Sağlık sistemlerinde yöneylem araştırması teknikleri: 2007-2017 yılları arası literatür taraması

Year 2018, Volume: 24 Issue: 1, 153 - 166, 27.02.2018

Gül Didem Batur , Serpil Erol

Abstract

Yöneylem
Araştırması tekniklerinin sağlık alanındaki problemlerin çözümünde kullanımının
son yıllarda dikkat çekici boyutlara ulaştığı görülmektedir. Bu çalışmada,
sağlık sistemlerinde karşılaşılan problemler ele alınmış ve planlama, yönetim
ve uygulama başlıkları altında sınıflandırılmıştır. Bu konularda çalışma yapacak
araştırmacılara yön göstermesi amacıyla, 2007-2017 yılları arasında yayınlanan
çalışmalar, konu başlıklarına göre, çözüm yöntemleri ve gerçek hayat
problemleri üzerindeki uygulamaları açısından değerlendirilmiş ve Yöneylem
Araştırması’nın bu tür problemlerin çözümünde uygulanabilirliği ortaya
koyulmuştur.

Keywords

Sağlık sistemleri, Yöneylem araştırması, Optimizasyon, Literatür araştırması

Operations research in healthcare systems: Literature review of years 2007-2017

Abstract

The
use of Operations Research techniques for problems in healthcare systems is
observed to get remarkable attention in recent years. In this study, problems
encountered in healthcare systems are taken into consideration and are
classified under the headings of planning, management and application. In order
to guide the researchers who will work on these issues, studies published
between the years

2007-2017 are evaluated according to their headings, solution methods and
applications on real life problems, and the applicability of Operation Research
over these problems is presented.

Keywords

Healthcare, Operations research, Optimization, Literature survey

References

• Rais A, Viana A. “Operations research in healthcare: a survey”. International Transactions in Operational Research, 18(1), 1-31, 2011.
• Kulkarni VG. “Stochastic Models in Health Care”. http://www.unc.edu/~vkulkarn/HealthCare.pdf (01.10.2012).
• Brailsford S, Vissers, J. “OR in healthcare: a european perspective”. European Journal of Operational Research, 212(2), 223-234, 2011.
• Mielczarek B, Uziałko-Mydlikowska J. “Application of computer simulation modeling in the health care sector: a survey”. Simulation: Transactions of the Society for Modeling and Simulation International, 88(2), 197-216, 2010.
• Ganguly A, Nandi S. “Using statistical forecasting to optimize staff scheduling in healthcare organizations”. Journal of Health Management, 18(1), 172-181, 2016.
• Kim K, Lee C, O’Leary K, Rosenauer S, Mehrotra S. “Predicting Patient Volumes in Hospital Medicine: A Comparative Study of Different Time Series Forecasting Methods”. Northwestern University, Illinois, USA, Scientific Report, 2014.
• Jalalpour M, Gel Y, Levin S. “Forecasting demand for health services: development of a publicly available toolbox”. Operations Research for Health Care, 5, 1-9, 2015.
• Boutsioli Z. “Hospital costs and unexpected demand: The case of greece”. The Open Economics Journal, 4, 49-58, 2011.
• Renaud-Théry F, Avila-Figueroa C, Stover J, Thierry S, Vitoria M, Habiyambere V, Souteyrand Y. “Utilization patterns and projected demand of antiretroviral drugs in low- and middle-income countries”. AIDS Research and Treatment, 1-8, 2011.
• Afilal M, Yalaoui F, Dugardin F, Amodeo L, Laplanche D, Blua P. “Forecasting the emergency department patients flows”. Journal of Medical Systems, 40, 1-18, 2016.
• Kadri F, Harrou F, Chaabane S, Tahon C. “Time series modelling and forecasting of emergency department overcrowding”. Journal of Medical Systems, 38(9), 1-20, 2014.
• Xu M, Wong TC, Chin KS. “Modeling daily patient arrivals at emergency department and quantifying the relative importance of contributing variables using artificial neural network”. Decision Support Systems, 54, 1488-1498, 2013.
• Shan S, Yoon SW, Khasawneh MT, Gandhi T. “A decision support system for estimating short-term hospital inpatient demands”. 8th International Conference on Service Systems and Service Management (ICSSSM), Tianjin, China, 1-4 June 2011.
• Eitel DR, Rudkin S, Malvehy MA, Killeen JP, Pines J. “Improving service quality by understanding emergency department flow: a white paper and position statement prepared for the american academy of emergency medicine”. The Journal of Emergency Medicine, 38(1), 70-79, 2010.
• Jones S, Evans RS, Allen T, Thomas A, Haug P, Welch S, Snow G. “A multivariate time series approach to modeling and forecasting demand in the emergency department”. Journal of Biomedical Informatics, 42, 123-139, 2009.
• Champion R, Kinsman L, Lee G, Masman K, May E, Mills T, Taylor M, Thomas P, Williams R. “Forecasting emergency department presentations”. Exploring Nosokinetics, 31(1), 83-90, 2007.
• Erikson C, Salsberg E, Forte G, Bruinooge S, Goldstein M. “future supply and demand for oncologists: challenges to assuring access to oncology services”. Journal of Oncology Practice, 3(2), 79-86, 2007.
• Ahmadi-Javid A, Seyedi P, Syam SS. “A survey of healthcare facility location”. Computers and Operations Research, 79, 223-263, 2017.
• Song L, Liu C, Li B. “Optimal selection of location for community hospitals: a case of huilongguan region in beijing”. International Conference on Information and Automation, Lijiang, China, 08-10 August 2015.
• Dehe B, Bamford D. “Development, test and comparison of two multiple criteria decision analysis (MCDA) models: A case of healthcare infrastructure location”. Expert Systems with Applications, 42, 6717-6727, 2015.
• Du G, Sun C. “Location planning problem of service centers for sustainable home healthcare: Evidence from the Empirical Analysis of Shanghai”. Sustainability, 7(12), 15812-15832, 2015.
• Kim JI, Senaratna DM, Ruza J, Kam C, Sandy N. “Feasibility study on an evidence-based decision-support system for hospital site selection for an aging population”. Sustainability, 7, 2730-2744, 2015.
• Vafaei N, Oztaysi B. “Selecting the field hospital place for disasters: A case study in Istanbul”. Joint International Conference of the INFORMS GDN Section and the EURO Working Group on DSS, Toulouse, France, 04-07 June 2014.
• Chiu JE, Tsai HH. “Applying analytic hierarchy process to select optimal expansion of hospital location: The case of a regional teaching hospital in yunlin”. International Conference on Service Systems and Service Management, Hong Kong, China, 12-14 July 2013.
• Chatterjee D, Mukherjee B. “Potential hospital location selection using fuzzy-AHP: An empirical study in rural India”. International Journal of Innovative Technology and Research, 1(4), 304-314, 2013.
• Soltani A, Marandi EZ. “Hospital site selection using two-stage fuzzy multi-criteria decision making process”. Journal of Urban and Environmental Engineering, 5(1), 32-43, 2011.
• Syam SS, Côté MJ. “A location–allocation model for service providers with application to not-for-profit health care organizations”. Omega, 38, 157-166, 2010.
• Vahidnia MH, Alesheikh AA, Alimohammadi A. “Hospital site selection using fuzzy AHP and its derivatives”. Journal of Environmental Management, 90(10), 3048-3056, 2009.
• Paul DP. “Dental practice location: Some aspects of the importance of selection of place”. Health Marketing Quarterly, 14(4), 55-69, 2009.
• Wu CR, Lin CT, Chen HC. “Optimal selection of location for taiwanese hospitals to ensure a competitive advantage by using the analytic hierarchy process and sensitivity analysis”. Building and Environment, 42, 1431-1444, 2007.
• Wu CR, Lin CT, Chen HC. “Integrated environmental assessment of the location selection with fuzzy analytical network process”. Quality and Quantity, 43, 351-380, 2009.
• Aboueljinane L, Sahin E, Jemai Z. “A review on simulation models applied to emergency medical service operations”. Computers and Industrial Engineering, 66, 734-750, 2013.
• Schmid V. “Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming”. European Journal of Operational Research, 219, 611-621, 2012.
• Rajagopalan HK, Saydam C, Xiao J. “A multiperiod set covering location model for dynamic redeployment of ambulances”. Computers and Operations Research, 35(3), 814-826, 2008.
• Pelone F, Kringos DS, Romaniello A, Archibugi M, Salsiri C, Ricciardi W. “Primary care efficiency measurement using data envelopment analysis: a systematic review”. Journal of Medical Systems, 39, 156, 2015.
• Mitrović Z, Vujošević M, Savić G. “Data envelopment analysis for evaluating serbia’s health care system”. Management, 75, 39-46, 2015.
• Wang C, Wang X, Su Q, Du J. “How can hospitals perform more efficiently? A case study in China based on data envelopment analysis”. 12th International Conference on Service Systems and Service Management, Guangzhou, China, 1-4 June 2015.
• Siew LW, Fai LK, Hoe LW. “Evaluation on the efficiency of healthcare companies in malaysia with data envelopment analysis model”. SCIREA Journal of Mathematics, 1(1), 95-106, 2016.
• Shen SL. “Efficiency of the healthcare system in Taiwan: an illustration with multidimensional scaling and data envelopment analysis”. International Journal of Organizational Innovation (Online), 9(3), 48A, 2017.
• Helal SMA, Elimam HA. “Measuring the efficiency of health services areas in Kingdom of Saudi Arabia using data envelopment analysis (DEA): A comparative study between the Years 2014 and 2006”. International Journal of Economics and Finance, 9(4), 172-184, 2017.
• Temür Y. “İllerin gelişmişlik derecelerine göre hastanelerin etkinlik analizi”. Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 29(2), 1-22, 2010.
• Aytekin S. “Yatak işgal oranı düşük olan sağlık bakanlığı hastanelerinin performans ölçümü: bir veri zarflama analizi uygulaması”. Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 1(30), 113-138, 2011.
• Tekin B. “Sağlık hizmeti sistemlerinin etkinliği: bir kamu hastanesi ve özel hastane karşılaştırması”. Çankırı Karatekin Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 6(1), 483-506, 2015.
• Fikar C, Hirsch P. “Home health care routing and scheduling: a review”. Computers and Operations Research, 77, 86-95, 2017.
• Ahmadi-Javid A, Jalali Z, Klassen KJ. “Outpatient appointment systems in healthcare: a review of optimization studies”. European Journal of Operational Research, 258(1), 3-34, 2017.
• Chen PS, Lin YJ, Peng NC. “A two-stage method to determine the allocation and scheduling of medical staff in uncertain environments”. Computers and Industrial Engineering, 99, 174-188, 2016.
• Tassopoulos IX, Solos IP, Beligiannis GN. “Α two-phase adaptive variable neighborhood approach for nurse rostering”. Computers and Operations Research, 60, 150-169, 2015.
• Burke EK, Curtois T. “New approaches to nurse rostering benchmark instances”. European Journal of Operational Research, 237(1), 71-81, 2014.
• Wong TC, Xu M, Chin KS. “A two-stage heuristic approach for nurse scheduling problem: a case study in an emergency department”. Computers and Operations Research, 51, 99-110, 2014.
• Hadwan M, Ayob M, Sabar NR, Qu R. “A harmony search algorithm for nurse rostering problems”. Information Sciences, 233, 126-140, 2013.
• Valouxis C, Gogos C, Goulas G, Alefragis P, Housos E. “A systematic two phase approach for the nurse rostering problem”. European Journal of Operational Research, 219, 2, 425-433, 2012.
• Lü Z, Hao JK. “Adaptive neighborhood search for nurse rostering”. European Journal of Operational Research, 218(3), 865-876, 2012.
• Burke EK, Li J, Qu R. “a pareto-based search methodology for multi-objective nurse scheduling”. Annals of Operations Research, 196(1), 91-109, 2012.
• He F, Qu R. “A constraint programming based column generation approach to nurse rostering problems”. Computers and Operations Research, 39, 3331-3343, 2012.
• Wang W, Gupta D, Potthoff S., “On evaluating the impact of flexibility enhancing strategies on the performance of nurse schedules”. Health Policy, 93, 188-200, 2009.
• Bruni R, Detti P. “A Flexible discrete optimization approach to the physician scheduling problem”. Operations Research for Health Care, 3, 191-199, 2014.
• Jerić SV, Figueira JR. “Multi-Objective scheduling and a resource allocation problem in hospitals”. Journal of Scheduling, 15(5), 513-535, 2012.
• Cardoen B, Demeulemeester E, Beliën J. “Operating room planning and scheduling: a literature review”. European Journal of Operational Research, 201(3), 921-932, 2010.
• Çekiç B. “Ameliyathanelerin çizelgelenmesi, bir karişik tamsayili programlama yaklaşımı”. Verimlilik Dergisi, 2, 7-28, 2015.
• Fei H, Meskens N, Chu C. “A Planning and scheduling problem for an operating theatre using an open scheduling strategy”. Computers and Industrial Engineering, 58(2), 221-230, 2010.
• Coelli F, Ferreira R, Almeida RM, Pereira WC. “Computer simulation and discrete-event models in the analysis of a mammography clinic patient flow”. Computer Methods and Programs in Biomedicine, 87(3), 201-207, 2007.
• Volland J, Fügener A, Schoenfelder J, Brunner JO. “Material logistics in hospitals: a literature review”. Omega, 69, 82-101, 2017.
• Beliën J, Forcé H. “Supply chain management of blood products: A literature review”. European Journal of Operational Research, 217(1), 1-16, 2012.
• Sousa RT, Liu S, Papageorgiou LG, Shah N. “Global supply chain planning for pharmaceuticals”. Chemical Engineering Research and Design, 89(11), 2396-2409, 2011.
• Pedroso MC, Nakano D. “Knowledge and information flows in supply chains: a study on pharmaceutical companies”. International Journal of Production Economics, 122(1), 376-384, 2009.
• Rais A, Alvelos F, Figueiredo J, Nobre A. “Optimization of logistics services in hospitals”. International Transactions in Operational Research, 25(1), 111-132, 2017.
• Longaray A. Ensslin L, Ensslin S, Alves G, Dutra A, Munhoz P. “Using MCDA to evaluate the performance of the logistics process in public hospitals: the case of a Brazilian teaching hospital”. International Transactions in Operational Research, 25(1), 133-136, 2017.
• Štefko R, Nemec J, Kubák M. “The analysis of logistics in selected healthcare facilities and the e-health perspectives in the slovak republic”. The International Journal of Transport and Logistics, 24, 1-8, 2012.
• Villa S, Barbieri M, Lega F. “Restructuring patient flow logistics around patient care needs: Implications and practicalities from three critical cases”. Health Care Management Science, 12(2), 155-165, 2009.
• Zahiri B, Pishvaee MS. “Blood supply chain network design considering blood group compatibility”. International Journal of Production Research, 55(7), 2013-2033, 2017.
• Randa AC, Cömert A, Adigüzel B, Balikçioğlu C, Örnekol C, Bayindir P, Bakal İS. “Türk Kızılayı Orta Anadolu Bölgesi kan ürünleri tedarik zinciri yönetimi projesi”. Journal of Industrial Engineering (Turkish Chamber of Mechanical Engineers), 22(3), 22-70, 2011.
• Silva ML, Perrier L, Cohen JM, Paget WJ, Mosnier A, Späth HM. “A literature review to identify factors that determine policies for influenza vaccination”. Health Policy, 119(6), 697-708, 2015.
• Yarmand H, Ivy JS, Denton B, Lloyd AL. “Optimal two-phase vaccine allocation to geographically different regions under uncertainty”. European Journal of Operational Research, 233(1), 208-219, 2014.
• Mamani H, Chick SE, Simchi-Levi D. “A game-theoretic model of international influenza vaccination coordination”. Management Science, 59(7), 1650-1670, 2013.
• Tang L. Machine Learning Methods for Annual Influenza Vaccine Update. MSc Thesis, University of Waterloo, Ontario, Canada, 2013.
• Araz OM, Lant T, Fowler JW, Jehn M. “Simulation modeling for pandemic decision making: A case study with bi-criteria analysis on school closures”. Decision Support Systems, 55(2), 564-575, 2013.
• Teytelman A, Larson RC. “Modeling influenza progression within a continuous-attribute heterogeneous population”. European Journal of Operational Research, 220(1), 238-250, 2012.
• Araz OM, Fowler JW, Lant TW, Jehn M. “A pandemic influenza simulation model for preparedness planning”. Winter Simulation Conference, Austin, Texas, 05-07 December 2009.
• Larson RC. “Simple models of influenza progression within a heterogeneous population”. Operations Research, 55(3), 399-412, 2007.
• Kim SP, Gupta D, Israni AK, Kasiske BL. “Accept/Decline decision module for the liver simulated allocation model”. Health Care Management Science, 18(1), 35-57, 2015.
• Davis AE, Mehrotra S, Friedewald JJ, Daskin MS, Skaro AI, Abecassis MM, Ladner DP. “Improving geographic equity in kidney transplantation using alternative kidney sharing and optimization modeling”. Medical Decision Making, 35(6)797-807, 2015.
• Bertsimas D, Farias VF, Trichakis N. “Fairness, efficiency, and flexibility in organ allocation for kidney transplantation”. Operations Research, 61(1), 73-87, 2013.
• Akan M, Alagoz O, Ata B, Erenay FS, Said A. “A broader view of designing the liver allocation system” Operations Research, 60(4), 757-770, 2012.
• Kong N, Schaefer AJ, Hunsaker B, Roberts MS. “Maximizing the efficiency of the US liver allocation system through region design”. Management Science, 56(12), 2111-2122, 2010.
• Alagoz O, Maillart LM, Schaefer AJ, Roberts MS. “Determining the acceptance of cadaveric livers using an implicit model of the waiting list”. Operations Research, 55(1), 24-36, 2007.
• Alagoz O, Maillart LM, Schaefer AJ, Roberts MS. “Choosing among living-donor and cadaveric livers”. Management Science, 53(11), 1702-1715, 2007.
• Vieira B, Hans EW, van Vliet-Vroegindeweij C, van de Kamer J, van Harten W. “Operations research for resource planning and -use in radiotherapy: A literature review”. BMC Medical Informatics and Decision Making, 16(1), 149, 2016.
• Lee EK, Wu TL. Encyclopedia of Optimization, Editors: Floudas CA, Pardalos PM. Disease Diagnosis: Optimization-Based Methods, 753-784, Springer, Berlin, 2008.
• Liberatore MJ, Nydick RL. “The analytic hierarchy process in medical and health care decision making: a literature review”. European Journal of Operational Research, 189(1), 194-207, 2008.
• Hong TS, Shang PP, Arumugam M, Yusuff RM. “Use of simulation to solve outpatient clinic problems: a review of the literature”. South African Journal of Industrial Engineering, 24(3), 27-42, 2013.
• Bortfeld T, Ramakrishnan J, Tsitsiklis JN, Unkelbach J. “Optimization of radiation therapy fractionation schedules in the presence of tumor repopulation”. INFORMS Journal on Computing, 27(4), 788-803, 2015.
• Salari E, Unkelbach G, Bortfeld T. “A mathematical programming approach to the fractionation problem in chemoradiotherapy”. IIE Transactions on Healthcare Systems Engineering, 5(2), 55-73, 2015.
• Shi J, Alagoz O, Erenay FS, Su Q. “A survey of optimization models on cancer chemotherapy treatment planning”. Annals of Operations Research, 221(1), 331-356, 2014.
• Werker G, Sauré A, French J, Shechter S. “The use of discrete-event simulation modelling to improve radiation therapy planning processes”. Radiotherapy and Oncology, 92(1), 76-82, 2009.
• Esen S, Çetin E. “Operation research techniques in cancer therapy”. Yönetim, 18(58), 23-27, 2007.
• Lim GJ, Ferris MC, Wright SJ, Shepard DM, Earl MA. “An optimization framework for conformal radiation treatment planning”. INFORMS Journal on Computing, 19(3), 366-380, 2007.
• Kurt M. Dynamic Decision Models for Managing the Major Complications of Diabetes. Phd Thesis, University of Pittsburgh, Pittsburgh, USA, 2012.
• Geng N, Xie X, Augusto V, Jiang Z. “A monte carlo optimization and dynamic programming approach for managing MRI examinations of stroke patients”. IEEE Transactions on Automatic Control, 56(11), 2515-2529, 2011.
• Zhang J, Mason JE, Denton BT, Pierskalla WP. Encyclopedia of Operations Research and Management Science. Editors: Gass SI, Fu MC. Applications of Operations Research to the Prevention, Detection and Treatment of Disease, 437-447, New York, USA, Springer, 2013.