Mahmoud Elbattah
ABSTRACT
Healthcare systems are increasingly challenged by the phenomenal growth of population ageing. Healthcare executives are, and will be, in an inevitable need of evidence-based artifacts for decision making. The paper addresses issues in the context of discharge planning for elderly patients with application to hip fracture care in Ireland. A hybrid approach is embraced that integrates simulation modeling with machine learning in an attempt to improve the validity of the simulation model outputs. In terms of simulation modeling, a discrete event simulation model is used to model the elderly patient's journey through the care scheme of hip fracture. In tandem with the simulation model, predictive models are used to guide the simulation model. Specifically, the predictive models are used to make predictions on the inpatient length of stay and discharge destination of simulation-generated patients. On a population basis, the simulation model provides demand predictions for healthcare resources related to discharge destinations, with a focus on long-stay care such as nursing homes. Our results suggest that there may be a need to reconsider the geographic distribution of nursing homes within particular areas in Ireland in order to keep abreast of the foreseen shift in demographics. Furthermore, the incorporation of machine learning within simulation modeling is claimed to improve the predictive power of the simulation model.
- Cohen, K.J. and Cyert, R.M., 1965. Simulation of organizational behavior. Handbook of Organizations, Rand McNally.Google Scholar
- Ashby, W.R., 1970. Analysis of the system to be modeled. The process of model building in the behavioral sciences, pp.94--114.Google Scholar
- Stanislaw, H., 1986. Tests of computer simulation validity: what do they measure?. Simulation & Games. Google ScholarDigital Library
- Gershenson, C., 2001. Complex philosophy. arXiv preprint nlin/0109001.Google Scholar
- Abu-Taieh, E.M. ed., 2009. Handbook of Research on Discrete Event Simulation Environments: Technologies and Applications: Technologies and Applications. IGI Global. Google ScholarDigital Library
- https://www.noca.ie/irish-hip-fracture-database, accessed on 26/12/2015.Google Scholar
- Barga, R., Fontama, V. and Tok, W.H., 2015. Predictive Analytics with Microsoft Azure Machine Learning. Google ScholarDigital Library
- O'Keefe, G.E., Jurkovich, G.J. and Maier, R.V., 1999. Defining excess resource utilization and identifying associated factors for trauma victims. Journal of Trauma and Acute Care Surgery, 46(3), pp.473--478.Google ScholarCross Ref
- Englert, J., Davis, K.M. and Koch, K.E., 2001. Using clinical practice analysis to improve care. Joint Commission Journal on Quality and Patient Safety, 27(6), pp.291--301.Google Scholar
- Guru, V., Anderson, G.M., Fremes, S.E., O'Connor, G.T., Grover, F.L., Tu, J.V. and Consensus, C.C.S.Q.I., 2005. The identification and development of Canadian coronary artery bypass graft surgery quality indicators. The Journal of thoracic and cardiovascular surgery, 130(5), pp.1257-e1.Google ScholarCross Ref
- Brasel, K.J., Lim, H.J., Nirula, R. and Weigelt, J.A., 2007. Length of stay: an appropriate quality measure?. Archives of Surgery, 142(5), pp.461--466.Google ScholarCross Ref
- Japkowicz, N. and Stephen, S., 2002. The class imbalance problem: A systematic study. Intelligent data analysis, 6(5), pp.429--449. Google ScholarDigital Library
- Yang, Q. and Wu, X., 2006. 10 challenging problems in data mining research. International Journal of Information Technology & Decision Making, 5(04), pp.597--604.Google ScholarCross Ref
- Japkowicz, N., 2000, July. Learning from imbalanced data sets: a comparison of various strategies. In AAAI workshop on learning from imbalanced data sets (Vol. 68, pp. 10--15).Google Scholar
- Hodge, V.J. and Austin, J., 2004. A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), pp.85--126. Google ScholarDigital Library
- Sarsfield, S., 2009. The data governance imperative. IT Governance Publishing. Google ScholarDigital Library
- Johansen, A., Wakeman, R., Boulton, C., Plant, F., Roberts, J. and Williams, A., 2013. National Hip Fracture Database: National Report 2013. Clinical Effectiveness and Evaluation Unit at the Royal College of Physicians.Google Scholar
- Altmann, A., Toloşi, L., Sander, O. and Lengauer, T., 2010. Permutation importance: a corrected feature importance measure. Bioinformatics, 26(10), pp.1340--1347. Google ScholarDigital Library
- Law, A.M. and Kelton, W.D., 1991. Simulation modeling and analysis, 1991. New York: McGrae-Hill Inc. Google ScholarDigital Library
- http://www.cso.ie/en/statistics/population/Google Scholar
- Shannon, R.E., 1975. Systems simulation: the art and science (Vol. 1). Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
- British Orthopaedic Association and British Geriatric Society, The Care of Patients with Fragility Fracture ("Blue Book"), BritishOrthopaedic Association and BritishGeriatric Society, London,UK, 2007.Google Scholar
- http://www.hse.ie/eng/services/publications/corporate/CHO_Chapter_1.pdfGoogle Scholar
- Dodds, M.K., Codd, M.B., Looney, A. and Mulhall, K.J., 2009. Incidence of hip fracture in the Republic of Ireland and future projections: a population-based study. Osteoporosis international, 20(12), pp.2105--2110.Google ScholarCross Ref
- Rosenberg, M. and Everitt, J., 2001. Planning for aging populations: inside or outside the walls. Progress in Planning, 56(3), pp.119--168.Google ScholarCross Ref
- Lechler, T. and Page, B., 1999, October. DESMO-J: An object oriented discrete simulation framework in Java. In Proceedings of the 11th European Simulation Symposium (pp. 46--50).Google Scholar
- Law, A.M., 2009, December. How to build valid and credible simulation models. In Simulation Conference (WSC), Proceedings of the 2009 Winter (pp. 24--33). IEEE. Google ScholarDigital Library
- Martis, M.S., 2006. Validation of simulation based models: a theoretical outlook. The Electronic Journal of Business Research Methods, 4(1), pp.39--46.Google Scholar
- Lloyd-Sherlock, P., 2000. Population ageing in developed and developing regions: implications for health policy. Social science & medicine, 51(6), pp.887--895.Google Scholar
- United Nations. World Population Ageing 2007 (United Nations, New York, 2007).Google ScholarCross Ref
- Christensen, K., Doblhammer, G., Rau, R. and Vaupel, J.W., 2009. Ageing populations: the challenges ahead. The Lancet, 374(9696), pp. 1196--1208.Google ScholarCross Ref
- Lutz, W., Sanderson, W. and Scherbov, S., 2008. The coming acceleration of global population ageing. Nature, 451(7179), pp. 716--719.Google ScholarCross Ref
- Rechel, B., Grundy, E., Robine, J.M., Cylus, J., Mackenbach, J.P., Knai, C. and McKee, M., 2013. Ageing in the European union. The Lancet, 381(9874), pp. 1312--1322.Google ScholarCross Ref
- European Commission. 2009 Ageing report: economic and budgetary projections for the EU-27 Member States (2008-2060). Luxembourg: Office for Official Publications of the European Communities, 2009.Google Scholar
- Health Service Executive. Annual Report and Financial Statements 2014, 2014.Google Scholar
- https://www.hiqa.ie/social-care/find-a-centre/nursing-homesGoogle Scholar
- Cooper, C., Campion, G. and Melton Iii, L.J., 1992. Hip fractures in the elderly: a world-wide projection. Osteoporosis international, 2(6), pp.285--289.Google ScholarCross Ref
- Melton, L.J., 1996. Epidemiology of hip fractures: implications of the exponential increase with age. Bone, 18(3), pp.S121--S125.Google ScholarCross Ref
- Gullberg, B., Johnell, O. and Kanis, J.A., 1997. World-wide projections for hip fracture. Osteoporosis international, 7(5), pp.407--413.Google ScholarCross Ref
- Ellanti, P., Cushen, B., Galbraith, A., Brent, L., Hurson, C. and Ahern, E., 2014. Improving Hip Fracture Care in Ireland: A Preliminary Report of the Irish Hip Fracture Database. Journal of osteoporosis, 2014.Google Scholar
- Ragab, M., Abo-Hamad, W. and Arisha, A., 2012. Capacity Planning for Elderly Care in Ireland Using Simulation Modeling.Google Scholar
- Rashwan, W., Ragab, M., Abo-Hamad, W. and Arisha, A., 2013, December. Evaluating policy interventions for delayed discharge: a system dynamics approach. In Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World (pp. 2463--2474). IEEE Press. Google ScholarDigital Library
- http://www.hpo.ie/hipe/hipe_data_dictionary/HIPE_Data_Dictionary_2015_V7.0.pdfGoogle Scholar
- http://www.hse.ie/eng/services/publications/olderpeople/Executive_Summary_-_Strategy_to_Prevent_Falls_and_Fractures_in_Ireland%E2%80%99s_Ageing_Population.pdfGoogle Scholar
- Breiman, L., 2001. Random forests. Machine learning, 45(1), pp.5--32. Google ScholarDigital Library
Index Terms
- Coupling Simulation with Machine Learning: A Hybrid Approach for Elderly Discharge Planning
Recommendations
Data-driven patient segmentation using K-means clustering: the case of hip fracture care in Ireland
ACSW '17: Proceedings of the Australasian Computer Science Week MulticonferenceMachine learning continues to forge the future of decision making in a broad diversity of domains including healthcare. Data-driven methods are increasingly geared towards leveraging evidence-based insights from large volumes of patient data. In this ...
Modeling and simulation of oncology clinic operations in discrete event system specification
Oncology clinics are often burdened with scheduling large volumes of cancer patients for chemotherapy under limited resources, such as nurses and chemotherapy chairs. Chemotherapy is a cancer treatment method that is administered orally or intravenously ...
Discrete Event Simulation and Real Time Locating Systems: Technology Integration for Process Improvement
The Veteran's Health Administration VHA is the largest integrated health care system in the United States, forming the arm of the Department of Veterans Affairs VA that delivers medical services. From a troubled past, the VHA today is regarded as a ...
Comments