OBJECTIVES:

Implementation of universal Lynch Syndrome (LS) screening in newly diagnosed colorectal cancer patients remains suboptimal. We previously developed decision analytic models to compare the relative effectiveness, costs, and efficiency of eight LS screening protocols from a healthcare system perspective. The objective of this study is to convert the models into a user-friendly modeling-tool and to assess its potential applications in supporting local decision-making of LS screening implementation in healthcare systems.

METHODS:

We converted the models into an Excel-based modeling-tool, allowing end-users to modify model variables and calculate model outputs specific to their local healthcare system. We pilot-tested and refined the tool with potential end-users from three healthcare systems. We distributed the validated and refined tool to identified end-users in genetic counseling, precision medicine, and pathology across eight healthcare systems. We then conducted semi-structured interviews with the end-users on the applications of the tool and how it was used to inform and guide local decision-making.

RESULTS:

We have successfully developed a modeling-tool containing an overview, an input sheet with instructions and definitions of parameters, and a results table with model outcomes. We completed 9 interviews with 15 end-users across 6 of the 8 healthcare systems. The end-users highly valued the tool’s capabilities to quantify and prove “why a certain protocol would be ideal over another”, and to compare, contrast, and customize implementation efforts based on local experience. The tool was deemed highly helpful to set up a program or to expand upon an existing program.

CONCLUSIONS:

Outcome metrics, including program costs to the healthcare system, are important factors in organizational decision-making. However, most decision tools do not incorporate local costs and clinical data. This newly developed tool, estimating outcomes using system-specific data, has been enthusiastically endorsed by end-users across multiple healthcare systems in informing and supporting local decision-making and implementation.