A Novel Machine Learning Predictive Tool Assessing Outpatient or Inpatient Designation for Medicare Patients
David N Kugelman1, Greg Teo1, Shengnan Huang1
1New York University Langone Orthopaedic Hospital, New York, NY.
Insights
A new machine learning model accurately predicts inpatient vs. outpatient status for total hip arthroplasty (THA) in Medicare patients. Key factors include BMI, age, and comorbidities, aiding in designation decisions post-THA.
Area of Science:
- Orthopedic Surgery
- Health Services Research
- Machine Learning in Healthcare
Background:
- Centers for Medicare and Medicaid Services removed total hip arthroplasty (THA) from the inpatient-only list.
- This removal created confusion regarding patient inpatient designation criteria.
- A predictive tool is needed to objectively determine outpatient vs. inpatient status for THA.
Purpose of the Study:
- To develop and validate a novel predictive tool.
- To objectively determine outpatient vs. inpatient status for THA.
- To aid in Medicare patient designation post-THA removal from inpatient-only list.
Main Methods:
- Retrospective review of Medicare patients undergoing primary THA (Jan 2017-Sep 2019).
- Machine learning model trained on 80% of cohort, tested on 20%.
- Model performance evaluated using accuracy and area under the receiver operating characteristic curve (AUROC).
Main Results:
- 1091 patients had outpatient stays, 318 qualified for inpatient designation.
- Inpatient designation associated with higher BMI, older age, better functional scores, higher ASA PS, higher MFI, higher CCI, female gender, and comorbidities.
- XGBoost model achieved 78.7% accuracy and 81.5% AUROC for predicting inpatient/outpatient stay.
Conclusions:
- Machine learning model accurately predicts inpatient/outpatient stay for THA in Medicare population.
- Readily available baseline characteristics, functional scores, and comorbidities are key predictors.
- BMI, age, functional scores, and ASA PS classification were most influential factors.
Background:
The Centers for Medicare and Medicaid Services removed total hip arthroplasty (THA) from the inpatient-only list. This has created significant confusion regarding which patients qualify for an inpatient designation. The purpose of this study is to develop and validate a novel predictive tool for preoperatively objectively determining "outpatient" vs "inpatient" status for THA in the Medicare population.
Methods:
A cohort of Medicare patients undergoing primary THA between January 2017 and September 2019 was retrospectively reviewed. A machine learning model was trained using 80% of the THA patients, and the remaining 20% was used for testing the model performance in terms of accuracy and the average area under the receiver operating characteristic curve. Feature importance was obtained for each feature used in the model.
Results:
One thousand ninety-one patients had outpatient stays, and 318 qualified for inpatient designation. Significant associations were demonstrated between inpatient designations and the following: higher BMI, increased patient age, better preoperative functional scores, higher American Society of Anesthesiologist Physical Status Classification, higher Modified Frailty Index, higher Charlson Comorbidity Index, female gender, and numerous comorbidities. The XGBoost model for predicting an inpatient or outpatient stay was 78.7% accurate with the area under the receiver operating characteristic curve to be 81.5%.
Conclusions:
Using readily available key baseline characteristics, functional scores and comorbidities, this machine-learning model accurately predicts an "outpatient" or "inpatient" stay after THA in the Medicare population. BMI, age, functional scores, and American Society of Anesthesiologist Physical Status Classification had the highest influence on this predictive model.
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