Custom Value Based Care Analytics Platform
Purpose-Built Analytics Software for Value-Based Care Organizations
VBC success depends on unified, standards-driven value based care analytics that align clinical outcomes to payment models (ACO REACH, MSSP, Medicare Advantage, bundled payments).
We design secure, auditable data pipelines and governance to enable real-time healthcare risk stratification and contract-level financial forecasting while keeping pace with CMS program updates.
Problems we solve:
- Fragmented clinical, claims, PBM and SDOH datasets
- Inconsistent terminology and measure logic across systems
- Lack of model governance, explainability and clinical validation
- Automation that must remain auditable and compliant
Data sources, standards & integration
We implement production connectors and normalize data to standard vocabularies and implementation guides.
Data Source | Key Metrics Captured | Integration Protocols / Standards |
EHR Systems | Encounters, labs, problems, care plans, immunizations | SMART on FHIR (OAuth2/OpenID Connect), FHIR R4 / R4B, US Core / USCDI, HL7 v2.x, C-CDA. |
Claims (payers) | PMPM cost, adjudicated claims, utilization, remittances | X12 837 (claims), 835 (remittance), 270/271 (eligibility), payer REST APIs per CMS Interoperability rule. |
HIEs / Registries | Document exchange, referrals, immunizations | C-CDA, FHIR Document / CarePlan bundles |
PBM / Pharmacy | Medication history, refill gaps, PDC, formulary events | NCPDP SCRIPT / ePrescribing standards, pharmacy data feeds. |
SDOH sources | Food insecurity, housing instability, and transportation barriers | Gravity Project / SDOH Clinical Care IG, FHIR SDOH profiles, referral workflows. |
Remote monitoring | Home vitals, device activity, adherence | FHIR Observation, secure streaming APIs (MQTT/HTTP) |
Notes: we support SMART app launch, server-to-server flows, and Bulk FHIR (population exports) for efficient, auditable population extracts. Bulk FHIR is widely used for population-level exports to research, value based care analytics, and measurement workflows.
Descriptive Analytics in Healthcare
What it does: Provides retrospective dashboards and analytic tools for quality tracking, contract benchmarking, and cost-of-care analysis (HEDIS, NCQA, CMS Stars).
Technical approach
- Data ingestion: Connect via certified APIs and EDI feeds (SMART/Bulk FHIR; HL7 v2.x / C-CDA; X12 837/835; NCPDP).
- Data quality & preparation: Deduplicate, validate, and reconcile claims and clinical events; normalize to SNOMED CT, LOINC, RxNorm, and ICD-10-CM.
- Mapping & enrichment: Align with NCQA/HEDIS value sets and US Core/USCDI profiles for digital quality measurement.
- Analytics layer: Perform aggregations, stratifications, and slicing by cohorts, risk tiers, providers, and contracts.
- Visualization & insights: Provide dashboards, retrospective trend analysis, and drill-down capabilities for care gaps and benchmarking.
- Storage & governance: Store in a governed data lake/warehouse with lineage tracking and de-identification (Safe Harbor or Expert Determination).
Deliverables: HEDIS/NCQA-ready electronic measure libraries, CMS Stars dashboards, PMPM rollups, provider and cohort benchmarking, and equity audit dashboards.
Descriptive — Data Sources & Metrics
Data Source | Key Metrics Captured | Integration Protocols |
EHR Systems | Encounters, labs, chronic condition registries, care plans | SMART on FHIR (App + S2S), FHIR Encounter/Observation, US Core. |
Claims Data | PMPM cost, utilization, readmissions, LOS | X12 837/835, 270/271, payer REST APIs under CMS Interoperability rules. |
HIE Registries | Vaccination records, referrals, documents | C-CDA, FHIR Document, CarePlan bundles |
PBM/Pharmacy Data | Medication adherence (PDC), refill gaps, formulary compliance | NCPDP SCRIPT, pharmacy transaction feeds. |
SDOH Repositories | Food insecurity, housing instability, transportation gaps | Gravity Project SDOH Clinical Care IG, FHIR SDOH profiles. |
Remote Monitoring | Home vitals, medication adherence, activity | FHIR Observation, streaming APIs (MQTT/HTTP) |
Predictive Analytics in Healthcare
What it does: Forecasts hospitalizations, clinical deterioration, cost escalation, and preventive care gaps so care teams can act before events occur.
Technical approach
- Data ingestion & preparation: Ingest structured data from claims, EHR, pharmacy, and SDoH sources; perform patient identity resolution, deduplication, validation, and harmonization into longitudinal patient records.
- Feature store & pipelines: Build reproducible pipelines for feature engineering, metadata management, and versioning to ensure model transparency and auditability.
- Model training: Apply survival analysis for time-to-event predictions; sequential models (LSTM or transformers) for patient trajectories; and machine learning frameworks (Scikit-learn, XGBoost, TensorFlow, PyTorch) for classification/regression.
- Deployment & monitoring: Implement model explainability (e.g., SHAP), continuous monitoring, and drift detection to maintain reliability and compliance.
Operationalization: real-time inference APIs, clinician-facing explainability (feature attributions), human-in-the-loop overrides, and automatic alerts for care managers.
Model Objectives & Algorithms
Model Objective | Algorithms Used | Feature Domains |
Readmission risk forecasting | Gradient boosting, logistic regression | Prior admissions, comorbidities, discharge notes, vitals |
Chronic disease progression | LSTM, Random Forest / Gradient boosting | HbA1c, blood pressure, med adherence, labs |
High-cost patient prediction | XGBoost, survival analysis | Claims history, utilization, SDOH signals |
Preventive care gap prediction | NLP (clinical notes), decision trees/ensembles | Screening history, care gaps, social risk |
Star rating impact forecasting | Gradient boosting, Bayesian simulation | Adherence, screenings, outcome measures |
Predictive analytics use cases in healthcare: identify members at high risk of 30-day readmission, predict uncontrolled diabetes within 90–180 days, flag patients likely to exceed PMPM thresholds under MSSP/MA contracts, and simulate how closing care gaps affects CMS Stars.
Prescriptive Analytics in Healthcare
What it does: Turn predictions into auditable actions (triage, outreach, scheduling, referral routing) embedded in care workflows.
Technical approach
- Decision engines combining rules and optimization: Drools/rules + optimization (OptaPlanner, OR-Tools) — commercial solvers (Gurobi) supported with appropriate licensing.
- Integrations (SMART on FHIR apps, secure APIs) to push tasks/notifications into EHR inboxes, care management platforms, and patient messaging.
Engine Types & Mapped Measures
Engine Type | Input Signals | Automated Recommendation | Example Measures Supported |
Care management prioritization | Risk scores, utilization, SDOH | Rank patients for outreach by ROI / risk | Readmission reduction, follow-up after hospitalization |
Preventive care optimizer | Age, comorbidities, screening history | Recommend screenings & scheduling nudges | Breast & colorectal cancer screening, immunizations |
Utilization redirection engine | ED admissions, PCP access | Suggest urgent care/telehealth alternatives | Avoidable ED use, PCP access/utilization |
Medication adherence agent | Pharmacy claims, refill gaps, vitals | Trigger reminders, pharmacy outreach | Medication adherence (PDC), statin use |
Resource allocation optimizer | Provider load, referral demand | Recommend staffing or referral redistribution | Care coordination & access measures |
Prescriptive analytics use cases in healthcare: automated outreach lists for care managers, targeted preventive screening campaigns, ED diversion recommendations in real time, automated refill reminders, and pharmacist work queues.
Security, privacy & compliance — concrete, accurate statements
Technical controls we implement
- Encryption: TLS 1.3 for in-transit; AES-256 (or equivalent) at rest.
- Access controls: Role-based and attribute-based access control (RBAC/ABAC), SSO (SAML/OAuth), and admin MFA.
- Contracts & attestations: BAA available for covered entities; SOC 2 Type II readiness and audit support.
- De-identification: Safe Harbor or Expert Determination pipelines for secondary use data.
- Auditability: Immutable audit logs, data lineage, and evidence packages for audits.
Regulatory & program notes (fact-checked)
- ONC / 21st Century Cures Act & information blocking: our API-first, SMART/Bulk approach aligns with ONC Cures Act requirements (information blocking, certified health IT APIs).
- CMS Interoperability & Patient Access: payer APIs and certain claims data availability are required under CMS rules; we design payer integrations to meet these expectations.
- 21 CFR Part 11: Part 11 applies to FDA-regulated records and clinical investigation systems; we provide Part 11-capable controls where clients require regulatory compliance (research/submissions). FDA guidance clarifies the scope and applicability.
- 42 CFR Part 2 (SUD): SUD records have enhanced confidentiality; pipelines can be configured with consent gating and special handling. (Legal counsel required for operational rules.)
Model governance, fairness & clinical validation
We embed governance across the model lifecycle: versioned model registry (MLflow/Kubeflow), validation & calibration suites, drift detection, documented clinical validation plans (pilot design, clinician review), subgroup performance and fairness reports, and human override workflows to prevent automated harm.
Deployment & operations
- Deployment options: multi-tenant cloud (AWS/Azure/GCP), private VPC, hybrid on-premises.
- Observability: Prometheus + Grafana for infra & pipeline metrics; automated data quality and lineage dashboards.
- SLAs & disaster recovery: configurable RTO/RPO, backup, and incident playbooks.
Pilot timeline & delivery model (sample)
Typical pilot: 8–12 weeks (discovery, data access & BAA, ingest & ETL mapping, HEDIS measure proofing, model pilot with clinical validation). Deliverables include a data readiness report, HEDIS/NCQA measure pass/fail audit, and a production-ready inference endpoint.
Why choose our VBC healthcare analytics solutions
- Standards-first (SMART/Bulk FHIR, X12, NCPDP, Gravity SDOH IG) — reduces time to production and audit risk.
- Production controls: BAAs, SOC 2 Type II readiness, RBAC/ABAC, model governance, and documented clinical validation.
- Deep mapping to NCQA/HEDIS and CMS program logic (ACO REACH, MSSP, MA).
Contact us to design a pilot that reduces avoidable costs, improves quality scores, and empowers care teams with real-time, auditable insights.
Ready to Solve Your Value-Based Care Challenge?
Let’s talk about your unique workflows and design a custom digital health solution that supports outcome-based care, improves population health, and aligns with value-based reimbursement models.
Whether you’re navigating HEDIS metrics, improving care coordination, or optimizing performance-based contracts, we can help.
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Build Your Custom Implementation Plan
Your implementation plan includes integrations, MVP timelines, and long-term support strategies. We build your value-based care solution around real workflows, compliance requirements, and measurable outcome goals.
Launch and Optimize for Outcome-Based Development
Our solutions combine predictive analytics, AI-driven clinical insights, and secure, interoperable data flows. Whether you need compliance tools, shared savings tracking, or a care coordination engine, we align it with your quality metrics, reimbursement goals, and care delivery model.
Ready to Improve Outcomes with Custom Value-Based Solutions?
We design and build custom software for value-based healthcare, built around your data, workflows, and objectives. Whether you need to unify data, support attribution, or track performance across contracts—we’re here to build what works.
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