Journal article
Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure
Journal of nursing scholarship, Vol.57(1), pp.165-177
01/2025
DOI: 10.1111/jnu.13030
PMCID: PMC11771534
PMID: 39508345
Appears in UI Libraries Support Open Access
Abstract
Abstract Background The healthcare industry increasingly values high‐quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations. This study aims to (1) describe important variables associated with a higher risk of ED visits and hospitalizations in HF patients receiving HHC; (2) map data requirements of a clinical decision support (CDS) tool to the exchangeable data standard for integrating a CDS tool into the care of patients with HF; (3) outline a pipeline for developing a real‐time artificial intelligence (AI)‐based CDS tool. Methods We used patient data from a large HHC organization in the Northeastern US to determine the factors that can predict ED visits and hospitalizations among patients with HF in HHC (9362 patients in 12,223 care episodes). We examined vital signs, HHC visit details (e.g., the purpose of the visit), and clinical note–derived variables. The study identified critical factors that can predict ED visits and hospitalizations and used these findings to suggest a practical CDS tool for nurses. The tool's proposed design includes a system that can analyze data quickly to offer timely advice to healthcare clinicians. Results Our research showed that the length of time since a patient was admitted to HHC and how recently they have shown symptoms of HF were significant factors predicting an adverse event. Additionally, we found this information from the last few HHC visits before the occurrence of an ED visit or hospitalization were particularly important in the prediction. One hundred percent of clinical demographic profiles from the Outcome and Assessment Information Set variables were mapped to the exchangeable data standard, while natural language processing–driven variables couldn't be mapped due to their nature, as they are generated from unstructured data. The suggested CDS tool alerts nurses about newly emerging or rising risks, helping them make informed decisions. Conclusions This study discusses the creation of a time‐series risk prediction model and its potential CDS applications within HHC, aiming to enhance patient outcomes, streamline resource utilization, and improve the quality of care for individuals with HF. Clinical Relevance This study provides a detailed plan for a CDS tool that uses the latest AI technology designed to aid nurses in their day‐to‐day HHC service. Our proposed CDS tool includes an alert system that serves as a guard rail to prevent ED visits and hospitalizations. This tool can potentially improve how nurses make decisions and improve patient outcomes by providing early warnings about ED visits and hospitalizations.
Details
- Title: Subtitle
- Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure
- Creators
- Sena Chae - University of IowaAnahita Davoudi - Merseburg University of Applied SciencesJiyoun Song - University of PennsylvaniaLauren Evans - Merseburg University of Applied SciencesKathryn H. Bowles - University of PennsylvaniaMargaret V. Mcdonald - Merseburg University of Applied SciencesYolanda Barrón - Merseburg University of Applied SciencesSe Hee Min - University of PennsylvaniaSungho Oh - University of PennsylvaniaDanielle Scharp - Icahn School of Medicine at Mount SinaiZidu Xu - Columbia UniversityMaxim Topaz - Columbia University
- Resource Type
- Journal article
- Publication Details
- Journal of nursing scholarship, Vol.57(1), pp.165-177
- DOI
- 10.1111/jnu.13030
- PMID
- 39508345
- PMCID
- PMC11771534
- NLM abbreviation
- J Nurs Scholarsh
- ISSN
- 1527-6546
- eISSN
- 1547-5069
- Publisher
- Wiley
- Grant note
- Agency for Healthcare Research and Quality
This study was funded by the Agency for Healthcare Research and Quality [AHRQ] (R01 HS027742), "Building risk models for prevent-able hospitalizations and emergency department visits in homecare (Homecare-CONCERN)." The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Dr. Se Hee Min and Danielle Scharp are supported by the National Institute for Nursing Research training grant Reducing Health Disparities through Informatics (T32NR007969). Dr. Sungho Oh is supported by the National Institute for Nursing Research training grant Individualized Care for At Risk Older Adults (T32NR009356).
- Language
- English
- Electronic publication date
- 11/07/2024
- Date published
- 01/2025
- Academic Unit
- Nursing
- Record Identifier
- 9984748158402771
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