Development and Validation of the Hospital-to-Home-Health Transition Quality (H3TQ) Index: A Novel Measure to Engage Patients and Home Health Providers in Evaluating Hospital-to-Home Care Transition Quality.


Journal

Quality management in health care
ISSN: 1550-5154
Titre abrégé: Qual Manag Health Care
Pays: United States
ID NLM: 9306156

Informations de publication

Date de publication:
20 Jun 2023
Historique:
medline: 22 6 2023
pubmed: 22 6 2023
entrez: 22 6 2023
Statut: aheadofprint

Résumé

Patients requiring skilled home health care (HH) after hospitalization are at high risk of adverse events. Human factors engineering (HFE) approaches can be useful for measure development to optimize hospital-to-home transitions. To describe the development, initial psychometric validation, and feasibility of the Hospital-to-Home-Health-Transition Quality (H3TQ) Index to identify patient safety risks. Development: A multisite, mixed-methods study at 5 HH agencies in rural and urban sites across the United States. Testing: Prospective H3TQ implementation on older adults' hospital-to-HH transitions. Populations Studied: Older adults and caregivers receiving HH services after hospital discharge, and their HH providers (nurses and rehabilitation therapists). The H3TQ is a 12-item count of hospital-to-HH transitions best practices for safety that we developed through more than 180 hours of observations and more than 80 hours of interviews. The H3TQ demonstrated feasibility of use, stability, construct validity, and concurrent validity when tested on 75 transitions. The vast majority (70%) of hospital-to-HH transitions had at least one safety issue, and HH providers identified more patient safety threats than did patients/caregivers. The most frequently identified issues were unsafe home environments (32%), medication issues (29%), incomplete information (27%), and patients' lack of general understanding of care plans (27%). The H3TQ is a novel measure to assess the quality of hospital-to-HH transitions and proactively identify transitions issues. Patients, caregivers, and HH providers offered valuable perspectives and should be included in safety reporting. Study findings can guide the design of interventions to optimize quality during the high-risk hospital-to-HH transition.

Sections du résumé

BACKGROUND BACKGROUND
Patients requiring skilled home health care (HH) after hospitalization are at high risk of adverse events. Human factors engineering (HFE) approaches can be useful for measure development to optimize hospital-to-home transitions.
OBJECTIVE OBJECTIVE
To describe the development, initial psychometric validation, and feasibility of the Hospital-to-Home-Health-Transition Quality (H3TQ) Index to identify patient safety risks.
METHODS METHODS
Development: A multisite, mixed-methods study at 5 HH agencies in rural and urban sites across the United States. Testing: Prospective H3TQ implementation on older adults' hospital-to-HH transitions. Populations Studied: Older adults and caregivers receiving HH services after hospital discharge, and their HH providers (nurses and rehabilitation therapists).
RESULTS RESULTS
The H3TQ is a 12-item count of hospital-to-HH transitions best practices for safety that we developed through more than 180 hours of observations and more than 80 hours of interviews. The H3TQ demonstrated feasibility of use, stability, construct validity, and concurrent validity when tested on 75 transitions. The vast majority (70%) of hospital-to-HH transitions had at least one safety issue, and HH providers identified more patient safety threats than did patients/caregivers. The most frequently identified issues were unsafe home environments (32%), medication issues (29%), incomplete information (27%), and patients' lack of general understanding of care plans (27%).
CONCLUSIONS CONCLUSIONS
The H3TQ is a novel measure to assess the quality of hospital-to-HH transitions and proactively identify transitions issues. Patients, caregivers, and HH providers offered valuable perspectives and should be included in safety reporting. Study findings can guide the design of interventions to optimize quality during the high-risk hospital-to-HH transition.

Identifiants

pubmed: 37348080
doi: 10.1097/QMH.0000000000000419
pii: 00019514-990000000-00046
doi:

Types de publication

Journal Article

Langues

eng

Informations de copyright

Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.

Déclaration de conflit d'intérêts

The authors declare no conflicts of interest.

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Auteurs

Alicia I Arbaje (AI)

Division of Geriatric Medicine and Gerontology, Department of Medicine, Center for Transformative Geriatric Research, Johns Hopkins University School of Medicine, Baltimore, Maryland (Drs Arbaje, Keita, and Leff and Ms Greyson); Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland (Drs Arbaje, Hsu, Keita, Marsteller, Gurses, and Leff); Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (Ms Wang); Armstrong Institute Center for Health Care Human Factors, Johns Hopkins University School of Medicine, Baltimore, Maryland (Drs Arbaje and Gurses); Department of Industrial and Systems Engineering, College of Engineering, University of Wisconsin-Madison, Madison (Dr Werner); Johns Hopkins Home Care Group, Baltimore, Maryland (Ms Carl and Dr Hohl); College of Nursing, University of South Carolina, Columbia (Dr Jones); Department of Biobehavioral Health Sciences, NewCourtland Center for Transitions and Health, School of Nursing, University of Pennsylvania, Philadelphia, and Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York (Dr Bowles); MedStar-Georgetown Surgical Outcomes Research Center, MedStar Health Research Institute and Medstar Georgetown University Hospital, Washington, District of Columbia (Dr Chan); Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland (Dr Gurses); and Department of Community and Public Health, Johns Hopkins School of Nursing, Baltimore, Maryland (Dr Leff).

Classifications MeSH