Prescriptive Analytics for Laboratory Workflow Optimisation: A Discrete-Event Simulation Approach to Reducing Diagnostic Bottlenecks in a Sub-Saharan African District Hospital

Prescriptive Analytics for Laboratory Workflow Optimisation: A Discrete-Event Simulation Approach to Reducing Diagnostic Bottlenecks in a Sub-Saharan African District Hospital | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prescriptive Analytics for Laboratory Workflow Optimisation: A Discrete-Event Simulation Approach to Reducing Diagnostic Bottlenecks in a Sub-Saharan African District Hospital William Mawunyo Agbo, Lawrence Quaye, John Serbe Marfo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9423252/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Clinical laboratory turnaround time (TAT) is a critical determinant of patient safety and clinical decision-making, yet prescriptive analytics approaches that recommend specific corrective actions remain underutilised in laboratory management. This study develops and evaluates a discrete-event simulation (DES)-based prescriptive analytics framework to identify and resolve workflow bottlenecks in a clinical chemistry laboratory at a district-level hospital in Ghana. A digital twin of the Liver Function Test (LFT) workflow was constructed using the R simmer package, parameterised from 1,615 LFT specimen records extracted over a five-month observation period, and validated against historical performance data. Baseline simulation revealed a compounded “Master Bottleneck” at the analytical stage, where 100% analyser utilisation drove the 95th-percentile (P95) TAT to 847.03 minutes and produced zero service level agreement (SLA) compliance. Single-factor resource interventions proved insufficient due to a serial bottleneck structure, confirmed by Cohen’s d effect sizes ranging from 0.00 to 32.22. A multi-factor prescriptive model comprising three phlebotomists, two analysers, and two technicians reduced P95 TAT by 17.52% (148.42 minutes) and transformed process stability, reducing the coefficient of variation from 543.8% to 0.003. The findings demonstrate that synchronised, multi-resource prescriptive optimisation substantially outperforms single-resource interventions, and that simulation-based decision support system (DSS) tools are feasible in resource-constrained district hospital settings. Operations Research Laboratory Diagnostics Prescriptive analytics Discrete-event simulation Laboratory turnaround time Workflow optimisation Decision support system Healthcare operations management Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9423252","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623747333,"identity":"f8fdbe46-a806-4586-aed4-ad7038cdfbed","order_by":0,"name":"William Mawunyo 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This study develops and evaluates a discrete-event simulation (DES)-based prescriptive analytics framework to identify and resolve workflow bottlenecks in a clinical chemistry laboratory at a district-level hospital in Ghana. A digital twin of the Liver Function Test (LFT) workflow was constructed using the R simmer package, parameterised from 1,615 LFT specimen records extracted over a five-month observation period, and validated against historical performance data. Baseline simulation revealed a compounded \u0026ldquo;Master Bottleneck\u0026rdquo; at the analytical stage, where 100% analyser utilisation drove the 95th-percentile (P95) TAT to 847.03 minutes and produced zero service level agreement (SLA) compliance. Single-factor resource interventions proved insufficient due to a serial bottleneck structure, confirmed by Cohen\u0026rsquo;s d effect sizes ranging from 0.00 to 32.22. A multi-factor prescriptive model comprising three phlebotomists, two analysers, and two technicians reduced P95 TAT by 17.52% (148.42 minutes) and transformed process stability, reducing the coefficient of variation from 543.8% to 0.003. The findings demonstrate that synchronised, multi-resource prescriptive optimisation substantially outperforms single-resource interventions, and that simulation-based decision support system (DSS) tools are feasible in resource-constrained district hospital settings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Prescriptive Analytics for Laboratory Workflow Optimisation: A Discrete-Event Simulation Approach to Reducing Diagnostic Bottlenecks in a Sub-Saharan African District Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 12:25:12","doi":"10.21203/rs.3.rs-9423252/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2acf58a4-e13c-4bda-9860-6e2e5362defa","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66386977,"name":"Operations Research"},{"id":66386978,"name":"Laboratory Diagnostics"}],"tags":[],"updatedAt":"2026-04-16T12:25:12+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 12:25:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9423252","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9423252","identity":"rs-9423252","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}