ArticleJournal of general internal medicine2020
Preventing Hospital Readmissions: Healthcare Providers' Perspectives on "Impactibility" Beyond EHR 30-Day Readmission Risk Prediction.
Article in Journal of general internal medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis.Journal of the American Medical Informatics Association : JAMIA · 2025Observational
- Article
- Development and validation of 'Patient Optimizer' (POP) algorithms for predicting surgical risk with machine learning.BMC medical informatics and decision making · 2024Article
- Implementation Experience with a 30-Day Hospital Readmission Risk Score in a Large, Integrated Health System: A Retrospective Study.Journal of general internal medicine · 2022Article
- What Is the Return on Investment of Caring for Complex High-need, High-cost Patients?Journal of general internal medicine · 2021Article
- LACE Score-Based Risk Management Tool for Long-Term Home Care Patients: A Proof-of-Concept Study in Taiwan.International journal of environmental research and public health · 2021Article
- Predicting preventable hospital readmissions with causal machine learning.Health services research · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredictive models based on electronic health records (EHRs) are used to identify patients at high risk for 30-day hospital readmission. However, these models' ability to accurately detect who could benefit from inclusion in prevention interventions, also termed "perceived impactibility", has yet to be realized.
objectiveWe aimed to explore healthcare providers' perspectives of patient characteristics associated with decisions about which patients should be referred to readmission prevention programs (RPPs) beyond the EHR preadmission readmission detection model (PREADM).
designThis cross-sectional study employed a multi-source mixed-method design, combining EHR data with nurses' and physicians' self-reported surveys from 15 internal medicine units in three general hospitals in Israel between May 2016 and June 2017, using a mini-Delphi approach.
participantsNurses and physicians were asked to provide information about patients 65 years or older who were hospitalized at least one night. MAIN MEASURES: We performed a decision-tree analysis to identify characteristics for consideration when deciding whether a patient should be included in an RPP. KEY
resultsWe collected 817 questionnaires on 435 patients. PREADM score and RPP inclusion were congruent in 65% of patients, whereas 19% had a high PREADM score but were not referred to an RPP, and 16% had a low-medium PREADM score but were referred to an RPP. The decision-tree analysis identified five patient characteristics that were statistically associated with RPP referral: high PREADM score, eligibility for a nursing home, having a condition not under control, need for social-services support, and need for special equipment at home.
conclusionsOur study provides empirical evidence for the partial congruence between classifications of a high PREADM score and perceived impactibility. Findings emphasize the need for additional research to understand the extent to which combining EHR data with provider insights leads to better selection of patients for RPP inclusion.
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