An Assessment of Patient, Caregiver, and Clinician Perspectives on the Post-discharge Phase of Care.

OBJECTIVE: We sought to elicit patients', caregivers', and health care providers' perceptions of home recovery to inform care personalization in the learning health system. SUMMARY BACKGROUND DATA: Postsurgical care has shifted from the hospital into the home. Daily care responsibilities fall to patients and their caregivers, yet stakeholder concerns in these heterogeneous environments, especially as they relate to racial inequities, are poorly understood. METHODS: Surgical oncology patients, caregivers, and clinicians participated in freelisting; an open-ended interviewing technique used to identify essential elements of a domain. Within 2 weeks after discharge, participants were queried on 5 domains: home independence, social support, pain control, immediate, and overall surgical impact. Salience indices, measures of the most important words of interest, were calculated using Anthropac by domain and group. RESULTS: Forty patients [20 whites and 20 African-Americans (AAs)], 30 caregivers (17 whites and 13 AAs), and 20 providers (8 residents, 4 nurses, 4 nurse practitioners, and 4 attending surgeons) were interviewed. Patients and caregivers attended to the personal recovery experience, whereas providers described activities and individuals associated with recovery. All groups defined surgery as life-changing, with providers and caregivers discussing financial and mortality concerns. Patients shared similar thoughts about social support and self-care ability by race, whereas AA patients described heterogeneous pain management and more hopeful recovery perceptions. AA caregivers expressed more positive responses than white caregivers. CONCLUSIONS: Patients live the day-to-day of recovery, whereas caregivers and clinicians also contemplate more expansive concerns. Incorporating relevant perceptions into traditional clinical outcomes and concepts could enhance the surgical experience for all stakeholders.

Predicting and Preventing Acute Care Re-Utilization by Patients with Diabetes.

PURPOSE OF REVIEW: Acute care re-utilization, i.e., hospital readmission and post-discharge Emergency Department (ED) use, is a significant driver of healthcare costs and a marker for healthcare quality. Diabetes is a major contributor to acute care re-utilization and associated costs. The goals of this paper are to (1) review the epidemiology of readmissions among patients with diabetes, (2) describe models that predict readmission risk, and (3) address various strategies for reducing the risk of acute care re-utilization. RECENT FINDINGS: Hospital readmissions and ED visits by diabetes patients are common and costly. Major risk factors for readmission include sociodemographics, comorbidities, insulin use, hospital length of stay (LOS), and history of readmissions, most of which are non-modifiable. Several models for predicting the risk of readmission among diabetes patients have been developed, two of which have reasonable accuracy in external validation. In retrospective studies and mostly small randomized controlled trials (RCTs), interventions such as inpatient diabetes education, inpatient diabetes management services, transition of care support, and outpatient follow-up are generally associated with a reduction in the risk of acute care re-utilization. Data on readmission risk and readmission risk reduction interventions are limited or lacking among patients with diabetes hospitalized for COVID-19. The evidence supporting post-discharge follow-up by telephone is equivocal and also limited. Acute care re-utilization of patients with diabetes presents an important opportunity to improve healthcare quality and reduce costs. Currently available predictive models are useful for identifying higher risk patients but could be improved. Machine learning models, which are becoming more common, have the potential to generate more accurate acute care re-utilization risk predictions. Tools embedded in electronic health record systems are needed to translate readmission risk prediction models into clinical practice. Several risk reduction interventions hold promise but require testing in multi-site RCTs to prove their generalizability, scalability, and effectiveness.