Batch Population Health Score: 3.8/5.0

Care Gap Outreach (Gaps-in-Care)

Scheduled Batch & Periodic Processing | External audience

The Problem

Preventive care gaps (mammograms, colonoscopies, well-child visits, adult wellness exams) drive poor quality scores and CMS payment penalties (MIPS, Star Ratings, HEDIS). Health systems struggle to identify and outreach patients with overdue preventive services. Systematic identification and personalized outreach improves quality measure performance and patient health outcomes.

What the Agent Does

Batch runs weekly: queries claims data and EHR for entire attributed population
Agent identifies patients overdue for preventive services: mammogram (age 40 to 74, not done >2 years), colonoscopy (age 45 to 75, not done >10 years for normal screening), well-child visits (age-specific HEDIS measures), wellness visits (Medicare annual wellness exams)
Generates outreach list, prioritised by overdue-ness: "High priority: 62F due for mammogram (3 years overdue)"
Initiates outreach: SMS reminder, automated voice call, patient portal message
Facilitates scheduling: patient clicks appointment link; offers available slots
Coordinates with provider: reserves screening capacity; sends confirming reminder
Tracks completion: confirms screening completed; documents in EHR and claims system
Generates report: % of population with screening gaps; closure rate; quality measure performance

Data Requirements

Primary Data Sources: Claims data (service history), EHR preventive care records, quality measure specifications (HEDIS, MIPS, CMS Star Ratings), patient contact info, scheduling system capacity, patient demographics (age, sex, risk factors)
Classification: PHI/PII (patient identifiers, preventive care history)
Quality Metrics: Gap identification accuracy >95%; outreach completion rate >70%; screening scheduling rate >50%; completion rate >60%
Complexity: Medium (data integration, gap logic, outreach orchestration, scheduling coordination)
Systems: Epic (EHR/scheduling), Arcadia (population health analytics), Innovaccer, claims system

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	4	0.40
Cost Avoidance	10%	4	0.40
Strategic Leverage	5%	4	0.20
Data Availability	15%	4	0.60
Process Clarity	15%	4	0.60
Ease of Implementation	10%	4	0.40
Fallback Available	10%	4	0.40
Audience (Int/Ext)	10%	3	0.30
Composite	100%		3.80

Why It Scores Well

Regulatory Mandate: Quality measures (MIPS, HEDIS, Star Ratings) directly incentivize preventive care gaps closure
Population Health Impact: Improved preventive care improves health outcomes; cancer detection rates increase
Operational Efficiency: Automated identification and outreach frees care coordinators to focus on complex patients
Scalability: Applies to entire attributed population; batch processing
Evidence Base: Proactive outreach proven to increase screening completion by 20 to 40%

Regulatory Alignment

CMS Quality Measures: MIPS and HEDIS preventive care measures explicitly tracked
Medicare Star Ratings: Preventive care is major quality score component
HIPAA: Preventive care screening history is PHI; standard access controls apply
State Screening Laws: Vary by state (e.g., some states mandate free cancer screening); agent respects state requirements

Sprint Factory Fit

2 to 3 sprints (claims/EHR data integration, quality measure logic, outreach workflow, scheduling coordination, compliance review)

Sprints Required: 2 to 3 sprints (claims/EHR data integration, quality measure logic, outreach workflow, scheduling coordination, compliance review)
Timeline: 4 to 6 weeks from requirements to pilot
Governance: Quality officer and primary care leader approval of gap definitions; monitor outreach completion and screening rates monthly
Success Metrics: Gap identification >95% accuracy; outreach completion >70%; screening scheduling >50%; completion rate >60%; quality measure score improvement >10%

Comparable Implementations

Arcadia Health: population health analytics
Innovaccer: care coordination platform
Epic Quality Analytics
CMS MIPS and HEDIS Measure Specifications

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Governance Risks to Consider

Before deploying this use case, review these agentic AI risks from the Corvair Risk Catalogue. Each is scored on the DAMAGE framework and mapped to regulatory expectations.

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