Optimizing Service Provision Process in the Emergency Department Using Value Flow Mapping and Simulation; a Qualitative Study
Iranian Journal of Emergency Medicine,
Vol. 9 No. 1 (2022),
16 March 2022
,
Page e10
https://doi.org/10.22037/ijem.v9i1.38410
Abstract
Introduction: In addition to having high costs, making changes to any of the wards in medical centers requires legal coordination and accepting many risks to prevent a decline in quantity and quality of health care provision. The present study was performed with the aim of evaluating optimization of service provision in the emergency department (ED) using value flow mapping and simulation.
Methods: In the present qualitative study, initially, the processes in the ED of Shohadaye Tajrish hospital were drawn and a graphic model was built for simulating the processes. To perform simulation, in addition to the sequence of activities, the time required for each activity, waiting time, present resources, and etc. were extracted as the inputs of the simulation model. Then, after determining the mean time frames obtained, intended scenarios of the emergency team were executed on the simulated model so that the best scenario can be determined by comparing the outputs of each scenario.
Results: Based on the patient flow map, the patient’s journey in the ED begins from the first visit and continues until discharge. Stages such as asking for consultations or visits by other services, laboratory test requests, imaging requests, and asking for medications are passed during this time. Results of analyzing the flow of 60 patients showed that in this department, the mean time interval between first visit to first laboratory test was 58.5 minutes, mean interval between requesting the first laboratory test and its implementation was 28.4 minutes, mean interval between the first visit and computed tomography (CT) scan request was 45.8 minutes, mean interval between CT scan request and its performance was 16.3 minutes, mean interval between the first visit to asking for medication was 46.2 minutes, and, finally, mean interval between the first visit to discharge was 275 minutes (4 hours 35 minutes). The overall ED flow might be optimized through: having one senior nurse and physician for all patients in a supervision unit; having an information systems that makes observation of capacity and flow in the ED and all the hospital possible; having patient sites that are observable from central positions, while preserving privacy; easy access to acute care unit, radiology, and other acute care spaces; easy access to clinical history of patients from other hospitals and primary care.
Conclusion: It seems that solutions for improving health care provision for patients and increasing patient safety can be reached through drawing patient flow map in the ED and using smart modeling.
- Emergency Service
- Hospital
- Process Assessment
- Health Care
- Computer Simulation
- Patient Navigation
How to Cite
References
2. Montesarchio V, Grimaldi AM, Fox BA, Rea A, Marincola FM, Ascierto PA. Lean oncology: a new model for oncologists. BioMed Central; 2012. p. 1-3.
3. Robinson S, Radnor ZJ, Burgess N, Worthington C. SimLean: Utilising simulation in the implementation of lean in healthcare. European Journal of Operational Research. 2012;219(1):188-97.
4. Papadopoulos T. Continuous improvement and dynamic actor associations: A study of lean thinking implementation in the UK National Health Service. Leadership in Health Services. 2011.
5. Doğan NÖ, Unutulmaz O. Lean production in healthcare: a simulation-based value stream mapping in the physical therapy and rehabilitation department of a public hospital. Total Quality Management & Business Excellence. 2016;27(1-2):64-80.
6. Dickson EW, Singh S, Cheung DS, Wyatt CC, Nugent AS. Application of lean manufacturing techniques in the emergency department. The Journal of emergency medicine. 2009;37(2):177-82.
7. Lummus RR, Vokurka RJ, Rodeghiero B. Improving quality through value stream mapping: A case study of a physician's clinic. Total Quality Management. 2006;17(8):1063-75.
8. Mazur LM, Chen S-JG. Understanding and reducing the medication delivery waste via systems mapping and analysis. Health Care Management Science. 2008;11(1):55-65.
9. Abo-Hamad W, Crowe J, Arisha A, editors. Towards leaner healthcare facility: application of simulation modelling and value stream mapping. Proceedings of the International Workshop on Innovative Simulation for Healthcare (I-WISH) Vienna, Austria; 2012: Citeseer.
10. Mahfouz A, Crowe J, Arisha A, editors. Integrating current state and future state value stream mapping with discrete event simulation: a lean distribution case study. The Third International Conference on Advances in System Simulation (SIMUL 2011); 2011.
11. Standridge CR, Marvel JH, editors. Why lean needs simulation. Proceedings of the 2006 Winter Simulation Conference; 2006: IEEE.
12. Swallmeh E, Tobail A, Abo-Hamad W, Gray J, Arisha A, editors. Integrating simulation modelling and value stream mapping for leaner capacity planning of an emergency department. The Sixth International Conference on Advances in System Simulation; 2014.
13. Khurma N, Bacioiu GM, Pasek ZJ, editors. Simulation-based verification of lean improvement for emergency room process. 2008 Winter Simulation Conference; 2008: IEEE.
14. Weerawat W, Pichitlamken J, Subsombat P. A generic discrete-event simulation model for outpatient clinics in a large public hospital. Journal of healthcare engineering. 2013;4(2):285-305.
15. Ismayyir DK, Haleel AJ. Application of Lean System Principles in Healthcare Services Using Arena Simulation. Journal of University of Babylon for Engineering Sciences. 2019:1-7.
16. Rogers P, Ward L, Salisbury C, Purdy S. Does a general practitioner support unit reduce admissions following medical referrals from general practitioners? Quality in Primary Care. 2011;19(1):23-33.
17. Wright PN, Tan G, Iliffe S, Lee D. The impact of a new emergency admission avoidance system for older people on length of stay and same-day discharges. Age and ageing. 2014;43(1):116-21.
18. Ham C, Imison C, Jennings M. Avoiding hospital admissions: lessons from evidence and experience. London: The King's Fund. 2010.
19. Purdy S, Griffin T, Salisbury C, Sharp D. Prioritizing ambulatory care sensitive hospital admissions in England for research and intervention: a Delphi exercise. Primary Health Care Research & Development. 2010;11(1):41-50.
20. Carter A. The ambulatory care unit at Derriford Hospital. Clinical medicine. 2014;14(3):250.
21. Lattimer V, Burgess A, Knapp F, Dalton S, Brailsford S, Junior E, et al. The impact of changing workforce patterns in emergency and urgent out-of-hours care on patient experience, staff practice and health system performance. 2010.
22. Müller-Engelmann M, Keller H, Donner-Banzhoff N, Krones T. Shared decision making in medicine: the influence of situational treatment factors. Patient education and counseling. 2011;82(2):240-6.
23. O'Hara R, O'Keeffe C, Mason S, Coster JE, Hutchinson A. Quality and safety of care provided by emergency care practitioners. Emergency Medicine Journal. 2012;29(4):327-32.
24. Adams RJ, Smith BJ, Ruffin RE. Patient preferences for autonomy in decision making in asthma management. Thorax. 2001;56(2):126-32.
25. Hensher M, Fulop N, Coast J, Jefferys E. The hospital of the future: better out than in? Alternatives to acute hospital care. BMJ: British Medical Journal. 1999;319(7217):1127.
26. Booker MJ. Patients who call emergency ambulances for'primary care'problems: how are decisions made? Primary Health Care Research and Development: Cambridge University Press; 2012. p. S1-152.
- Abstract Viewed: 196 times
- pdf (فارسی) Downloaded: 110 times