Inselman, Shealeigh A.

Abstract Background Hospital‐at‐home programs (HaH) in the United States have evolved to include a virtual‐hybrid delivery model, where all physician encounters are virtual and partnered with a home care team. Objective To examine whether a virtual hybrid HaH program enabled by technology has similar clinical outcomes to traditional brick‐and‐mortar (B&M) hospital care. Methods We conducted a pragmatic trial at three hospitals, randomizing 1150 acutely ill patients requiring hospital care between July 10, 2023, and October 31, 2023 one‐to‐one into two groups: intervention (HaH) and control (B&M). The primary analysis was an intention‐to‐treat non‐inferiority analysis of the primary outcome, which was a composite of 30‐day all‐cause mortality and unplanned readmissions. Secondary outcomes included 30‐day readmission, all‐cause mortality, and patient experience. Results The mean age was 67.8 (standard deviation [SD] 16.3) years, and 52.2% were female. The primary outcome occurred in 99 (17.3%) HaH patients and 114 (19.8%) B&M patients (odds ratio [OR] 0.85, 95% confidence interval [CI] 0.63–1.14, p = .28), meeting the non‐inferiority criterion. Thirty‐day unplanned readmission occurred in 84 (14.7%) HaH patients and 101 (17.5%) B&M patients (OR 0.81, 95% CI 0.59–1.11, p = .19). Thirty‐day all‐cause mortality occurred in 25 (4.4%) HaH patients and 19 (3.3%) B&M patients (OR 1.34, 95% CI 0.73–2.46, p = .35). No HaH patients died while receiving their hospital care at home. HaH program was associated with a higher likelihood of patients reporting feeling extremely comfortable or very comfortable (84.4% HaH; 60.9% B&M, p = .001). Conclusion A hybrid HaH model is a safe and comfortable alternative to traditional B&M hospital care.

Background: Benefits of hospice care, such as improvement in quality of life and reduced costs, depend on duration of enrollment in hospice services, making timely hospice referral essential. Patient-, provider-, and system-level factors associated with hospice referral timing and length of stay (LOS) are unclear. 1 – 6 Objective: To review existing hospice referral patterns to identify patient- and system-level factors associated with hospice LOS. Methods: We performed a retrospective review of all adult patients referred from our institution (located in Rochester, Minnesota, USA) to a nonprofit hospice agency between 2013 and 2017. The primary dependent variable was hospice LOS. Patient-level variables included demographic characteristics, place of residence, and hospice diagnosis. System-level variables included referral setting and provider-specific variables, such as title and gender. Statistical analyses were performed using multivariate logistic regression. Results: A total of 2072 patients were referred to hospice during the study period. Patient-level factors associated with LOS included hospice diagnosis and place of residence. Patients referred for cancer had a higher odds of a long LOS, while patients from long-term care facilities had a higher odds of a short LOS. System-level factors associated with LOS included provider gender and title. Referral by a female provider or by a physician, rather than an advanced practice provider, was associated with a lower odds of a short LOS. Conclusion: Based on a review of hospice referral patterns, the integration of hospice care into subspecialty practices, long-term care facilities, and advanced practice education could be an effective strategy to improve hospice LOS.

AbstractThe Adult Medical Care Coordination program (“the program”) was implemented at Mayo Clinic to promote patient self‐management and improve 30‐day unplanned readmission for patients with high risk for readmission after hospital discharge. This study aimed to evaluate the impact of the program compared to usual care using a pragmatic, stepped wedge cluster randomized trial (“stepped wedge trial”). However, several challenges arose including large differences between the study arms. Our goal is to describe the challenges and present lessons learned on how to overcome such challenges and generate evidence to support practice decisions. We describe the challenges encountered during the trial, the approach to addressing these challenges, and lessons learned for other learning health system researchers facing similar challenges. The trial experienced several challenges in implementation including several clinics dropping from the study and care disruptions due to COVID‐19. Additionally, there were large differences in the patient population between the program and usual care arms. For example, the mean age was 76.8 for the program and 68.1 for usual care. Due to these differences, we adapted the methods using the propensity score matching approach that is traditionally applied to observational designs and adjusted for differences in observable characteristics. When conducting pragmatic research, researchers will encounter factors beyond their control that may introduce bias. The lessons learned include the need to weigh the tradeoffs of pragmatic design elements and the potential value of adaptive designs for pragmatic trials. Applying these lessons would promote the successful generation of evidence that informs practice decisions.

The objective was to assess the value of routinely collected patient-reported health-related social needs (HRSNs) measures for predicting utilization and health outcomes. The authors identified Mayo Clinic patients with cancer, diabetes, or heart failure. The HRSN measures were collected as part of patient-reported screenings from June to December 2019 and outcomes (hospitalization, 30-day readmission, and death) were ascertained in 2020. For each outcome and disease combination, 4 models were used: gradient boosting machine (GBM), random forest (RF), generalized linear model (GLM), and elastic net (EN). Other predictors included clinical factors, demographics, and area-based HRSN measures—area deprivation index (ADI) and rurality. Predictive performance for models was evaluated with and without the routinely collected HRSN measures as change in area under the curve (AUC). Variable importance was also assessed. The differences in AUC were mixed. Significant improvements existed in 3 models of death for cancer (GBM: 0.0421, RF: 0.0496, EN: 0.0428), 3 models of hospitalization (GBM: 0.0372, RF: 0.0640, EN: 0.0441), and 1 of death (RF: 0.0754) for diabetes, and 1 model of readmissions (GBM: 0.1817), and 3 models of death (GBM: 0.0333, RF: 0.0519, GLM: 0.0489) for heart failure. Age, ADI, and the Charlson comorbidity index were the top 3 in variable importance and were consistently more important than routinely collected HRSN measures. The addition of routinely collected HRSN measures resulted in mixed improvement in the predictive performance of the models. These findings suggest that existing factors and the ADI are more important for prediction in these contexts. More work is needed to identify predictors that consistently improve model performance.

To understand why US patients refused participation in hospital-at-home (H@H) during the coronavirus disease 2019 Public Health Emergency, eligible adult patients seen at 2 Mayo Clinic sites, Mayo Clinic Health System—Northwest Wisconsin region (NWWI) and Mayo Clinic Florida (MCF), from August 2021 through March 2022, were invited to participate in a convergent-parallel study. Quantitative associations between H@H participation status and patient baseline data at hospital admission were investigated. H@H patients were more likely to have a Mayo Clinic patient portal at baseline ( P-value: .014), indicating a familiarity with telehealth. Patients who refused were more likely to be from NWWI ( P-value < .001) and have a higher Epic Deterioration Index score ( P-value: .004). The groups also had different quarters (in terms of fiscal calendar) of admission ( P-value: .040). Analyzing qualitative interviews (n = 13) about refusal reasons, 2 themes portraying the quantitative associations emerged: lack of clarity about H@H and perceived domestic challenges. To improve access to H@H and increase patient recruitment, improved education about the dynamics of H@H, for both hospital staff and patients, and inclusive strategies for navigating domestic barriers and diagnostic challenges are needed.