Does Last Year's Cost Predict the Present Cost? An Application of Machine Leaning for the Japanese Area-Basis Public Health Insurance Database

Identification of high-risk beneficiaries in private healthcare insurance

The objective of this study was to apply the Knowledge Discovery in Databases process to find out if beneficiaries of a private healthcare insurance would belong, at least once, to the 'very high cost' and 'complex cases' groups throughout the 12 months after the month when algorithms were applied. Datasets were built containing information on beneficiaries' effective use of their health plan, as well as their characteristics. Five machine learning algorithms were used, namely Random forest, Extra tree, Xgboost, Naive bayes and K-nearest neighbor. The K-nearest neighbor algorithm had a recall rate of 81.12%, 83.77% precision and an Area Under the Curve (AUC) value of 0.9045. The study also revealed that categorization occurs, on average, 8.11 months before a beneficiary entering, for the first time, a high-risk group, considering the dataset classification from January 2019 to June 2020.

The application of artificial intelligence in health financing: a scoping review

INTRODUCTION

Artificial Intelligence (AI) represents a significant advancement in technology, and it is crucial for policymakers to incorporate AI thinking into policies and to fully explore, analyze and utilize massive data and conduct AI-related policies. AI has the potential to optimize healthcare financing systems. This study provides an overview of the AI application domains in healthcare financing.

METHOD

We conducted a scoping review in six steps: formulating research questions, identifying relevant studies by conducting a comprehensive literature search using appropriate keywords, screening titles and abstracts for relevance, reviewing full texts of relevant articles, charting extracted data, and compiling and summarizing findings. Specifically, the research question sought to identify the applications of artificial intelligence in health financing supported by the published literature and explore potential future applications. PubMed, Scopus, and Web of Science databases were searched between 2000 and 2023.

RESULTS

We discovered that AI has a significant impact on various aspects of health financing, such as governance, revenue raising, pooling, and strategic purchasing. We provide evidence-based recommendations for establishing and improving the health financing system based on AI.

CONCLUSIONS

To ensure that vulnerable groups face minimum challenges and benefit from improved health financing, we urge national and international institutions worldwide to use and adopt AI tools and applications.

How the use of an online healthcare community affects the doctor-patient relationship: An empirical study in China

Possible improvements to the doctor-patient relationship are an important subject confronting national healthcare policy and health institutions. In recent years, online healthcare communities have changed the ways in which doctors and patients communicate, especially during the COVID-19 pandemic. However, previous research on how usage of online healthcare communities has affected the doctor-patient relationship is rather limited. This paper proposes a research model to investigate the relationship between online healthcare community usage and the doctor-patient relationship. An analysis of 313 patients' data using structural equation modeling showed the following. First, the use of an online healthcare community has a positive impact on doctor-patient communication, helps improve the performance of healthcare procedures, and reduces healthcare costs. Second, doctor-patient communication and healthcare costs have a positive impact on patients' emotional dependence and patients' perception of healthcare quality, while healthcare procedures do not have this impact. Finally, patients' emotional dependence and perception of healthcare quality have a positive effect on doctor-patient relationship through the mediator of patients' satisfaction.