Preventing Hospital Readmissions: Healthcare Providers’ Perspectives on “Impactibility” Beyond EHR 30-Day Readmission Risk Prediction

Natalie Flaks-Manov, Einav Srulovici, Rina Yahalom, Henia Perry-Mezre, Ran Balicer, Efrat Shadmi

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Background: Predictive 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. Objective: We 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). Design: This 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. Participants: Nurses 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 Results: We 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. Conclusions: Our 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.

Original languageEnglish
Pages (from-to)1484-1489
Number of pages6
JournalJournal of General Internal Medicine
Volume35
Issue number5
DOIs
StatePublished - 1 May 2020

Keywords

  • electronic health records
  • high-risk classification
  • impactibility
  • readmission prevention

Fingerprint

Dive into the research topics of 'Preventing Hospital Readmissions: Healthcare Providers’ Perspectives on “Impactibility” Beyond EHR 30-Day Readmission Risk Prediction'. Together they form a unique fingerprint.

Cite this