Preventive care gaps remain a persistent challenge for health plans, particularly among disengaged or underserved populations. These gaps often exist not because members are noncompliant—but because the clinical data used to identify them is outdated or incomplete. As a result, care teams spend time and resources on outreach that may be unnecessary, while missing opportunities to identify and address emerging or undocumented needs. This session will explore how combining real-time clinical data aggregation with in-home care delivery can improve the accuracy and efficiency of preventive care programs. By integrating claims, pharmacy history, and health information exchange (HIE) data, health plans can build a more complete and current clinical profile for each member. This enables more targeted outreach, reduces false positive care gaps, and surfaces opportunities for earlier intervention. We will share insights and outcomes from a national Medicare program where this approach was applied to hundreds of thousands of disengaged members in 2024, particularly targeting those flagged as high risk due to social barriers, with the program continuing and expanding into 2025. Across [NUMBER; currently 23 and counting] states, the health plan identified, documented, and closed [XX]% of false positive care gaps prior to outreach and identified [XX]% new gaps that had previously gone undetected. As a result, more than [XX]% of contacted members received in-home preventive care across [XX,000] in-home visits addressing an average of [NUMBER; >2 in 2024 and targeting >3 in 2025] HEDIS measures per visit. (The latest metrics will be provided closer to event in order to provide the most up-to-date data.) False positive gaps—such as those related to blood pressure control, A1C, or colorectal cancer screening—can often be resolved without a clinical encounter when more current evidence is available through recent labs or screenings conducted elsewhere. By cleaning up outdated gap lists using aggregated clinical data, health plans can avoid unnecessary outreach, reduce member abrasion, and lower operational costs. Equally important, clinical data aggregation helps surface previously unknown or unreported care needs. Longitudinal data can reveal trends that signal deterioration in chronic conditions or identify members who have fallen out of care. These insights support more proactive risk stratification and allow care teams to intervene earlier—before gaps widen or acuity increases. In this model, only members with verified, open preventive care gaps receive outreach and are offered a an in-home visit. These visits are conducted by clinically trained professionals who can perform diagnostics, screenings, and assessments—delivering 20 or more preventive services in a single encounter at half the cost of a traditional Nurse Practitioner house call. This structure supports the closure of gaps across more than a dozen HEDIS measures, including blood pressure screening, A1C control, diabetic eye exams, and colorectal cancer screening. After each visit, results are compiled into a clinical summary and shared with the member’s primary care provider, with the ability to send results directly into providers’ EHR inboxes. All encounter data is also surfaced to HIEs to support broader care coordination, avoid future redundancy, and ensure continuity of care. This session will focus on the operational and clinical implications of combining clinical data aggregation with in-home care delivery at scale. We’ll discuss how health plans can apply this model to improve HEDIS performance, address disparities in access to preventive services, and make better use of clinical resources. By targeting members who truly need care and enabling delivery of multiple services in a single visit, plans can drive quality improvement while containing costs. Attendees will leave with a clearer understanding of how real-time clinical data can reduce unnecessary outreach, surface unmet needs, and enable more efficient, precise, and equitable preventive care.