Literature DB >> 30811679

Patients with minor diseases who access high-tier medical care facilities: New evidence from classification and regression trees.

Chung Jen Yang1, Ying Che Tsai1, Joseph J Tien2.   

Abstract

OBJECTIVES: Patients in Taiwan's National Health Insurance (NHI) program can choose a medical care facility of any tier for outpatient visits, without a referral. However, this system results in high medical expenditures and costs of outpatient visits. In this study, patients who had only minor diseases but who accessed high-tier medical care facilities were investigated using classification and regression trees.
METHODS: For this study, data were obtained from the Taiwan NHI Research Database. First, 280 diseases, coded according to the Clinical Classification Software (CCS), were examined to determine whether patients chose the most appropriate facility when seeking medical care. After controlling for the CCS codes, an investigation into the types of patients who visit high-tier medical care facilities was conducted.
RESULTS: Chronic disease status and CCS code were critical for constructing the classification trees. Male patients living in urban areas and earning a higher income were more likely to access high-tier medical care facilities. However, changes to the NHI copayment policies have significantly reduced the probability of utilizing high-tier medical care facilities.
CONCLUSIONS: Factors relevant to patients' selection of high-tier medical care facilities were identified. Overall, increasing patients' out-of-pocket payments significantly reduced the probability of accessing high-tier medical facilities.
© 2019 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Clinical Classification Software (CCS); classification and regression tree (CART) model; outpatient visit; referral system

Year:  2019        PMID: 30811679     DOI: 10.1002/hpm.2745

Source DB:  PubMed          Journal:  Int J Health Plann Manage        ISSN: 0749-6753


  2 in total

1.  Why people select the outpatient clinic of medical centers: a nationwide analysis in Taiwan.

Authors:  Ming-Hwai Lin; Hsiao-Ting Chang; Tzeng-Ji Chen; Shinn-Jang Hwang
Journal:  PeerJ       Date:  2020-08-27       Impact factor: 2.984

2.  Mathematical Modeling and Computational Prediction of High-Risk Types of Human Papillomaviruses.

Authors:  Junchao Zhang; Kechao Wang
Journal:  Comput Math Methods Med       Date:  2022-07-21       Impact factor: 2.809

  2 in total

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