Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지
DOI QR코드

DOI QR Code

Healthcare Optimization : Current Status and Vitalization Suggestions

의료서비스 최적화 : 현황 및 활성화 방안

  • Kang, Sung-Hong (Department of Health Policy and Management, Inje University) ;
  • Kim, Byung-In (Department of Industrial and Management Engineering, POSTECH) ;
  • Jun, Chi-Hyuck (Department of Industrial and Management Engineering, POSTECH) ;
  • Choi, Byung Kwan (Department of Neurosurgery, School of Medicine, Pusan National University) ;
  • Lee, Shin-Ho (Korea Health Industry Development Institute, Bureau of Health Industry Policy)
  • 강성홍 (인제대학교 보건행정학과) ;
  • 김병인 (포항공과대학교 산업경영공학과) ;
  • 전치혁 (포항공과대학교 산업경영공학과) ;
  • 최병관 (부산대학교 의과대학 신경외과) ;
  • 이신호 (한국보건산업진흥원)
  • Received : 2013.01.02
  • Accepted : 2013.03.27
  • Published : 2013.08.15
Download PDF
⟨ Previous Next ⟩

Abstract

Healthcare optimization is mandatory to strengthen the competitiveness of domestic healthcare industry. Healthcare optimization aims to increase service quality, patient safety, and system efficiency. This paper reviews various healthcare optimization cases of developed countries, synopsizes the current status of domestic healthcare industry, points out several reasons why healthcare optimization is not active in Korea, and suggests some vitalization ways.

Keywords

References

  1. Abdel-Aal, R. E. and Mangoud, A. M. (1998), Modeling and forecasting monthly patient volume at a primary health care clinic using univariate time-series analysis, Computer Methods and Programs in Biomedicine, 56, 235-247. https://doi.org/10.1016/S0169-2607(98)00032-7
  2. Abraham, G., Byrnes, G. B., and Bain, C. A. (2009), Short-term forecasting of emergency inpatient flow, IEEE Transactions on Information Technology in Biomedicine, 13(3), 380-388. https://doi.org/10.1109/TITB.2009.2014565
  3. Ahmed, A. and Amagoh, F. (2008), Modeling hospital resources with process oriented simulation, Central Asia Business, 1(1), 5-20.
  4. Ahn, J.-H. and Hornberger, J. C. (1996), Involving patients in the cadaveric kidney transplant allocation process : a decision-theoretic perspective, Management Science, 42(5), 629-641. https://doi.org/10.1287/mnsc.42.5.629
  5. Bartolozzi, F., de Gaetano, A., Lena, E. D., Marino, S., Nieddu, L. and Patrizi, G. (2000), Operational research techniques in medical treatment and diagnosis : a review, European Journal of Operational Research, 121, 435-466. https://doi.org/10.1016/S0377-2217(99)00017-X
  6. Bellazzi, R. and Zupan, B. (2008), Predictive data mining in clinical medicine : current issues and guidelines, International Journal of Medical Informatics, 77(2), 81-97. https://doi.org/10.1016/j.ijmedinf.2006.11.006
  7. Bertels, S. and Fahle, T. (2006), A hybrid setup for a hybrid scenario : combining heuristics for the home health care problem, Computers and Operations Research, 33, 2866-2890. https://doi.org/10.1016/j.cor.2005.01.015
  8. Bertsimas, D., Bjarnadottir, M. V., Kane, M. A., Kryder, J. C., Pandey, R., Vempale, S., and Wang, G. (2008), Algorithmic prediction of health-care costs, Operations Research, 56(6), 1382-1392. https://doi.org/10.1287/opre.1080.0619
  9. Bolton, R. J. and Hand, D. J. (2002), Statistical fraud detection : a review, Statistical Science, 17(3), 235-249. https://doi.org/10.1214/ss/1042727940
  10. Bowers, J. and G. Mould. (2005), Ambulatory care and orthopaedic capacity planning, Health Care Management Science, 8, 41-47. https://doi.org/10.1007/s10729-005-5215-4
  11. Brailsford, S. C., Harper, R. R., and Sykes, J. (2012), Incorporating human behaviour in simulation models of screening for breast cancer, European Journal of Operational Research, 219, 491-507. https://doi.org/10.1016/j.ejor.2011.10.041
  12. Brandeau, M. L., Sainfort, F., and Pierskalla, W. P. (Eds) (2004), Operations Research and Health Care, A Handbook of Methods and Applications, Kluwer's International Series, Dordrecht.
  13. Burke, E. K., Curtois, T., Post, G., Qu, R., and Veltman, B. (2008), A hybrid heuristic ordering and variable neighborhood search for the nurse rostering problem, European Journal of Operational Research, 188, 330-341. https://doi.org/10.1016/j.ejor.2007.04.030
  14. Cardoen, B. and Demeulemeester, E. (2007), Evaluating the capacity of clinical pathways through discrete-event simulation, KU Leuven Department of Decision Sciences and Information Management Working Paper No. KBI 0712, 1-23.
  15. Cardoen, B., Demeulemeester, E., and Belien, J. (2010), Operating room planning and scheduling : a literature review, European Journal of Operational Research, 201, 921-932. https://doi.org/10.1016/j.ejor.2009.04.011
  16. Carrigan, M. D. and Kujawa, D. (2006), Six sigma in health care management and strategy, Health Care Manager, 25(2), 133-141. https://doi.org/10.1097/00126450-200604000-00006
  17. Chang, H. and Lee, T. (2010), A study on effectiveness of simulation optimization for outpatient appointment scheduling, Proc. Joint Conf. of KIIE/KORMS, Jeju RamaDa Hotel.
  18. Cheng, E. and Rich, J. L. (1998), A Home Health Care Routing and Scheduling Problem, Technical Report 98-04, Computational and Applied Mathematics, Rice University.
  19. Cho, I.-S. and Kim, K.-A. (2011), Electronic Health Records-based clinical decision support system, Journal of the Korean Institute of Information Scientists and Engineers, 29(2), 92-100.
  20. Chung, H. (2011), Korean National Health Accounts and Total Health Expenditure in 2010, Ministry of Health and Welfare and Yonsei Institute of Health and Welfare.
  21. Cios, K. J. and Moore, G. W. (2002), Uniqueness of medical data mining, Artificial Intelligence in Medicine, 26(1/2), 1-24. https://doi.org/10.1016/S0933-3657(02)00049-0
  22. Committee on Advancement of Health Security (2010), Future Strategy for Health Security Advancement : 2010 Committee Report, National Health Insurance Service.
  23. Committee on Quality of Health Care in America (2001), Crossing the Quality Chasm : a New Health Systems for the 21st Century, Institute of Medicine, National Academy Press, Washing, D. C.
  24. Cooper, W. W. (1999), Operational research/management science : where it's been. where it should be going?, Journal of the Operational Research Society, 50(1), 3-11.
  25. Copeland, L., Edberg, D., and Wendel, J. (2012), Applying business intelligence concepts to medicaid claim fraud detection, Journal of Information Systems Applied Research, 5(1), 51-61.
  26. Creemers, S., Belien, J., and Lambrecht, M. (2012), The optimal allocation of server time slots over different classes of patients, European Journal of Operational Research, 219, 508-521. https://doi.org/10.1016/j.ejor.2011.10.045
  27. Davies, R., Brailsford, S. C., Roderick, P. J., Canning, C. R., and Crabbe, D. N. (2000), Using simulation modelling for evaluating screening services for diabetic retinopathy, The Journal of the Operational Research Society, 51(4), 476-484. https://doi.org/10.1057/palgrave.jors.2600890
  28. De Koning, H., Verver, J. P. S., van den Heuvel, J., Bisgaard, S., and Does, R. J. M. M. (2006), Lean six sigma in healthcare, Journal for Healthcare Quality, 28, 4-11.
  29. Diefenbach, M. and Kozan, E. (2008), Hospital emergency department simulation for resource analysis, Industrial Engineering and Management Systems, 7(2), 133-142.
  30. Eveborn, P., Flisberg, P., and Ronnqvist, M. (2006), LAPS CARE-an operational system for staff planning of home care, European Journal of Operational Research, 171, 962-976. https://doi.org/10.1016/j.ejor.2005.01.011
  31. Fetter, R. B. and Thompson, J. D. (1965), The simulation of hospital systems, Operations Research, 13, 689-711. https://doi.org/10.1287/opre.13.5.689
  32. Finarelli Jr, H. J. and Johnson, T. (2004), Effective demand forecasting in 9 steps, Helathcare Financial Management, 58(11), 52-58.
  33. Fletcher, A., Halsall, D., Huxham, S. and Worthington, D. (2006), The DH accident and emergency department model-a national generic model used locally, Journal of the Operational Research Society, 58, 1554-1562.
  34. Hall, R. (Ed.) (2006), Patient Flow : Reducing Delay in Healthcare Delivery, International Series in Operations Research and Management Science, 91.
  35. Hall, R. (Ed.) (2012), Handbook of Healthcare System Scheduling, International Series in Operations Research and Management Science, 168.
  36. Hall, S. N., Jacobson, S. H., and Sewell, E. C. (2008), An analysis of pediatric vaccine formulary selection problems, Operations Research, 56(6), 1348-1365. https://doi.org/10.1287/opre.1080.0612
  37. Han, J., Lee, K., Ahn, M., and Lee, T. (2011), Process improvement of emergency roll using Lean six sigma and simulation, Health and Social Welfare Review, 31(4), 454-477. https://doi.org/10.15709/hswr.2011.31.4.454
  38. Hans, E., Wullink, G., van Houdenhoven, M., and Kazemier, G. (2008), Robust surgery loading, European Journal of Operational Research, 185, 1038-1050. https://doi.org/10.1016/j.ejor.2006.08.022
  39. Hauskrecht, M. and Fraser, H. (2000), Planning treatment of ischemic heart disease with partially observable Markov decision processes, Artificial Intelligence in Medicine, 18, 221-244. https://doi.org/10.1016/S0933-3657(99)00042-1
  40. Herrin, A. N. (1995), Operations Research for Program Planning and Management, The Population Council.
  41. Isken, M. W. and Rajagopalan, B. (2002), Data mining to support simulation modeling of patient flow in hospitals, Journal of Medical Systems, 26(2), 179-197. https://doi.org/10.1023/A:1014814111524
  42. Izady, N. and Worthington, D. (2012), Setting staffing requirements for time dependent queuing networks : the case of accident and emergency departments, European Journal of Operational Research, 219, 531-540. https://doi.org/10.1016/j.ejor.2011.10.040
  43. Jacobson, S. H., Hall, S. N., and Swisher, J. R. (2006), Discrete-event simulation of health care systems, Patient Flow : Reducing Delay in Healthcare Delivery, International Series in Operations Research and Management Science, 91, 211-252. https://doi.org/10.1007/978-0-387-33636-7_8
  44. Johnson, M. P. and Smilowitz, K. (2008), Community-based operations research, OR/MS Today.
  45. Jones, S. S., Thomas, A., Evans, R. S., Welch, S. J., Haug, P. J., and Snow, G. L. (2008), Forecasting daily patient volumes in the emergency department, Academic Emergency Medicine, 15(2), 159-170. https://doi.org/10.1111/j.1553-2712.2007.00032.x
  46. Jun, J. B., Jacobson, S. H., and Swisher, J. R. (1999), Application of discrete- event simulation in health care clinics : a survey, Journal of the Operational Research Society, 50, 109-123. https://doi.org/10.1057/palgrave.jors.2600669
  47. KB Financial Group (2011), Domestic health industry status and financial operation analysis.
  48. Kim, M., Chate, A., and Phillips, M. H. (2012), A stochastic control formalism for dynamic biologically conformal radiation therapy, European Journal of Operational Research, 219, 541-556. https://doi.org/10.1016/j.ejor.2011.10.039
  49. Koeleman, P. M., Bhulai, S., and van Meersbergen, M. (2012), Optimal patient and personnel scheduling policies for care-at-home service facilities, European Journal of Operational Research, 219, 557-563. https://doi.org/10.1016/j.ejor.2011.10.046
  50. Kohn, L.T., Corrigan, J. M., and Donaldson, M. S. (Eds.) (2000), To Err Is Human : Building a Safer System, Institute of Medicine, National Academy Press, Washing, D. C.
  51. Kolker, A. and Story, P. (Eds.) (2011), Management Engineering for Effective Healthcare Delivery : Principles and Applications, Medical Information Science Reference.
  52. Kong, G., Xu, D.-L., Body, R., Yang, J.-B., Machway-Jones, K., and Carley, S. (2012), A belief rule-based decision support system for clinical risk assessment of cardiac chest pain, European Journal of Operational Research, 219, 564-573. https://doi.org/10.1016/j.ejor.2011.10.044
  53. Koo, J., Lee, G., Lee, J., Li, H., and Kim, B. (2012), Internet-based generic simulation model for outpatient clinics, Journal of the Korean Operations Research and Management Science Society, 40(2), 408-417.
  54. Kusters, R. J. and Groot, P. M. A. (1996), Modelling resource availability in general hospitals Design and implementation of a decision support model, European Journal of Operational Research, 88, 428-445. https://doi.org/10.1016/0377-2217(95)00201-4
  55. Lee, K. (2005), Medical Security and Medical System, Kyechukmunhwasa.
  56. Lee, S. (2010), Domestic and Foreign Trend of Patient Safety, the 19th Health Insurance Review and Assessment Service Forum.
  57. Lee, Y. and Lee, T. (2010), A comprehensive study on patient flow improvement solutions and their implementation strategies in an outpatient system, IE Interfaces, 23(1), 1-11.
  58. Lefevre, C. (1981), Optimal control of a birth and death epidemic process, Operations Research, 29(5), 971-982. https://doi.org/10.1287/opre.29.5.971
  59. Li, J., Huang, K.-Y., Jin, J., and Shi, J. (2008), A survey on statistical methods for health care fraud detection, Health Care Management Science, 11(3), 275-287. https://doi.org/10.1007/s10729-007-9045-4
  60. Li, W., Liu, K., Li, S., and Yang, H. (2010), A semiotic multi-agent modeling approach for clinical pathway management, Journal of Computers, 5(2), 266-273.
  61. Lienhardt, C. and Cobelens, F. G. J. (2011), Operational research for improved tuberculosis control : the scope, the needs and the way forward, The International Journal of Tuberculosis and Lung Disease, 15(1), 6-13. https://doi.org/10.5588/ijtld.11.0158
  62. Lim, J.-H., Kang, S.-H., and Kim, W.-J. (2012), Patient management through simulation modeling in the medical center, The Journal of Digital Policy and Management, 10, 287-295.
  63. Litvak, N., van Rijsbergen, M., Boucherie, R. J., and van Houdenhoven, M. (2008), Managing the overflow of intensive care patients, European Journal of Operational Research, 185, 998-1010. https://doi.org/10.1016/j.ejor.2006.08.021
  64. Loechl, C., Ruel, M. T., Pelto, G., and Menon, P. (2005), The Use of Operations Research as a Tool for Monitoring and managing Food-Assisted Maternal/Child Health and Nutrition(MCHN) Programs : An Example from Haiti, International Food Policy Research Institute.
  65. Lubicz, M. (Eds.)(2009), Operational Research Applied to Health Services in Action, Oficyna Wydawnicza Politechniki Wroclawskiej Wroclaw.
  66. Major, J. A. and Riedinger, D. R. (2002), EFD : A hybrid knowledge/statistical-based system for the detection of fraud, Journal of Risk and Insurance, 69(3), 309-324. https://doi.org/10.1111/1539-6975.00025
  67. Martin, K. E., Rogal, D. L., and Arnold, S. B. (2004), Health-Based Risk Assessment : Risk-Adjusted Payments and Beyond, Academy Health, Washington.
  68. Ministry of Health and Welfare (2011), Health Expenditure Report for 2010-2020.
  69. Moreno, L., Aguilar, R. M., Martin, C. A., Pineiro, J. D., Estevez, J. I., Sigut, J. F., and Sanchez, J. L. (2000), Patient-centered simulation to aid decision-making in hospital management, Simulation, 74, 290-304. https://doi.org/10.1177/003754970007400504
  70. Najmuddin, A. F., Ibrahim, I. M., and Ismail, S. R. (2010), A simulation approach : improving patient waiting time for multiphase patient flow of obstetrics and gynecology department in local specialist centre, Wseas Transactions on Mathematics, 9(10), 778-790.
  71. Nickel, S., Schroder, M., and Steeg, J. (2012), Mid-term and short-term planning support for home health care services, European Journal of Operational Research, 219, 574-587. https://doi.org/10.1016/j.ejor.2011.10.042
  72. OECD (2011), Health at a Glance 2011 : OECD Indicators, OECD Publishing.
  73. Park, H. (2002), Healthcare industry and Industrial engineering : hospital sector, IE Magazine, 9(2), 20-21.
  74. Park, K., Choi, I., Ji, W., Park, H., and Shin, H. (2008), Chronic delinquency index for hospital bill claim, Proc. 2008 Conf. of KIIE.
  75. Pitt, M., Dodds, S., Bensley, D., Royston, G., and Stein, K. (2009), The potential for operational research, British Journal of Healthcare Management, 15(1), 346-351.
  76. Raikundalia, G. K., Mastan, M., and Bain, C. A. (2011), A web-based visual simulator for hospital management using discrete event simulation, Research Journal of Information Technology, 3, 55-67. https://doi.org/10.5507/jtie.2011.010
  77. Rais, A. and Viana, A. (2010), Operations research in healthcare : a survey, International Transactions in Operational Research, 18, 1-31.
  78. Rasmussen, M. S., Justesen, T., Dohn, A., and Larsen, J. (2012), The home care crew scheduling problem : preference-based visit clustering and temporal dependencies, European Journal of Operational Research, 219, 598-610. https://doi.org/10.1016/j.ejor.2011.10.048
  79. Reid, P. P., Compton, W. D., Grossman, J. H., and Fanjiang, G. (Eds.) (2005), Building a Better Delivery System : A New Engineering/Health Care Partnership, National Academies Press.
  80. Revere, L. and Black, K. (2003), Integrating six sigma with total quality management : a case example for measuring medication errors, Journal of Healthcare Management, 48(6), 377-391. https://doi.org/10.1097/00115514-200311000-00007
  81. Royston, G. (2011), Meeting global health challenges through operational research and management science, Bulletin of the World Health Organization, 89, 683-688. https://doi.org/10.2471/BLT.11.086066
  82. Sainfort, F., Blake, J., Gupta, D., and Rardin, R. L. (2005), Operations Research for Healthcare Delivery Systems, World Technology Evaluation Center, Inc., Boston.
  83. Schaefer, A. J., Bailey, M. D., Shechter, S. M., and Roberts, M. S. (2005), Modeling medical treatment using Markov decision processes, International Series in Operations Research and Management Science, 70(4), 593-612. https://doi.org/10.1007/1-4020-8066-2_23
  84. Schweigler, L. M., Desmond, J. S., McCarthy, M. L., Bukowski, K. J., Ionides, E. L. and Younger, J. G. (2009), Forecasting models of emergency department crowding, Academic Emergency Medicine, 16(4), 301-308. https://doi.org/10.1111/j.1553-2712.2009.00356.x
  85. Sermeus, W., Linda, H. A., van den Heede, K. et al. (2011), Nurse forecasting in Europe (RN4CAST) : rationale, design and methodology, BMC Nursing, 10(6), http://www.biomedcentral.com/1472-6955/10/6.
  86. Sibbel, R. and Urban, C. (2001), Agent-based modeling and simulation for hospital management, in : Saam, N. and Schmidt, B. (eds.) : Cooperative Agents, Kluwer Academic Publishers, Boston.
  87. Sundaramoorthi, D., Chen, V. C. P., Rosenberger, J. M., Kim, S. and Buckley-Behan, D. F. (2009), A data-integrated simulation model to evaluate nurse-patient assignments, Health Care Management Science, 12, 252-268. https://doi.org/10.1007/s10729-008-9090-7
  88. The Korea Economic Daily (2012), Best Hospital it is me.
  89. The Global Fund (2010), Framework for Operations and Implementation Research in Health and Disease Control Programs, The Global Fund.
  90. USAID (2008), Using Operations Research to Enhance Delivery of Postpartum/Postabortion Family Planning Services in the Arab Region, Frontiers in Reproductive Health(FRONTIERS) Population Council.
  91. Valdez, R. S., Ramly, E. and Brennan, P. F. (2010), Industrial and Systems Engineering and Health Care : Critical Areas of Research-Final Report (Prepared by Professional and Scientific Associates under Contract No. 290-09-00027U), AHRQ Publication No. 10-0079, Rockville, MD : Agency for Healthcare Research and Quality.
  92. van den Heuvel, J., Does, R. J. M. M., and Verver, J. P. S. (2005), Six sigma in healthcare : lessons learned from a hospital, International Journal of Six Sigma and Competitive Advantage, 1(4), 380-388. https://doi.org/10.1504/IJSSCA.2005.008504
  93. van Dijk, N. M. and Kortbeek, N. (2009), Erlang loss bounds for OT-ICU systems, Queueing System, 63, 253-280. https://doi.org/10.1007/s11134-009-9149-2
  94. van Houdenhoven, M., Hans, E. W., Klein, J., Wullink, G., and Kazemier, G. (2007), A norm utilisation for scarce hospital resources : evidence from operating rooms in a Dutch university hospital, Journal of Medical Systems, 31(4), 231-236. https://doi.org/10.1007/s10916-007-9060-5
  95. van Houdenhoven, M., van Oostrum, J. M., Wullink, G., Hans, E., Hurink, J. L., Bakker, J., and Kazemier, G. (2008), Fewer intensive care unit refusals and a higher capacity utilization by using a cyclic surgical case schedule, Journal of Critical Care, 23, 222-226. https://doi.org/10.1016/j.jcrc.2007.07.002
  96. van Oostrum, J. M., Bredenhoff, E., and Hans, E. W. (2008), Managerial Implications and Suitability of a Master Surgical Scheduling Approach, Econometric Institute.
  97. van Oostrum, J. M., van Houdenhoven, M., Vrielink, M. M. J., Klein, J., Hans, E. W., Klimek, M., Wullink, G., Steyerberg, E. W., and Kazemier, G. (2008), A simulation model for determining the optimal size of emergency teams on call in the operating room at night, International Anesthesia Research society, 107(5), 1655-1662.
  98. Woodard, T. D. (2005), Addressing variation in hospital quality: is six sigma the answer?, Journal of Healthcare Management, 50(4), 226-236. https://doi.org/10.1097/00115514-200507000-00005
  99. Zonderland, M. E., Boer, F., Boucherie, R. J., de Roode, A., and van Kleef, J. W. (2009), Redesign of a university hospital preanesthesia evaluation clinic using a queuing theory approach, International Anesthesia Research Society, 109(5), 1612-1621.
  100. Zonderland, M. E., Boucherie, R. J., Litvak, N., and Vleggeert-Lankamp, C. L. (2010), Planning and scheduling of semi-urgent surgeries, Health Care Management Science, 13, 256-267. https://doi.org/10.1007/s10729-010-9127-6
  101. Zonderland, M. E. and Timmer, J. (2012), Optimal allocation of MRI scan capacity among competing hospital department, European Journal of Operational Research, 219, 630-637. https://doi.org/10.1016/j.ejor.2011.10.036

Cited by

  1. Epidemic Disease Spreading Simulation Model Based on Census Data vol.40, pp.2, 2014, https://doi.org/10.7232/JKIIE.2014.40.2.163