Clinical Recommender Algorithms to Simulate Digital Specialty Consultations

Advances in medical science simultaneously benefit patients while contributing to an over-whelming complexity of medicine with a decision space of thousands of possible diagnoses, tests, and treatment options. Medical expertise becomes the most important scarce health-care resource, reflected in tens of millions in the US alone with deficient access to specialty care. Combining the growing wealth of electronic medical record data with modern recommender algorithms has the potential to synthesize the clinical community's expertise into an executable format to manage this information overload and improve access to personalized care suggestions. We focus here specifically on outpatient consultations for (Endocrine) specialty expertise, one of the highest demand and most amenable areas for electronic consultation systems. Specifically we develop and evaluate models to predict the clinical orders of these initial specialty referral consultations using an ensemble of feed-forward neural networks as compared to multiple baseline algorithms. As benchmarks closer to the existing standard of care, we used diagnosis-based clinical checklists based on our review of literature and practice guidelines (e.g., Up-to-Date) for each common referral diagnosis as well as existing electronic consult referral guides. Results indicate that such automated algorithms trained on historical data can provide more personalized decision support with greater accuracy than existing benchmarks, with the potential to power fully digital consultation services that could consolidate utilization of scarce medical expertise, improving consistency of quality and access to care for more patients.