Utilization of a multidisciplinary hospital-based approach to reduce readmission rates.

Background: Hospital readmissions remain a significant and pressing issue in our healthcare system. In 2010, the Affordable Care Act helped establish the Hospital Readmissions Reduction Program, which incentivized reducing readmission rates by instituting penalties. Hospital readmission, specifically unplanned, refers to a patient returning to the hospital shortly after discharge due to the same or a related medical condition, signaling potential issues in initial care, discharge processes, or post-hospitalization management. For this study, we defined readmission as a return to the hospital within 30 days. In 2018, Staten Island University Hospital started a multidisciplinary and coordinated initiative to reduce patient readmissions. The approach involved the departments of emergency medicine, medicine, cardiology, case management, nursing, pharmacy, and transitional care management. This study aimed to determine if this approach reduced 30-day readmissions. Methods: This case-control retrospective study reviewed electronic health records between January 2018 and November 2019. Readmission rates within 30 days of index discharge were compared between patients who received transitional care management before and after establishing a multidisciplinary communication of transitional care. Readmission rates were unadjusted and adjusted for patient demographics and predisposed risk for readmission and compared across demographics and select clinical characteristics. Results: A total of 772 patients were included in the analyses; 323 were in the control group (41.8%), and 449 were in the intervention group (58.2%). After the hospital adopted the workflow for multidisciplinary communication of transitional care, there was 45.2% less adjusted incidence of readmission, or approximately seven fewer overall readmissions per 100 patients (16.4% readmission vs 9.0% readmission; incident rate ratio, 0.55; 95% CI: 0.34-0.88). Conclusions: Multidisciplinary communication approaches led by emergency medicine can help reduce readmissions significantly. Adopting a structured communication workflow can enhance co-managing patients with a high risk of readmission between the emergency department and hospital medicine teams.

Augmenting a Transitional Care Model With Artificial Intelligence Decreased Readmissions.

OBJECTIVES: Evaluate if augmenting a transitions of care delivery model with insights from artificial intelligence (AI) that applied clinical and exogenous social determinants of health data would reduce rehospitalization in older adults. DESIGN: Retrospective case-control study. SETTING AND PARTICIPANTS: Adult patients discharged from integrated health system between November 1, 2019, and February 31, 2020, and enrolled in a rehospitalization reduction transitional care management program. INTERVENTION: An AI algorithm utilizing multiple data sources including clinical, socioeconomic, and behavioral data was developed to predict patients at highest risk for readmitting within 30 days and provide care navigators five care recommendations to prevent rehospitalization. METHODS: Adjusted incidence of rehospitalization was estimated with Poisson regression and compared between transitional care management enrollees that used AI insights and matched enrollees for whom AI insights were not used. RESULTS: Analyses included 6371 hospital encounters between November 2019 and February 2020 across 12 hospitals. Of the encounters 29.3% were identified by AI as being medium-high risk for re-hospitalizing within 30 days, for which AI provided transitional care recommendations to the transitional care management team. The navigation team completed 40.2% of AI recommendations for these high-risk older adults. These patients had overall 21.0% less adjusted incidence of 30-day rehospitalization compared with matched control encounters, or 69 fewer rehospitalizations per 1000 encounters (95% CI 0.65‒0.95). CONCLUSIONS AND IMPLICATIONS: Coordinating a patient's care continuum is critical for safe and effective transition of care. This study found that augmenting an existing transition of care navigation program with patient insights from AI reduced rehospitalization more than without AI insights. Augmenting transitional care with insights from AI could be a cost-effective intervention to improve transitional care outcomes and reduce unnecessary rehospitalization. Future studies should examine cost-effectiveness of augmenting transitional care models of care with AI when hospitals and post-acute providers partner with AI companies.

Postpartum navigation decreases severe maternal morbidity most among Black women.

BACKGROUND: Postpartum care is crucial for addressing conditions associated with severe maternal morbidity and mortality. Examination of programs that affect these outcomes for women at high risk, including disparate populations, is needed. OBJECTIVE: This study aimed to examine whether a postpartum navigation program decreases all-cause 30-day postpartum hospitalizations and hospitalizations because of severe maternal morbidity identified using the US Centers for Disease Control and Prevention guidelines. The effect of this program was explored across patient demographics, including race and ethnicity. STUDY DESIGN: This was a retrospective cohort study that used health records of women who delivered at 3 large hospitals in the New York metropolitan area (Queens and Long Island) between April 2020 and November 2021 and who were at high risk of severe maternal morbidity. The incidence rates of 30-day postpartum all-cause hospitalization and hospitalization because of severe maternal morbidity were compared between women who were and were not enrolled in a novel postpartum transitional care management program. Navigation included standardized assessments, development of care plans, clinical management, and connection to clinical and social services that would extend beyond the postpartum period. Because the program prioritized enrolling women of the greatest risk, the risk-adjusted incidence was estimated using multivariate Poisson regression and stratified across patient demographics. RESULTS: Patient health records of 5819 women were included for analysis. Of note, 5819 of 19,258 deliveries (30.2%) during the study period were identified as having a higher risk of severe maternal morbidity. This was consistent with the incidence of high-risk pregnancies for tertiary hospitals in the New York metropolitan area. The condition most identified for risk of severe maternal morbidity at the time of delivery was hypertension (3171/5819 [54.5%]). The adjusted incidence of all-cause rehospitalization was 20% lower in enrollees than in nonenrollees (incident rate ratio, 0.80; 95% confidence interval, 0.67-0.95). Rehospitalization was decreased the most among Black women (incident rate ratio, 0.57; 95% confidence interval, 0.42-0.80). The adjusted incidence of rehospitalization because of indicators of severe maternal morbidity was 56% lower in enrollees than in nonenrollees (incident rate ratio, 0.44; 95% confidence interval, 0.24-0.77). Furthermore, it decreased most among Black women (incident rate ratio, 0.23; 95% confidence interval, 0.07-0.73). CONCLUSION: High-risk medical conditions at the time of delivery increased the risk of postpartum hospitalization, including hospitalizations because of severe maternal morbidity. A postpartum navigation program designed to identify and resolve clinical and social needs reduced postpartum hospitalizations and racial disparities with hospitalizations. Hospitals and healthcare systems should adopt this type of care model for women at high risk of severe maternal morbidity. Cost analyses are needed to evaluate the financial effect of postpartum navigation programs for women at high risk of severe maternal morbidity or mortality, which could influence reimbursement for these types of services. Further evidence and details of novel postpartum interventional models are needed for future studies.