Optimisation of Patient Flow and Scheduling in an Outpatient Haemodialysis Clinic
Keywords:
Dialysis, Genetic Algorithm, Optimisation, Patient Scheduling, SimulationAbstract
The demand for renal replacement therapy (RRT) from the growing number of patients suffering from chronic kidney disease (CKD) and end stage renal disease (ESRD) in Nigeria is reported to be on the rise. However, dialysis clinics are few with limited facilities to meet the increasing demand leading to congestion, long waiting time and increased length of stay (LOS) in dialysis clinics. This paper presents an optimisation model for scheduling patient flow in an outpatient haemodialysis clinic. The objective is to minimize patient LOS using Genetic Algorithm (GA), implemented in Python programming language with Spyder Integrated Development Environment (IDE). The model was tested using data obtained from a haemodialysis clinic, in Lagos, Nigeria. The model generated optimum LOS values (193.01, 275.02 and 390.01) minutes compared to the mean LOS values at the haemodialysis clinic (235.50, 296.62 and 424.50) minutes for the 3-hour, 4-hour and 6-hour dialysis sessions. Furthermore, a simulation experiment of patient flow in a typical haemodialysis clinic was performed by gradual variations in patient arrival rates, Simulation results at revealed mean LOS (minutes) as (312.85 ± 73.45, 348.18 ± 84.89, 342.18 ± 81.30, 305, 28 ± 63.67) respectively. The optimisation model was effective in reducing patient LOS.
References
Adejumo, A. O.; J.D. Olusoji and F.O. Andrew. (2017). Factors associated with TB/HIV co-infection among drug sensitive tuberculosis patients managed in a secondary health facility in Lagos, Nigeria. African Journal of Infectious Diseases, 11(2): 75 - 82.
Afrane, S. and Appah, A. (2014). Queuing theory and the management of waiting-time in hospitals: The case of Anglo Gold Ashanti Hospital in Ghana. International Journal of Academic Research in Business and Social Sciences, 4(2):34 – 44.
Ajayi S.; Y. Raji; T. Bello; L. Jinadu and B. Salako (2016). Unaffordability of renal replacement therapy in Nigeria. Hong Kong Journal of Nephrology, 18:15 – 18.
Augusto V.; X. Xie and V. Perdomo (2010). Operating theatre scheduling with patient recovery in both rooms and recovery beds. Computers and Industrial Engineering, 58:231 – 238.
Ayandele, I.A. and Nnamseh, M.P. (2012). Healthcare Service Delivery: The Queuing theory approach. International Journal of Social Sciences and Humanities Reviews, 3(3): 51 – 64.
Bajpai, P. and Kumar, M. (2010). Genetic algorithm – an approach to solve global optimisation problem. Indian Journal of Computer Science and Engineering, 1(3):199 – 206.
Bakker M.; V. Post; C. D. Langevin; J. D. Hughes; J. T. White; J. J. Starn; M. N. Fienen. (2016). Scripting MODFLOW model development using Python and Floppy. Groundwater, 54(5):733 – 739.
Bamgboye, E. (2003). Haemodialysis: Management Problems in Developing Countries, with Nigeria as a Surrogate. Kidney International, Supplement 63(83): S93-S595.
Chan C.T.; P.J. Blankestin; L.M. Dember; M. Gallieni; D.C.H. Harris; et al., (2019). Dialysis initiation, modality choice, access, and prescription. Kidney International Journal, 96:37 – 47.
Chen B.; E. Li; Y. Kazunobu; K. Ken; N. Shinji and W. Miao (2010). Impact of adjustment measures on reducing outpatient waiting time in a community hospital: application of a computer simulation. Chinese Medical Journal, 123(5):574 – 580.
Chiesa M.; G. Maioli; G.I. Colombo and L. Piacentini (2020). GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets. Available online at: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3400-6. Accessed on March 20, 2020.
Collins N.J.; K.M. Crossley; R. Darnell; et al. (2010). Predictors of short- and long-term outcome in patellofemoral pain syndrome: a prospective longitudinal study. Available online at: https://bmcmusculoskeletdisord.biomedcentral.com/articles/10.1186/1471-2474-11-11. Accessed on March 21, 2020.
Fei H.; N. Meskens and C. Chu (2010) A planning and Scheduling problem for an operating theatre using an open scheduling strategy. Computers and Industrial Engineering, 58: 221 – 230.
Fleming R.; D. Gartner; R. Padman and D. James. (2019). Analytic approach for improving patient-centric delivery of dialysis services. AMIA Annual Symposium Proceedings Archives, 418 -427.
Forrest G.; M. Nagao; A. Iqbal and R. Kakar (2005). Inpatient rehabilitation of patients requiring haemodialysis: improving efficiency of care. Archives of Physical Medicine, 86(10):1949 – 1952.
Goldberg, D.E. (1989). Genetic Algorithms in Search. Optimization and Machine Learning. Addison Wesley, New York, USA.
Gross D.; J.F. Shortie; J.M. Thompson and C.M. Harris (2008). Fundamentals of Queueing Theory. Fourth Edition, John Wiley & Son, New Jersey, USA.
Jafar T.H.; R.C. John; O. Tewari A. Cobb B. Legido-Quigley Y. Sungwon and V. Jha (2020). Access to CKD Care in Rural Communities of India: a qualitative study exploring the barriers and potential facilitators. Available online at: https://bmcnephrol.biomedcentral.com/articles/10.1186/s12882-020-1702-6. Accessed on March 21, 2020.
Hegazy, T. (1999). Optimisation of resource allocation and leveling using genetic algorithm. Journal of Construction Engineering and Management, 167 -175.
Horn S.; P. Buerhaus; N. Bergstrom and R. Smout (2005). RN Staffing Time and Outcomes of Long-Stay Nursing Home Residents: Pressure ulcers and other adverse outcomes are less likely as RNs spend more time on direct patientcare. American Journal of Nursing, 105(11):58 -70.
Hribar M.R.; S. Read-Brown; L. Reznick and M.F. Chiang (2017). Evaluating and improving an outpatient clinic scheduling template using secondary electronic health record. AMIA Annual Symposium Proceedings Archives, 921 – 929.
Karkar A.; L. Dammang and M. Bouhaha (2015). Stress and burnout among haemodialysis nurses: a single-centre, prospective survey study. Saudi Journal of Kidney Diseases and Transplantation, 26(1): 12 – 18.
Lee S.; D. Min; J. Ryu and Y. Yih (2013). A simulation study of appointment scheduling in outpatient clinics: open access and overbooking. Simulation: Transactions of the Society for Modelling and Simulation International 89(12):1459–1473.
Lin K.Y.L.; T.W.C Ling and W.K. Yeung (2017). Resource allocation and outpatient appointment scheduling using simulation optimization. Available online at: https://doi.org/10.1155/2017/9034737. Accessed on March 28, 2020.
Liu Z.; L. Jiongbing; L. Zaisheng; L. Guangrui; Z. Hao and D. Junwu (2017). Patient scheduling in hemodialysis service. Available online at: https://link.springer.com/article/10.1007%2Fs10878-017-0232-z. Accessed on April 14, 2020.
Liyanage T.; T. Ninomiya; V. Jha; et al. (2015). World access to treatment for end-stage kidney disease: a systematic review. Lancet, 365:1975 – 1982.
Man N.K.; J. Zingraff and P. Jungers (1995). Basic Principles of Haemodialysis, In: Long-Term Hemodialysis. Available online at: https://doi.org/10.1007/978-94-011-0027-4_2. Accessed on June 20, 2020.
Nappo R.W. and Ross, E.A. (2020). Improved outpatient haemodialysis adequacy using queued schedules. Journal of Renal Care, 46(1):62 – 68.
Nwaneri, S.C. and Anyaeche, C.O. (2018). Application of Time-Cost-Quality-Risk Trade-off Model in Magnetic
Resonance imaging machine installation project. Nigerian Journal of Technology, 37(2): 387 – 396.
Odubanjo M.O.; A.O. Oluwasola and S. Kadiri (2011). The epidemiology of end-stage renal disease in Nigeria: The way forward. International Urology and Nephrology, 43(3):785-792.
Oche, M.O. and Adamu, H. (2013). Determinants of patient waiting time in the general outpatient department of a tertiary health institution in North Western Nigeria. Annals of Medical and Health Science Research, 3(4):588 – 592.
Rossiter C.E. and Raynolds, F.A. (1968). Automatic monitoring of the time waited in an outpatient clinic. Journal Storage: Med Care. 1:218–25.
Samar A. E.; B. Davide; D. Graziella; D. Evangelia; T. Giovanni and Z. Carmine (2016). Prevalence and burden of chronic kidney disease among the general population and high-risk groups in Africa: a systematic review. Available online at:https://bmjopen.bmj.com/content/8/1/e015069.info. Accessed on March 22, 2020.
Sethi S.K.; V. Krishnappa; N. Nangethu; P. Nemer; L.A. Frazee and R. Raina (2018). Antibiotic Dosing in Sustained Low-Efficiency Dialysis in Critically Ill Patients. Available online at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6088477/. Accessed on December 30, 2020.
Stecz W.; R. Pytlak K. Rymarz and S. Niemczyk (2019). Application of Dynamic Optimisation for planning for planning a haemodialysis process. Available online at:https://doi.org/10.1186/s12882-019-1409-8. Accessed on June 5, 2020.
Zhang S.; H. Morgenstem; P. Albertus; B.K. Nallamothu; K. He and R. Saran (2019). Emergency Department visits and hospitalizations among haemodialysis patients by day of the week and dialysis schedule in the United States. Available online at: https://doi.org/10.1371/journal.pone.0220966. Accessed on June 5, 2020.
Additional Files
Published
Issue
Section
License
Copyright (c) 2021 Nigerian Journal of Technological Development
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Nigerian Journal of Technological Development, in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approve the submitted version of the manuscript and agree to its publication in the Nigerian Journal of Technological Development." A copyright transfer form should be downloaded from the NJTD Website ( http://njtd.com.ng/index.php/njtd). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.
