Medicare.AI: Design, Implementation, and Safety Evaluation of an Intelligent Healthcare Management Platform

Abstract

Healthcare software prototypes usually have conversational help, scheduling, role-specific dashboards, and real-time data. Their evaluations focus more on feature lists than on failure modes. This paper reports a reproducible evaluation of safety and access control for Medicare. React, Node.js/Express, MongoDB, and socket.io. AI.IO educational health care platform. We froze a 20-prompt safety benchmark covering emergencies, first aid, medication use, mental health and general questions. We defined eight source-level security checks, including JSON Web Token middleware, role guards, appointment ownership, patient-only creation, password hashing, secret handling and Socket.IO room access. The baseline deterministic chatbot passed 7 out of 20 prompts (35%) and 5 out of 8 security checks. The dynamic loopback test also showed that an unauthenticated client could join the doctor telemetry room. Remediation introduced specific intent-matching, explicit high-risk routes, authenticated socket.io middleware, room-level role restrictions, and appointment ownership enforcement. Our final benchmark passed all 20 prompts and all eight source-level checks. The study does not claim clinical efficacy: no patient records, clinician participants, diagnostic model, or clinical outcomes were assessed. The Atlas network allowlist blocked database-backed exploitation and post-remediation dynamic socket tests, which were reported as unexecuted. Thus, the contribution is a transparent case study of how an ambitious student prototype can be transformed into a more testable, safety-aware system.