Browse > Article
http://dx.doi.org/10.4332/KJHPA.2020.30.1.82

Medical Service Variation of Urinary Incontinence Surgery and Uterine Polypectomy Using a Multilevel Analysis  

Kim, Sang Me (Department of Big Data Analytics, Ewha Womans University)
Ahn, Bo Ryung (Review and Assessment Research Department, Health Insurance Review and Assessment Service)
Kim, Jeong Lim (Review and Assessment Research Department, Health Insurance Review and Assessment Service)
Lee, Hae Jong (Department of Health Administration, Yonsei University)
Publication Information
Health Policy and Management / v.30, no.1, 2020 , pp. 82-91 More about this Journal
Abstract
Background: This study investigates the influence factors of medical service variations using medical charge and the length of stay (LOS) for urinary incontinence surgery and uterine polypectomy. Methods: The National Health Insurance claims data and Medical Resource Report by the Health Insurance Review & Assessment Service in 2016 were used. Frequency analysis, one-way analysis of variance, and Bonferroni post-hoc tests were executed for each surgery. A multilevel analysis was executed to assess the factors to the medical charge and LOS for each surgery in patient, doctor, and hospital level. Results: Fifty-two point eight percent of urinary incontinence surgery and 87.1% of uterine polypectomy were distributed in general and tertiary hospitals. Among three levels, the patient level variation was 61.5% or 77.2% in medical charge and 93.9% or 96.3% in LOS, respectively. The doctor level variation was 29.6% or 22.6% in medical charge and 0.6% or 0.0% in LOS, respectively. The institution level variation was 8.9% or 0.2% in medical charge and 5.5% or 3.7% in LOS, respectively. Number of other disease and organizational type were main factors that affected the charge and LOS for urinary incontinence surgery and uterine polypectomy. Conclusion: Medical service variations of the urinary incontinence surgery and uterine polypectomy were the largest for the patient level, followed by doctor level for the medical charge, and the institution level for the LOS.
Keywords
Medical service variation; Urinary incontinence surgery; Uterine polypectomy; Medical charge; Length of stay;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Organization for Economic Cooperation and Development. Geographic variations in health care [Internet]. Paris: Organization for Economic Cooperation and Development; 2014 [cited 2019 Sep 13]. Available from: https://www.oecd.org/health/geographic-variationsin-health-care-9789264216594-en.htm.
2 Andersen R, Newman JF. Societal and individual determinants of medical care utilization in the United States. Milbank Mem Fund Q Health Soc 1973;51(1):95-124. DOI: https://doi.org/10.2307/3349613.   DOI
3 Hox JH. Applied multilevel analysis. Amsterdam: TT-Publikaties; 1995.
4 Raudrnbush SW, Beyk AS. Hierarchical liner model. 2nd ed. Thousand Oaks (CA): Sage; 2002.
5 Grieve R, Nixon R, Thompson SG, Normand C. Using multilevel models for assessing the variability of multinational resource use and cost data. Health Econ 2005;14(2):185-196. DOI: https://doi.org/10.1002/hec.916.   DOI
6 Kim Y, Kim TH, Park SK, Balk HM, Lee YS, Jung YG. A study on the optimization of the establishment criteria for the types of medical institutions over the hospital level. Wonju: National Health Insurance Service; 2018.
7 Ahn HS. The effect of hospital, department and physician factors on hospital resource use. Korean J Health Policy Adm 1997;7(1):125-154.
8 Youn KI, Doh SR. An analysis of the diseases specific medical service organization selection factors of patients. Korea J Hosp Manag 2007;12(4):1-21.
9 Kim DR. The effect of having usual source of care on the choice among different types of medical facilities. Health Policy Manag 2016;26(3):195-206. DOI: https://doi.org/10.4332/KJHPA.2016.26.3.195.   DOI
10 Organization for Economic Cooperation and Development. OECD health statistics [Internet]. Paris: Organization for Economic Cooperation and Development; 2018 [cited 2019 Apr 10]. Available from: http://www.oecd.org/els/health-systems/health-data.htm.
11 Kim GH. Establishment of medical delivery system, primary medical activation. Health Policy Forum 2016;14(1):10-13.
12 Kim SY, Kim HS, Park YS. Lee JC. A study on the status of simple surgery by type of medical institution: focused on five specialized departments. Sejong: Korea Institute of Health Policy Institute; 2018.
13 Organization for Economic Cooperation and Development. Strengthening Social Cohesion in Korea (Korean version). Paris: OECD Publishing; 2013. DOI: https://doi.org/10.1787/9789264188938-ko.
14 Shin HY, Ahn HS, Lee CS. Estimation of social welfare loss due to small area variations in health care utilization. Health Soc Welf Rev 2007;27(1):52-80.   DOI
15 Dutton D. Financial, organizational and professional factors affecting health care utilization. Soc Sci Med 1986;23(7):721-735. DOI: https://doi.org/10.1016/0277-9536(86)90121-8.   DOI
16 Goodney PP, Stukel TA, Lucas FL, Finlayson EV, Birkmeyer JD. Hospital volume, length of stay, and readmission rates in high-risk surgery. Ann Surg 2003;238(2):161-167. DOI: https://doi.org/10.1097/01.SLA.0000081094.66659.c3.   DOI
17 Eisenberg JM. Doctor's decisions and the cost of medical care: the reasons for doctors' practice patterns and ways to change them. Ann Arbor (MI): Health Administration Press Perspective; 1986.
18 Jeong EK, Moon OR, Kim CY. A study on the practice variations according to physician characteristics. Korean J Prev Med 1993;26(4):614-627.
19 Ahn HS. The effect of hospital, department and physician factors on hospital resource use. Korean J Health Policy Adm 1997;7(1):125-154.
20 Kim YM, Yang BM. Small area variation in rates of common surgery in general surgery department. Korean J Health Policy Adm 2004;14(2):138-162.   DOI
21 Lee KS. Health insurance theory. Seoul: Gyechuk Munwhasa; 2016.
22 Kang HJ, Hong JS, Cho YY, Oh DK. Current status and direction of medical service variation research. Wonju: Health Insurance Review and Assessment Service; 2012.
23 Bae JY. Surgical departments are omitting from the strengthen primary medical policy. Digital Medical News. 2017 Nov 27.
24 The fall of the Korean surgical system II: is not there a breakthrough? Proceedings of the co-hosted policy debate of the National Assembly & Surgical Society Association; 2018 Apr 24; Seoul, Korea. Seoul: Surgical Society Association; 2018.
25 Kang HJ. OECD medical service mutation expert meeting report. Wonju: Health Insurance Review and Assessment Service; 2013.
26 Statistics Korea. Prevalence (based on doctors' group) and current treatment rates of chronic diseases by gender of the elderly. Daejeon: Statistics Korea; 2017.
27 Antunes A Jr, Costa-Paiva L, Arthuso M, Costa JV, Pinto-Neto AM. Endometrial polyps in pre- and postmenopausal women: factors associated with malignancy. Maturitas 2007;57(4):415-421. DOI: https://doi.org/10.1016/j.maturitas.2007.04.010.   DOI
28 Sosa JA, Mehta PJ, Wang TS, Boudourakis L, Roman SA. A population-based study of outcomes from thyroidectomy in aging Americans: at what cost? J Am Coll Surg 2008;206(6):1097-1105. DOI: https://doi.org/10.1016/j.jamcollsurg.2007.11.023.   DOI
29 Lasmar RB, Dias R, Barrozo PR, Oliveira MA, Coutinho Eda S, da Rosa DB. Prevalence of hysteroscopic findings and histologic diagnoses in patients with abnormal uterine bleeding. Fertil Steril 2008;89(6):1803-1807. DOI: https://doi.org/10.1016/j.fertnstert.2007.05.045.   DOI
30 Kim SM, Hwang SW. Factors influencing high length of stay outlier. Korean Health Econ Rev 2013;19(2):81-96.
31 Diez-Roux AV. Bringing context back into epidemiology: variables and fallacies in multilevel analysis. Am J Public Health 1998;88(2):216-222. DOI: https://doi.org/10.2105/ajph.88.2.216.   DOI
32 Burns LR, Wholey DR. The effects of patient, hospital, and physician characteristics on length of stay and mortality. Med Care 1991;29(3):251-271. DOI: https://doi.org/10.1097/00005650-199103000-00007.   DOI
33 Kim SR. A study on practice variations for hospitalized cataract patient [dissertation]. Seoul: Yonsei University; 1994.
34 Raudenbush SW, Bryk AS. Hierarchical linear models. 2nd ed. Thousand Oaks (CA): Sage; 2002.
35 Snijders TA, Bosker RJ. Multilevel analysis: an introduction to basic and advanced multilevel modeling. 2nd ed. Thousand Oaks (CA): Sage; 2012.
36 Brook RH, Lohr KN. Efficacy, effectiveness, variations, and quality: boundary-crossing research. Med Care 1985;23(5):710-722. DOI: https://doi.org/10.1097/00005650-198505000-00030.   DOI
37 Kim Y, Lee TS, Park SK. KNHI: ATLAS project. Wonju: National Health Insurance Service; 2016.