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A Neurosurgical Readmissions Reduction Program in an Academic Hospital Leveraging Machine Learning, Workflow
Tzu-Chun Wu1,2, Abraham Kim1,2,3, Ching-Tzu Tsai1,2
1Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States.
Applied Clinical Informatics
|June 19, 2024
Summary
Machine learning models can predict and reduce neurosurgery readmissions. Interventions identified through interviews show potential for improved patient outcomes and significant financial savings.
Area of Science:
- Neurosurgery
- Health Informatics
- Machine Learning
Background:
- Predicting 30-day hospital readmissions is vital for patient care and resource management.
- Existing machine learning (ML) models for neurosurgery readmissions lack clinical implementation details.
- This study addresses the need for practical, implementable ML solutions in neurosurgery readmission prediction.
Purpose of the Study:
- Develop high-performing ML models (AUROC > 0.8) for predicting 30-day neurosurgical readmissions.
- Identify actionable interventions through clinical workflow analysis and interviews.
- Simulate the clinical and financial impact of implementing these predictive models and interventions.
Main Methods:
- Utilized electronic health records and five ML methodologies: gradient boosting, decision tree, random forest, ridge logistic regression, and linear support vector machine.
- Conducted semi-structured interviews to identify intervention points across preoperative, inpatient, discharge, and follow-up phases.
- Applied calibrated agent-based models (ABMs) to simulate readmission rate and cost reductions.
Main Results:
- The random forest model achieved an AUROC of 0.89 for neurosurgical intensive care unit (NSICU) admissions.
- Six interventions were identified, targeting key phases of patient care.
- Simulations indicated significant reductions in readmission rates (e.g., NSICU from 13.13% to 10.12%) and projected savings of over $1.3 million.
Conclusions:
- Successfully developed and simulated an ML-based approach for predicting and reducing 30-day hospital readmissions in neurosurgery.
- The proposed interventions demonstrate feasibility for enhancing patient outcomes and mitigating financial losses.
