Abstract
The thought process of this book needs to be complemented with guidance about how to use the framework to decide what actually to do, and this in turn requires suitable modelling. A vast range of models is used in healthcare and hospitals—accounting financial analysis, comparative system analysis (e.g. data envelopment analysis), and operational research including simulation of stocks and flows to elucidate capacity (often enumerated in hospitals in terms of bed numbers). These analytical methods do not combine physical and economic processes well, do not address the long term, and fail to connect to the wider health system. The focus of serious quantitative analyses should instead be in the domain of modelling investment appraisal. Models of business should be expressed as business cases, at whole system level, and via optimisation approaches—learning from other process industries.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
As a curiosity, the levels of efficiency of the services analysed for the PPP hospitals were above the mean (cf. Chap. 5).
- 2.
An early example of this calculation was the so-called Hill-Burton model, developed from the eponymous act of 1946 in the US. It was developed to justify augmenting hospital capacity in the South and in areas of deprivation and implicitly with a high black population; it worked. Hill-Burton originally proposed a target bed number per state based on 4.5 beds per 1000 population, but this caused issues in that the national average was anyway only 3.4. From 1963, the calculation was amended to look at population forecasts over five years, “use rate” in terms of patient days per 1000 population, and an occupancy factor. See DEHW (1974 op. cit., p. 4). It is the essentials of this analysis which is widely replicated today in hospital planning everywhere.
- 3.
Such a three-part characterisation of business models, taken from Christiansen, may well be insufficient in many senses, but serves for the moment as an initial thought experiment.
- 4.
The earliest use of this idea was in the “input-output tables” developed from the 1940s (Leontief 1986).
- 5.
Of course, it could be pointed out that a power system produces just one output: kilowatt hours (kWh). However, this is not quite true, since a kWh at 03.00 on a weekend in summer is worth a great deal less than one at 17.00 on a winter weekday. The relevant models at least take this sort of timing and quality issue into account. Hospitals have hundreds—perhaps thousands—of outputs, and this would need gross simplification to be calculable (a traditional hospital NPSV analysis already uses a comparable degree of simplification, but without the connection to the wider healthcare system).
- 6.
The discussion in this section “A Further Reflection: How Other Process Industries Do It” is not intended to be more than outline-illustrative on power system economics, and particularly not in an age of renewable energy such as wind and solar. These raise special problems, because they are high capital cost/zero marginal cost, but cannot be “called” as required; they work when the sun shines or the wind blows, which may not be when the system most needs the power.
- 7.
There is of course a quality angle which needs careful thought. That is, if the new hospital produces better outcomes than other settings, that ought to be taken into account. We acknowledge that, at present, just as a thought experiment, that is being ignored here.
References
Adam, T., Evans, D. B., & Murray, C. J. L. (2003). Econometric Estimation of Country-Specific Hospital Costs. Cost Effectiveness and Resource Allocation, 1, 3. Retrieved December 19, 2018, from http://www.resource-allocation.com/content/1/1/3.
Brailsford, S. C., Lattimer, V. A., Tarnaras, P., & Turnbull, J. C. (2004). Emergency and On-Demand Health Care: Modelling a Large Complex System. Journal of the Operational Research Society, 55(1), 34–42.
Caballer-Tarazona, M., Moya-Clemente, I., Vivas-Consuelo, D., & Barrachina-Martínez, I. (2010). A Model to Measure the Efficiency of Hospital Performance. Mathematical and Computer Modelling, 52, 1095–1102. Retrieved from https://ac.els-cdn.com/S089571771000124X/1-s2.0-S089571771000124X-main.pdf?_tid=76f432ae-c61c-4add-ac37-da815453cfb7&acdnat=1552640153_44ac12bc173625e179c7eefb9a0566b5.
Clissold, A., Filar, J., Mackay, M., Qin, S., & Ward, D. (2015). Simulating Hospital Patient Flow for Insight and Improvement (Sidney: Health Informatics and Knowledge Management). In A. Maeder & J. Warren (Eds.), Health Informatics and Knowledge Management 2015 (HIKM 2015). Conferences in Research and Practice in Information Technology (CRPIT), Vol. 164. Retrieved from https://50years.acs.org.au/content/dam/acs/50-years/journals/crpit/Vol164.pdf.
Cromwell, D. A., Viney, R., Hassall, J., & Hindle, D. (1998). Linking Measures of Health Gain to Explicit Priority Setting by an Area Health Service in Australia. Social Science & Medicine, 47(12), 2067–2074.
Cylus, J., Papanicolas, I., & Smith, P. (2017). How to Make Sense of Health System Efficiency Comparisons? Brussels: European Observatory on Health Systems and Policies, Policy Brief.
Ehrhardt, M., & Brigham, E. (2008). Corporate Finance: A Focused Approach (3rd ed.). p. 131. ISBN 978-0-324-65568-1.
Epstein, D., Chalabi, Z., Claxton, K., & Sculper, M. J. (2005). Mathematical Programming for the Optimal Allocation of Health Care Resources. York: Centre for Health Economics, University of York, Published Online. Retrieved January 10, 2019, from https://www.york.ac.uk/che/pdf/mathprog.pdf.
Erlandsen, E. (2008). Improving the Efficiency of Health Care Spending: What Can Be Learnt from Partial and Selected Analyses of Hospital Performance? (Paris: OECD Publishing). OECD Journal: Economic Studies, 2008(1), 1–33.
Fletcher, A., & Worthington, D. (2009). What Is a ‘Generic’ Hospital Model?—A Comparison of ‘Generic’ and ‘Specific’ Hospital Models of Emergency Patient Flows. Health Care Management Science, 12, 374–391. https://doi.org/10.1007/s10729-009-9108-9.
Gunal, M. M. (2012). A Guide for Building Hospital Simulation Models. Health Systems, 1, 17–25.
Health Services Research Network. (2014). Change by Design: Systems Modelling and Simulation in Healthcare (Published Online) June 2014, Version 1. Retrieved December 28, 2018, from https://mashnet.info/wp-content/files/2016/09/Change-By-Design-Booklet.pdf.
HM Treasury. (2018). The Green Book: Central Government Guidance on Appraisal and Evaluation. London: Her Majesty’s Treasury. Retrieved from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/685903/The_Green_Book.pdf.
Jacobs, R. (2000). Alternative Methods to Examine Hospital Efficiency: Data Envelopment Analysis and Stochastic Frontier Analysis. York: Centre for Health Economics, University of York, Discussion Paper 177, February 2000.
Joumard, I., André, C., & Nicq, C. H. (2010). Health Care Systems: Efficiency and Institutions. Paris: OECD, Economics Department Working Papers, No. 769, ECO/WKP(2010)25.
Karakusevic, S. (2016). Understanding Patient Flow in Hospitals. London: Nuffield Trust. Retrieved from https://www.nuffieldtrust.org.uk/files/2017-01/understanding-patient-flow-in-hospitals-web-final.pdf.
Kivisaari, S., Saranummi, N., & Väyrynen, E. (2004). Knowledge-Intensive Service Activities in Health Care Innovation. Case Pirkana. Tampere: VTT Technology Studies, VTT Research Notes 2267, 2004. Retrieved from https://www.vtt.fi/inf/pdf/tiedotteet/2004/T2267.pdf.
Leontief, W. (1986). Input-Output Economics. New York: Oxford University Press.
Mackay, M., Qin, S., Clissold, A., Hakendorf, P., Ben-Tovim, D., & McDonnell, G. (2013). Patient Flow Simulation Modelling – An Approach Conducive to Multi-Disciplinary Collaboration Towards Hospital Capacity Management. Adelaida: 20th International Congress on Modelling and Simulation, 1–6 December 2013. Retrieved from http://www.mssanz.org.au/modsim2013/A1/mackay.pdf.
Medeiros, J., & Schwierz, C. (2015). Efficiency Estimates of Health Care Systems. European Economy Economic Papers 549, June 2015.
Mohiuddin, S., Busby, J., Savovic, J., Richards, A., Northstone, K., Donovan, J. L., & Vasilakis, C. (2017). Patient Flow Within UK Emergency Departments: A Systematic Review of the Use of Computer Simulation Modelling Methods. BMJ Open, 7(5), e015007. https://doi.org/10.1136/bmjopen-2016-015007.
Montgomery, J. B., & Davis, K. (2013). The Hospital Patient Flow Model: A Simulation Decision Support Tool. New Orleans: Society for Health Systems, 2013 Healthcare Systems Process Improvement Conference Proceedings. Retrieved from https://www.promodel.com/pdf/7.%20Montgomery%20SHS%20Conf%20Paper%20(IEE).pdf.
Mulholland, M. W., Abrahamse, P., & Bahl, V. (2005). Linear Programming to Optimize Performance in a Department of Surgery. Journal of the American College of Surgeons, 200(6), 861–868.
Naylor, C., & Gregory, S. (2009). Briefing: The Independent Sector Treatment Centres. London: The King’s Fund, October 2009. Retrieved from https://www.kingsfund.org.uk/sites/default/files/Briefing-Independent-sector-treatment-centres-ISTC-Chris-Naylor-Sarah-Gregory-Kings-Fund-October-2009.pdf.
Pitt, M., Monks, T., Crowe, S., & Vasilakis, C. (2016). Systems Modelling and Simulation in Health Service Design, Delivery and Decision Making. BMJ Quality and Safety, 25(1), 38–45. https://doi.org/10.1136/bmjqs-2015-004430.
Proudlove, N. C., Black, S., & Fletcher, A. (2007). OR and the Challenge to Improve the NHS: Modelling for Insight and Improvement in In-Patient Flows. Journal of the Operational Research Society, 58, 145–158.
Schull, M. J., Szalai, J. P., Schwartz, B., & Redelmeier, D. A. (2001). Emergency Department Overcrowding Following Systematic Hospital Restructuring: Trends at Twenty Hospitals over Ten Years. Academic Emergency Medicine, 8(11), 1037–1043.
Shepperd, S., Doll, H., Angus, R. M., Clarke, M. J., Iliffe, S., Kalra, L., Ricauda, N. A., Tibaldi, V., & Wilson, A. D. (2009). Avoiding Hospital Admission Through Provision of Hospital Care at Home: A Systematic Review and Meta-Analysis of Individual Patient Data. Canadian Medical Association Journal, 180(2), 175–182.
de Silva, D. (2013). Improving Patient Flow Across Organisations and Pathways. London: The Health Foundation, Evidence Scan No.19.
Steiner, A. (2001). Intermediate Care – A Good Thing? Age and Ageing, 30(S3), 33–39.
Trilling, L., Guinet, A., & Le Magny, D. (2006). Nurse Scheduling Using Integer Linear Programming and Constraint Programming. IFAC Proceedings Volumes, 39(3), 671–676. Retrieved January 10, 2019, from https://ac.els-cdn.com/S1474667015360602/1-s2.0-S1474667015360602-main.pdf?_tid=f57341b5-3bf3-4dad-b602-016443298d50&acdnat=1547163583_34df586882268d16d88cffd5e2cdfc5d.
US Department of Health, Education and Welfare. (1974). Report to the Health Sub-Committee, Committee on Labor and Public Welfare United States Senate by the Comptroller General of the United States. Retrieved January 4, 2019, from https://www.gao.gov/assets/120/113233.pdf.
Vargas-Palacios, A. 2015. Economic Evaluation of Complex Intervention Using Simulation Modelling Techniques. Leeds Institute of Health Sciences, Mimeo.
Wang, L. Y., Haddix, A. C., Teutsch, S. M., & Caldwell, B. (1999). The Role of Resource Allocation Models in Selecting Clinical Preventive Services. The American Journal of Managed Care, 5(4), 445–454.
WHO – World Health Organization. (2000). World Health Report 2000. Health System Improving Performance. Geneva: World Health Organization.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Author(s)
About this chapter
Cite this chapter
Wright, S., Durán, A. (2020). Decision Analysis. In: Durán, A., Wright, S. (eds) Understanding Hospitals in Changing Health Systems. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-28172-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-28172-4_9
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-28171-7
Online ISBN: 978-3-030-28172-4
eBook Packages: Political Science and International StudiesPolitical Science and International Studies (R0)