The Use of Decision Analytic Modeling in the Evaluation of Surgical Innovations: A Scoping Review

The main objective of this review was to map how decision analytic models are used in surgical innovation (in which research phase, with what aim) and to understand how challenges related to the assessment of surgical interventions are incorporated.

We systematically searched PubMed, Embase, and the Cochrane Library for studies published in 2018. We included original articles using a decision analytic model to compare surgical strategies. We included modeling studies of surgical innovations. General, innovation, and modeling characteristics were extracted, as were outcomes, recommendations, and handling of challenges related to the assessment of surgical interventions (learning curve, incremental innovation, dynamic pricing, quality variation, organizational impact).

We included 46 studies. The number of studies increased with each research phase, from 4% (n = 2) in the preclinical phase to 40% (n = 20) in phase 3 studies. Eighty-one studies were excluded because they investigated established surgical procedures, indicating that modeling is predominantly applied after the innovation process. Regardless of the research stage, the aim to determine cost-effectiveness was most frequently identified (n = 40, 87%), whereas exploratory aims (eg, exploring when a strategy becomes cost-effective) were less common (n = 9, 20%). Most challenges related to the assessment of surgical interventions were rarely incorporated in models (eg, learning curve [n = 1, 2%], organizational impact [n = 2, 4%], and incremental innovation [n = 1, 2%]), except for dynamic pricing (n = 10, 22%) and quality variation (n = 6, 13%).

In surgical innovation, modeling is predominantly used in later research stages to assess cost-effectiveness. The exploratory use of modeling seems still largely overlooked in surgery; therefore, the opportunity to inform research and development may not be optimally used.