Improving transitions from acute care to home among complex older adults using the LACE Index and care coordination

Can prediction models for hospital readmission be improved by incorporating patient-reported outcome measures? A systematic review and narrative synthesis

PURPOSE

To investigate the roles, challenges, and implications of using patient-reported outcome measures (PROMs) in predicting the risk of hospital readmissions.

METHODS

We systematically searched four bibliometric databases for peer-reviewed studies published in English between 1 January 2000 and 15 June 2023 and used validated PROMs to predict readmission risks for adult populations. Reported studies were analysed and narratively synthesised in accordance with the CHARMS and PRISMA guidelines.

RESULTS

Of the 2858 abstracts reviewed, 23 studies met predefined eligibility criteria, representing diverse geographic regions and medical specialties. Among those, 19 identified the positive contributions of PROMs in predicting readmission risks. Seven studies utilised generic PROMs exclusively, eleven used generic and condition-specific PROMs, while 5 focussed solely on condition-specific PROMs. Logistic regression was the most used modelling approach, with 13 studies aiming at predicting 30-day all-cause readmission risks. The c-statistic, ranging from 0.54 to 0.84, was reported in 22/23 studies as a measure of model discrimination. Nine studies reported model calibration in addition to c-statistic. Thirteen studies detailed their approaches to dealing with missing data.

CONCLUSION

Our study highlights the potential of PROMs to enhance predictive accuracy in readmission models, while acknowledging the diversity in data collection methods, readmission definitions, and model evaluation approaches. Recognizing that PROMs serve various purposes beyond readmission reduction, our study supports routine data collection and strategic integration of PROMs in healthcare practices to improve patient outcomes. To facilitate comparative analysis and broaden the use of PROMs in the prediction framework, it is imperative to consider the methodological aspects involved.

Differential Effects of an Emergency Department-to-Home Care Transitions Intervention in an Older Adult Population: A Latent Class Analysis

BACKGROUND

Older adults frequently return to the emergency department (ED) within 30 days of a visit. High-risk patients can differentially benefit from transitional care interventions. Latent class analysis (LCA) is a model-based method used to segment the population and test intervention effects by subgroup.

OBJECTIVES

We aimed to identify latent classes within an older adult population from a randomized controlled trial evaluating the effectiveness of an ED-to-home transitional care program and test whether class membership modified the intervention effect.

RESEARCH DESIGN

Participants were randomized to receive the Care Transitions Intervention or usual care. Study staff collected outcomes data through medical record reviews and surveys. We performed LCA and logistic regression to evaluate the differential effects of the intervention by class membership.

SUBJECTS

Participants were ED patients (age 60 y and above) discharged to a community residence.

MEASURES

Indicator variables for the LCA included clinically available and patient-reported data from the initial ED visit. Our primary outcome was ED revisits within 30 days. Secondary outcomes included ED revisits within 14 days, outpatient follow-up within 7 and 30 days, and self-management behaviors.

RESULTS

We interpreted 6 latent classes in this study population. Classes 1, 4, 5, and 6 showed a reduction in ED revisit rates with the intervention; classes 2 and 3 showed an increase in ED revisit rates. In class 5, we found evidence that the intervention increased outpatient follow-up within 7 and 30 days (odds ratio: 1.81, 95% CI: 1.13-2.91; odds ratio: 2.24, 95% CI: 1.25-4.03).

CONCLUSIONS

Class membership modified the intervention effect. Population segmentation is an important step in evaluating a transitional care intervention.

The effectiveness of tele-transitions of care interventions in high-risk older adults: A systematic review and meta-analysis

BACKGROUND

Chronically ill older patients are often vulnerable to detrimental health outcomes and have increased risk of preventable readmission. Tele-transitions of care interventions utilizing telecommunications and surveillance technologies help monitor patients' conditions after discharge to prevent negative health outcomes.

OBJECTIVES

This systematic review and meta-analysis aimed to identify and synthesize available evidence on the effectiveness of tele-transitions of care interventions on various health outcomes in older adults at high risk for readmission discharged from acute setting.

METHODS

Published, unpublished studies and gray literatures were identified through searching PubMed, Medline, Embase, PsycINFO, Cochrane Library, CINAHL, Scopus, ProQuest Dissertations and theses and Google Scholar from inception to December 2021. Only randomized controlled trials published in English language assessing tele-transitions of care interventions on high-risk older adults were included. Meta-analyses were performed using random-effects model in RevMan 5.4. Sensitivity and subgroup and narrative analyses were conducted.

RESULTS

Fourteen studies were included, of which thirteen were considered for meta-analyses. Tele-transitions of care interventions were effective in reducing readmission rate (RR = 0.59, 95%CI 0.50-0.69, z = 6.28, p < 0.00001), mortality rate (RR = 0.72, 95%CI 0.53-0.98, z = 2.12, p = 0.03), and improving health-related quality of life (SMD = 0.24, Z = 2.04, p = 0.04). However, reduction of emergency department visit (RR = 1.10, 95%CI 0.59-2.06, z = 0.31, p = 0.76) and improvement of functional status (SMD = -0.06, Z = 0.19, p = 0.85) was not observed following intervention. Subgroup analysis found that the positive effects of tele-transitions of care interventions persist up to 180 days even after the intervention.

CONCLUSION

This systematic review and meta-analysis concluded that tele-transitions of care interventions have promising effects on readmission, mortality rate and health-related quality of life. Tele-transitions of care interventions are cost-effective and suitable for large-scale implementation in healthcare settings.

REGISTRATION

The protocol was registered on PROSPERO (CRD42022295665).

TWEETABLE ABSTRACT

Systematic review demonstrates that monitoring older patients at high risk of readmission, following discharge from hospital, using telecommunication and surveillance technologies significantly reduces readmission and mortality rates and improves their quality of life.

Emergency Department Care Transitions for Patients With Cognitive Impairment: A Scoping Review

OBJECTIVES

We aimed to describe emergency department (ED) care transition interventions delivered to older adults with cognitive impairment, identify relevant patient-centered outcomes, and determine priority research areas for future investigation.

DESIGN

Systematic scoping review.

SETTING AND PARTICIPANTS

ED patients with cognitive impairment and/or their care partners.

METHODS

Informed by the clinical questions, we conducted systematic electronic searches of medical research databases for relevant publications following published guidelines. The results were presented to a stakeholder group representing ED-based and non-ED-based clinicians, individuals living with cognitive impairment, care partners, and advocacy organizations. After discussion, they voted on potential research areas to prioritize for future investigations.

RESULTS

From 3848 publications identified, 78 eligible studies underwent full text review, and 10 articles were abstracted. Common ED-to-community care transition interventions for older adults with cognitive impairment included interdisciplinary geriatric assessments, home visits from medical personnel, and telephone follow-ups. Intervention effects were mixed, with improvements observed in 30-day ED revisit rates but most largely ineffective at promoting connections to outpatient care or improving secondary outcomes such as physical function. Outcomes identified as important to adults with cognitive impairment and their care partners included care coordination between providers and inclusion of care partners in care management within the ED setting. The highest priority research area for future investigation identified by stakeholders was identifying strategies to tailor ED-to-community care transitions for adults living with cognitive impairment complicated by other vulnerabilities such as social isolation or economic disadvantage.

CONCLUSIONS AND IMPLICATIONS

This scoping review identified key gaps in ED-to-community care transition interventions delivered to older adults with cognitive impairment. Combined with a stakeholder assessment and prioritization, it identified relevant patient-centered outcomes and clarifies priority areas for future investigation to improve ED care for individuals with impaired cognition, an area of critical need given the current population trends.

Recommendations for the Design and Delivery of Transitions-Focused Digital Health Interventions: Rapid Review

Background

Older adults experience a high risk of adverse events during hospital-to-home transitions. Implementation barriers have prevented widespread clinical uptake of the various digital health technologies that aim to support hospital-to-home transitions.

Objective

To guide the development of a digital health intervention to support transitions from hospital to home (the Digital Bridge intervention), the specific objectives of this review were to describe the various roles and functions of health care providers supporting hospital-to-home transitions for older adults, allowing future technologies to be more targeted to support their work; describe the types of digital health interventions used to facilitate the transition from hospital to home for older adults and elucidate how these interventions support the roles and functions of providers; describe the lessons learned from the design and implementation of these interventions; and identify opportunities to improve the fit between technology and provider functions within the Digital Bridge intervention and other transition-focused digital health interventions.

Methods

This 2-phase rapid review involved a selective review of providers’ roles and their functions during hospital-to-home transitions (phase 1) and a structured literature review on digital health interventions used to support older adults’ hospital-to-home transitions (phase 2). During the analysis, the technology functions identified in phase 2 were linked to the provider roles and functions identified in phase 1.

Results

In phase 1, various provider roles were identified that facilitated hospital-to-home transitions, including navigation-specific roles and the roles of nurses and physicians. The key transition functions performed by providers were related to the 3 categories of continuity of care (ie, informational, management, and relational continuity). Phase 2, included articles (n=142) that reported digital health interventions targeting various medical conditions or groups. Most digital health interventions supported management continuity (eg, follow-up, assessment, and monitoring of patients’ status after hospital discharge), whereas informational and relational continuity were the least supported. The lessons learned from the interventions were categorized into technology- and research-related challenges and opportunities and informed several recommendations to guide the design of transition-focused digital health interventions.

Conclusions

This review highlights the need for Digital Bridge and other digital health interventions to align the design and delivery of digital health interventions with provider functions, design and test interventions with older adults, and examine multilevel outcomes.

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2020-045596

AI Models for Predicting Readmission of Pneumonia Patients within 30 Days after Discharge

A model with capability for precisely predicting readmission is a target being pursued worldwide. The objective of this study is to design predictive models using artificial intelligence methods and data retrieved from the National Health Insurance Research Database of Taiwan for identifying high-risk pneumonia patients with 30-day all-cause readmissions. An integrated genetic algorithm (GA) and support vector machine (SVM), namely IGS, were used to design predictive models optimized with three objective functions. In IGS, GA was used for selecting salient features and optimal SVM parameters, while SVM was used for constructing the models. For comparison, logistic regression (LR) and deep neural network (DNN) were also applied for model construction. The IGS model with AUC used as the objective function achieved an accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 70.11%, 73.46%, 69.26%, and 0.7758, respectively, outperforming the models designed with LR (65.77%, 78.44%, 62.54%, and 0.7689, respectively) and DNN (61.50%, 79.34%, 56.95%, and 0.7547, respectively), as well as previously reported models constructed using thedata of electronic health records with an AUC of 0.71–0.74. It can be used for automatically detecting pneumonia patients with a risk of all-cause readmissions within 30 days after discharge so as to administer suitable interventions to reduce readmission and healthcare costs.

Understanding the role of informal caregivers in postoperative care transitions for older patients

BACKGROUND

Older adults may have new care needs and functional limitations after surgery. Many rely on informal caregivers (unpaid family or friends) after discharge but the extent of informal support is unknown. We sought to examine the role of informal postoperative caregiving on transitions of care for older adults undergoing routine surgical procedures.

MATERIALS AND METHODS

We performed a retrospective cohort study using ACS NSQIP Geriatric Pilot Project data, 2014-2018. Patients were ≥65 years and underwent an inpatient surgical procedure. Patients who lived at home alone were compared with those who lived with support from informal caregivers (family and/or friends). Primary outcomes were discharge destination (home vs. post-acute care) and readmission within 30 days. Multivariable logistic regression was used to determine the association between support at home, discharge destination, and readmission.

RESULTS

Of 18,494 patients, 25% lived alone before surgery. There was no difference in loss of independence (decline in functional status or new use of mobility aid) after surgery between patients who lived alone or with others (18.7% vs. 19.5%, p = 0.24). Nevertheless, twice as many patients who lived alone were discharged to a non-home location (10.2% vs. 5.1%; OR: 2.24, CI: 1.93-2.56). Patients who lived alone and were discharged home with new informal caregivers had increased odds of readmission (OR: 1.43, CI: 1.09-1.86).

CONCLUSION

Living alone independently predicts discharge to post-acute care, and patients who received new informal caregiver support at home have higher odds of readmission. These findings highlight opportunities to improve discharge planning and care.

Care transition for complex patients: a framework to analyse and develop the Operating Centres for Transition

Purpose

Transitional care (TC) models emerged to ensure healthcare coordination and continuity, as at-risk patients transfer between different settings or different levels of care within the same setting. TC models have been developed in many countries as well as within different healthcare service delivery models and organizations. This paper aims to focus on a TC model developed in Italy called Operating Centre for Transition (OCT), in order to (1) explore its distinctive features by establishing a framework of analysis, (2) apply the framework to study two OCTs and (3) provide recommendations on how to use the framework to evaluate and develop new OCTs in the future.

Design/methodology/approach

The authors adopted a grounded theory method to develop and validate the framework of analysis. The authors employed several qualitative methods following four iterative and recursive steps: (1) desk analysis of relevant documents, (2) in-depth interviews to key informants, (3) three meetings of an expert working group and (4) application of the framework to two case studies.

Findings

The framework of analysis identifies three core dimensions that are always present in any OCT: the service model, the functions and the organizational features. Moreover, for every dimension several variables that capture and understand OCTs’ nature, role and development level are identified.

Originality/value

The results of the study highlight the key elements of the OCT model in Italy and show that the proposed framework can be useful both to analyse existing OCTs and to support health managers and policy makers to create new OCTs or develop those already active.