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As healthcare organizations scale Remote Patient Monitoring (RPM), Chronic Care Management (CCM), and Continuous Glucose Monitoring (CGM) initiatives, the ability to identify high-risk patients, measure engagement, and maximize billing efficiency becomes mission-critical. Yet, disparate data silos and shifting enrollment trends threaten both clinical quality and revenue performance. This case study showcases how Scoop’s agentic automation rapidly bridged these gaps—enabling healthcare operations teams to precision-target interventions, improve patient adoption, and unlock missed billing opportunities.
Scoop surfaced mission-critical outcomes at both the patient and operations level. Dramatic variation in patient engagement and program enrollment was revealed—enabling the targeting of underperforming offices for rapid improvement. Importantly, agentic ML pinpointed the exact clinical and operational levers associated with improved patient outcomes and revenue realization. High patient participation in RPM, especially when cross-enrolled in CCM or CGM, correlated with measurable improvements in glycemic control and a lower incidence of uncontrolled chronic conditions. At the same time, significant inefficiencies in billing conversion—both within and across offices—were quantified, exposing previously unseen gaps in program monetization and patient care delivery.
RPM remains the most adopted program by patient count, serving as the backbone for digital chronic care management efforts.
A substantial share—up to 795 patients—recorded virtually zero engagement (≤4 minutes monthly) in CCM, highlighting a critical opportunity for targeted outreach.
A substantial share—up to 795 patients—recorded virtually zero engagement (≤4 minutes monthly) in CCM, highlighting a critical opportunity for targeted outreach.
Even at the leading site, fewer than half of RPM-eligible patient activities translated to billable events, exposing major revenue leakage.
Only 2.5% of eligible patients completed billable CGM services, demonstrating untapped opportunity for both clinical and financial impact.
Healthcare organizations are under mounting pressure to elevate quality of care while tightening operational efficiency in chronic disease management. Rapidly growing programs—like Remote Patient Monitoring (RPM), Chronic Care Management (CCM), and Continuous Glucose Monitoring (CGM)—generate a trove of granular data from diverse facilities and timeframes, but conventional BI tools struggle to harmonize this complexity. Common challenges include low engagement rates in CCM, inadequate risk stratification, opaque enrollment patterns across programs, and significant gaps in converting care activities into billable revenue. Leaders need actionable clarity on which patient populations benefit most from multi-program enrollment, the health indicators that signal rising risk, and why whole offices underutilize critical billing codes. Traditional reporting fails to diagnose where interventions or resource reallocation will yield the highest impact, often leaving the full value of these programs unrealized.
Scoop ingested a detailed, transactional dataset combining patient participation across RPM, CCM, and CGM programs, spanning several years and thousands of patients (with over 30 key attributes per case, such as condition presence, biometric averages, enrollment timelines, program status, and CPT billing activity). By unifying granular enrollment data, longitudinal health metrics, engagement minutes, and billing records across dozens of offices, Scoop’s agentic AI enabled true end-to-end analytics in a fraction of the time required by legacy BI.
Scoop ingested a detailed, transactional dataset combining patient participation across RPM, CCM, and CGM programs, spanning several years and thousands of patients (with over 30 key attributes per case, such as condition presence, biometric averages, enrollment timelines, program status, and CPT billing activity). By unifying granular enrollment data, longitudinal health metrics, engagement minutes, and billing records across dozens of offices, Scoop’s agentic AI enabled true end-to-end analytics in a fraction of the time required by legacy BI.
With zero manual scripting and no data engineering bottlenecks, the healthcare ops team transitioned from weeks of laborious reporting to instant, root-cause-level insights—empowering proactive, data-driven action.
Scoop’s agentic pipeline uncovered several non-obvious, high-impact patterns that traditional dashboards would routinely miss. Machine learning revealed that engagement in CCM is determined almost solely by the actual minutes recorded—demographic or diagnostic information contributed no meaningful predictive value. This streamlined the engagement model, allowing operational teams to focus intervention precisely where minutes lag. Patient risk was found to hinge not just on individual diagnoses, but on specific comorbidity clusters—namely, the simultaneous presence of congestive heart failure, diabetes, hypertension, and obesity. Notably, a BMI threshold effect was confirmed: patients with BMI ≤24 presented low risk regardless of their other conditions, while BMI ≥30 in conjunction with multiple comorbidities drove risk sharply higher. In diabetes management, real-time glucose monitoring averages established clear cutoffs correlating to A1C outcomes—empowering caregivers to anticipate glycemic loss of control before lab results confirm it. Additionally, the analysis surfaced critical operational nuances; for instance, high total enrollment at a facility did not guarantee billing efficiency, as administrative practices and engagement durations varied drastically by office. Without Scoop’s end-to-end automation—spanning enrichment, ML, and root-cause narrative synthesis—these multi-factorial insights would have required weeks of manual data science.
The healthcare operations team mobilized immediately to close the identified engagement and billing gaps. Offices with high enrollment but poor billing conversion were prioritized for workflow audits, enabling process changes targeting the specific predictors surfaced by Scoop. Patient engagement campaigns were tailored to the time-based thresholds for CCM, with automated triggers for outreach when minutes dropped below actionable levels. Risk stratification protocols were updated to leverage the revealed comorbidity patterns and BMI cutoffs, allowing for earlier identification and more proactive care of the highest-risk patients. Looking ahead, leaders plan to automate quarterly deep dives across all programs using Scoop—further integrating these data-driven rules into both clinical and revenue cycle processes.