20 Aug
Most dashboards describe yesterday. Trend analysis looks for small shifts as they form: a quality rate edging down in a subset of clinics, emergency arrivals rising above seasonal patterns, or social risks intensifying in a few neighborhoods. Public health treats this as early-warning surveillance; CDC’s National Syndromic Surveillance Program explicitly frames near-real-time signals as a way to trigger faster, proportionate responses.
Two developments make this practical for day-to-day operations. First, quality measures are becoming computable: NCQA’s digital quality measures (dQMs) use CQL and FHIR, while the Electronic Clinical Data Systems (ECDS) method defines acceptable electronic sources for HEDIS and reduces manual abstraction. NCQA reports 19 HEDIS measures are ECDS-reportable in MY 2025, with more shifting by 2029. Second, population-scale access is standardized: the HL7 FHIR Bulk Data Access guide lets an authorized client export data for all patients or defined cohorts, rather than pulling one record at a time. A shared data language via USCDI further aligns what gets exchanged.
How different teams can use real-time trends
Providers (health systems and groups)
- Clinical quality, more often: Recalculate HEDIS measures with digital specs or ECDS inputs, track weekly deltas by clinic or panel, and surface where performance is bending.
- ED operations: Watch arrivals, left-without-being-seen, and boarding time against baseline to anticipate flow issues. Evidence shows real-time analytics and AI methods can help reduce waits when paired with operational actions.
- Transitions of care (TRC): Follow TRC components (admission notice, discharge information, patient engagement, med reconciliation) to keep post-discharge risk from creeping up.
Payers (health plans and ACOs)
- Stars awareness: Stars affect Medicare Advantage Quality Bonus Payments; the ratings published for one year influence bonus determinations in the following payment year. Trends help teams move early.
- Network variation: Compare measure movement by contract and geography; focus outreach where drift starts, not after it spreads.
- Whole-person risk: Add social determinants to prediction to improve identification of members at risk of readmission or high utilization.
Public health partners
Community signals: Overlay syndromic or environmental feeds on chronic-disease cohorts to time outreach and supplies before spikes.
Two to three practical use cases
1) Behavioral health follow-up and transition safety
Follow-up after hospitalisation (FUH) and TRC are sensitive to small operational slips. Recomputing weekly and applying simple shift-detection can reveal a quiet decline in timely follow-up. NCQA’s public specs and descriptions clarify expectations for both measures, making the signal easy to interpret.
2) Emergency department surge alerts
Arrival volume and boarding time can be smoothed with short-horizon analytics to flag anomalies against seasonal baselines, prompting staffing and observation adjustments. Reviews of ED analytics report benefits for throughput and waits when organizations pair signals with established playbooks.
3) Readmission early warnings with social context
Combine clinical, utilization, pharmacy, and SDOH features to rank who is most at risk and most reachable. Studies show adding SDOH improves readmission prediction, which helps target transition bundles and home supports where they matter most.
Aigilx Health’s Trends Analyzer
Aigilx Health offers a Trends Analyzer capability that brings these ideas together so teams can observe population-level shifts and receive concise reports for preventive action and population health. It runs on a standards-based data layer and is designed to surface early movement, not just end-of-month summaries.
- Observe trends across clinical, behavioral, social, and claims data
- Receive concise, actionable reports on measure movement and utilization signals
- Align outputs to quality and operations workflows so teams can act in time
Aigilx Health’s FHIR expertise (the foundation that makes this work)
- Analytics-Ready Enhanced FHIR Server: Built to adapt with Medicare, Medicaid, and quality program changes; supports analytics at scale.
- FHIR-native unification: Consolidates multi-source data in a common format that supports computation and exchange.
- Bulk Data readiness: Implements cohort exports using HL7’s Bulk Data specification for secure, population-scale analytics.
Why act now
Real-time trend analysis moves teams from reporting to readiness. For payers, it reveals measure drift early enough to close gaps and strengthen Star Ratings. For providers, it surfaces rising risk in time to prevent avoidable events and improve outcomes. For public health agencies, it tracks community health shifts as they happen and supports coordinated responses. The result is improved population health, smarter resource allocation, more reliable compliance reporting, and quicker action when new trends emerge.
Ready to act on real-time health signals?
Aigilx Health helps payers, providers, and public health agencies FHIR-enable their workflows, unify data sources, and set up trend analysis pipelines that deliver measurable impact.
Reach out to our team if you’d like a working session on how to:
- Align to NCQA digital quality measures and ECDS
Aigilx health specializes in developing Interoperability solutions to create a healthcare ecosystem and aids in the delivery of efficient, patient-centric and population-focused healthcare.
