Am J Perinatol 2015; 32(08): 761-770
DOI: 10.1055/s-0034-1396074
Original Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Capacity Planning for Maternal–Fetal Medicine Using Discrete Event Simulation

Nicole M. Ferraro
1   School of Biomedical Engineering, Science, and Health Systems, Drexel University, Philadelphia, Pennsylvania
,
Courtney B. Reamer
2   Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania
,
Thomas A. Reynolds
3   Center for Fetal Diagnosis and Treatment, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
,
Lori J. Howell
3   Center for Fetal Diagnosis and Treatment, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
,
Julie S. Moldenhauer
3   Center for Fetal Diagnosis and Treatment, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
4   Clinical Obstetrics and Gynecology in Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
,
Theodore Eugene Day
5   Office of Safety and Medical Operations, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
› Author Affiliations
Further Information

Publication History

05 June 2014

02 October 2014

Publication Date:
17 December 2014 (online)

Abstract

Background Maternal–fetal medicine is a rapidly growing field requiring collaboration from many subspecialties. We provide an evidence-based estimate of capacity needs for our clinic, as well as demonstrate how simulation can aid in capacity planning in similar environments.

Methods A Discrete Event Simulation of the Center for Fetal Diagnosis and Treatment and Special Delivery Unit at The Children's Hospital of Philadelphia was designed and validated. This model was then used to determine the time until demand overwhelms inpatient bed availability under increasing capacity.

Findings No significant deviation was found between historical inpatient censuses and simulated censuses for the validation phase (p = 0.889). Prospectively increasing capacity was found to delay time to balk (the inability of the center to provide bed space for a patient in need of admission). With current capacity, the model predicts mean time to balk of 276 days. Adding three beds delays mean time to first balk to 762 days; an additional six beds to 1,335 days.

Conclusion Providing sufficient access is a patient safety issue, and good planning is crucial for targeting infrastructure investments appropriately. Computer-simulated analysis can provide an evidence base for both medical and administrative decision making in a complex clinical environment.

 
  • References

  • 1 Howell LJ. The Garbose Family Special Delivery Unit: a new paradigm for maternal-fetal and neonatal care. Semin Pediatr Surg 2013; 22 (1) 3-9
  • 2 Liley AW. Intrauterine transfusion of foetus in haemolytic disease. BMJ 1963; 2 (5365) 1107-1109
  • 3 Upadhyaya M, Lander A. Advances in fetal surgery. Surgery 2013; 31 (3) 114-118
  • 4 Deprest JA, Flake AW, Gratacos E , et al. The making of fetal surgery. Prenat Diagn 2010; 30 (7) 653-667
  • 5 Adzick NS. Prospects for fetal surgery. Early Hum Dev 2013; 89 (11) 881-886
  • 6 Ben Bachouch R, Guinet A, Hajri-Gabouj S. An integer linear model for hospital bed planning. Int J Prod Econ 2012; 140 (2) 833-843
  • 7 Harrison GW, Shafer A, Mackay M. Modelling variability in hospital bed occupancy. Health Care Manage Sci 2005; 8 (4) 325-334
  • 8 Cochran JK, Roche K. A queuing-based decision support methodology to estimate hospital inpatient bed demand. J Oper Res Soc 2008; 59 (11) 1471-1482
  • 9 Green L. Queueing analysis in healthcare. In: Patient Flow: Reducing Delay in Healthcare Delivery. US, New York: Springer; 2006: 281-307
  • 10 Marmor YN, Rohleder TR, Cook DJ, Huschka TR, Thompson JE. Recovery bed planning in cardiovascular surgery: a simulation case study. Health Care Manage Sci 2013; 16 (4) 314-327
  • 11 Ponis ST, Delis A, Gayialis SP, Kasimatis P, Tan J. Applying discrete event simulation (des) in healthcare: the case for outpatient facility capacity planning. Int J Healthc Inf Syst Inform 2013; 8 (3) 58-79
  • 12 Reynolds M, Vasilakis C, McLeod M , et al. Using discrete event simulation to design a more efficient hospital pharmacy for outpatients. Health Care Manage Sci 2011; 14 (3) 223-236
  • 13 Kunkel A, McLay LA. Determining minimum staffing levels during snowstorms using an integrated simulation, regression, and reliability model. Health Care Manage Sci 2013; 16 (1) 14-26
  • 14 McHugh ML. Computer simulation as a method for selecting nurse staffing levels in hospitals. In: MacNair EA, Musselman KI, Heidelberger P, , eds. Proceedings of the 1989 Winter Simulation Conference. 1989: 1121-1129
  • 15 Rossetti M, Trzcinski G, Syverud S. Emergency department simulation and determination of optimal attending physician staffing schedules. In: P. A. Farrington, H. B. Nembhard, D. T. Sturrock, and G. W. Evans, Eds. Proceedings of the 1999 Winter Simulation Conference. 1999: 1532-1540
  • 16 Duguay C, Chetouane F. Modeling and improving emergency department systems using discrete event simulation. Simulation 2007; 83 (4) 311-320
  • 17 McGuire F. Using simulation to reduce length of stay in emergency departments. In: Tew JD, Manivannan S, Sadowski DA, Seila AF, , eds. Proceedings of the 1994 Winter Simulation Conference. 1994: 861-867
  • 18 Vemuri S. Simulated analysis of patient waiting time in an outpatient pharmacy. Am J Hosp Pharm 1984; 41 (6) 1127-1130
  • 19 Al-Araidah O, Boran A, Wahsheh A. Reducing Delay In Healthcare Delivery At Outpatients Clinics Using Discrete Event Simulation. International Journal of Simulation Modelling 2012; 11 (4) 185-195
  • 20 Day TE, Mehrotra A, Ravi N. A Novel Use for Real Time Locating Systems: Discrete Event Simulation Validation in Medical Systems. Int J Healthc Deliv Reform Initiatives 2010; 2 (3) 11-19
  • 21 Günal MM, Pidd M. Discrete event simulation for performance modelling in health care: a review of the literature. J Simulation 2010; 4: 42-51
  • 22 Jun JB, Jacobson SH, Swisher JR. Application of discrete-event simulation in health care clinics: A survey. J Oper Res Soc 1999; 50 (2) 109-123
  • 23 Zhu Z, Hen BH, Teow KL. Estimating ICU bed capacity using discrete event simulation. Int J Health Care Qual Assur 2012; 25 (2) 134-144
  • 24 Rau CL, Tsai PFJ, Liang SFM , et al. Using discrete-event simulation in strategic capacity planning for an outpatient physical therapy service. Health Care Manage Sci 2013; 16 (4) 352-365
  • 25 Karnon J, Stahl J, Brennan A, Caro JJ, Mar J, Möller J. Modeling using discrete event simulation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-4. Med Decis Making 2012; 32 (5) 701-711
  • 26 Eddy DM, Hollingworth W, Caro JJ, Tsevat J, McDonald KM, Wong JB ; ISPOR-SMDM Modeling Good Research Practices Task Force. Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7. Med Decis Making 2012; 32 (5) 733-743
  • 27 Cochran JK, Bharti A. Stochastic bed balancing of an obstetrics hospital. Health Care Manage Sci 2006; 9 (1) 31-45
  • 28 Jahangirian M, Taylor SJR, Young T. Economics of modeling and simulation: reflections and implications. In B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yücesan, eds. Proceedings of the 2010 Winter Simulation Conference. 2010: 2283-2292
  • 29 Ritchie K, Bradbury I, Slattery J, Wright D, Iqbal K, Penney G. Economic modelling of antenatal screening and ultrasound scanning programmes for identification of fetal abnormalities. BJOG 2005; 112 (7) 866-874