Modeling and Analysis of E-Consults in Primary-Specialty Care Referrals

E-consults offer a digital platform where primary care physicians (PCPs) can consult specialists and obtain feedback or offer a direct specialty referral to their patients. The goal of this work is to investigate whether e-consults can advance the primary-specialty care interface and reduce referral delays. We provide a high-level abstraction of e-consult operations using an analytical framework that quantifies the benefit of e-consults in the context of efficient matching in a flexible service system. With heterogeneous patients who lack information regarding the severity of their condition, e-consults are conceptualized as Bayesian learning to estimate the true severity, parameterized by the level of e-consult efficacy, a measure of the degree of PCPs and specialists' communication and information sharing efforts. The analytical framework of games with incomplete information combines the Hotelling model, the Bayesian inference, and service science to fully capture the patient flow dynamics between the PCP and the specialist modulated by the revelation of hidden patient severity information enabled by e-consults. The fundamental e-consult economics is related to strategic patients who are oversensitive toward the service prices and congestion costs, which leads to the overuse of one type of service. Such a behavior can be mitigated by e-consults that facilitate shared diagnostic efforts that can navigate patients away from the more popular choice toward a more suitable one.