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.

Early Experience With Neutralizing Monoclonal Antibody Therapy for COVID-19: Retrospective Cohort Survival Analysis and Descriptive Study.

BACKGROUND: Neutralizing monoclonal antibody (MAB) therapies may benefit patients with mild to moderate COVID-19 at high risk for progressing to severe COVID-19 or hospitalization. Studies documenting approaches to deliver MAB infusions and demonstrating their efficacy are lacking. OBJECTIVE: We describe our experience and the outcomes of almost 3000 patients who received MAB infusion therapy at Northwell Health, a large integrated health care system in New York. METHODS: This is a descriptive study of adult patients who received MAB therapy between November 20, 2020, to January 31, 2021, and a retrospective cohort survival analysis comparing patients who received MAB therapy prior to admission versus those who did not. A multivariable Cox model with inverse probability weighting according to the propensity score including covariates (sociodemographic, comorbidities, and presenting vital signs) was used. The primary outcome was in-hospital mortality; additional evaluations included emergency department use and hospitalization within 28 days of a positive COVID-19 test for patients who received MAB therapy. RESULTS: During the study period, 2818 adult patients received MAB infusion. Following therapy and within 28 days of a COVID-19 test, 123 (4.4%) patients presented to the emergency department and were released, and 145 (5.1%) patients were hospitalized. These 145 patients were compared with 200 controls who were eligible for but did not receive MAB therapy and were hospitalized. In the MAB group, 16 (11%) patients met the primary outcome of in-hospital mortality, versus 21 (10.5%) in the control group. In an unadjusted Cox model, the hazard ratio (HR) for time to in-hospital mortality for the MAB group was 1.38 (95% CI 0.696-2.719). Models adjusting for demographics (HR 1.1, 95% CI 0.53-2.23), demographics and Charlson Comorbidity Index (HR 1.22, 95% CI 0.573-2.59), and with inverse probability weighting according to propensity scores (HR 1.19, 95% CI 0.619-2.29) did not demonstrate significance. The hospitalization rate was 4.4% for patients who received MAB therapy within 0 to 4 days, 5% within 5 to 7 days, and 6.1% in ≥8 days of symptom onset (P=.15). CONCLUSIONS: Establishing the capability to provide neutralizing MAB infusion therapy requires substantial planning and coordination. Although this therapy may be an important treatment option for early mild to moderate COVID-19 in patients who are at high risk, further investigations are needed to define the optimal timing of MAB treatment to reduce hospitalization and mortality.

Variation in Treatment Patterns Correlate With Resource Utilization in the 30-Day Episode of Care of Displaced Femoral Neck Fractures.

BACKGROUND: We evaluated which treatment decisions in the management of displaced femoral neck fractures (FNFs) may associate with measures of resource utilization relevant to a value-based episode-of-care model. METHODS: A total of 1139 FNFs treated with hip arthroplasty at 7 hospitals were retrospectively reviewed. Treatment choices were procedure (hemiarthroplasty vs total hip arthroplasty [THA]), surgeon training status, admitting service, and time to surgery. Dependent variables were length of stay, discharge disposition, 30-day readmission, and in-hospital mortality. Variation across hospitals was evaluated with analysis of variance and chi-square tests. Treatment choices were evaluated for the dependent variables of interest with univariable and multivariable regression. RESULTS: There was significant variation between hospitals regarding proportion of cases treated with THA (range = 3.0%-73.2%, P < .001), proportion treated by arthroplasty fellowship-trained surgeons (range = 0%-74.9%, P < .001), proportion admitted to the orthopedic service (range = 2.8%-91.3%, P < .001), mean time to surgery (range = 0.9-2.1 days, P < .001), and proportion of discharge home (range = 63.9%-97.8%, P < .001). Multivariable analysis adjusting for age, gender, and Charlson Comorbidity Index demonstrated correlations between (1) decreased length of stay and admission to orthopedics (B = -1.256, P < .001); (2) lower 30-day readmission and THA (odds ratio [OR] = .376, P = .004), and (3) decreased discharge to a care facility and admission to orthopedics (OR = 0.402, P = <.001), THA (OR = 0.435, P = .002), and treatment by an arthroplasty fellowship-trained surgeon (OR = 0.572, P = .016). None of the treatment variables tested associated with in-hospital mortality. CONCLUSION: We observed significant variation in the treatment of displaced FNF patients across 7 hospitals and identified treatment choices that associated with resource utilization within the episode of care. Future, prospective study is necessary to understand whether care pathways that adapt some combination of these characteristics may result in more value-based care.

Transitioning From Volume to Value: A Strategic Approach to Design and Implementation.

As the health care delivery system migrates toward a model based on value rather than volume, nursing leaders play a key role in assisting in the design and implementation of new models of care to support this transition. This article provides an overview of one organization's approach to evolve in the direction of value while gaining the experience needed to scope and scale cross-continuum assets to meet this growing demand. This article outlines the development and deployment of an organizational structure, information technology integration, clinical implementation strategies, and tools and metrics utilized to evaluate the outcomes of value-based programs. Experience in Bundled Payments for Care Improvement program is highlighted. The outcomes and lessons learned are incorporated for those interested in advancing value-based endeavors in their own organizations.