers to the ability to access, understand, and apply numerical data to health-related decisions.¹¹ It is estimated that 110 million people in the United States have limited numeracy skills.¹² Low numeracy is a risk factor for poor glycemic control among patients with diabetes,¹³ medication adherence in HIV/AIDS,¹⁴ and worse blood pressure control in hypertensives.¹⁵ Much like these conditions, HF requires that patients understand, use, and act on numerical information. Maintaining a low-salt diet, monitoring weight, adjusting diuretic doses, and measuring blood pressure are tasks that HF patients are asked to perform on a daily or near-daily basis. These tasks are particularly important in the posthospitalization period and could be complicated by medication changes, which might create additional challenges for patients with inadequate numeracy. Additionally, cognitive impairment, which is a highly prevalent comorbid condition among adults with HF,^16,17 might impose additional barriers for those with inadequate numeracy who do not have adequate social support. However, to date, numeracy in the context of HF has not been well described.

Herein, we examined the effects of numeracy, alongside health literacy and cognition, on 30-day readmission risk among patients hospitalized for acute decompensated HF (ADHF).

Study Design

The Vanderbilt Inpatient Cohort Study (VICS) is a prospective observational study of patients admitted with cardiovascular disease to Vanderbilt University Medical Center (VUMC), an academic tertiary care hospital. VICS was designed to investigate the impact of social determinants of health on postdischarge health outcomes. A detailed description of the study rationale, design, and methods is described elsewhere.³

Briefly, participants completed a baseline interview while hospitalized, and follow-up phone calls were conducted within 1 week of discharge, at 30 days, and at 90 days. At 30 and 90 days postdischarge, healthcare utilization was ascertained by review of medical records and patient report. Clinical data about the index hospitalization were also abstracted. The Vanderbilt University Institutional Review Board approved the study.

Study Population

Patients hospitalized from 2011 to 2015 with a likely diagnosis of acute coronary syndrome and/or ADHF, as determined by a physician’s review of the medical record, were identified as potentially eligible. Research assistants assessed these patients for the presence of the following exclusion criteria: less than 18 years of age, non-English speaking, unstable psychiatric illness, a low likelihood of follow-up (eg, no reliable telephone number), on hospice, or otherwise too ill to complete an interview. Additionally, those with severe cognitive impairment, as assessed from the medical record (such as seeing a note describing dementia), and those with delirium, as assessed by the brief confusion assessment method, were excluded from enrollment in the study.^18,19 Those who died before discharge or during the 30-day follow-up period were excluded. For this analysis, we restricted our sample to only include participants who were hospitalized for ADHF.

Outcome Measure: 30-Day Readmission

The main outcome was all-cause readmission to any hospital within 30 days of discharge, as determined by patient interview, review of electronic medical records from VUMC, and review of outside hospital records.

Main Exposures: Numeracy, Health Literacy, and Cognitive Impairment

Numeracy was assessed with a 3-item version of the Subjective Numeracy Scale (SNS-3), which quantifies the patients perceived quantitative abilities.²⁰ Other authors have shown that the SNS-3 has a correlation coefficient of 0.88 with the full-length SNS-8 and a Cronbach’s alpha of 0.78.^20-22 The SNS-3 is reported as the mean on a scale from 1 to 6, with higher scores reflecting higher numeracy.

Subjective health literacy was assessed by using the 3-item Brief Health Literacy Screen (BHLS).²³ Scores range from 3 to 15, with higher scores reflecting higher literacy. Objective health literacy was assessed with the short form of the Test of Functional Health Literacy in Adults (sTOFHLA).^24,25 Scores may be categorized as inadequate (0-16), marginal (17-22), or adequate (23-36).

We assessed cognition by using the 10-item Short Portable Mental Status Questionnaire (SPMSQ).²⁶ The SPMSQ, which describes a person’s capacity for memory, structured thought, and orientation, has been validated and has demonstrated good reliability and validity.²⁷ Scores of 0 were considered to reflect intact cognition, and scores of 1 or more were considered to reflect any cognitive impairment, a scoring approach employed by other authors.²⁸ We used this approach, rather than the traditional scoring system developed by Pfeiffer et al.²⁶ (1975), because it would be the most sensitive to detect any cognitive impairment in the VICS cohort, which excluded those with severe cognition impairment, dementia, and delirium.

Covariates

During the hospitalization, participants completed an in-person interviewer-administered baseline assessment composed of demographic information, including age, self-reported race (white and nonwhite), educational attainment, home status (married, not married and living with someone, not married and living alone), and household income.

Clinical and diagnostic characteristics abstracted from the medical record included a medical history of HF, HF subtype (classified by left ventricular ejection fraction [LVEF]), coronary artery disease, chronic obstructive pulmonary disease (COPD), diabetes mellitus (DM), and comorbidity burden as summarized by the van Walraven-Elixhauser score.^29,30 Depressive symptoms were assessed during the 2 weeks prior to the hospitalization by using the first 8 items of the Patient Health Questionnaire.³¹ Scores ranged from 0 to 24, with higher scores reflecting more severe depressive symptoms. Laboratory values included estimated glomerular filtration rate (eGFR), hemoglobin (g/dl), sodium (mg/L), and brain natriuretic peptide (BNP) (pg/ml) from the last laboratory draw before discharge. Smoking status was also assessed (current and former/nonsmokers).

Hospitalization characteristics included length of stay in days, number of prior admissions in the last year, and transfer to the intensive care unit during the index admission.

Statistical Analysis

Descriptive statistics were used to summarize patient characteristics. The Kruskal-Wallis test and the Pearson χ² test were used to determine the association between patient characteristics and levels of numeracy, literacy, and cognition separately. The unadjusted relationship between patient characteristics and 30-day readmission was assessed by using Wilcoxon rank sums tests for continuous variables and Pearson χ² tests for categorical variables. In addition, a correlation matrix was performed to assess the correlations between numeracy, health literacy, and cognition (supplementary Figure 1).

To examine the association between numeracy, health literacy, and cognition and 30-day readmissions, a series of multivariable Poisson (log-linear) regression models were fit.³² Like other studies, numeracy, health literacy, and cognition were examined as categorical and continuous measures in models.³³ Each model was modified with a sandwich estimator for robust standard errors. Log-linear models were chosen over logistic regression models for ease of interpretation because (exponentiated) parameters correspond to risk ratios (RRs) as opposed to odds ratios. Furthermore, the fitting challenges associated with log-linear models when predicted probabilities are near 0 or 1 were not present in these analyses. Redundancy analyses were conducted to ensure that independent variables were not highly correlated with a linear combination of the other independent variables. To avoid case-wise deletion of records with missing covariates, we employed multiple imputation with 10 imputation samples by using predictive mean matching.^34,35 All analyses were conducted in R version 3.1.2 (The R Foundation, Vienna, Austria).³⁶

Overall, 883 patients were included in this analysis (supplementary Figure 2). Of the 883 participants, 46% were female and 76% were white (Table 1). Their median age was 60 years (interdecile range [IDR] 39-78) and the median educational attainment was 13.5 years (IDR 11-18).

This is the first study to examine the effect of numeracy alongside literacy and cognition on 30-day readmission risk among patients hospitalized with ADHF. Overall, we found that 33.9% of participants had inadequate numeracy skills, and 24.6% had inadequate or marginal health literacy. In unadjusted and adjusted models, numeracy was not associated with 30-day readmission. Although (objective) low health literacy was associated with 30-day readmission in unadjusted models, it was not in adjusted models. Additionally, though 53% of participants had any cognitive impairment, readmission did not differ significantly by this factor. Taken together, these findings suggest that other factors may be greater determinants of 30-day readmissions among patients hospitalized for ADHF.

Only 1 other study has examined the effect of numeracy on readmission risk among patients hospitalized for HF. In this multicenter prospective study, McNaughton et al.³⁷ found low numeracy to be associated with higher odds of recidivism to the emergency department (ED) or hospital within 30 days. Our findings may differ from theirs for a few reasons. First, their study had a significantly higher percentage of individuals with low numeracy (55%) compared with ours (33.9%). This may be because they did not exclude individuals with severe cognitive impairment, and their patient population was of lower socioeconomic status (SES) than ours. Low SES is associated with higher 30-day readmissions among HF patients^1,10 throughout the literature, and low numeracy is associated with low SES in other diseases.^13,38,39 Finally, they studied recidivism, which was defined as any unplanned return to the ED or hospital within 30 days of the index ED visit for acute HF. We only focused on 30-day readmissions, which also may explain why our results differed.

We found that health literacy was not associated with 30-day readmissions, which is consistent with the literature. Although an association between health literacy and mortality exists among adults with HF, several studies have not found an association between health literacy and 30- and 90-day readmission among adults hospitalized for HF.^8,9,40 Although we found an association between objective health literacy and 30-day readmission in unadjusted analyses, we did not find one in the multivariable model. This, along with our numeracy finding, suggests that numeracy and literacy may not be driving the 30-day readmission risk among patients hospitalized with ADHF.

We examined cognition alongside numeracy and literacy because it is a prevalent condition among HF patients and because it is associated with adverse outcomes among patients with HF, including readmission.^41,42 Studies have shown that HF preferentially affects certain cognitive domains,⁴³ some of which are vital to HF self-care activities. We found that 53% of patients had any cognitive impairment, which is consistent with the literature of adults hospitalized for ADHF.^44,45 Cognitive impairment was not, however, associated with 30-day readmissions. There may be a couple reasons for this. First, we measured cognitive impairment with the SPMSQ, which, although widely used and well-validated, does not assess executive function, the domain most commonly affected in HF patients with cognitive impairment.⁴⁶ Second, patients with severe cognitive impairment and those with delirium were excluded from this study, which may have limited our ability to detect differences in readmission by this factor.

As in prior studies, we found that a history of DM and more hospitalizations in the prior year were independently associated with 30-day readmissions in fully adjusted models. Like other studies, in adjusted models, we found that LVEF and a history of HF were not independently associated with 30-day readmission.^47-49 This, however, is not surprising because recent studies have shown that, although HF patients are at risk for multiple hospitalizations, early readmission after a hospitalization for ADHF specifically is often because of reasons unrelated to HF or a non-cardiovascular cause in general.^50,51

Although a negative study, several important themes emerged. First, while we were able to assess numeracy, health literacy, and cognition, none of these measures were HF-specific. It is possible that we did not see an effect on readmission because our instruments failed to assess domains specific to HF, such as monitoring weight changes, following a low-salt diet, and interpreting blood pressure. Currently, however, no HF-specific objective numeracy measure exists. With respect to health literacy, only 1 HF-specific measure exists,⁵² although it was only recently developed and validated. Second, while numeracy may not be a driving influence of all-cause 30-day readmissions, it may be associated with other health behaviors and quality metrics that we did not examine here, such as self-care, medication adherence, and HF-specific readmissions. Third, it is likely that the progression of HF itself, as well as the clinical management of patients following discharge, contribute significantly to 30-day readmissions. Increased attention to predischarge processes for HF patients occurred at VUMC during the study period; close follow-up and evidence-directed therapies may have mitigated some of the expected associations. Finally, we were not able to assess numeracy of participants’ primary caregivers who may help patients at home, especially postdischarge. Though a number of studies have examined the role of family caregivers in the management of HF,^53,54 none have examined numeracy levels of caregivers in the context of HF, and this may be worth doing in future studies.

Overall, our study has several strengths. The size of the cohort is large and there were high response rates during the follow-up period. Unlike other HF readmission studies, VICS accounts for readmissions to outside hospitals. Approximately 35% of all hospitalizations in VICS are to outside facilities. Thus, the ascertainment of readmissions to hospitals other than Vanderbilt is more comprehensive than if readmissions to VUMC were only considered. We were able to include a number of clinical comorbidities, laboratory and diagnostic tests from the index admission, and hospitalization characteristics in our analyses. Finally, we performed additional analyses to investigate the correlation between numeracy, literacy, and cognition; ultimately, we found that the majority of these correlations were weak, which supports our ability to study them simultaneously among VICS participants.

Nonetheless, we note some limitations. Although we captured readmissions to outside hospitals, the study took place at a single referral center in Tennessee. Though patients were diverse in age and comorbidities, they were mostly white and of higher SES. Finally, we used home status as a proxy for social support, which may underestimate the support that home care workers provide.

In conclusion, in this prospective longitudinal study of adults hospitalized with ADHF, inadequate numeracy was present in more than a third of patients, and low health literacy was present in roughly a quarter of patients. Neither numeracy nor health literacy, however, were associated with 30-day readmissions in adjusted analyses. Any cognitive impairment, although present in roughly one-half of patients, was not associated with 30-day readmission either. Our findings suggest that other influences may play a more dominant role in determining 30-day readmission rates in patients hospitalized for ADHF than inadequate numeracy, low health literacy, or cognitive impairment as assessed here.

Acknowledgments

This research was supported by the National Heart, Lung, and Blood Institute (R01 HL109388) and in part by the National Center for Advancing Translational Sciences (UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the National Institutes of Health. The authors’ funding sources did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication. Dr. Sterling is supported by T32HS000066 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Dr. Mixon has a VA Health Services Research and Development Service Career Development Award at the Tennessee Valley Healthcare System, Department of Veterans Affairs (CDA 12-168). This material was presented at the Society of General Internal Medicine Annual Meeting on April 20, 2017, in Washington, DC.

Disclosure

Dr. Kripalani reports personal fees from Verustat, personal fees from SAI Interactive, and equity from Bioscape Digital, all outside of the submitted work. Dr. Rothman and Dr. Wallston report personal fees from EdLogics outside of the submitted work. All of the other authors have nothing to disclose

A special concern since the institution of hospital readmission penalties¹ is the transitions in care of a patient from one care setting to another, often at hospital discharge. Burke et al.² proposed a framework for an ideal transition in care (ITC) to study and improve transitions from the hospital to home. The features in the ITC were identified based upon their inclusion in the interventions that improved discharge outcomes.^3-5 Inspired by the ITC and other patient risk tools,⁶ we identified 10 domains of transitional care needs ([TCN] specified below), which we define as patient-centered risk factors that should be addressed to foster a safe and effective transition in care.⁷

One particularly important risk factor in patient self-management at transition points is health literacy, a patient’s ability to obtain, understand, and use basic health information and services. Low health literacy affects approximately 26% to 36% of adults in the United States.^8,9 Health literacy is associated with many factors that may affect successful navigation of care transitions, including doctor-patient communication,^10,11 understanding of the medication regimen,¹² and self-management.^13-15 Research has also demonstrated an association between low health literacy and poor outcomes after hospital discharge, including medication errors,¹⁶ 30-day hospital readmission,¹⁷ and mortality.¹⁸ Transitional care initiatives have begun to incorporate health literacy into patient risk assessments⁶ and provide specific attention to low health literacy in interventions to reduce adverse drug events and readmission.^4,19 Training programs for medical students and nurses advise teaching skills in health literacy as part of fostering effective transitions in care.^20,21

Although low health literacy is generally recognized as a barrier to patient education and self-management, little is known about whether patients with low health literacy are more likely to have other risk factors that could further increase their risk for poor transitions in care. A better understanding of associated risks would inform and improve patient care. We hypothesized that TCNs are more common among patients with low health literacy, as compared with those with adequate health literacy. We also aimed to describe the relationship between low health literacy and specific TCNs in order to guide clinical care and future interventions.

Setting

The present study is a cross-sectional analysis of data from a quality improvement (QI) intervention that was performed at Vanderbilt University Medical Center, a tertiary care facility in Nashville, Tennessee. The QI intervention, My Health Team (MHT), was funded by the Centers for Medicare and Medicaid Services Innovation Award program. The overall MHT program included outpatient care coordination for chronic disease management as well as a transitional care program that was designed to reduce hospital readmission. The latter included an inpatient needs assessment (which provided data for the present analysis), inpatient intervention, and postdischarge phone follow-up. The MHT initiative was reviewed by the institutional review board (IRB), which deemed it a QI program and granted a waiver of informed consent. The present secondary data analysis was reviewed and approved by the IRB.

Sample

Patients were identified for inclusion in the MHT transitions of care program if the presenting problem for hospital admission was pneumonia, chronic obstructive pulmonary disease (COPD) exacerbation, or decompensated heart failure, as determined by the review of clinical documentation by nurse transition care coordinators (TCCs). Adults over the age of 18 years were eligible, though priority was given to patients aged 65 years or older. This study includes the first inpatient encounter between June 2013 and December 31, 2014, for patients having a completed needs assessment and documentation of health literacy data in the medical record.

Data Collection

TCN assessment was developed from published patient risk tools and the ITC framework.^2,6,22 The assessment has 10 domains composed of 49 individual items as follows: (1) caregiver support (caregiver support not sufficient for patient needs), (2) transportation (relies on public or others for transportation and misses medical care because of transportation), (3) health care utilization (no primary care physician, unplanned hospitalization in the last year, emergency department [ED] visit in the last 6 months, or home health services in the last 60 days), (4) high-risk medical comorbidities (malnutrition or body mass index <18.5, renal failure, chronic pain, diabetes, heart failure, COPD, or stroke), (5) medication management provider or caregiver concern (cannot provide medication list, >10 preadmission medications, high-risk medications [eg, insulin, warfarin], poor medication understanding, or adherence issue identified), (6) medical devices (vascular access, urinary catheter, wounds, or home supplemental oxygen), (7) functional status (weakness of extremities, limited extremity range of motion, difficulty with mobility, falls at home, or activities of daily living challenges), (8) mental health comorbidities (over the past month has felt down, depressed, or hopeless or over the past month has felt little interest or pleasure in doing things, high-risk alcohol use, or high-risk substance use), (9) communication (limited English proficiency or at risk for limited health literacy), and (10) financial resources (no health insurance, skips or rations medicines because of cost, misses medical care because of cost, or misses medical care because of job).

The 49 items of the TCN assessment were documented as being present or absent by nurse TCCs at the time patients were enrolled in the transitional care program, based on patient and family interview and chart review, and the items were later extracted for analysis. Patients were determined to have a domain-level need if they reported a need on any individual item within that domain, resulting in a binary score (any need present, absent) for each of the 10 TCN domains.

Health literacy was assessed by using the Brief Health Literacy Screen (BHLS), which is administered routinely by nurses at hospital intake and documented in the medical record, with completion rates of approximately 90%.²³ The BHLS is a 3-question subjective health literacy assessment (scoring range 3-15) that has been validated against longer objective measures²⁴ and shown to predict disease control and mortality.^18,25 To improve the stability of scores (for patients who completed the BHLS more than once because of repeat hospitalizations) and to reduce missing values, we calculated the patient’s mean BHLS score for assessments obtained between January 1, 2013, and December 31, 2014. Patients were then categorized as having inadequate health literacy (BHLS ≤ 9) or adequate health literacy (BHLS > 9).^18,25 Demographic information was extracted from patient records and included age, sex (male/female), marital status (married/without a partner), race (white/nonwhite), and years of education. Income level and primary language were not available for analysis.

Statistical Analysis

Patient characteristics and TCNs were summarized by using the frequency and percentages for categorical variables and the mean and standard deviation (SD) for continuous variables. We compared patient characteristics (age, sex, marital status, race, and education) between health literacy groups (inadequate vs adequate) by using χ² or analysis of variance as appropriate. We assessed Pearson correlations among the 10 TCN domains, and we examined differences in reported needs for each of 10 TCN domains by the level of health literacy by using the χ² test. Because the TCN domain of communication included low health literacy as one of its items, we excluded this domain from subsequent analyses. We then compared differences in the number of TCNs documented (scoring range 0-9) by using an independent samples Student t test.

Multivariate logistic regression models were then constructed to examine the independent association of inadequate health literacy with 8 TCN domains while controlling for age, sex, marital status, race, and education. Patients with incomplete demographic data were excluded from these models. Additionally, these analyses excluded 2 TCN domains: the communication domain for reasons noted above and the high-risk medical comorbidity domain because it ended up being positive in 98.4% of patients. Statistical significance was set at an alpha of 0.05. All analyses were performed by using SPSS Statistics for Mac, version 23.0 (IBM Corp., Armonk, New York)

A structured patient risk factor assessment derived from literature was used to record TCNs in preparation for hospital discharge. On average, patients had needs in about half of the TCN domains (4.6 of 9). The most common areas identified were related to the presence of high-risk comorbidities (98.4%), frequent or prior healthcare utilization (76.6%), medication management (76.3%), functional status (54.9%), and transportation (48.7%). Many of the TCNs were significantly correlated with one another. The prevalence of these needs highlights the importance of using a structured assessment to identify patient concerns so that they may be addressed through discharge planning and follow-up. In addition, using a standardized TCN instrument based on a framework for ITC promotes further research in understanding patient needs and in developing personalized interventions to address them.

As hypothesized, we found that TCNs were more common in patients with inadequate health literacy. After adjustment for demographic factors, inadequate health literacy was significantly associated with transportation barriers and inadequate caregiver support. Analyses also suggested a relationship with needs related to medical devices, functional status, and mental health comorbidities. A review of the literature substantiates a link between inadequate health literacy and these needs and also suggests solutions to address these barriers.

The association with inadequate caregiver support is concerning because there is often a high degree of reliance on caregivers at transitions in care.^3-5 Caregivers are routinely called upon to provide assistance with activities that may be difficult for patients with low health literacy, including medication adherence, provider communication, and self-care activities.^26,27 Our finding that patients with inadequate health literacy are more likely to have inadequate caregiver support indicates additional vulnerability. This may be because of the absence of a caregiver, or in many cases, the presence of a caregiver who is underprepared to assist with care. Prior research has shown that when caregivers are present, up to 33% have low health literacy, even when they are paid nonfamilial caregivers.^26,28 Other studies have noted the inadequacy of information and patient training for caregivers.^29,30 Transitional care programs to improve caregiver understanding have been developed³¹ and have been demonstrated to lower rehospitalization and ED visits.³²

Patients with inadequate health literacy were also more likely to have transportation barriers. Lack of transportation has been recorded as a factor in early hospital readmission in patients with chronic disease,³³ and it has been shown to have a negative effect on a variety of health outcomes.³⁴ A likely link between readmission and lack of transportation is poor follow-up care. Wheeler et al.³⁵ found that 59% of patients expected difficulty keeping postdischarge appointments because of transportation needs. Instead of expecting patients to navigate their own transportation, the Agency for Healthcare Research and Quality recommends identifying community resources for patients with low health literacy.³⁶

In this sample, inadequate health literacy also had near significant associations with TCNs in the use of medical devices, lower functional status, and mental health comorbidities. The use of a medical device, such as home oxygen, is a risk factor for readmission,³⁷ and early reports suggest that interventions, including education related to home oxygen use, can dramatically reduce these readmissions.³⁸ Lower functional capacity and faster functional decline are associated with inadequate health literacy,³⁹ which may have to do with the inability to appropriately utilize health resources.⁴⁰ If so, structured discharge planning could alleviate the known connection between functional impairment and hospital readmissions.⁴¹ A relationship between low health literacy and depression has been demonstrated repeatedly,⁴² with worsened symptoms in those with addiction.⁴³ As has been shown in other domains where health literacy is a factor, literacy-focused interventions provide greater benefits to these depressed patients.⁴⁴

The TCN assessment worked well overall, but certain domains proved less valuable and could be removed in the future. First, it was not useful to separately identify communication barriers, because doing so did not add to information beyond the measurement of health literacy. Second, high-risk comorbidities were ubiquitous within the sample and therefore unhelpful for group comparisons. In hindsight, this is unsurprising because the sample was comprised primarily of elderly patients admitted to medical services. Still, in a younger population or a surgical setting, identifying patients with high-risk medical comorbidities may be more useful.

We acknowledge several limitations of this study. First, the study was performed at a single center, and the TCN assessments were conducted by a small number of registered nurses who received training. Therefore, the results may not generalize to the profile of patient needs at other settings, and the instrument may perform differently when scaled across an organization. Second, the needs assessment was developed for this QI initiative and did not undergo formal validation, although it was developed from published frameworks and similar assessments. Third, for the measure of health literacy, we relied on data collected by nurses as part of their normal workflow. As is often the case with data collected during routine care, the scores are imperfect,⁴⁵ but they have proven to be a valuable and valid indicator of health literacy in our previous research.^18,24,25,46 Fourth, we chose to declare a domain as positive if any item in that domain was positive and to perform a domain-level analysis (for greater clarity). We did not take into account the variable number of items within each domain or attempt to grade their severity, as this would be a subjective exercise and impractical in the discharge planning process. Finally, we were unable to address associations among socioeconomic status,⁴⁷ primary language,⁴⁸ and health literacy, because relevant data were not available for this analysis.

In this sample of hospitalized patients who were administered a structured needs assessment, patients commonly had needs that placed them at a higher risk of adverse outcomes, such as hospital readmission. Patients with low health literacy had more TCNs that extended beyond the areas that we normally associate with low health literacy, namely patient education and self-management. Healthcare professionals should be aware of the greater likelihood of transportation barriers and inadequate caregiver support among patients with low health literacy. Screening for health literacy and TCN at admission or as part of the discharge planning process will elevate such risks, better positioning clinicians and hospitals to address them as a part of the efforts to ensure a quality transition of care.

Disclosure 

This work was funded by the Centers for Medicare and Medicaid Services (1C1CMS330979) and in part by the National Center for Advancing Translational Sciences (2 UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the funding agencies, which did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication.

Dr. Dittus reports personal fees as a board member of the Robert Wood Johnson Foundation Medical Faculty Scholars Program National Advisory Committee; consultancy fees from the University of Virginia, Indiana University, University of Michigan, Northwestern University, Montana State University, and Purdue University; has grants/grants pending from NIH (research grants), PCORI (research grant), CME (innovation award), VA (training grant); payment for lectures including service on speakers bureaus from Corporate Parity (conference organizer) for the Global Hospital Management & Innovation Summit; and other from Medical Decision Making, Inc. (passive owner); all outside the submitted work. Dr. Kripalani has grants from NIH (research grant), PCORI (research grant), and CMS (QI grant); outside the submitted work. All other authors have nothing to disclose.

A special concern since the institution of hospital readmission penalties¹ is the transitions in care of a patient from one care setting to another, often at hospital discharge. Burke et al.² proposed a framework for an ideal transition in care (ITC) to study and improve transitions from the hospital to home. The features in the ITC were identified based upon their inclusion in the interventions that improved discharge outcomes.^3-5 Inspired by the ITC and other patient risk tools,⁶ we identified 10 domains of transitional care needs ([TCN] specified below), which we define as patient-centered risk factors that should be addressed to foster a safe and effective transition in care.⁷

One particularly important risk factor in patient self-management at transition points is health literacy, a patient’s ability to obtain, understand, and use basic health information and services. Low health literacy affects approximately 26% to 36% of adults in the United States.^8,9 Health literacy is associated with many factors that may affect successful navigation of care transitions, including doctor-patient communication,^10,11 understanding of the medication regimen,¹² and self-management.^13-15 Research has also demonstrated an association between low health literacy and poor outcomes after hospital discharge, including medication errors,¹⁶ 30-day hospital readmission,¹⁷ and mortality.¹⁸ Transitional care initiatives have begun to incorporate health literacy into patient risk assessments⁶ and provide specific attention to low health literacy in interventions to reduce adverse drug events and readmission.^4,19 Training programs for medical students and nurses advise teaching skills in health literacy as part of fostering effective transitions in care.^20,21

Although low health literacy is generally recognized as a barrier to patient education and self-management, little is known about whether patients with low health literacy are more likely to have other risk factors that could further increase their risk for poor transitions in care. A better understanding of associated risks would inform and improve patient care. We hypothesized that TCNs are more common among patients with low health literacy, as compared with those with adequate health literacy. We also aimed to describe the relationship between low health literacy and specific TCNs in order to guide clinical care and future interventions.

Setting

The present study is a cross-sectional analysis of data from a quality improvement (QI) intervention that was performed at Vanderbilt University Medical Center, a tertiary care facility in Nashville, Tennessee. The QI intervention, My Health Team (MHT), was funded by the Centers for Medicare and Medicaid Services Innovation Award program. The overall MHT program included outpatient care coordination for chronic disease management as well as a transitional care program that was designed to reduce hospital readmission. The latter included an inpatient needs assessment (which provided data for the present analysis), inpatient intervention, and postdischarge phone follow-up. The MHT initiative was reviewed by the institutional review board (IRB), which deemed it a QI program and granted a waiver of informed consent. The present secondary data analysis was reviewed and approved by the IRB.

Sample

Patients were identified for inclusion in the MHT transitions of care program if the presenting problem for hospital admission was pneumonia, chronic obstructive pulmonary disease (COPD) exacerbation, or decompensated heart failure, as determined by the review of clinical documentation by nurse transition care coordinators (TCCs). Adults over the age of 18 years were eligible, though priority was given to patients aged 65 years or older. This study includes the first inpatient encounter between June 2013 and December 31, 2014, for patients having a completed needs assessment and documentation of health literacy data in the medical record.

Data Collection

TCN assessment was developed from published patient risk tools and the ITC framework.^2,6,22 The assessment has 10 domains composed of 49 individual items as follows: (1) caregiver support (caregiver support not sufficient for patient needs), (2) transportation (relies on public or others for transportation and misses medical care because of transportation), (3) health care utilization (no primary care physician, unplanned hospitalization in the last year, emergency department [ED] visit in the last 6 months, or home health services in the last 60 days), (4) high-risk medical comorbidities (malnutrition or body mass index <18.5, renal failure, chronic pain, diabetes, heart failure, COPD, or stroke), (5) medication management provider or caregiver concern (cannot provide medication list, >10 preadmission medications, high-risk medications [eg, insulin, warfarin], poor medication understanding, or adherence issue identified), (6) medical devices (vascular access, urinary catheter, wounds, or home supplemental oxygen), (7) functional status (weakness of extremities, limited extremity range of motion, difficulty with mobility, falls at home, or activities of daily living challenges), (8) mental health comorbidities (over the past month has felt down, depressed, or hopeless or over the past month has felt little interest or pleasure in doing things, high-risk alcohol use, or high-risk substance use), (9) communication (limited English proficiency or at risk for limited health literacy), and (10) financial resources (no health insurance, skips or rations medicines because of cost, misses medical care because of cost, or misses medical care because of job).

The 49 items of the TCN assessment were documented as being present or absent by nurse TCCs at the time patients were enrolled in the transitional care program, based on patient and family interview and chart review, and the items were later extracted for analysis. Patients were determined to have a domain-level need if they reported a need on any individual item within that domain, resulting in a binary score (any need present, absent) for each of the 10 TCN domains.

Health literacy was assessed by using the Brief Health Literacy Screen (BHLS), which is administered routinely by nurses at hospital intake and documented in the medical record, with completion rates of approximately 90%.²³ The BHLS is a 3-question subjective health literacy assessment (scoring range 3-15) that has been validated against longer objective measures²⁴ and shown to predict disease control and mortality.^18,25 To improve the stability of scores (for patients who completed the BHLS more than once because of repeat hospitalizations) and to reduce missing values, we calculated the patient’s mean BHLS score for assessments obtained between January 1, 2013, and December 31, 2014. Patients were then categorized as having inadequate health literacy (BHLS ≤ 9) or adequate health literacy (BHLS > 9).^18,25 Demographic information was extracted from patient records and included age, sex (male/female), marital status (married/without a partner), race (white/nonwhite), and years of education. Income level and primary language were not available for analysis.

Statistical Analysis

Patient characteristics and TCNs were summarized by using the frequency and percentages for categorical variables and the mean and standard deviation (SD) for continuous variables. We compared patient characteristics (age, sex, marital status, race, and education) between health literacy groups (inadequate vs adequate) by using χ² or analysis of variance as appropriate. We assessed Pearson correlations among the 10 TCN domains, and we examined differences in reported needs for each of 10 TCN domains by the level of health literacy by using the χ² test. Because the TCN domain of communication included low health literacy as one of its items, we excluded this domain from subsequent analyses. We then compared differences in the number of TCNs documented (scoring range 0-9) by using an independent samples Student t test.

Multivariate logistic regression models were then constructed to examine the independent association of inadequate health literacy with 8 TCN domains while controlling for age, sex, marital status, race, and education. Patients with incomplete demographic data were excluded from these models. Additionally, these analyses excluded 2 TCN domains: the communication domain for reasons noted above and the high-risk medical comorbidity domain because it ended up being positive in 98.4% of patients. Statistical significance was set at an alpha of 0.05. All analyses were performed by using SPSS Statistics for Mac, version 23.0 (IBM Corp., Armonk, New York)

A structured patient risk factor assessment derived from literature was used to record TCNs in preparation for hospital discharge. On average, patients had needs in about half of the TCN domains (4.6 of 9). The most common areas identified were related to the presence of high-risk comorbidities (98.4%), frequent or prior healthcare utilization (76.6%), medication management (76.3%), functional status (54.9%), and transportation (48.7%). Many of the TCNs were significantly correlated with one another. The prevalence of these needs highlights the importance of using a structured assessment to identify patient concerns so that they may be addressed through discharge planning and follow-up. In addition, using a standardized TCN instrument based on a framework for ITC promotes further research in understanding patient needs and in developing personalized interventions to address them.

As hypothesized, we found that TCNs were more common in patients with inadequate health literacy. After adjustment for demographic factors, inadequate health literacy was significantly associated with transportation barriers and inadequate caregiver support. Analyses also suggested a relationship with needs related to medical devices, functional status, and mental health comorbidities. A review of the literature substantiates a link between inadequate health literacy and these needs and also suggests solutions to address these barriers.

The association with inadequate caregiver support is concerning because there is often a high degree of reliance on caregivers at transitions in care.^3-5 Caregivers are routinely called upon to provide assistance with activities that may be difficult for patients with low health literacy, including medication adherence, provider communication, and self-care activities.^26,27 Our finding that patients with inadequate health literacy are more likely to have inadequate caregiver support indicates additional vulnerability. This may be because of the absence of a caregiver, or in many cases, the presence of a caregiver who is underprepared to assist with care. Prior research has shown that when caregivers are present, up to 33% have low health literacy, even when they are paid nonfamilial caregivers.^26,28 Other studies have noted the inadequacy of information and patient training for caregivers.^29,30 Transitional care programs to improve caregiver understanding have been developed³¹ and have been demonstrated to lower rehospitalization and ED visits.³²

Patients with inadequate health literacy were also more likely to have transportation barriers. Lack of transportation has been recorded as a factor in early hospital readmission in patients with chronic disease,³³ and it has been shown to have a negative effect on a variety of health outcomes.³⁴ A likely link between readmission and lack of transportation is poor follow-up care. Wheeler et al.³⁵ found that 59% of patients expected difficulty keeping postdischarge appointments because of transportation needs. Instead of expecting patients to navigate their own transportation, the Agency for Healthcare Research and Quality recommends identifying community resources for patients with low health literacy.³⁶

In this sample, inadequate health literacy also had near significant associations with TCNs in the use of medical devices, lower functional status, and mental health comorbidities. The use of a medical device, such as home oxygen, is a risk factor for readmission,³⁷ and early reports suggest that interventions, including education related to home oxygen use, can dramatically reduce these readmissions.³⁸ Lower functional capacity and faster functional decline are associated with inadequate health literacy,³⁹ which may have to do with the inability to appropriately utilize health resources.⁴⁰ If so, structured discharge planning could alleviate the known connection between functional impairment and hospital readmissions.⁴¹ A relationship between low health literacy and depression has been demonstrated repeatedly,⁴² with worsened symptoms in those with addiction.⁴³ As has been shown in other domains where health literacy is a factor, literacy-focused interventions provide greater benefits to these depressed patients.⁴⁴

The TCN assessment worked well overall, but certain domains proved less valuable and could be removed in the future. First, it was not useful to separately identify communication barriers, because doing so did not add to information beyond the measurement of health literacy. Second, high-risk comorbidities were ubiquitous within the sample and therefore unhelpful for group comparisons. In hindsight, this is unsurprising because the sample was comprised primarily of elderly patients admitted to medical services. Still, in a younger population or a surgical setting, identifying patients with high-risk medical comorbidities may be more useful.

We acknowledge several limitations of this study. First, the study was performed at a single center, and the TCN assessments were conducted by a small number of registered nurses who received training. Therefore, the results may not generalize to the profile of patient needs at other settings, and the instrument may perform differently when scaled across an organization. Second, the needs assessment was developed for this QI initiative and did not undergo formal validation, although it was developed from published frameworks and similar assessments. Third, for the measure of health literacy, we relied on data collected by nurses as part of their normal workflow. As is often the case with data collected during routine care, the scores are imperfect,⁴⁵ but they have proven to be a valuable and valid indicator of health literacy in our previous research.^18,24,25,46 Fourth, we chose to declare a domain as positive if any item in that domain was positive and to perform a domain-level analysis (for greater clarity). We did not take into account the variable number of items within each domain or attempt to grade their severity, as this would be a subjective exercise and impractical in the discharge planning process. Finally, we were unable to address associations among socioeconomic status,⁴⁷ primary language,⁴⁸ and health literacy, because relevant data were not available for this analysis.

In this sample of hospitalized patients who were administered a structured needs assessment, patients commonly had needs that placed them at a higher risk of adverse outcomes, such as hospital readmission. Patients with low health literacy had more TCNs that extended beyond the areas that we normally associate with low health literacy, namely patient education and self-management. Healthcare professionals should be aware of the greater likelihood of transportation barriers and inadequate caregiver support among patients with low health literacy. Screening for health literacy and TCN at admission or as part of the discharge planning process will elevate such risks, better positioning clinicians and hospitals to address them as a part of the efforts to ensure a quality transition of care.

Disclosure 

This work was funded by the Centers for Medicare and Medicaid Services (1C1CMS330979) and in part by the National Center for Advancing Translational Sciences (2 UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the funding agencies, which did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication.

Dr. Dittus reports personal fees as a board member of the Robert Wood Johnson Foundation Medical Faculty Scholars Program National Advisory Committee; consultancy fees from the University of Virginia, Indiana University, University of Michigan, Northwestern University, Montana State University, and Purdue University; has grants/grants pending from NIH (research grants), PCORI (research grant), CME (innovation award), VA (training grant); payment for lectures including service on speakers bureaus from Corporate Parity (conference organizer) for the Global Hospital Management & Innovation Summit; and other from Medical Decision Making, Inc. (passive owner); all outside the submitted work. Dr. Kripalani has grants from NIH (research grant), PCORI (research grant), and CMS (QI grant); outside the submitted work. All other authors have nothing to disclose.

A special concern since the institution of hospital readmission penalties¹ is the transitions in care of a patient from one care setting to another, often at hospital discharge. Burke et al.² proposed a framework for an ideal transition in care (ITC) to study and improve transitions from the hospital to home. The features in the ITC were identified based upon their inclusion in the interventions that improved discharge outcomes.^3-5 Inspired by the ITC and other patient risk tools,⁶ we identified 10 domains of transitional care needs ([TCN] specified below), which we define as patient-centered risk factors that should be addressed to foster a safe and effective transition in care.⁷

One particularly important risk factor in patient self-management at transition points is health literacy, a patient’s ability to obtain, understand, and use basic health information and services. Low health literacy affects approximately 26% to 36% of adults in the United States.^8,9 Health literacy is associated with many factors that may affect successful navigation of care transitions, including doctor-patient communication,^10,11 understanding of the medication regimen,¹² and self-management.^13-15 Research has also demonstrated an association between low health literacy and poor outcomes after hospital discharge, including medication errors,¹⁶ 30-day hospital readmission,¹⁷ and mortality.¹⁸ Transitional care initiatives have begun to incorporate health literacy into patient risk assessments⁶ and provide specific attention to low health literacy in interventions to reduce adverse drug events and readmission.^4,19 Training programs for medical students and nurses advise teaching skills in health literacy as part of fostering effective transitions in care.^20,21

Although low health literacy is generally recognized as a barrier to patient education and self-management, little is known about whether patients with low health literacy are more likely to have other risk factors that could further increase their risk for poor transitions in care. A better understanding of associated risks would inform and improve patient care. We hypothesized that TCNs are more common among patients with low health literacy, as compared with those with adequate health literacy. We also aimed to describe the relationship between low health literacy and specific TCNs in order to guide clinical care and future interventions.

Setting

The present study is a cross-sectional analysis of data from a quality improvement (QI) intervention that was performed at Vanderbilt University Medical Center, a tertiary care facility in Nashville, Tennessee. The QI intervention, My Health Team (MHT), was funded by the Centers for Medicare and Medicaid Services Innovation Award program. The overall MHT program included outpatient care coordination for chronic disease management as well as a transitional care program that was designed to reduce hospital readmission. The latter included an inpatient needs assessment (which provided data for the present analysis), inpatient intervention, and postdischarge phone follow-up. The MHT initiative was reviewed by the institutional review board (IRB), which deemed it a QI program and granted a waiver of informed consent. The present secondary data analysis was reviewed and approved by the IRB.

Sample

Patients were identified for inclusion in the MHT transitions of care program if the presenting problem for hospital admission was pneumonia, chronic obstructive pulmonary disease (COPD) exacerbation, or decompensated heart failure, as determined by the review of clinical documentation by nurse transition care coordinators (TCCs). Adults over the age of 18 years were eligible, though priority was given to patients aged 65 years or older. This study includes the first inpatient encounter between June 2013 and December 31, 2014, for patients having a completed needs assessment and documentation of health literacy data in the medical record.

Data Collection

TCN assessment was developed from published patient risk tools and the ITC framework.^2,6,22 The assessment has 10 domains composed of 49 individual items as follows: (1) caregiver support (caregiver support not sufficient for patient needs), (2) transportation (relies on public or others for transportation and misses medical care because of transportation), (3) health care utilization (no primary care physician, unplanned hospitalization in the last year, emergency department [ED] visit in the last 6 months, or home health services in the last 60 days), (4) high-risk medical comorbidities (malnutrition or body mass index <18.5, renal failure, chronic pain, diabetes, heart failure, COPD, or stroke), (5) medication management provider or caregiver concern (cannot provide medication list, >10 preadmission medications, high-risk medications [eg, insulin, warfarin], poor medication understanding, or adherence issue identified), (6) medical devices (vascular access, urinary catheter, wounds, or home supplemental oxygen), (7) functional status (weakness of extremities, limited extremity range of motion, difficulty with mobility, falls at home, or activities of daily living challenges), (8) mental health comorbidities (over the past month has felt down, depressed, or hopeless or over the past month has felt little interest or pleasure in doing things, high-risk alcohol use, or high-risk substance use), (9) communication (limited English proficiency or at risk for limited health literacy), and (10) financial resources (no health insurance, skips or rations medicines because of cost, misses medical care because of cost, or misses medical care because of job).

The 49 items of the TCN assessment were documented as being present or absent by nurse TCCs at the time patients were enrolled in the transitional care program, based on patient and family interview and chart review, and the items were later extracted for analysis. Patients were determined to have a domain-level need if they reported a need on any individual item within that domain, resulting in a binary score (any need present, absent) for each of the 10 TCN domains.

Health literacy was assessed by using the Brief Health Literacy Screen (BHLS), which is administered routinely by nurses at hospital intake and documented in the medical record, with completion rates of approximately 90%.²³ The BHLS is a 3-question subjective health literacy assessment (scoring range 3-15) that has been validated against longer objective measures²⁴ and shown to predict disease control and mortality.^18,25 To improve the stability of scores (for patients who completed the BHLS more than once because of repeat hospitalizations) and to reduce missing values, we calculated the patient’s mean BHLS score for assessments obtained between January 1, 2013, and December 31, 2014. Patients were then categorized as having inadequate health literacy (BHLS ≤ 9) or adequate health literacy (BHLS > 9).^18,25 Demographic information was extracted from patient records and included age, sex (male/female), marital status (married/without a partner), race (white/nonwhite), and years of education. Income level and primary language were not available for analysis.

Statistical Analysis

Patient characteristics and TCNs were summarized by using the frequency and percentages for categorical variables and the mean and standard deviation (SD) for continuous variables. We compared patient characteristics (age, sex, marital status, race, and education) between health literacy groups (inadequate vs adequate) by using χ² or analysis of variance as appropriate. We assessed Pearson correlations among the 10 TCN domains, and we examined differences in reported needs for each of 10 TCN domains by the level of health literacy by using the χ² test. Because the TCN domain of communication included low health literacy as one of its items, we excluded this domain from subsequent analyses. We then compared differences in the number of TCNs documented (scoring range 0-9) by using an independent samples Student t test.

Multivariate logistic regression models were then constructed to examine the independent association of inadequate health literacy with 8 TCN domains while controlling for age, sex, marital status, race, and education. Patients with incomplete demographic data were excluded from these models. Additionally, these analyses excluded 2 TCN domains: the communication domain for reasons noted above and the high-risk medical comorbidity domain because it ended up being positive in 98.4% of patients. Statistical significance was set at an alpha of 0.05. All analyses were performed by using SPSS Statistics for Mac, version 23.0 (IBM Corp., Armonk, New York)

A structured patient risk factor assessment derived from literature was used to record TCNs in preparation for hospital discharge. On average, patients had needs in about half of the TCN domains (4.6 of 9). The most common areas identified were related to the presence of high-risk comorbidities (98.4%), frequent or prior healthcare utilization (76.6%), medication management (76.3%), functional status (54.9%), and transportation (48.7%). Many of the TCNs were significantly correlated with one another. The prevalence of these needs highlights the importance of using a structured assessment to identify patient concerns so that they may be addressed through discharge planning and follow-up. In addition, using a standardized TCN instrument based on a framework for ITC promotes further research in understanding patient needs and in developing personalized interventions to address them.

As hypothesized, we found that TCNs were more common in patients with inadequate health literacy. After adjustment for demographic factors, inadequate health literacy was significantly associated with transportation barriers and inadequate caregiver support. Analyses also suggested a relationship with needs related to medical devices, functional status, and mental health comorbidities. A review of the literature substantiates a link between inadequate health literacy and these needs and also suggests solutions to address these barriers.

The association with inadequate caregiver support is concerning because there is often a high degree of reliance on caregivers at transitions in care.^3-5 Caregivers are routinely called upon to provide assistance with activities that may be difficult for patients with low health literacy, including medication adherence, provider communication, and self-care activities.^26,27 Our finding that patients with inadequate health literacy are more likely to have inadequate caregiver support indicates additional vulnerability. This may be because of the absence of a caregiver, or in many cases, the presence of a caregiver who is underprepared to assist with care. Prior research has shown that when caregivers are present, up to 33% have low health literacy, even when they are paid nonfamilial caregivers.^26,28 Other studies have noted the inadequacy of information and patient training for caregivers.^29,30 Transitional care programs to improve caregiver understanding have been developed³¹ and have been demonstrated to lower rehospitalization and ED visits.³²

Patients with inadequate health literacy were also more likely to have transportation barriers. Lack of transportation has been recorded as a factor in early hospital readmission in patients with chronic disease,³³ and it has been shown to have a negative effect on a variety of health outcomes.³⁴ A likely link between readmission and lack of transportation is poor follow-up care. Wheeler et al.³⁵ found that 59% of patients expected difficulty keeping postdischarge appointments because of transportation needs. Instead of expecting patients to navigate their own transportation, the Agency for Healthcare Research and Quality recommends identifying community resources for patients with low health literacy.³⁶

In this sample, inadequate health literacy also had near significant associations with TCNs in the use of medical devices, lower functional status, and mental health comorbidities. The use of a medical device, such as home oxygen, is a risk factor for readmission,³⁷ and early reports suggest that interventions, including education related to home oxygen use, can dramatically reduce these readmissions.³⁸ Lower functional capacity and faster functional decline are associated with inadequate health literacy,³⁹ which may have to do with the inability to appropriately utilize health resources.⁴⁰ If so, structured discharge planning could alleviate the known connection between functional impairment and hospital readmissions.⁴¹ A relationship between low health literacy and depression has been demonstrated repeatedly,⁴² with worsened symptoms in those with addiction.⁴³ As has been shown in other domains where health literacy is a factor, literacy-focused interventions provide greater benefits to these depressed patients.⁴⁴

The TCN assessment worked well overall, but certain domains proved less valuable and could be removed in the future. First, it was not useful to separately identify communication barriers, because doing so did not add to information beyond the measurement of health literacy. Second, high-risk comorbidities were ubiquitous within the sample and therefore unhelpful for group comparisons. In hindsight, this is unsurprising because the sample was comprised primarily of elderly patients admitted to medical services. Still, in a younger population or a surgical setting, identifying patients with high-risk medical comorbidities may be more useful.

We acknowledge several limitations of this study. First, the study was performed at a single center, and the TCN assessments were conducted by a small number of registered nurses who received training. Therefore, the results may not generalize to the profile of patient needs at other settings, and the instrument may perform differently when scaled across an organization. Second, the needs assessment was developed for this QI initiative and did not undergo formal validation, although it was developed from published frameworks and similar assessments. Third, for the measure of health literacy, we relied on data collected by nurses as part of their normal workflow. As is often the case with data collected during routine care, the scores are imperfect,⁴⁵ but they have proven to be a valuable and valid indicator of health literacy in our previous research.^18,24,25,46 Fourth, we chose to declare a domain as positive if any item in that domain was positive and to perform a domain-level analysis (for greater clarity). We did not take into account the variable number of items within each domain or attempt to grade their severity, as this would be a subjective exercise and impractical in the discharge planning process. Finally, we were unable to address associations among socioeconomic status,⁴⁷ primary language,⁴⁸ and health literacy, because relevant data were not available for this analysis.

In this sample of hospitalized patients who were administered a structured needs assessment, patients commonly had needs that placed them at a higher risk of adverse outcomes, such as hospital readmission. Patients with low health literacy had more TCNs that extended beyond the areas that we normally associate with low health literacy, namely patient education and self-management. Healthcare professionals should be aware of the greater likelihood of transportation barriers and inadequate caregiver support among patients with low health literacy. Screening for health literacy and TCN at admission or as part of the discharge planning process will elevate such risks, better positioning clinicians and hospitals to address them as a part of the efforts to ensure a quality transition of care.

Disclosure 

This work was funded by the Centers for Medicare and Medicaid Services (1C1CMS330979) and in part by the National Center for Advancing Translational Sciences (2 UL1 TR000445-06). The content is solely the responsibility of the authors and does not necessarily represent official views of the funding agencies, which did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication.

Dr. Dittus reports personal fees as a board member of the Robert Wood Johnson Foundation Medical Faculty Scholars Program National Advisory Committee; consultancy fees from the University of Virginia, Indiana University, University of Michigan, Northwestern University, Montana State University, and Purdue University; has grants/grants pending from NIH (research grants), PCORI (research grant), CME (innovation award), VA (training grant); payment for lectures including service on speakers bureaus from Corporate Parity (conference organizer) for the Global Hospital Management & Innovation Summit; and other from Medical Decision Making, Inc. (passive owner); all outside the submitted work. Dr. Kripalani has grants from NIH (research grant), PCORI (research grant), and CMS (QI grant); outside the submitted work. All other authors have nothing to disclose.