Capacity and patient flow planning in post-term pregnancy outpatient clinics: a computer simulation modelling study.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read Online Read More Add to Saved list
  • Additional Information
    • Source:
      Publisher: BioMed Central Country of Publication: England NLM ID: 101088677 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6963 (Electronic) Linking ISSN: 14726963 NLM ISO Abbreviation: BMC Health Serv Res Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : BioMed Central, [2001-
    • Subject Terms:
      Hospital Bed Capacity*; Outpatient Clinics, Hospital/*organization & administration ; Patient Care Planning/*organization & administration; Computer Simulation ; Female ; Health Services Research ; Humans ; Norway ; Pregnancy
    • Abstract:
      Background: The demand for a large Norwegian hospital's post-term pregnancy outpatient clinic has increased substantially over the last 10 years due to changes in the hospital's catchment area and to clinical guidelines. Planning the clinic is further complicated due to the high did not attend rates as a result of women giving birth. The aim of this study is to determine the maximum number of women specified clinic configurations, combination of specified clinic resources, can feasibly serve within clinic opening times.
      Methods: A hybrid agent based discrete event simulation model of the clinic was used to evaluate alternative configurations to gain insight into clinic planning and to support decision making. Clinic configurations consisted of six factors: X0: Arrivals. X1: Arrival pattern. X2: Order of midwife and doctor consultations. X3: Number of midwives. X4: Number of doctors. X5: Number of cardiotocography (CTGs) machines. A full factorial experimental design of the six factors generated 608 configurations.
      Results: Each configuration was evaluated using the following measures: Y1: Arrivals. Y2: Time last woman checks out. Y3: Women's length of stay (LoS). Y4: Clinic overrun time. Y5: Midwife waiting time (WT). Y6: Doctor WT. Y7: CTG connection WT. Optimisation was used to maximise X0 with respect to the 32 combinations of X1-X5. Configuration 0a, the base case Y1 = 7 women and Y3 = 102.97 [0.21] mins. Changing the arrival pattern (X1) and the order of the midwife and doctor consultations (X2) configuration 0d, where X3, X4, X5 = 0a, Y1 = 8 woman and Y3 86.06 [0.10] mins.
      Conclusions: The simulation model identified the availability of CTG machines as a bottleneck in the clinic, indicated by the WT for CTG connection effect on LoS. One additional CTG machine improved clinic performance to the same degree as an extra midwife and an extra doctor. The simulation model demonstrated significant reductions to LoS can be achieved without additional resources, by changing the clinic pathway and scheduling of appointments. A more general finding is that a simulation model can be used to identify bottlenecks, and efficient ways of restructuring an outpatient clinic.
    • References:
      Value Health. 2012 Sep-Oct;15(6):796-803. (PMID: 22999128)
      Oper Res. 1982 Nov-Dec;30(6):1082-104. (PMID: 10259645)
      Aust N Z J Obstet Gynaecol. 2018 Jun;58(3):315-320. (PMID: 29067683)
      BJOG. 2018 May;125(6):686-692. (PMID: 28941068)
      BJOG. 2005 Jul;112(7):866-74. (PMID: 15957985)
      Acta Obstet Gynecol Scand. 2009;88(1):6-17. (PMID: 19140042)
      J Public Health Med. 2003 Dec;25(4):325-35. (PMID: 14747592)
      Health Serv Res. 2013 Apr;48(2 Pt 2):713-34. (PMID: 23398502)
      Midwifery. 2015 May;31(5):526-31. (PMID: 25726005)
      BJOG. 2013 Jan;120(1):100-7. (PMID: 23078233)
      BMC Health Serv Res. 2020 Feb 14;20(1):117. (PMID: 32059727)
      Acta Obstet Gynecol Scand. 1977;56(3):247-53. (PMID: 560099)
      BMC Health Serv Res. 2011 May 19;11:108. (PMID: 21595946)
      BMC Health Serv Res. 2018 Apr 2;18(1):235. (PMID: 29609612)
      BMC Health Serv Res. 2015 Sep 16;15:387. (PMID: 26376782)
      Value Health. 2007 May-Jun;10(3):195-203. (PMID: 17532812)
      J Med Syst. 2002 Apr;26(2):179-97. (PMID: 11993573)
      Value Health. 2012 Sep-Oct;15(6):821-7. (PMID: 22999131)
      Health Policy. 2018 Apr;122(4):412-421. (PMID: 29482948)
      Health Care Manag Sci. 2006 Feb;9(1):31-45. (PMID: 16613015)
      Obstet Gynecol. 2009 Aug;114(2 Pt 1):386-397. (PMID: 19623003)
      BJOG. 2017 Sep;124 Suppl 4:19-25. (PMID: 28940875)
      Sex Reprod Healthc. 2016 Dec;10:19-24. (PMID: 27938867)
      J Nurse Midwifery. 1980 Sep-Oct;25(5):34-7. (PMID: 6902766)
      Aust N Z J Obstet Gynaecol. 2016 Oct;56(5):496-502. (PMID: 27302150)
      Health Care Manag Sci. 2001 Dec;4(4):249-55. (PMID: 11718457)
      Lancet. 1952 May 31;1(6718):1105-8. (PMID: 14928595)
      Cochrane Database Syst Rev. 2018 May 09;5:CD004945. (PMID: 29741208)
      BJOG. 2007 Sep;114(9):1097-103. (PMID: 17617197)
      Health Care Manag Sci. 2012 Mar;15(1):1-14. (PMID: 21901533)
      BMJ Open. 2017 May 9;7(5):e015007. (PMID: 28487459)
      Med Care. 1981 Apr;19(4):393-409. (PMID: 7230933)
      BJOG. 2015 Sep;122(10):1377-85. (PMID: 25690911)
    • Grant Information:
      237766 Norges Forskningsråd
    • Contributed Indexing:
      Keywords: Capacity planning; Optimisation; Outpatient; Patient flow; Post-term pregnancies; Simulation
    • Publication Date:
      Date Created: 20200216 Date Completed: 20200624 Latest Revision: 20240328
    • Publication Date:
      20240329
    • Accession Number:
      PMC7023739
    • Accession Number:
      10.1186/s12913-020-4943-y
    • Accession Number:
      32059727