Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms
Abdulkadir Atalan1, Cem Çağrı Dönmez2
1Department of Industrial Engineering, Çanakkale Onsekiz Mart University, Çanakkale 17100, Turkey.
Healthcare (Basel, Switzerland)
|July 13, 2024
Summary
This study introduces a dynamic pricing model to reduce hospital financial losses from missed appointments. Machine learning algorithms like AdaBoost, Gradient Boosting, and Random Forest predict no-shows, enabling penalty fee calculations.
Area of Science:
- Healthcare Management
- Operations Research
- Applied Machine Learning
Background:
- Non-profit hospitals aim to minimize losses, not solely maximize profit.
- Patient no-shows create significant financial deficits in hospital revenue streams.
- Dynamic pricing strategies can mitigate losses from appointment no-shows.
Purpose of the Study:
- To develop a dynamic pricing model for hospital appointments.
- To assess the financial impact of patient no-shows.
- To evaluate the effectiveness of machine learning algorithms in predicting no-shows and calculating penalty fees.
Main Methods:
- Utilized three machine learning algorithms: Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB).
- Analyzed appointment data for 1073 patients across nine hospital departments.
- Developed a mathematical formula to calculate penalty fees for no-shows and reappointment gaps.
Main Results:
- AdaBoost (AB) yielded the lowest average penalty cost rate at 14.28%.
- Gradient Boosting (GB) resulted in a 19.47% penalty cost rate.
- Random Forest (RF) showed the highest average penalty cost rate at 22.87%.
Conclusions:
- Machine learning models can effectively estimate financial losses due to missed appointments.
- Algorithm choice impacts the calculated penalty costs for no-shows.
- Findings offer criteria for hospital management to understand and mitigate financial risks from patient no-shows.
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