A Generalizable Discrete Event Simulation Framework to Assess Clinical and Economic Impact of Prescription Protocol Revisions
Author(s)
Panos Stafylas, MD, MSc, PhD1, Christiana Tychala, BSc, MSc1, Andreas Georgiou, BSc, PhD2, Konstantinos Kaparis, BSc, PhD2, Demosthenes Panagiotakos, BSc, PhD3, ZOI STEFANIDOU, BA, MSc4, Christos Stafylas, BSc5, KELLY AVGITIDOU, BSc, PhD1, Vassilis Homer Aletras, BSc, MSc, PhD2.
1HealThink, Thessaloniki, Greece, 2Department of Business Administration, University of Macedonia, Thessaloniki, Greece, 3Department of Nutrition and Dietetics, School of Health Sciences and Education, Harokopio University, Athens, Greece, 4ELPEN Pharmaceutical SA, Athens, Greece, 5School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
1HealThink, Thessaloniki, Greece, 2Department of Business Administration, University of Macedonia, Thessaloniki, Greece, 3Department of Nutrition and Dietetics, School of Health Sciences and Education, Harokopio University, Athens, Greece, 4ELPEN Pharmaceutical SA, Athens, Greece, 5School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
OBJECTIVES: National Prescription Protocols (NPP) and Clinical Guidelines (CG) are frequently revised in response to emerging clinical evidence. However, revisions often lack structured, quantitative assessment of their real-world clinical and economic implications. This study aimed to develop and validate a dynamic, generalizable methodological framework to evaluate the impact of NPP/CG revisions on population health outcomes, healthcare utilization, and budgetary consequences - supporting evidence-informed policy decisions across different therapeutic areas.
METHODS: A targeted review of ISPOR-SMDM Modeling Good Research Practices and related literature was conducted to identify best practices in modeling. Discrete-event simulation (DES) was selected for its ability to model patient-level heterogeneity, event sequencing, and dynamic treatment effects over time. A modular DES model was developed in Simul8 software to simulate alternative care pathways, disease progression, and associated costs. The framework incorporates local epidemiological data, real-world treatment patterns, and cost inputs. A feasibility case study in dyslipidemia management in Greece was used to validate the model. Model transparency, calibration, internal validation, and scenario-based uncertainty analyses were conducted in line with best practice standards.
RESULTS: The framework successfully captured complex treatment pathways and population-level dynamics across cardiovascular risk strata. Scenario analyses revealed that modest protocol changes - such as increased statin use - produced measurable changes in cardiovascular event rates and healthcare expenditures. The dyslipidemia case study confirmed feasibility and policy relevance, while also revealing limitations related to real-world data (RWD) availability. The framework’s modular structure enables ongoing updates as more robust data sources become available.
CONCLUSIONS: This DES-based framework provides a robust, adaptable tool for evaluating the clinical and economic impact of CG and NPP revisions before proceeding to real-world implementations. It supports early health technology assessment (HTA), reimbursement decisions, and NPP optimization. Its dynamic architecture facilitates integration with RWD and aligns with learning healthcare system principles.
METHODS: A targeted review of ISPOR-SMDM Modeling Good Research Practices and related literature was conducted to identify best practices in modeling. Discrete-event simulation (DES) was selected for its ability to model patient-level heterogeneity, event sequencing, and dynamic treatment effects over time. A modular DES model was developed in Simul8 software to simulate alternative care pathways, disease progression, and associated costs. The framework incorporates local epidemiological data, real-world treatment patterns, and cost inputs. A feasibility case study in dyslipidemia management in Greece was used to validate the model. Model transparency, calibration, internal validation, and scenario-based uncertainty analyses were conducted in line with best practice standards.
RESULTS: The framework successfully captured complex treatment pathways and population-level dynamics across cardiovascular risk strata. Scenario analyses revealed that modest protocol changes - such as increased statin use - produced measurable changes in cardiovascular event rates and healthcare expenditures. The dyslipidemia case study confirmed feasibility and policy relevance, while also revealing limitations related to real-world data (RWD) availability. The framework’s modular structure enables ongoing updates as more robust data sources become available.
CONCLUSIONS: This DES-based framework provides a robust, adaptable tool for evaluating the clinical and economic impact of CG and NPP revisions before proceeding to real-world implementations. It supports early health technology assessment (HTA), reimbursement decisions, and NPP optimization. Its dynamic architecture facilitates integration with RWD and aligns with learning healthcare system principles.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
MSR3
Topic
Health Policy & Regulatory, Health Service Delivery & Process of Care, Methodological & Statistical Research
Disease
Cardiovascular Disorders (including MI, Stroke, Circulatory), Diabetes/Endocrine/Metabolic Disorders (including obesity), No Additional Disease & Conditions/Specialized Treatment Areas, Oncology, Urinary/Kidney Disorders