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Healthcare providers today are under mounting pressure to improve care quality while streamlining operations and maximizing reimbursement. This case study demonstrates how end-to-end AI automation—via Scoop—enables data-rich, efficient decision-making by surfacing granular enrollment, engagement, and billing patterns. As value-based care models accelerate, organizations unable to unify and analyze diverse program, clinical, and financial records risk missed revenue and lagging patient outcomes. Here, AI-powered analysis converts fragmented datasets into actionable insights, empowering leaders to drive both operational excellence and improved patient health.
Scoop’s end-to-end analysis uncovered both precise revenue opportunities and actionable engagement insights. Most strikingly, it quantified significant under-billing relative to eligibility, and illuminated which patient groups and offices outperformed in engagement and health outcomes—enabling targeted interventions to maximize both care and financial results.
Key takeaways include high adoption and long-term retention in RPM programs, marked engagement disparities across offices, and critical gaps in cross-program integration. Moreover, the automated identification of key billing predictors, such as enrollment status and monitoring duration, equipped management to close revenue gaps more systematically than with manual review. Finally, stratified risk modeling allowed early identification of high-risk clinical cohorts, focusing quality improvement efforts where they will yield maximum ROI.
A substantial majority of patients were successfully enrolled in Remote Patient Monitoring, establishing RPM as the primary foundation for digital care delivery.
RPM participants averaged over 1.5 years in the program, more than 4 times CCM duration, highlighting RPM’s superior retention capacity.
RPM participants averaged over 1.5 years in the program, more than 4 times CCM duration, highlighting RPM’s superior retention capacity.
Agentic analysis showed most RPM patients engage meaningfully with care, while 78% of CCM patients exhibit no engagement—directing focus toward CCM improvement.
Less than half of eligible RPM patients were billed appropriately, surfacing a significant and actionable reimbursement gap.
Outpatient healthcare organizations increasingly rely on multi-program care models—such as Remote Patient Monitoring (RPM), Chronic Care Management (CCM), and Continuous Glucose Monitoring (CGM)—to address chronic disease management and grow reimbursement. However, rapid program expansion has introduced data fragmentation, inconsistent engagement monitoring, and variable billing performance across offices and patient cohorts. Traditional BI tools struggle to connect program participation, patient health conditions, engagement trajectories, and revenue cycle processes in a single, dynamic view. Leaders face critical questions: Which program structures drive sustained engagement and lasting outcomes? Where are hidden billing gaps? Can risk stratification be automated? Without integrated, agentic analysis, teams risk lost revenues and less effective interventions, particularly as CMS and payers prioritize measurable, high-quality care delivery.
The dataset comprised patient-level records spanning RPM, CCM, and CGM enrollment over a 12+ month period across multiple office locations, featuring thousands of rows and dozens of columns. Primary fields included patient demographics, program enrollments and durations, comprehensive health metrics (blood pressure, heart rate, glucose, oxygen, weight), condition prevalence (hypertension, diabetes, CKD, COPD, CHF), and granular billing events (CPT codes, eligibility, recorded minutes) keyed to time-stamped participation.
Scoop deployed its intelligent pipeline as follows:
The dataset comprised patient-level records spanning RPM, CCM, and CGM enrollment over a 12+ month period across multiple office locations, featuring thousands of rows and dozens of columns. Primary fields included patient demographics, program enrollments and durations, comprehensive health metrics (blood pressure, heart rate, glucose, oxygen, weight), condition prevalence (hypertension, diabetes, CKD, COPD, CHF), and granular billing events (CPT codes, eligibility, recorded minutes) keyed to time-stamped participation.
Scoop deployed its intelligent pipeline as follows:
Through this fully automated, agentic process, the Scoop platform abstracted away manual analytics, codified best-practice rules, and enabled cross-team alignment on clinical, operational, and billing priorities.
Scoop’s agentic ML surface went far beyond standard dashboarding, uncovering complex, actionable patterns previously invisible to traditional BI tools. For example, it flagged that early enrollment age combined with high cholesterol consistently predicts elevated patient risk—signal unobtainable by simple cross-tabs. Moreover, it revealed that most CCM patients show no program engagement (78%), while RPM patients predominantly engage, highlighting structural differences requiring targeted program redesign.
Billing optimization rules extracted from the dataset demonstrated that patient RPM enrollment status (especially "declined" or "unenrolled") is the central predictor of missed opportunities, prompting tailored re-engagement campaigns. Additionally, program duration and engagement interact non-linearly: a subset of patients achieve optimal health improvements within narrow enrollment windows (27-31 days), suggesting critical periods for intervention. Finally, Scoop’s ML identified nuanced BMI and condition-count interplay on engagement—such as long-term, multi-condition patients with overweight BMI responding best to sustained care—insights unreachable via static dashboards. These findings enable precision policymaking and resource allocation that reactive, siloed BI solutions inherently miss.
Providers have already prioritized targeted outreach to under-engaged CCM cohorts and initiated systematic reviews of RPM unenrolled/declined patients to improve billing capture. Data-driven adjustments to patient engagement strategies are underway, with high-value interventions being developed for those at greatest risk based on nuanced ML risk stratification. Next steps include integrating Scoop’s stratification models with care coordination workflows, optimizing program eligibility processes, and leveling engagement practices across varying office locations. Leadership intends to scale AI-driven program and billing monitoring, ensuring continuous margin improvement and better patient outcomes as payer requirements—and patient complexity—continue to rise.