Walk through every stage of the CAHPS/HOS data lifecycle. See how healthcare organizations handle it today — and how an intelligent approach eliminates weeks of manual work, fragile processes, and tribal knowledge.
Membership file pulled from enrollment system. Manually cross-referenced against Do Not Contact lists in separate spreadsheets. Hospice flags checked in another system. Continuous enrollment verified with ad hoc SQL queries. Cleaned list exported to CSV and emailed.
Membership data flows through automated validation rules — DNC, hospice, deceased, continuous enrollment — all checked instantly. Exceptions flagged for human review with recommended actions. Full audit trail maintained.
Sample frames manually calculated per CMS specifications. Files sent to survey vendor via secure transfer. Fielding progress tracked through vendor's separate portal. No real-time visibility into response rates.
Sample frames auto-generated based on current CMS methodology. Real-time response tracking as vendor data flows in. Disposition codes validated automatically. Response rate alerts trigger when targets are at risk.
Vendor returns flat files. Someone loads into SQL Server manually. Scoring logic maintained in fragile legacy scripts — top-box conversion, 0-100 scaling, composite calculations. One person understands the code. CMS methodology changes require manual script updates.
Returned data auto-validated against expected schemas. CMS scoring methodology maintained as versioned, testable rule sets. When CMS updates specs, rules update once and cascade everywhere. Composite calculations run instantly with full transparency.
Complex SQL joins to attribute members to providers. Provider to clinic to clinic group to market to region — each level a separate query. NPI mismatches cause silent failures. Attribution logic lives in one analyst's head. When they leave, institutional knowledge walks out the door.
Attribution hierarchy codified and centrally managed. NPI mismatches flagged with AI-suggested resolutions based on claims patterns and credentialing data. Changes at any level cascade automatically through the rollup. Documentation is built into the system, not dependent on tribal knowledge.
Scored data exported to Tableau or Excel. Static dashboards built manually. CAHPS scores, visit data, HEDIS measures, and claims analyzed in completely separate tools. Correlations discovered by accident if at all. The visit-to-score relationship? Nobody's connecting that.
Unified intelligence layer connects CAHPS, HOS, HEDIS, claims, utilization, and provider data. AI identifies patterns and anomalies automatically — like the direct correlation between PCP visit frequency and CAHPS scores. Insights surface proactively instead of waiting for a human to stumble onto them.
Quality team reviews dashboards and manually identifies intervention targets. Outreach lists built in spreadsheets. Phone scripts created ad hoc. Progress tracked informally. No closed loop connecting outreach back to score improvement.
System automatically generates prioritized intervention lists — which members to contact, which providers need support, which outreach approach to use — based on integrated data. Pre-built, evidence-based scripts ready to deploy. Closed-loop tracking connects every outreach to measurable score movement.