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.

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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
Controls
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article

Development and validation of a machine learning model integrated with the clinical workflow for inpatient discharge date prediction

Ajit Shukla, Kacie Dougherty, Mohammed A. Mahyoub, Raul L Berio-Dorta et al.
3 citations
Frontiers in Digital Health
Emergency and Acute Care Studies
2.07
article

Development and validation of a machine learning model integrated with the clinical workflow for inpatient discharge date prediction

Ajit Shukla, Kacie Dougherty, Mohammed A. Mahyoub, Raul L Berio-Dorta, Ravi Yadav
article en
3 citations

Abstract

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.

Frontiers in Digital HealthVol. 6
Binghamton University (US), Virtua Health (US)
Openalex Percentile: Top 13%
Emergency and Acute Care Studies
2.07
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