Development and validation of a machine learning model integrated with the clinical workflow for inpatient discharge date prediction
Our findings highlight the effectiveness and potential value of the developed discharge date prediction model in clinical practice. By improving the accuracy of discharge date estimations, the model has the potential to enhance healthcare resource management and patient care planning. Additional research endeavors should prioritize the evaluation of the model's long-term applicability across diverse scenarios and the comprehensive analysis of its influence on patient outcomes.
Authors
- Ajit Shukla
- Kacie Dougherty (ORCID: https://orcid.org/0000-0001-6295-0026)
- Mohammed A. Mahyoub (ORCID: https://orcid.org/0000-0002-0808-3205)
- Raul L Berio-Dorta
- Ravi Yadav (ORCID: https://orcid.org/0009-0006-8185-9889)
Institutions
- Binghamton University (US)
- Virtua Health (US)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2024-09-30
- DOI
- https://doi.org/10.3389/fdgth.2024.1455446
- Citations
- 3
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.07