摘要

Objectives: Attendees will be able to: (1) define AI-based patient profiling and its ethical boundaries; (2) build a privacy-preserving data model combining EHR, CRM and marketing signals; (3) segment patients with clustering/propensity models to surface “next best action”; (4) operationalize profiles in the front office (scripts, offers, timing, channel); (5) run A/B tests; (6) track business impact with CR, CAC, LTV, no-show, and retention metrics. Introduction: Many clinics overspend on ads while losing patients in generic funnels. We introduce a practical framework for AI-driven patient profiling focused on the commercial journey (not diagnosis), designed to raise conversions and lifetime value with rigorous privacy, consent and governance. We summarize common data pitfalls, why prior “one-size-fits-all” CRM rules fail, and how personalization aligned to clinical criteria improves both revenue and experience. Materials / method: Data: de-identified EHR encounters, scheduling logs, CRM interactions, campaign source, demographics, consent flags. Features: recency/frequency/monetary, channel responsiveness, lead score, no-show risk. Models: k-means/GBDT for segments and uplift; SHAP for explainability; fairness checks. Deployment: profiles to CRM as “next best action” (message, offer, timing, channel); staff playbooks. Evaluation: randomized A/B by site and week; mixed-effects analysis on CR, CAC, LTV, no-show Results: Across multi-clinic deployments, AI profiling outperformed business-as-usual: median conversion +11.8pp, acquisition cost −21%, no-show −19%, time-to-booking −28%, and 6-month LTV +27%. Gains were consistent across specialties; parity difference <3pp across age/gender segments. Human-override rate ~8% improved acceptance. Effects remained significant in mixed-effects models (p<0.05) and robust in sensitivity analyses excluding heavy-promo weeks. Conclusion: AI patient profiling, when governed by consent, transparency and clinician oversight, reliably lifts revenue and patient experience. The biggest wins come from small, testable nudges—right channel, right timing, right script. We provide a deployment checklist, governance template and KPI dashboard so clinics can replicate results within 60–90 days while maintaining fairness and data protection by design.