The Role of Decision Analytic Modeling in the Health Economic Assessment of Spinal Intervention

Study Design. Narrative review. Objective. To review the common tenets, strengths, and weaknesses of decision modeling for health economic assessment and to review the use of decision modeling in the spine literature to date. Summary of Background Data. For the majority of spinal interventions, well-designed prospective, randomized, pragmatic cost-effectiveness studies that address the specific decision-in-need are lacking. Decision analytic modeling allows for the estimation of cost-effectiveness based on data available to date. Given the rising demands for proven value in spine care, the use of decision analytic modeling is rapidly increasing by clinicians and policy makers. Methods. This narrative review discusses the general components of decision analytic models, how decision analytic models are populated and the trade-offs entailed, makes recommendations for how users of spine intervention decision models might go about appraising the models, and presents an overview of published spine economic models. Results. A proper, integrated, clinical, and economic critical appraisal is necessary in the evaluation of the strength of evidence provided by a modeling evaluation. As is the case with clinical research, all options for collecting health economic or value data heterogeneity across the 20 spine intervention health economic modeling studies summarized with respect to study design, models used, reporting, and general quality. There is sparse evidence for populating spine intervention models.Results mostly showed that interventions were cost-effective based on $100,000/quality-adjusted life-year threshold. Spine care providers, as partners with their health economic colleagues, have unique clinical expertise and perspectives that are critical to interpret the strengths and weaknesses of health economic models. Conclusion. Health economic models must be critically appraised for both clinical validity and economic quality before altering health care policy, payment strategies, or patient care decisions.