Abstract
Background and objectives: Machine learning approaches using random forest have been effectively used to provide decision support in health and medical informatics. This is especially true when predicting variables associated with Medicare reimbursements. However, more work is needed to analyze and predict data associated with reimbursements through Medicare and Medicaid services for physical therapy practices in the United States. The key objective of this study is to analyze different machine learning models to predict key variables associated with Medicare standardized payments for physical therapy practices in the United States. Materials and Methods: This study employs five methods, namely, multiple linear regression, decision tree regression, random forest regression, K-nearest neighbors, and linear generalized additive model, (GAM) to predict key variables associated with Medicare payments for physical therapy practices in the United States. Results: The study described in this article adds to the body of knowledge on the effective use of random forest regression and linear generalized additive model in predicting Medicare Standardized payment. It turns out that random forest regression may have any edge over other methods employed for this purpose. Conclusions: The study provides a useful insight into comparing the performance of the aforementioned methods, while identifying a few intricate details associated with predicting Medicare costs while also ascertaining that linear generalized additive model and random forest regression as the most suitable machine learning models for predicting key variables associated with standardized Medicare payments.
Highlights
IntroductionWe would like to point out that Medicare is defined by Rajaram and Bilimoria [6] as “a federal program that provides health insurance coverage to people aged 65 years or older and younger people with permanent disabilities.”
Investigators have used various methods and techniques to analyze results in healthcare delivery. While many of these studies have involved methods such as ANOVA and MANOVA, regression, and more recently deep learning techniques [1,2,3], there has been a dearth of literature on the use of random forests [4] and other ensemble learning methods [5] for analyzing health and medical data when compared to other machine learning algorithms
We would like to point out that Medicare is defined by Rajaram and Bilimoria [6] as “a federal program that provides health insurance coverage to people aged 65 years or older and younger people with permanent disabilities.”. Based on this aforementioned comparison the broader research goals targeted by this study are as follows: (a) increasing the information available to the health informaticians on Medicare payments with respect to physical therapy practices in the United States [7,8], and (b) analyzing the computational techniques available to the researchers in deciphering the necessary information that can assist in the development of a knowledge base for decision making purposes [1]
Summary
We would like to point out that Medicare is defined by Rajaram and Bilimoria [6] as “a federal program that provides health insurance coverage to people aged 65 years or older and younger people with permanent disabilities.” Based on this aforementioned comparison the broader research goals targeted by this study are as follows: (a) increasing the information available to the health informaticians on Medicare payments with respect to physical therapy practices in the United States [7,8], and (b) analyzing the computational techniques available to the researchers in deciphering the necessary information that can assist in the development of a knowledge base for decision making purposes [1].
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