Care Management Platform

Part of the worlds-biggest-software-project initiative.

An open-source, AI-native platform for population-scale chronic disease management, care gap closure, and coordinated patient outreach.

Care Management Platform is a comprehensive tool for health systems, ACOs, FQHCs, and managed care organisations that need to identify at-risk patients, enroll them in evidence-based disease management programs, and systematically close care gaps across large populations. It replaces fragmented manual workflows with automated risk stratification, multi-channel outreach, collaborative care planning, and real-time quality measure reporting -- all designed from the ground up to leverage AI where it matters most.

Why Care Management Platform?

Every incumbent is proprietary and expensive. All seven major platforms analysed (Epic Healthy Planet, Azara, Innovaccer, Arcadia, Oracle Health, Persivia, Salesforce Health Cloud) are closed-source commercial products with no open-source alternative identified in the market.
Vendor lock-in is structural. Epic Healthy Planet only works well inside Epic; Oracle HealtheIntent is optimised for the Oracle/Cerner ecosystem. Organisations running mixed EHR environments are forced into costly integration projects or second-best tools.
SDOH is bolted on, not built in. Most platforms treat social determinants of health screening as an add-on feature. Validated instruments like AHC-HRSN and PRAPARE, community resource referral, and SDOH outcome tracking remain fragmented across separate tools.
Risk models carry hidden bias. Commercial risk stratification models trained on historical claims data systematically underestimate risk in under-served populations who have historically used less care, leading to inequitable program enrollment.
Small organisations are priced out. Most platforms target large health systems. Community health centres, small primary care practices, and resource-constrained organisations lack access to affordable, purpose-built care management software.

Key Features

Risk Stratification and Population Segmentation

Multi-factor risk scoring combining claims, clinical, and demographic data
Chronic disease registries for diabetes, hypertension, COPD, heart failure, and depression
Explainable risk scores showing specific contributing factors for targeted interventions
AI-driven predictive models that go beyond rule-based scoring
Equity-aware risk model validation detecting and correcting bias against under-represented populations

Care Gap Identification and Quality Measures

Automated detection of overdue preventive services, missing labs, medication adherence gaps, and unmet screenings
Mapping to HEDIS, MIPS, and value-based contract quality measures
Real-time performance dashboards with drill-down to individual patient opportunities
Predictive quality measure scoring identifying patients at risk of missing targets
CMS CCM billing compliance with CPT 99490, 99491, 99487, and 99489 time tracking and documentation

Care Plan Management and Coordination

Collaborative care plan documentation with problem lists, goals, interventions, and patient-reported outcomes
Care team task assignment and workflow routing across coordinators, nurses, social workers, and physicians
Caseload management dashboards with capacity visibility and dynamic workload balancing
Disease management program templates with configurable evidence-based protocols and escalation pathways
Mobile access for field-based care coordinators

Patient Outreach and Engagement

Multi-channel outreach: automated call, SMS, email, and patient portal messaging
Consent management and communication preference tracking
ML-optimised outreach timing and modality selection per patient
Contact rate tracking with engagement and ROI metrics
Non-responder escalation strategies

Data Integration and SDOH

Bidirectional EHR integration (Epic, Cerner, Athena, and others) with claims, labs, and pharmacy data
Master patient index for longitudinal patient record construction across disparate sources
SDOH screening with validated instruments (AHC-HRSN, PRAPARE)
Community resource referral integration with eligibility checking and outcome tracking
Health information exchange (HIE) connectivity

AI-Native Advantage

Unlike incumbents that have retrofitted AI onto legacy architectures, Care Management Platform is designed from the start to apply machine learning where it creates the most clinical and operational value. AI-driven intervention matching predicts which patients will respond to which programs based on characteristics, preferences, and past outcomes -- moving beyond one-size-fits-all care plans. Outreach timing optimisation learns each patient's engagement patterns to maximise contact rates. Readmission risk prediction and medication adherence models enable preventive action before complications occur. Equity-aware risk model validation continuously monitors for demographic bias and auto-recalibrates to ensure fair program enrollment.

Tech Stack & Deployment

The platform targets self-hosted and cloud deployment, with a hybrid model for organisations that require on-premise data residency for PHI. EHR integration uses standard healthcare interoperability protocols (HL7 FHIR, HL7 v2) alongside direct API connectors for major EHR platforms. SDOH screening instruments (AHC-HRSN, PRAPARE) are publicly available and licence-free. CMS CCM billing requirements follow published regulatory specifications. The architecture supports multi-source data aggregation and normalisation across EHRs, claims systems, pharmacy benefit managers, and HIEs.

Market Context

The chronic disease management application market is projected to reach $20.28 billion by 2032, growing at a CAGR of 10.32%. The CMS ACCESS model launching in July 2026 introduces performance-contingent payments of $15--$35 per member per month for enrolled chronic care management programs, creating strong new financial incentives for platform adoption. Target buyers include health systems with value-based contracts, Medicare Advantage plans, Medicaid managed care organisations, FQHCs, and ACOs.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.