A Scoping Review of Resource Modeling in HTA Using Discrete Event Simulation
Author(s)
Syed Salleh, PhD1, Aishee Ghatak, MSc2, Raju Gautam, PhD1, Shilpi Swami, MSc1.
1ConnectHEOR, London, United Kingdom, 2ConnectHEOR, Delhi, India.
1ConnectHEOR, London, United Kingdom, 2ConnectHEOR, Delhi, India.
OBJECTIVES: Resource modeling (RM) involves incorporating and measuring the effects of physical resource constraints (e.g. beds, doctors, nurses) within health technology assessment (HTA) models. While discrete event simulation (DES)-based RM is common in operational research, its application in HTA remains limited. Excluding RM can lead HTA models to overlook key implementation and feasibility issues, such as capacity bottlenecks, which may delay or limit the adoption of new technologies. Hence, this study aimed to summarize recent literature on the use of DES for RM in HTA.
METHODS: A targeted literature review was conducted using PubMed database (January-2019-April-2025) to identify studies incorporating RM in HTAs. Studies such as cost-effectiveness analysis (CEA) and budget impact analysis (BIA) were included. Data on modeling methodology, assumptions, outcomes, and challenges was analyzed.
RESULTS: The search yielded 219 potentially relevant citations. After the screening, five studies (all CEA, no BIA) were included. Three studies were national-level models, while two were organizational-level. One study explicitly modelled physical capacity constraints, while others addressed them indirectly using throughput measures. Data sources for constraints varied, with literature reviews being the most common, followed by medical records and modeler assumptions. All studies explored parameter uncertainty using deterministic and probabilistic sensitivity analyses. Two also employed stochastic simulation to model uncertainty in patient-level outcomes. Four studies examined process-related impacts of RM in HTA, such as delays in service flow and associated costs, while only one examined health impacts of delayed treatment. In addition to traditional CEA outcomes, all studies reported waiting time as an RM outcome, some also included queue length and resource utilization.
CONCLUSIONS: DES appears to be an effective RM technique in HTA, but only a small number of studies were identified, and its application in BIA was not observed. Future studies should focus on creating consensus guidelines and addressing these gaps.
METHODS: A targeted literature review was conducted using PubMed database (January-2019-April-2025) to identify studies incorporating RM in HTAs. Studies such as cost-effectiveness analysis (CEA) and budget impact analysis (BIA) were included. Data on modeling methodology, assumptions, outcomes, and challenges was analyzed.
RESULTS: The search yielded 219 potentially relevant citations. After the screening, five studies (all CEA, no BIA) were included. Three studies were national-level models, while two were organizational-level. One study explicitly modelled physical capacity constraints, while others addressed them indirectly using throughput measures. Data sources for constraints varied, with literature reviews being the most common, followed by medical records and modeler assumptions. All studies explored parameter uncertainty using deterministic and probabilistic sensitivity analyses. Two also employed stochastic simulation to model uncertainty in patient-level outcomes. Four studies examined process-related impacts of RM in HTA, such as delays in service flow and associated costs, while only one examined health impacts of delayed treatment. In addition to traditional CEA outcomes, all studies reported waiting time as an RM outcome, some also included queue length and resource utilization.
CONCLUSIONS: DES appears to be an effective RM technique in HTA, but only a small number of studies were identified, and its application in BIA was not observed. Future studies should focus on creating consensus guidelines and addressing these gaps.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
SA3
Topic
Study Approaches
Topic Subcategory
Decision Modeling & Simulation
Disease
No Additional Disease & Conditions/Specialized Treatment Areas