A Discrete Event Simulation Model to Manage Bed Usage for Non-Elective Admissions in a Geriatric Medicine Speciality
Over the past decade, the non-elective admissions in the UK have increased significantly. Taking into account limited resources (i.e. beds), the related service managers are obliged to manage their resources effectively due to the non-elective admissions which are mostly admitted to inpatient specialities via A&E departments. Geriatric medicine is one of specialities that have long length of stay for the non-elective admissions. This study aims to develop a discrete event simulation model to understand how possible increases on non-elective demand over the next 12 months affect the bed occupancy rate and to determine required number of beds in a geriatric medicine speciality in a UK hospital. In our validated simulation model, we take into account observed frequency distributions which are derived from a big data covering the period April, 2009 to January, 2013, for the non-elective admission and the length of stay. An experimental analysis, which consists of 16 experiments, is carried out to better understand possible effects of case studies and scenarios related to increase on demand and number of bed. As a result, the speciality does not achieve the target level in the base model although the bed occupancy rate decreases from 125.94% to 96.41% by increasing the number of beds by 30%. In addition, the number of required beds is more than the number of beds considered in the scenario analysis in order to meet the bed requirement. This paper sheds light on bed management for service managers in geriatric medicine specialities.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316285Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 556
 National Health Service England (2018). Bed Availability and Occupancy. https://www.england.nhs.uk/statistics/statistical-work-areas/bed-availability-and-occupancy/, accessed 11 February 2018.
 L. G. Connelly and A. E. Bair, “Discrete event simulation of emergency department activity: a platform for system-level operations,” Academic Emergency Medicine, vol. 11, no. 11, pp. 1177–1185, Nov. 2004.
 M. Gunal and M. Pidd, “Understanding accident and emergency department performance using simulation,” in Proc. 39th Winter Simulation Conference, Monterey, 2006, pp. 446–452.
 D. J. Medeiros, E. Swenson and C. DeFlitch, “Improving patient flow in a hospital emergency department,” in Proc. 41st Winter Simulation Conference, Miami, 2008, pp. 1526–1531.
 A. Ozdagoglu, O. Yalcinkaya and G. Ozdagoglu, “Ege Bölgesindeki bir araştırma ve uygulama hastanesinin acil hasta verilerinin simüle edilerek analizi (A Simulation Based Analysis of a Research and Application Hospital Emergency Patient Data in Aegean Region),” İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, vol. 8, no. 16, pp. 61–73, Dec. 2009.
 A. Virtue, J. Kelly and T. Chaussalet. “Using simplified discrete-event simulation models for health care applications,” in Proc. 44th Winter Simulation Conference, Phoenix, 2011, pp. 1154–1165.
 C. Duguay and F. Chetouane, “Modeling and improving emergency department systems using discrete event simulation,” Simulation, vol. 83, no. 4, pp. 311–320, Apr. 2007.
 L. Y. Meng and T. Spedding, “Modelling patient arrivals when simulating an accident and emergency unit,” in Proc. 41st Winter Simulation Conference, Miami, 2008, pp. 1509–1515.
 J. Wang, J. Li, K. Tussey and K. Ross, “Reducing length of stay in emergency department: A simulation study at a community hospital,” Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transaction, vol. 42, no. 6, pp. 1314–1322, Nov. 2012.
 F. C. Coelli, R. B. Ferreira, R. M. V. R. Almeida and W. C. A. Pereira, “Computer simulation and discrete-event models in the analysis of a mammography clinic patient flow,” Computer Methods and Programs in Biomedicine, vol. 87, no. 3, pp. 201–207, Sep. 2007.
 P. T. VanBerkel and J. T. Blake, “A comprehensive simulation for wait time reduction and capacity planning applied in general surgery,” Health Care Management Science, vol. 10, no. 4, pp. 373–385, Dec. 2007.
 A. Komashie, A. Mousavi and J. Gore, “Using discrete event simulation (DES) to manage theatre operations in healthcare: An audit-based case study,” in Proc. 10th International Conference on Computer Modeling and Simulation, Cambridge, 2008, pp. 360–365.
 J. Bowers and G. Mould, “Ambulatory and orthopaedic capacity planning,” Health Care Management Science, vol. 8, no. 1, pp. 41–47, Feb. 2005.
 S. R. Levin, R. Dittus, D. Aronsky, M. B. Weinger, J. Han, J. Boord and D. France, “Optimizing cardiology capacity to reduce emergency department boarding: a system engineering approach,” American Heart Journal, vol. 158, no. 6, pp. 1202–1209, Dec. 2008.
 N. Ahmad, N. A. Ghani, A. A. Kamil, R. M. Tahar and A. H. Teo, “Evaluating emergency department resource capacity using simulation,” Modern Applied Science, vol. 6, no. 11, pp. 9–19, Nov. 2012.
 E. Demir, M. M. Gunal and D. Southern, “Demand and capacity modelling for acute services using discrete event simulation,” Health Systems, pp. 1–8, Mar. 2016.
 P. Landa, M. Sonnessa, E. Tanfani and A. Testi, “A discrete event simulation model to support bed management,” in Proc. 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, Vienna, 2014, pp. 901–912.
 A. Kumar and J. Mo, “Models for bed occupancy management of a hospital in Singapore,” in Proc. The 2010 International Conference on Industrial Engineering and Operations Management, Dhaka, 2010, pp. 242–247.
 K. S. Mathews and E. F. Long, “A conceptual framework for improving critical care patient flow and bed use,” Annals of the American Thoracic Society, vol. 12, no. 6, pp. 886–894, Jun. 2015.
 E. Martin and K. Haugene, “Proposals to reduce over-crowding, lengthy stays and improve patient care: Study of the geriatric department in Norway’s largest hospital,” in Proc. 35th Winter Simulation Conference, New Orleans, 2003, pp. 1876–1881.
 M. M. Gunal, “A guide for building hospital simulation models,” Health Systems, vol. 1, no. 1, pp. 17–25, Jun. 2012.
 M. Pidd, Computer Simulation in Management Science. Chichester: John Wiley and Sons, 2004, pp. 3–4, 35–36.
 J. Banks, J. S. Carson II, B. L. Nelson and D. M. Nicol, Discrete-Event System Simulation. New Jersey: Pearson, 2005, pp. 5–6, 374–378.
 NHS Digital (2017), Hospital Data Dictionary: Admitted Patient Care. Accessed on 5th February 2018. Available at: http://content.digital.nhs.uk/media/25188/DD-APC-V10/pdf/DD-APC-V10.pdf.
 A. M. Law and W. D. Kelton, Simulation Modeling and Analysis. New York: McGraw-Hill, 2000, pp. 505–515, 519–525.
 National Health Service England (2016). Bed Occupancy Rate. https://www.nhs.uk/Scorecard/Pages/IndicatorFacts.aspx?MetricId=8120, accessed 11 February 2018.