PandoraPractitioner: Navigating the Labyrinth of Mental Health Care Preferences with Optimal Search Algorithms and Analysis of Player Strategies

Abstract

The landscape of mental healthcare is intricate and ever-evolving, with a significant demand for support due to the prevalence of mental illness, exacerbated by the COVID-19 pandemic. The challenge of finding a therapist tailored to individual needs further complicates this landscape, with existing platforms falling short in providing effective solutions. This thesis proposes the Pandora's Box Modified Algorithm (PBMA) – rooted in Martin Weitzman’s Pandora's Box Problem, a well-regarded optimal search problem in computer science theory – to assist individuals in navigating the therapist selection process. PBMA enhances existing search algorithms, incorporates user preferences, accounts for uncertainty, and evaluates the costs and benefits of therapist options. The thesis also offers an extensive literature review on the various Pandora’s Box extensions and variations. Finally, the thesis considers the provider's perspective, addressing concerns of profile accuracy and patient retention. By introducing a more sophisticated method, we aim to streamline decision-making and improve outcomes for both patients and providers in the mental healthcare ecosystem.