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.
Insights
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.
Abstract:
Hospitals that are considered non-profit take into consideration not to make any losses other than seeking profit. A model that ensures that hospital price policies are variable due to hospital revenues depending on patients with appointments is presented in this study. A dynamic pricing approach is presented to prevent patients who have an appointment but do not show up to the hospital from causing financial loss to the hospital. The research leverages three distinct machine learning (ML) algorithms, namely Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB), to analyze the appointment status of 1073 patients across nine different departments in a hospital. A mathematical formula has been developed to apply the penalty fee to evaluate the reappointment situations of the same patients in the first 100 days and the gaps in the appointment system, considering the estimated patient appointment statuses. Average penalty cost rates were calculated based on the ML algorithms used to determine the penalty costs patients will face if they do not show up, such as 22.87% for RF, 19.47% for GB, and 14.28% for AB. As a result, this study provides essential criteria that can help hospital management better understand the potential financial impact of patients missing appointments and can be considered when choosing between these algorithms.
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