Assortment Optimization for Patient-Provider Matching

Primary care providers are essential to the healthcare ecosystem because they are the first point of contact for many patients (Pearson and Raeke, 2000; Wu et al., 2022). Patients rely on primary care providers for routine checkups and referrals to specialists. Moreover, care continuity can instill trust and improve medication uptake rates and patient health (Wu et al., 2022). Unfortunately, high provider turnover rates frequently lead to patients without an assigned provider (Reddy et al., 2015). Provider turnover disrupts patient care and leads to worse care (Reddy et al., 2015). In principle, healthcare administrators reassign unmatched patients to other providers; however, in practice, the process takes months due to provider scarcity and the logistical burden of rematching and coordinating patient matches (Hedden et al., 2021). While many patients find their new provider quickly, others have to wait years to find a new provider due to large numbers of patients, high turnover rates, and provider scarcity (Hedden et al., 2021; Shanafelt et al., 2012). Algorithms that automatically match patients and providers can reduce logistical hassle but require balancing patient autonomy and system-wide utility. For example, while automatically assigning each patient to a provider would decrease wait times, it also reduces patient autonomy because patients cannot select their provider (Entwistle et al., 2010; Gaynor et al., 2016). 1