title: Estimating the Ratio of Patients with a Certain Disease Between Hospitals for the Allocation of Patients to Clinical Trials Using Health Insurance Claims Data in Japan.
abstract: In clinical trials, investigating the ratio of patients with each disease who are treated in a hospital is important for determining the number of patients who are allocated to hospitals. The Japanese health insurance claims data includes standardized disease and medicine data. However, the disease data has some problems in terms of reliability, because the healed diseases are sometimes not deleted or because a disease that a patient does not actually have is registered to claim the cost of the examination. On the other hand, therapeutic medicines are administered to target particular diseases. In this study, we developed a system for estimating the number of patients with each disease using the disease data and the therapeutic medicine data. We converted the ICD-10 code to a 4-grade classification code so that we could predict the diseases in the shallow layer (e.g. gastrointestinal disease) when it was difficult to predict the precise diseases in the deep layer (e.g. gastric ulcers). A table showing the disease code and the corresponding therapeutic medicine code was provided by the Japan Pharmaceutical Information Center (JAPIC). We calculated the disease probability score from the diseases and therapeutic medicines and recorded the predicted disease. For the system evaluation, we used the health insurance claims data from Osaka University Hospital for January 2015. A total of 58,526 diseases were predicted from the health insurance claims data of 18,393 patients. One hundred twenty patients were randomly extracted for use in a chart review that was performed by an expert physician. Two hundred twenty-four of 329 predicted diseases, were correctly predicted; 56 were reasonably predicted, and 49 were incorrectly predicted. The main disease was correctly predicted in 71 patients. In conclusion, we could estimate the number of patients with each disease using the health insurance claims data with a certain degree of accuracy.
authors: Department of Medical Informatics, Osaka University Graduate School of Medicine.
authors: Toshihiro
authors: T
authors: Takeda
authors: Department of Medical Informatics, Osaka University Graduate School of Medicine.
authors: Naoki
authors: N
authors: Mihara
authors: Division of Medical Informatics, Osaka University Hospital.
authors: Taizo
authors: T
authors: Murata
authors: Department of Medical Informatics, Osaka University Graduate School of Medicine.
authors: Yoshie
authors: Y
authors: Shimai
authors: Department of Medical Informatics, Osaka University Graduate School of Medicine.
authors: Katsuki
authors: K
authors: Okada
authors: Department of Medical Informatics, Osaka University Graduate School of Medicine.
authors: Shiro
authors: S
authors: Manabe
authors: Department of Medical Informatics, Osaka University Graduate School of Medicine.
authors: Yasushi
authors: Y
authors: Matsumura
mesh_terms: D059019:Clinical Coding / Q000706:statistics & numerical data*; D002986:Clinical Trials as Topic; D005260:Female; D006785:Hospitals, University; D006801:Humans; D007348:Insurance, Health / Q000706:statistics & numerical data*; D038801:International Classification of Diseases; D007564:Japan; D008297:Male; D004364:Pharmaceutical Preparations / Q000008:administration & dosage*; D012189:Retrospective Studies
chemical_list: D004364:Pharmaceutical Preparations