Optimal Planning of Health Services through Genetic Algorithm and Discrete Event Simulation: A Proposed Model and Its Application to Stroke Rehabilitation Care.

Publisher: SAGE Publications Country of Publication: United States NLM ID: 101707716 Publication Model: eCollection Cited Medium: Internet ISSN: 2381-4683 (Electronic) Linking ISSN: 23814683 NLM ISO Abbreviation: MDM Policy Pract Subsets: PubMed not MEDLINE

Original Publication: Thousand Oaks, CA : SAGE Publications, [2016]-

Background. Increasing demand for provision of care to stroke survivors creates challenges for health care planners. A key concern is the optimal alignment of health care resources between provision of acute care, rehabilitation, and among different segments of rehabilitation, including inpatient rehabilitation, early supported discharge (ESD), and outpatient rehabilitation (OPR). We propose a novel application of discrete event simulation (DES) combined with a genetic algorithm (GA) to identify the optimal configuration of rehabilitation that maximizes patient benefits subject to finite health care resources. Design. Our stroke rehabilitation optimal model (sROM) combines DES and GA to identify an optimal solution that minimizes wait time for each segment of rehabilitation by changing care capacity across different segments. sROM is initiated by generating parameters for DES. GA is used to evaluate wait time from DES. If wait time meets specified stopping criteria, the search process stops at a point at which optimal capacity is reached. If not, capacity estimates are updated, and an additional iteration of the DES is run. To parameterize the model, we standardized real-world data from medical records by fitting them into probability distributions. A meta-analysis was conducted to determine the likelihood of stroke survivors flowing across rehabilitation segments. Results. We predict that rehabilitation planners in Alberta, Canada, have the potential to improve services by increasing capacity from 75 to 113 patients per day for ESD and from 101 to 143 patients per day for OPR. Compared with the status quo, optimal capacity would provide ESD to 138 ( s = 29.5) more survivors and OPR to 262 ( s = 45.5) more annually while having an estimated net annual cost savings of $25.45 ( s = 15.02) million. Conclusions. The combination of DES and GA can be used to estimate optimal service capacity.
Highlights: We created a hybrid model combining a genetic algorithm and discrete event simulation to search for the optimal configuration of health care service capacity that maximizes patient outcomes subject to finite health system resources.We applied a probability distribution fitting process to standardize real-world data to probability distributions. The process consists of choosing the distribution type and estimating the parameters of that distribution that best reflects the data. Standardizing real-word data to a best-fitted distribution can increase model generalizability.In an illustrative study of stroke rehabilitation care, resource allocation to stroke rehabilitation services under an optimal configuration allows provision of care to more stroke survivors who need services while reducing wait time.Resources needed to expand rehabilitation services could be reallocated from the savings due to reduced wait time in acute care units. In general, the predicted optimal configuration of stroke rehabilitation services is associated with a net cost savings to the health care system.
Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
(© The Author(s) 2022.)

Stroke. 2016 Jun;47(6):e98-e169. (PMID: 27145936)
Lancet. 2011 May 14;377(9778):1693-702. (PMID: 21571152)
Int J Health Geogr. 2010 Jan 28;9:4. (PMID: 20109172)
Stroke. 2016 Jun;47(6):1685-91. (PMID: 27143275)
MDM Policy Pract. 2020 Jun 6;5(1):2381468320915242. (PMID: 32551365)
Neuroepidemiology. 2017;49(1-2):45-61. (PMID: 28848165)
J Allergy Clin Immunol Pract. 2020 May;8(5):1489-1491. (PMID: 32220575)
Pharmacoeconomics. 2002;20(8):537-52. (PMID: 12109919)
Stroke. 2009 Jan;40(1):24-9. (PMID: 19008473)
Can J Neurol Sci. 2016 May;43(3):353-9. (PMID: 26742718)
Med Decis Making. 2012 Sep-Oct;32(5):701-11. (PMID: 22990085)
PLoS One. 2015 Jul 17;10(7):e0133536. (PMID: 26186211)
BMC Health Serv Res. 2016 Sep 29;16(1):530. (PMID: 27688152)
Ann Phys Rehabil Med. 2016 Sep;59(4):248-54. (PMID: 27009910)
Lancet. 2005 Feb 5-11;365(9458):501-6. (PMID: 15705460)
Cochrane Database Syst Rev. 2020 Jan 31;1:CD010255. (PMID: 32002991)
BMC Health Serv Res. 2019 Jun 20;19(1):399. (PMID: 31221167)
Lancet. 2016 Oct 8;388(10053):1545-1602. (PMID: 27733282)
Int J Stroke. 2020 Oct;15(7):789-806. (PMID: 31983292)
J Am Heart Assoc. 2015 Feb 23;4(2):. (PMID: 25713291)
Eur J Health Econ. 2017 Jan;18(1):33-47. (PMID: 26715578)
Rev Neurol (Paris). 2018 Sep - Oct;174(7-8):555-563. (PMID: 29703444)
Lancet Neurol. 2019 Apr;18(4):357-375. (PMID: 30773428)
Stroke. 2003 Nov;34(11):2687-91. (PMID: 14576376)
Health Syst (Basingstoke). 2019 Jan 16;8(3):162-183. (PMID: 31839929)
Cochrane Database Syst Rev. 2020 Oct 15;10:CD012575. (PMID: 33058172)
Eur J Health Econ. 2019 Feb;20(1):107-134. (PMID: 29909569)
Pharmacoeconomics. 2016 Apr;34(4):349-61. (PMID: 26660529)
Int J Stroke. 2016 Jun;11(4):459-84. (PMID: 27079654)
Health Informatics J. 2010 Jun;16(2):129-43. (PMID: 20573645)
Cochrane Database Syst Rev. 2012 Sep 12;(9):CD000443. (PMID: 22972045)
Am J Occup Ther. 2020 Jan/Feb;74(1):7401205050p1-7401205050p14. (PMID: 32078516)
Lancet. 2014 Jan 18;383(9913):245-54. (PMID: 24449944)
Can J Hosp Pharm. 2015 May-Jun;68(3):232-7. (PMID: 26157185)
Top Stroke Rehabil. 2017 Jul;24(5):374-380. (PMID: 28218020)
Can J Neurol Sci. 2020 Jul;47(4):474-478. (PMID: 32654680)

Keywords: discrete event simulation; distribution fit; genetic algorithm; health service planning; real-world data; stroke rehabilitation

Date Created: 20221031 Latest Revision: 20221102

20231215

PMC9597031

10.1177/23814683221134098

36310567