Using Machine Learning to Predict Hospital Disposition With Geriatric Emergency Department Innovation Intervention - 20/02/23
, Steven Tran, BS b, Sae Han, MPH b, Carol Gu, BS d, Hanyin Wang, BMed b, Yuan Luo, PhD c, Scott Dresden, MD aAbstract |
Study objective |
The Geriatric Emergency Department Innovations (GEDI) program is a nurse-based geriatric assessment and care coordination program that reduces preventable admissions for older adults. Unfortunately, only 5% of older adults receive GEDI care because of resource limitations. The objective of this study was to predict the likelihood of hospitalization accurately and consistently with and without GEDI care using machine learning models to better target patients for the GEDI program.
Methods |
We performed a cross-sectional observational study of emergency department (ED) patients between 2010 and 2018. Using propensity-score matching, GEDI patients were matched to other older adult patients. Multiple models, including random forest, were used to predict hospital admission. Multiple second-layer models, including random forest, were then used to predict whether GEDI assessment would change predicted hospital admission. Final model performance was reported as the area under the curve using receiver operating characteristic models.
Results |
We included 128,050 patients aged over 65 years. The random forest ED disposition model had an area under the curve of 0.774 (95% confidence interval [CI] 0.741 to 0.806). In the random forest GEDI change-in-disposition model, 24,876 (97.3%) ED visits were predicted to have no change in disposition with GEDI assessment, and 695 (2.7%) ED visits were predicted to have a change in disposition with GEDI assessment.
Conclusion |
Our machine learning models could predict who will likely be discharged with GEDI assessment with good accuracy and thus select a cohort appropriate for GEDI care. In addition, future implementation through integration into the electronic health record may assist in selecting patients to be prioritized for GEDI care.
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| Please see page 354 for the Editor’s Capsule Summary of this article. |
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| Supervising editor: Stephen Schenkel, MD, MPP. Specific detailed information about possible confliict of interest for individual editors is available at editors. |
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| Author contributions: GB and ST wrote, edited, and performed the analysis of the article. SH, CG, HW, and YL performed the analysis and edited the article. SD supplied data, performed analysis, and edited the article. All authors approved the final manuscript. GB takes responsibility for the paper as a whole. |
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| All authors attest to meeting the four ICMJE.org authorship criteria: (1) Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND (2) Drafting the work or revising it critically for important intellectual content; AND (3) Final approval of the version to be published; AND (4) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. |
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| Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). The data used in this study were collected as part of a Health Care Innovation Award from the Centers for Medicare and Medicaid Services #1C1CMS331055. The authors have stated that no such relationships exist. |
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| Presented the Abstract at the American College of Emergency Physicians Scientific Assembly in Boston, MA, October 2021. |
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| A podcast for this article is available at www.annemergmed.com. |
Vol 81 - N° 3
P. 353-363 - mars 2023 Retour au numéro
Article précédent
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