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

Abstract

The increasing healthcare cost imposes a large economic burden for the Japanese government. Predicting the healthcare cost may be a useful tool for policy making. A database of the area-basis public health insurance of one city was analyzed to predict the medical healthcare cost by the dental healthcare cost with a machine learning strategy. The 30,340 subjects who had continued registration of the area-basis public health insurance of Ebina city during April 2017 to September 2018 were analyzed. The sum of the healthcare cost was JPY 13,548,831,930. The per capita healthcare cost was JPY 446,567. The proportion of medical healthcare cost, medication cost, and dental healthcare cost was 78%, 15%, and 7%, respectively. By the results of the neural network model, the medical healthcare cost proportionally depended on the medical healthcare cost of the previous year. The dental healthcare cost of the previous year had a reducing effect on the medical healthcare cost. However, the effect was very small. Oral health may be a risk for chronic diseases. However, when evaluated by the healthcare cost, its effect was very small during the observation period.

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

“…The use of an online healthcare community has a positive impact on the healthcare treatment process because online healthcare community provides online registration, which is much more convenient for patients (17). Overall, the use of an online healthcare community can shorten the time required for patients to see a doctor and optimize the healthcare treatment process through online registration and appointment of examination items (18).…”

“…First, this paper forms a contribution to the doctor-patient relationship literature by investigating the relationship between online healthcare community use and the doctor-patient relationship. Most prior research has focused on patients' behavior, such as their willingness to engage in a community (18), their informational and emotional support (48), social support (50), their knowledge sharing (51) and information seeking (52), their information disclosure intention (53); physicians' behavior, such as group joining behavior (54), and physicians' teams' performance (55); differences in demographic characteristics (56), and value co-creation behaviors in online health communities (47). However, few studies have focused on the relationship between online healthcare community use and the doctor-patient relationship.…”

“…Healthcare costs are the comprehensive expenditures of economy, time, energy, and other resources in the process of seeking healthcare treatment, which includes two main sub-categories: providing free consultation services online and reducing the cost of healthcare treatment online ( 19 ). In traditional healthcare treatment, patients have higher healthcare costs, especially with respect to transportation, accommodation, registration, waiting for healthcare treatment, payment, and other costs of long-distance healthcare treatment.…”

A Computational Intelligence Approach for Predicting Medical Insurance Cost

“…Machine learning methods have been widely used to forecast healthcare costs, although the data used varies, such as the Japanese Public Health Insurance Database [19] and nationwide claims database in France [20] which are used in machine learning applications for predicting individual healthcare costs. Ensemble Regression and LR-based healthcare cost insurance prediction are performed in [21].…”

Teknik Data Science di Bidang Kesehatan : Menganalisis Faktor penting Ketidakhadiran Janji Medis Pasien

“…Banyak pasien frustrasi dengan biaya perawatan kesehatan yang terus meningkat di berbagai negara berkembang dan kurangnya transparansi dalam sistem pembayaran medis: harga prosedur dan tes tidak diketahui sebelumnya, berbeda dari satu lokasi ke lokasi lain, dan sering kali tampak tidak proporsional dengan yang sebenarnya. tingkat perawatan yang diterima (Jödicke et al 2019;Nomura et al 2021). Alat untuk membantu menegosiasikan sistem ini dapat memungkinkan pasien menemukan solusi paling ekonomis untuk kebutuhan perawatan kesehatan (Huang et al 2021;Morid et al 2019).…”