METHODOLOGICAL REVIEW OF CAPACITY CONSTRAINT MODELING IN SIMULATIONS INFORMING ORGAN TRANSPLANTATION
Author(s)
Lotte K. Staal, MSc1, Sopany Saing, BSc, MPH, PhD1, Sebastian Rachuba, PhD1, Olivier Manintveld, PhD, MD2, Erik Koffijberg, MSc, PhD1.
1University of Twente, Enschede, Netherlands, 2Erasmus MC, University Medical Center Rotterdam, Rotterdamn, Netherlands.
1University of Twente, Enschede, Netherlands, 2Erasmus MC, University Medical Center Rotterdam, Rotterdamn, Netherlands.
OBJECTIVES: Healthcare capacity constraints are widely recognized to restrict the ability to deliver care to patients, impacting the process, health, and economic outcomes of new care strategies. This systematic methodological review aims to identify and describe existing methods for modelling capacity constraints in simulation-based decision models in the solid organ transplantation pathway.
METHODS: We searched PubMed, Scopus, Web of Science, and IEEE for studies using simulation-based decision models to evaluate new strategies in the solid organ transplantation pathway. The models had to explicitly model time. We distinguished between implicit constraint modelling (limiting patient throughput) and explicit constraint modelling (quantifying single-use or reusable resources). Data extraction included study characteristics, simulation characteristics, capacity-related modelling components, outcome measures and reported model limitations. Quality assessment utilized the CHEQUE and STRESS checklists.
RESULTS: Of 3533 articles, 206 were included, covering 155 unique simulation models. Only 40% explicitly modeled capacity constraints, and just one study considered reusable resources. All studies using explicit modelling were individual-level and mainly used discrete-event simulations (84%). Studies with only implicit modelling were mostly state-transition models (STMs) (88%). Explicit modelling was only used in 10%, 16%, 65%, and 78% of models developed for health economic evaluations (HEEs), health outcomes research (HOR), process outcomes research (POR), and health and process outcome research (HPR), respectively. Only 30% reported a limitation regarding capacity modelling. Reasons included ‘not individual-model’ (25%) and simplified matching/allocation (72%) for implicit and explicit models, respectively.
CONCLUSIONS: Explicit modelling of capacity constraints in simulation models informing solid organ transplantation remains uncommon and exclusive to individual-level modelling approaches. While more common in POR and HPR, its application is rare in HEEs and HOR, where STMs are the most common. A broader adoption of explicit modelling of capacity constraints, using flexible, non-STM approaches, could improve the relevance of model-based evaluations in solid organ transplantation.
METHODS: We searched PubMed, Scopus, Web of Science, and IEEE for studies using simulation-based decision models to evaluate new strategies in the solid organ transplantation pathway. The models had to explicitly model time. We distinguished between implicit constraint modelling (limiting patient throughput) and explicit constraint modelling (quantifying single-use or reusable resources). Data extraction included study characteristics, simulation characteristics, capacity-related modelling components, outcome measures and reported model limitations. Quality assessment utilized the CHEQUE and STRESS checklists.
RESULTS: Of 3533 articles, 206 were included, covering 155 unique simulation models. Only 40% explicitly modeled capacity constraints, and just one study considered reusable resources. All studies using explicit modelling were individual-level and mainly used discrete-event simulations (84%). Studies with only implicit modelling were mostly state-transition models (STMs) (88%). Explicit modelling was only used in 10%, 16%, 65%, and 78% of models developed for health economic evaluations (HEEs), health outcomes research (HOR), process outcomes research (POR), and health and process outcome research (HPR), respectively. Only 30% reported a limitation regarding capacity modelling. Reasons included ‘not individual-model’ (25%) and simplified matching/allocation (72%) for implicit and explicit models, respectively.
CONCLUSIONS: Explicit modelling of capacity constraints in simulation models informing solid organ transplantation remains uncommon and exclusive to individual-level modelling approaches. While more common in POR and HPR, its application is rare in HEEs and HOR, where STMs are the most common. A broader adoption of explicit modelling of capacity constraints, using flexible, non-STM approaches, could improve the relevance of model-based evaluations in solid organ transplantation.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
MSR191
Topic
Health Technology Assessment, Methodological & Statistical Research, Study Approaches
Disease
No Additional Disease & Conditions/Specialized Treatment Areas