Redefining Health Economic Evaluation: A Call for Advanced Models to Assess Real-World Operational Efficiency
Author(s)
Felipe Fagundes, PhD1, Lívia Loamí R. Paula, MSc, PhD1, Marcos Rodrigues, MSc1, Talita Garcia do Nascimento Castro, Phd1, Bruno Tirotti Saragiotto, PhD2;
1Hi! Healthcare Intelligence, São José dos Campos, Brazil, 2University of Technology of Sydney, Sydney, Australia
1Hi! Healthcare Intelligence, São José dos Campos, Brazil, 2University of Technology of Sydney, Sydney, Australia
OBJECTIVES: As healthcare systems globally grapple with escalating costs and uneven outcomes, traditional economic evaluation frameworks are proving insufficient to capture the complexity and operational inefficiencies inherent in real-world healthcare delivery. While methods such as cost-effectiveness and cost-utility analyses remain pivotal, they often overlook the nuanced interplay between outcomes, resource utilization, and operational waste in health systems. This paper explores the pressing need for evolving health economic evaluation models that can integrate real-world data (RWD) and adapt to the varying levels of data maturity within healthcare systems.
METHODS: The current state of health economics and outcomes research (HEOR) underscores a gap in evaluating operational efficiency, a critical dimension in delivering value-based care. Methods like Data Envelopment Analysis (DEA) offer promising pathways by focusing on relative efficiency and benchmarking providers based on input-output ratios. For instance, DEA applications in oncology have successfully highlighted disparities in cost-effectiveness and waste reduction opportunities, uncovering inefficiencies in up to 23% of expenditures across care delivery pathways. Similarly, advanced regression and machine learning models have begun integrating fragmented datasets to identify patterns of resource misallocation and suboptimal clinical practices.
RESULTS: However, these approaches remain underutilized due to challenges such as data accessibility, fragmentation, and lack of standardization across health systems. This paper advocates for leveraging available datasets, even with their inherent limitations, to derive actionable insights. By adopting adaptive models tailored to existing data maturity levels, healthcare decision-makers can bridge the gap between theoretical evaluation frameworks and practical applications.
CONCLUSIONS: This conceptual paper aims to provoke discussion on the urgent need for innovative methodologies to evaluate operational efficiency in healthcare. Adapting to the constraints of real-world data, while simultaneously pushing for data maturity and integration, is essential to unlock the potential of HEOR in driving sustainable, value-based health systems.
METHODS: The current state of health economics and outcomes research (HEOR) underscores a gap in evaluating operational efficiency, a critical dimension in delivering value-based care. Methods like Data Envelopment Analysis (DEA) offer promising pathways by focusing on relative efficiency and benchmarking providers based on input-output ratios. For instance, DEA applications in oncology have successfully highlighted disparities in cost-effectiveness and waste reduction opportunities, uncovering inefficiencies in up to 23% of expenditures across care delivery pathways. Similarly, advanced regression and machine learning models have begun integrating fragmented datasets to identify patterns of resource misallocation and suboptimal clinical practices.
RESULTS: However, these approaches remain underutilized due to challenges such as data accessibility, fragmentation, and lack of standardization across health systems. This paper advocates for leveraging available datasets, even with their inherent limitations, to derive actionable insights. By adopting adaptive models tailored to existing data maturity levels, healthcare decision-makers can bridge the gap between theoretical evaluation frameworks and practical applications.
CONCLUSIONS: This conceptual paper aims to provoke discussion on the urgent need for innovative methodologies to evaluate operational efficiency in healthcare. Adapting to the constraints of real-world data, while simultaneously pushing for data maturity and integration, is essential to unlock the potential of HEOR in driving sustainable, value-based health systems.
Conference/Value in Health Info
2025-05, ISPOR 2025, Montréal, Quebec, CA
Value in Health, Volume 28, Issue S1
Code
CO99
Topic
Clinical Outcomes
Topic Subcategory
Clinician Reported Outcomes, Comparative Effectiveness or Efficacy, Performance-based Outcomes
Disease
No Additional Disease & Conditions/Specialized Treatment Areas