Operational research as implementation science: definitions, challenges and research priorities
Tóm tắt
Operational research (OR) is the discipline of using models, either quantitative or qualitative, to aid decision-making in complex implementation problems. The methods of OR have been used in healthcare since the 1950s in diverse areas such as emergency medicine and the interface between acute and community care; hospital performance; scheduling and management of patient home visits; scheduling of patient appointments; and many other complex implementation problems of an operational or logistical nature. To date, there has been limited debate about the role that operational research should take within implementation science. I detail three such roles for OR all grounded in upfront system thinking: structuring implementation problems, prospective evaluation of improvement interventions, and strategic reconfiguration. Case studies from mental health, emergency medicine, and stroke care are used to illustrate each role. I then describe the challenges for applied OR within implementation science at the organisational, interventional, and disciplinary levels. Two key challenges include the difficulty faced in achieving a position of mutual understanding between implementation scientists and research users and a stark lack of evaluation of OR interventions. To address these challenges, I propose a research agenda to evaluate applied OR through the lens of implementation science, the liberation of OR from the specialist research and consultancy environment, and co-design of models with service users. Operational research is a mature discipline that has developed a significant volume of methodology to improve health services. OR offers implementation scientists the opportunity to do more upfront system thinking before committing resources or taking risks. OR has three roles within implementation science: structuring an implementation problem, prospective evaluation of implementation problems, and a tool for strategic reconfiguration of health services. Challenges facing OR as implementation science include limited evidence and evaluation of impact, limited service user involvement, a lack of managerial awareness, effective communication between research users and OR modellers, and availability of healthcare data. To progress the science, a focus is needed in three key areas: evaluation of OR interventions, embedding the knowledge of OR in health services, and educating OR modellers about the aims and benefits of service user involvement.
Tài liệu tham khảo
Pitt M, Monks T, Crowe S, Vasilakis C. Systems modelling and simulation in health service design, delivery and decision making. BMJ Qual Saf. 2015. doi:10.1136/bmjqs-2015-004430.
Ackoff RL. The future of operational research is past. J Oper Res Soc. 1979;30(2):93–104. doi:10.2307/3009290.
Royston G. One hundred years of operational research in health—UK 1948-2048[star]. J Oper Res Soc. 2009;60(1):169–79.
Lane DC, Monefeldt C, Rosenhead JV. Looking in the wrong place for healthcare improvements: a system dynamics study of an accident and emergency department. J Oper Res Soc. 2000;51(5):518–31. doi:10.2307/254183.
Günal MM, Pidd M. Understanding target-driven action in emergency department performance using simulation. Emerg Med J. 2009;26(10):724–7. doi:10.1136/emj.2008.066969.
Fletcher A, Halsall D, Huxham S, Worthington D. The DH accident and emergency department model: a national generic model used locally. J Oper Res Soc. 2007;58(12):1554–62.
Knight VA, Harper PR. Modelling emergency medical services with phase-type distributions. HS. 2012;1(1):58–68.
Monks T, Pitt M, Stein K, James MA. Hyperacute stroke care and NHS England’s business plan. BMJ. 2014;348. doi: 10.1136/bmj.g3049.
Monks T, Pitt M, Stein K, James M. Maximizing the population benefit from thrombolysis in acute ischemic stroke: a modeling study of in-hospital delays. Stroke. 2012;43(10):2706–11. doi:10.1161/strokeaha.112.663187.
Lahr MMH, van der Zee D-J, Luijckx G-J, Vroomen PCAJ, Buskens E. A simulation-based approach for improving utilization of thrombolysis in acute brain infarction. Med Care. 2013;51(12):1101–5. doi:10.1097/MLR.0b013e3182a3e505.
Monks T, Pearn K, Allen M. Simulating stroke care systems. In: Yilmaz L, et al, editors. Proceedings of the 2015 Winter Simulation Conference. Piscataway, New Jersey: IEEE; 2015. p. 1391–1402. doi:10.1109/WSC.2015.7408262.
Jun J, Jacobson S, Swisher J. Application of discrete-event simulation in health care clinics: a survey. J Oper Res Soc. 1999;50(2):109–23.
Harper PR, Shahani AK, Gallagher JE, Bowie C. Planning health services with explicit geographical considerations: a stochastic location–allocation approach. Omega. 2005;33(2):141–52. doi:10.1016/j.omega.2004.03.011.
Gallivan S, Utley M, Treasure T, Valencia O. Booked inpatient admissions and hospital capacity: mathematical modelling study. BMJ. 2002;324(7332):280–2. doi:10.1136/bmj.324.7332.280.
Brailsford SC, Lattimer VA, Tarnaras P, Turnbull JC. Emergency and on-demand health care: modelling a large complex system. J Oper Res Soc. 2004;55(1):34–42.
Gunal MM. A guide for building hospital simulation models. Health Syst. 2012;1(1):17–25. doi:10.1057/hs.2012.8.
Bertels S, Fahle T. A hybrid setup for a hybrid scenario: combining heuristics for the home health care problem. Comput Oper Res. 2006;33(10):2866–90. doi:10.1016/j.cor.2005.01.015.
Gupta D, Denton B. Appointment scheduling in health care: challenges and opportunities. IIE Trans. 2008;40(9):800–19. doi:10.1080/07408170802165880.
Foy R et al. Implementation science: a reappraisal of our journal mission and scope. Implement Sci. 2015;10(1):1–7. doi:10.1186/s13012-015-0240-2.
Atkinson J-A, Page A, Wells R, Milat A, Wilson A. A modelling tool for policy analysis to support the design of efficient and effective policy responses for complex public health problems. Implement Sci. 2015;10(1):26.
Pitt M, Monks T, Allen M. Systems modelling for improving healthcare. In: Richards D, Rahm Hallberg I, editors. Complex interventions in health: an overview of research methods. London: Routledge; 2015.
Westcombe M, Alberto Franco L, Shaw D. Where next for PSMs—a grassroots revolution? J Oper Res Soc. 2006;57(7):776–8.
Mingers J, Rosenhead J. Problem structuring methods in action. Eur J Oper Res. 2004;152(3):530–54. http://dx.doi.org/10.1016/S0377-2217(03)00056-0.
Kotiadis K, Mingers J. Combining PSMs with hard OR methods: the philosophical and practical challenges. J Oper Res Soc. 2006;57(7):856–67. doi:10.1057/palgrave.jors.2602147.
Penn ML, Kennedy AP, Vassilev II, Chew-Graham CA, Protheroe J, Rogers A, Monks T. Modelling self-management pathways for people with diabetes in primary care. BMC Fam Pract. 2015;16(1):1–10. doi:10.1186/s12875-015-0325-7.
Vennix JAM. Group model-building: tackling messy problems. Syst Dyn Rev. 1999;15(4):379–401.
Cooke MW, Wilson S, Halsall J, Roalfe A. Total time in English accident and emergency departments is related to bed occupancy. Emerg Med J. 2004;21(5):575–6. doi:10.1136/emj.2004.015081.
Utley M, Worthington D. Capacity planning. In: Hall R, editor. Handbook of Healthcare System Scheduling. New York: Springer; 2012.
Robinson S. Simulation: the practice of model development and use. London: Wiley; 2004.
National Institute of Clinical Excellence, Stroke. In: NICE Clinical Guideline, editor. Diagnosis and initial management of acute stroke and transient ischemic attack (TIA). 2008.
Smith HK, Harper PR, Potts CN, Thyle A. Planning sustainable community health schemes in rural areas of developing countries. Eur J Oper Res. 2009;193(3):768–77. doi:10.1016/j.ejor.2007.07.031.
Franco AL, Lord E. Understanding multi-methodology: evaluating the perceived impact of mixing methods for group budgetary decisions. Omega. 2010;39:362–72.
Katsaliaki K, Mustafee N. Applications of simulation within the healthcare context. J Oper Res Soc. 2011;62(8):1431–51.
Günal M, Pidd M. Discrete event simulation for performance modelling in health care: a review of the literature. J Simul. 2011;4:42–51.
Fone D et al. Systematic review of the use and value of computer simulation modelling in population health and health care delivery. J Public Health. 2003;25(4):325–35. doi:10.1093/pubmed/fdg075.
Brailsford SC, Harper PR, Patel B, Pitt M. An analysis of the academic literature on simulation and modelling in health care. J Simul. 2009;3(3):130–40.
Monks T, Pearson M, Pitt M, Stein K, James MA. Evaluating the impact of a simulation study in emergency stroke care. Oper Res Health Care. 2015;6:40–9. http://dx.doi.org/10.1016/j.orhc.2015.09.002.
Pagel C et al. Real time monitoring of risk-adjusted paediatric cardiac surgery outcomes using variable life-adjusted display: implementation in three UK centres. Heart. 2013;99(19):1445–50. doi:10.1136/heartjnl-2013-303671.
Brailsford SC et al. Overcoming the barriers: a qualitative study of simulation adoption in the NHS. J Oper Res Soc. 2013;64(2):157–68.
Walsh M, Hostick T. Improving health care through community OR. J Oper Res Soc. 2004;56(2):193–201.
Pearson M et al. Involving patients and the public in healthcare operational research—the challenges and opportunities. Oper Res Health Care. 2013;2(4):86–9. http://dx.doi.org/10.1016/j.orhc.2013.09.001.
Jahangirian M, Taylor SJE, Eatock J, Stergioulas LK, Taylor PM. Causal study of low stakeholder engagement in healthcare simulation projects. J Oper Res Soc. 2015;66(3):369–79. doi:10.1057/jors.2014.1.
Young T, Eatock J, Jahangirian M, Naseer A, Lilford R. Three critical challenges for modeling and simulation in healthcare. In: Simulation Conference (WSC), Proceedings of the 2009 Winter. 2009.
Seila AF, Brailsford S. Opportunities and challenges in health care simulation. In: Alexopoulos C, Goldsman D, Wilson JR, editors. Advancing the Frontiers of Simulation. US: Springer; 2009. p. 195–229.
Jahangirian M, Eldabi T, Naseer A, Stergioulas LK, Young T. Simulation in manufacturing and business: a review. Eur J Oper Res. 2010;203(1):1–13. doi:10.1016/j.ejor.2009.06.004.
Churchman CW, Schainblatt AH. The researcher and the manager: a dialectic of implementation. Manag Sci. 1965;11(4):69–87. doi:10.2307/2628012.
Willemain TR. Model formulation: what experts think about and when. Oper Res. 1995;43(6):916–32. doi:10.1287/opre.43.6.916.
Pidd M, Woolley RN. A pilot study of problem structuring. J Oper Res Soc. 1980;31(12):1063–8. doi:10.2307/2581818.
Tako AA, Kotiadis K. PartiSim: a multi-methodology framework to support facilitated simulation modelling in healthcare. Eur J Oper Res. 2015;244(2):555–64. http://dx.doi.org/10.1016/j.ejor.2015.01.046.
Franco LA, Hämäläinen RP. Behavioural operational research: returning to the roots of the OR profession. Eur J Oper Res. 2016;249(3):791–5. http://dx.doi.org/10.1016/j.ejor.2015.10.034.
Gogi A, Tako AA, Robinson S. An experimental investigation into the role of simulation models in generating insights. Eur J Oper Res. 2016;249(3):931–44. http://dx.doi.org/10.1016/j.ejor.2015.09.042.
Monks T, Robinson S, Kotiadis K. Learning from discrete-event simulation: exploring the high involvement hypothesis. Eur J Oper Res. 2014;235(1):195–205. http://dx.doi.org/10.1016/j.ejor.2013.10.003.
Monks T, Robinson S, Kotiadis K. Can involving clients in simulation studies help them solve their future problems? A transfer of learning experiment. Eur J Oper Res. 2016;249(3):919–30. http://dx.doi.org/10.1016/j.ejor.2015.08.037.
Pitt M, Davies R, Brailsford SC, Chausselet T, Harper PR, Worthington D, Pidd M, Bucci G. Developing competence in modelling and simulation for commissioning and strategic planning. A guide for commissioners. 2009 [cited 2016 07/01/2016]; Available from: http://mashnet.info/wp-content/files/CurriculumInModellingAndSimulation4Commissioning.pdf.
Naseer A, Eldabi T, Young TP. RIGHT: a toolkit for selecting healthcare modelling methods. J Sim. 2010;4(1):2–13.