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장기입원 의료급여 환자의 재원일수에 미치는 영향요인: 요양병원 입원유형 중심으로
윤은지 ( Eun Ji Yun ),이요셉 ( Yo Seb Lee ),홍미영 ( Mi Yeong Hong ),박미숙 ( Mi Sook Park ) 한국보건행정학회 2021 보건행정학회지 Vol.31 No.2
Background: In Korea, the length of stay and medical expenses incurred by medical aid patients are increasing at a rate faster than the national health insurance. Therefore, there is a need to create a management strategy for each type of hospitalization to manage the length of stay of medical aid patients. Methods: The study used data from the 2019 National Health Insurance Claims. We analyzed the factors that affect the length of stay for 186,576 medical aid patients who were hospitalized for more than 31 days, with a focus on the type of hospitalization in long-term care hospitals. Results: The study found a significant correlation between gender, age, medical aid type, chronic disease ratio, long-term care hospital patient classification, and hospitalization type variables as factors that affect the length of hospital stay. The analysis of the differences in the length of stay for each type of hospitalization showed that the average length of stay is 291.4 days for type 1, 192.9 days for type 2, and 157.0 days for type 3, and that the difference is significant (p<0.0001). When type 3 was 0, type 1 significantly increased by 99.4 days, and type 2 by 36.6 days (p<0.0001). Conclusion: A model that can comprehensively view factors, such as provider factors and institutional factors, needs to be designed. In addition, to reduce long stays for medical aid patients, a mechanism to establish an early discharge plan should be prepared and concerns about underutilization should be simultaneously addressed.
영상진단 수가 변화가 의료공급자 진료행태에 미치는 영향: 전산화단층영상진단 검사건수를 중심으로
조수진 ( Su-jin Cho ),김동환 ( Donghwan Kim ),윤은지 ( Eun-ji Yun ) 한국보건행정학회 2018 보건행정학회지 Vol.28 No.2
Background: Diagnostic imaging fee had been reduced in May 2011, but it was recovered after 6 months because of strong opposition of medical providers. This study aimed to analyze the behavior of medical providers according to fee changes. Methods: The National Health Insurance claims data between November 2010 and December 2012 were used. The number of exams per computed tomography was analyzed to verify that the fee changes increased or decreased the number of exams. Multivariate regression model were applied. Results: The monthly number of exams increased by 92.5% after fee reduction, so the diagnostic imaging spending were remained before it. But medical provider decreased the number of exams after fee return. After adjusting characteristic of hospitals, fee reduction increased the monthly number of exams by 48.0% in a regression model. Regardless type of hospitals and severity of disease, the monthly number of exams increased during period of fee reduction. The number of exams in large-scaled hospitals (tertiary and general hospital) were increased more than those of small-scaled hospitals. Conclusion: Fee-reduction increased unnecessary diagnostic exams under the fee-for-service system. It is needed to define appropriate exam and change reimbursement system on the basis of guideline.