Association between Post-Hospital Clinic and Telephone Follow-up Provider Visits with 30-Day Readmission Risk in an Integrated Health System

BACKGROUND: Follow-up visits with clinic providers after hospital discharge may not be feasible for some patients due to functional limitations, transportation challenges, need for physical distancing, or fear of exposure especially during the current COVID-19 pandemic. METHODS: The aim of the study was to determine the effects of post-hospital clinic (POSH) and telephone (TPOSH) follow-up provider visits versus no visit on 30-day readmission. We used a retrospective cohort design based on data from 1/1/2017 to 12/31/2019 on adult patients (n = 213,513) discharged home from 15 Kaiser Permanente Southern California hospitals. Completion of POSH or TPOSH provider visits within 7 days of discharge was the exposure and all-cause 30-day inpatient and observation stay readmission was the primary outcome. We used matching weights to balance the groups and Fine-Gray subdistribution hazard model to assess for readmission risk. RESULTS: Unweighted all-cause 30-day readmission rate was highest for patients who completed a TPOSH (17.3%) followed by no visit (14.2%), non-POSH (evaluation and management visits that were not focused on the hospitalization: 13.6%) and POSH (12.6%) visits. The matching weighted models showed that the effects of POSH and TPOSH visits varied across patient subgroups. For high risk (LACE 11+) medicine patients, both POSH (HR: 0.77, 95% CI: 0.71, 0.85, P \u3c .001) and TPOSH (HR: 0.91, 95% CI: 0.83, 0.99, P = .03) were associated with 23 and 9% lower risk of 30-day readmission, respectively, compared to no visit. For medium to low risk medicine patients (LACE\u3c 11) and all surgical patients regardless of LACE score or age, there were no significant associations for either visit type with risk of 30-day readmission. CONCLUSIONS: Post-hospital telephone follow-up provider visits had only modest effects on 30-day readmission in high-risk medicine patients compared to clinic visits. It remains to be determined if greater use and comfort with virtual visits by providers and patients as a result of the pandemic might improve the effectiveness of these encounters

Understanding the groups of care transition strategies used by U.S. hospitals: An application of factor analytic and latent class methods

BACKGROUND: After activation of the Hospital Readmission Reduction Program (HRRP) in 2012, hospitals nationwide experimented broadly with the implementation of Transitional Care (TC) strategies to reduce hospital readmissions. Although numerous evidence-based TC models exist, they are often adapted to local contexts, rendering large-scale evaluation difficult. Little systematic evidence exists about prevailing implementation patterns of TC strategies among hospitals, nor which strategies in which combinations are most effective at improving patient outcomes. We aimed to identify and define combinations of TC strategies, or groups of transitional care activities, implemented among a large and diverse cohort of U.S. hospitals, with the ultimate goal of evaluating their comparative effectiveness. METHODS: We collected implementation data for 13 TC strategies through a nationwide, web-based survey of representatives from short-term acute-care and critical access hospitals (N = 370) and obtained Medicare claims data for patients discharged from participating hospitals. TC strategies were grouped separately through factor analysis and latent class analysis. RESULTS: We observed 348 variations in how hospitals implemented 13 TC strategies, highlighting the diversity of hospitals\u27 TC strategy implementation. Factor analysis resulted in five overlapping groups of TC strategies, including those characterized by 1) medication reconciliation, 2) shared decision making, 3) identifying high risk patients, 4) care plan, and 5) cross-setting information exchange. We determined that the groups suggested by factor analysis results provided a more logical grouping. Further, groups of TC strategies based on factor analysis performed better than the ones based on latent class analysis in detecting differences in 30-day readmission trends. CONCLUSIONS: U.S. hospitals uniquely combine TC strategies in ways that require further evaluation. Factor analysis provides a logical method for grouping such strategies for comparative effectiveness analysis when the groups are dependent. Our findings provide hospitals and health systems 1) information about what groups of TC strategies are commonly being implemented by hospitals, 2) strengths associated with the factor analysis approach for classifying these groups, and ultimately, 3) information upon which comparative effectiveness trials can be designed. Our results further reveal promising targets for comparative effectiveness analyses, including groups incorporating cross-setting information exchange

Frailty in Chronic Obstructive Pulmonary Disease and Risk of Exacerbations and Hospitalizations

Background: Frailty is a complex clinical syndrome associated with vulnerability to adverse health outcomes. While frailty is thought to be common in chronic obstructive pulmonary disease (COPD), the relationship between frailty and COPD-related outcomes such as risk of acute exacerbations of COPD (AE-COPD) and hospitalizations is unclear.Purpose: To examine the association between physical frailty and risk of acute exacerbations, hospitalizations, and mortality in patients with COPD.Methods: A longitudinal analysis of data from a cohort of 280 participants was performed. Baseline frailty measures included exhaustion, weakness, low activity, slowness, and undernutrition. Outcome measures included AE-COPD, hospitalizations, and mortality over 2 years. Negative binomial regression and Cox proportional hazard modeling were used.Results: Sixty-two percent of the study population met criteria for pre-frail and 23% were frail. In adjusted analyses, the frailty syndrome was not associated with COPD exacerbations. However, among the individual components of the frailty syndrome, weakness measured by handgrip strength was associated with increased risk of COPD exacerbations (IRR 1.46, 95% CI 1.09– 1.97). The frailty phenotype was not associated with all-cause hospitalizations but was associated with increased risk of non-COPD-related hospitalizations.Conclusion: This longitudinal cohort study shows that a high proportion of patients with COPD are pre-frail or frail. The frailty phenotype was associated with an increased risk of non-COPD hospitalizations but not with all-cause hospitalizations or COPD exacerbations. Among the individual frailty components, low handgrip strength was associated with increased risk of COPD exacerbations over a 2-year period. Measuring handgrip strength may identify COPD patients who could benefit from programs to reduce COPD exacerbations

Improving Evidence-Based Grouping of Transitional Care Strategies in Hospital Implementation Using Statistical Tools and Expert Review

BACKGROUND: As health systems transition to value-based care, improving transitional care (TC) remains a priority. Hospitals implementing evidence-based TC models often adapt them to local contexts. However, limited research has evaluated which groups of TC strategies, or transitional care activities, commonly implemented by hospitals correspond with improved patient outcomes. In order to identify TC strategy groups for evaluation, we applied a data-driven approach informed by literature review and expert opinion. METHODS: Based on a review of evidence-based TC models and the literature, focus groups with patients and family caregivers identifying what matters most to them during care transitions, and expert review, the Project ACHIEVE team identified 22 TC strategies to evaluate. Patient exposure to TC strategies was measured through a hospital survey (N = 42) and prospective survey of patients discharged from those hospitals (N = 8080). To define groups of TC strategies for evaluation, we performed a multistep process including: using ACHIEVE\u27S prior retrospective analysis; performing exploratory factor analysis, latent class analysis, and finite mixture model analysis on hospital and patient survey data; and confirming results through expert review. Machine learning (e.g., random forest) was performed using patient claims data to explore the predictive influence of individual strategies, strategy groups, and key covariates on 30-day hospital readmissions. RESULTS: The methodological approach identified five groups of TC strategies that were commonly delivered as a bundle by hospitals: 1) Patient Communication and Care Management, 2) Hospital-Based Trust, Plain Language, and Coordination, 3) Home-Based Trust, Plain language, and Coordination, 4) Patient/Family Caregiver Assessment and Information Exchange Among Providers, and 5) Assessment and Teach Back. Each TC strategy group comprises three to six, non-mutually exclusive TC strategies (i.e., some strategies are in multiple TC strategy groups). Results from random forest analyses revealed that TC strategies patients reported receiving were more important in predicting readmissions than TC strategies that hospitals reported delivering, and that other key co-variates, such as patient comorbidities, were the most important variables. CONCLUSION: Sophisticated statistical tools can help identify underlying patterns of hospitals\u27 TC efforts. Using such tools, this study identified five groups of TC strategies that have potential to improve patient outcomes

Healthcare Cost Differences with Participation in a Community-Based Group Physical Activity Benefit for Medicare Managed Care Health Plan Members: HEALTHCARE COST DIFFERENCES

To determine whether participation in a physical activity benefit by Medicare managed care enrollees is associated with lower healthcare utilization and costs

Development and Psychometric Properties of Surveys to Assess Provider Perspectives on the Barriers and Facilitators of Effective Care Transitions

Background The quality of the discharge process and effective care transitions between settings of care are critical to minimize gaps in patient care and reduce hospital readmissions. Few studies have explored which care transition components and strategies are most valuable to patients and providers. This study describes the development, pilot testing, and psychometric analysis of surveys designed to gain providers’ perspectives on current practices in delivering transitional care services. Methods We underwent a comprehensive process to develop items measuring unique aspects of care transitions from the perspectives of the three types of providers (downstream, ambulatory, and hospital providers). The process involved 1) an environmental scan, 2) provider interviews, 3) survey cognitive testing, 4) pilot testing, 5) a Stakeholder Advisory Group, 6) a Scientific Advisory Council, and 7) a collaborative Project ACHIEVE (Achieving Patient-Centered Care and Optimized Health in Care Transitions by Evaluating the Value of Evidence) research team. Three surveys were developed and fielded to providers affiliated with 43 hospitals participating in Project ACHIEVE. Web-based survey administration resulted in 948 provider respondents. We assessed response variability and response missingness. To evaluate the composites’ psychometric properties, we examined intercorrelations of survey items, item factor loadings, model fit indices, internal consistency reliability, and intercorrelations between the composite measures and overall rating items. Results Results from psychometric analyses of the three surveys provided support for five composite measures: 1) Effort in Coordinating Patient Care, 2) Quality of Patient Information Received, 3) Organizational Support for Transitional Care, 4) Access to Community Resources, and 5) Strength of Relationships Among Community Providers. All factor loadings and reliability estimates were acceptable (loadings ≥ 0.40, α ≥ 0.70), and the fit indices showed a good model fit. All composite measures positively and significantly correlated with the overall ratings (0.13 ≤ r ≤ 0.71). Conclusions We determined that the items and composite measures assessing the barriers and facilitators to care transitions within this survey are reliable and demonstrate satisfactory psychometric properties. The instruments may be useful to healthcare organizations and researchers to assess the quality of care transitions and target areas of improvement across different provider settings