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Innovation in healthcare payment and service delivery utilizes high cost,high risk pilots paired with traditional program evaluations. Decision-makersare unable to reliably forecast the impacts of pilot interventions in thiscomplex system, complicating the feasibility assessment of proposed healthcaremodels. We developed and validated a Discrete Event Simulation (DES) model ofprimary care for patients with Diabetes to allow rapid prototyping andassessment of models before pilot implementation. We replicated four outcomesfrom the Centers for Medicare and Medicaid Services Federally Qualified HealthCenter Advanced Primary Care Practice pilot. The DES model simulates asynthetic population's healthcare experience, including symptom onset,appointment scheduling, screening, and treatment, as well as the impact ofphysician training. A network of detailed event modules was developed frompeer-reviewed literature. Synthetic patients' attributes modify the probabilitydistributions for event outputs and direct them through an episode of care;attributes are in turn modified by patients' experiences. Our model replicatesthe direction of the effect of physician training on the selected outcomes, andthe strength of the effect increases with the number of trainings. Thesimulated effect strength replicates the pilot results for eye exams andnephropathy screening, but over-estimates results for HbA1c and LDL screening.Our model will improve decision-makers' abilities to assess the feasibility ofpilot success, with reproducible, literature-based systems models. Our modelidentifies intervention and healthcare system components to which outcomes aresensitive, so these aspects can be monitored and controlled during pilotimplementation. More work is needed to improve replication of HbA1c and LDLscreening, and to elaborate sub-models related to intervention components.

Rapid Prototyping Model for Healthcare Alternative Payment Models:  Replicating the Federally Qualified Health Center Advanced Primary Care  Practice Demonstration