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