Quick orientation, the measures you live with

• How many distinct quality measures does your team report across CMS, state, and commercial programs each year? Options: 1-25, 26-50, 51-100, 101-250, 250+
• Name the measure families that create the largest manual abstraction volume for your team Options: Outpatient preventive and HEDIS-style measures, Inpatient hospital quality measures, MIPS / eCQM measures, ACO / population health measures, State-specific programs, Other
• Walk me through the last reporting cycle where a missed data element changed a payment, a contract outcome, or a public score
• How many full-time equivalent abstractors support measure abstraction today? Options: 0 (relying on clinical staff), 1-3, 4-9, 10-25, 25+
• Imagine your team could eliminate one recurring manual abstraction task next quarter, which would it be and why?

Where the process breaks and quietly costs you

• Identify a recurring failure in your current workflow you have come to accept as normal, even though it costs reimbursement or reputation
• On average, how often does a measure fail validation during your pre-submission checks? Options: Almost every reporting cycle, Often, Occasionally, Rarely, Never
• Describe a recent example where conflicting measure definitions between payer programs or state rules caused rework or a missed submission
• Select the root causes that most often explain failed validations in your experience Options: Missing documentation in chart, EHR data mapping errors, Timing/window calculation mistakes, Abstractor interpretation differences, Payer or program spec conflicts, Extraction or API gaps, Other
• Which single failure, if resolved, would raise your overall quality score enough to change a contract payment or public ranking?

Where your data hides its exceptions and surprises

• How often does structured EHR data used for automated calculation contradict what your chart reviewers find? Options: Almost always, Frequently, Sometimes, Rarely, Never
• List the EHR modules and document types you rely on for abstraction, for example problem lists, flowsheets, discharge summaries, or clinical notes
• Select the data sources you currently feed into measure calculation Options: Primary EHR structured fields, Clinical notes (unstructured), Lab systems, Pharmacy/medication records, External registries, Claims data, Other
• Who owns access to the EHR endpoints and APIs, and who would grant credentials for a pilot?
• Is there an EHR endpoint or data domain your team cannot share that would block automating a specific measure? Options: Yes, blocking (please describe next), Yes, but a workaround is possible, No, required domains are shareable, Unsure
• If you answered yes, which domain or endpoint and what prevents access?

Who has to be involved so this does not stall

• Recall a missing stakeholder in past projects that caused delays or prevented acceptance, who was it and what happened?
• Identify the roles that must be available during configuration and testing Options: Chief quality officer or designee, Director of quality / quality manager, Abstractor leads, Health IT / EHR interface team, Compliance / legal, Data governance / privacy officer, Clinical subject matter experts, Other
• Who holds final sign-off authority on measure accuracy and submission readiness in your organization? Options: Quality leader, Clinical committee, CIO or IT leader, Compliance/legal, Other
• Describe current staffing capacity in the abstraction team and any planned changes to headcount or roles in the next 6 months
• Would lack of a named executive sponsor stop you from beginning a pilot this quarter? Options: Yes, No, Depends (please explain)

The other options you are weighing

• Choose the option you are most seriously considering instead of an external quality automation partner Options: Keep current vendor, Build an internal solution, Switch to a different external vendor, Continue manual processes and triage, Other
• Describe the incumbent or internal team currently responsible for measure reporting and what they say needs to change
• Rank the top criteria that will make you choose a vendor over staying with the current approach Options: Accuracy vs manual benchmark, Faster time to submission readiness, Lower total cost of ownership, Reduced abstraction FTEs, Demonstrated EHR integration capability, Compliance and security assurances, Other
• Has anyone proposed an internal build to solve abstraction and eCQM extraction? If so, who proposed it and what timeline was suggested?
• What would have to be true about your current approach for you to keep it instead of moving to a partner?

Practical gatekeepers: technical, legal, and scheduling constraints

• Which single technical dependency, if missing at go-live, would force a pause or scope reduction?
• Select all that are already available for a pilot Options: EHR test API endpoints, Named data owner contact, Sample de-identified patient set, Network access and firewall rules pre-approved, Existing data sharing agreement, Sandbox credentials, None of the above
• Estimate how clean and mapped your data currently is against measure specifications, on a 1-5 scale Options: 1 - not mapped at all, 2 - sparse mapping, 3 - partially mapped, 4 - mostly mapped, 5 - fully mapped and clean
• List any regulatory or privacy approvals required before data can be used in a pilot and the expected time to obtain them
• Who on your team will be the primary technical point of contact for API and integration work?
• If we find gaps in sample data during testing, how quickly can your team provide additional examples? Options: Within 48 hours, Within 1 week, Within 2-4 weeks, Longer than 4 weeks, Unsure

How you will judge accuracy and say yes

• Assume automated abstraction results differ from manual benchmarks, what tolerance will leadership accept before rejecting the pilot? Options: ±1-2 percentage points, ±3-5 percentage points, ±6-10 percentage points, No tolerance, must match exactly, Unsure
• Describe your current manual benchmark process, including sample size, reviewer selection, and reconciliation steps
• Pick the acceptance criteria that must be met before you consider the system submission-ready Options: Accuracy threshold vs manual benchmark, Reproducible extraction for target measures, Successful end-to-end submission test, User acceptance by abstractors, Clinical sign-off on edge cases, Operational runbook and owners documented
• Who will approve the acceptance criteria and the final go-live decision? Options: Quality leader, Quality committee, C-suite (CIO/COO), Clinical leaders, Other
• If the pilot meets your acceptance criteria, what is the earliest date your organization could complete contract approval and data access?

Decision triggers, timing, and the very next steps

• If the pilot proves the numbers, what stops you from signing the agreement that week?
• Choose the timeline that best matches your target for a pilot start Options: Within 2 weeks, Within 1 month, Within 2-3 months, 3-6 months, Unsure
• Which measures or measure families would you prioritize for a pilot and why?
• List the internal milestones that would trigger moving from pilot to full implementation Options: Accuracy thresholds met, Operational readiness checklist complete, Executive sign-off, Budget approval, Successful payer submission test, Other
• Who else should we involve in the next conversation and what decision or information do they need to commit?
• On a scale, how urgent is this initiative for your organization right now? Options: Critical - must progress now, High priority - this quarter, Medium priority - 3-6 months, Low priority - 6+ months, Undecided

• Solution Experience Session — Measure Automation

• Confirm the current state and its cost to your team
• You confirm the demonstrated workflow eliminates the manual rework and mapping gaps you described for the two representative measures.
• Provide two representative measures, the associated measure specifications, and a de-identified sample of charts or extracts for the scenarios demonstrated.
• You agree that the side-by-side score comparison and exception handling approach provides a path to an acceptance threshold for measure accuracy.
• Map two representative measures and scenarios
• Deliver a benchmark comparison report showing platform-calculated scores versus your manual scores for the demonstrated measures within 10 business days after receipt of sample data.
• Run an end-to-end scenario using your measure and data
• You identify remaining evidence and data access needed to complete validation and move toward a pilot or mutual commit.
• Identify named data owners and the target acceptance thresholds for measure accuracy and submission readiness.
• Compare calculated scores to your manual benchmark
• Validate the future state

• Solution Experience Session — Measure Automation
• Solution Experience Deck — Measure Automation
• Solution Brief — Measure Automation

• meeting
• slides
• document

• Configure EHR interface for eCQM extraction
• Map and encode measure specifications
• Deploy automated chart abstraction workflows
• Set abstraction exception handling and triage rules
• Run parallel abstraction and output reconciliation
• Configure measure library and program coverage
• Implement care gap detection and provider notifications
• Configure submission-ready reporting and exports
• Integrate with payer and regulatory submission endpoints
• Configure measure trend dashboards and alerts
• Train abstraction team on platform processes
• Activate provider worklists and outreach workflows

Configure EHR interface for eCQM extraction

• Which EHR integration surfaces should we connect for patient-level eCQM extraction (for example, FHIR R4, CCDA/CCD, HL7 v2 feeds, direct database extracts)? Options: FHIR R4, FHIR DSTU2, CCDA/CCD, HL7 v2 feed, Direct database extract, Other
• Do you have an existing FHIR server with Patient, Encounter, Condition, Procedure, MedicationStatement resources accessible for testing? Options: Yes, No
• Provide the technical contact and preferred method for coordinating EHR access and test credential exchange.
• Identify the authentication mechanism your EHR requires for API access (for example, OAuth 2.0, mutual TLS, API key). Options: OAuth 2.0, Mutual TLS (mTLS), API key, Basic Auth, Other
• What test window dates can the EHR team support for initial endpoint connectivity and sample extracts?
• Confirm the evidence that will validate EHR endpoints return all patient-level fields required by the eCQM specs (for example, sample FHIR Resource bundles or CCDA extracts covering demographics, encounters, labs, meds).

Map and encode measure specifications

• Select the measure families you want encoded and mapped into the platform (for example, HEDIS, CMS eCQM/MIPS, hospital inpatient, state-specific measures). Options: HEDIS, MIPS/eCQM, Hospital inpatient quality, State-specific measures, Commercial payer measures, Custom/local measures
• List the specific measure IDs or titles to be encoded (for example, CMS eCQM ID or HEDIS measure name and reporting year).
• Specify the output format you require for encoded specs (for example, machine-readable XML, FHIR Measure, human-readable spec sheet). Options: Machine-readable XML, FHIR Measure, Human-readable PDF/spec sheet, Custom JSON, Other
• Are there payer-specific logic variants or code set deviations we must capture when mapping (for example, alternate RxNorm value sets or CPT/HCPCS exceptions)? Options: Yes, No
• Describe the versioning and release control you expect for encoded measure specs (for example, include NQF/CMS release date and versioning metadata).
• Indicate any tolerance thresholds for logic mapping where near matches are acceptable (for example, accept equivalent code subsets versus exact code matching). Options: Exact code match required, Allow equivalent code subsets, Case-by-case, Other

Deploy automated chart abstraction workflows

• Do you want automated abstraction run on historical charts, prospective encounters, or both? Options: Historical only, Prospective only, Both
• How many patient charts per month should automated abstraction process initially? Options: <1,000, 1,000-5,000, 5,000-20,000, 20,000+
• Select the abstraction modules to enable (for example, structured-data extraction, natural language processing, rule-based engine, hybrid workflows). Options: Structured-data extractor, Natural language processing (NLP), Rule-based logic engine, Hybrid (NLP + rules), Manual review queue
• Who is the named abstraction lead on your team for configuration, validation, and escalation?
• When would you like to begin a pilot of automated abstraction (month and year) and for how many weeks should the pilot run?
• Specify the initial accuracy target for automated abstraction during rollout compared to manual abstraction (for example, >=95% agreement on sampled elements). Options: >=99%, >=97%, >=95%, Custom

Set abstraction exception handling and triage rules

• Which exception types should force manual triage (for example, missing encounter date, conflicting medication records, ambiguous problem list entries)?
• Are severity levels required for exceptions (for example, critical for potential submission-impacting items, medium for review, low for informational)? Options: Yes, No
• Specify the time-to-escalation for unresolved exceptions in the abstraction queue (for example, 24 hours, 3 business days, 1 week). Options: 24 hours, 3 business days, 1 week, Custom
• Who on your staff will own exception triage decisions and final disposition for contested measure logic?
• Which notification channels should be used for exception alerts (for example, EHR in-basket, email, webhook to your collaboration tool)? Options: EHR in-basket, Email, Webhook, In-platform alert, Other
• Explain any payer audit scenarios that should change triage handling (for example, immediate manual review for measures under active audit or retrospective chart requests).

Run parallel abstraction and output reconciliation

• Do you require a parallel run comparing automated abstraction output to your manual abstraction results? Options: Yes, No
• Estimate the sample size or percentage of charts to include in parallel validation (for example, 5%, 10%, 20%, or a fixed N). Options: 5%, 10%, 20%, Custom
• Choose the reconciliation metric to use for judging parity between automated and manual abstraction (for example, percent agreement, Cohen's kappa, sensitivity/specificity). Options: Percent agreement, Cohen's kappa, Sensitivity/Specificity, Custom metric
• What acceptance criteria will confirm calculated measure scores are equivalent to the manual benchmark (for example, >=95% agreement across sampled charts)?
• Who will approve the reconciliation report and sign off for production deployment once parity is met?
• Within what cadence should reconciliation cycles run during rollout (for example, weekly, biweekly, monthly)? Options: Weekly, Biweekly, Monthly, Custom

Configure measure library and program coverage

• Select the program coverages required in your measure library (for example, HEDIS, MIPS/eCQM, hospital inpatient, ACO, state registries). Options: HEDIS, MIPS/eCQM, Hospital inpatient, ACO measures, State registry measures, Commercial payer measures
• How many measures do you plan to activate at go-live (for example, <50, 50-150, 150-300, 300+)? Options: <50, 50-150, 150-300, 300+
• Are there custom or local measures we must author and maintain in the library? Options: Yes, No
• Specify the required historical years of measure data to enable trend analysis (for example, prior 3 calendar years). Options: Prior 1 year, Prior 3 years, Prior 5 years, All available
• Do you require measures tagged for submission formats (for example, QRDA Category I, QRDA Category III, FHIR MeasureReport)? Options: Yes, No
• Indicate the cadence required for measure spec updates when regulatory releases occur (for example, immediate update within 2 weeks, quarterly bundle). Options: Immediate (within 2 weeks), Quarterly, Annual, Custom

Implement care gap detection and provider notifications

• Which care gap definitions must be supported (for example, overdue preventive screening, missed immunization, medication refill gap)?
• Do you want notifications routed to individual providers, care teams, or centralized care coordinators? Options: Individual providers, Care teams, Centralized care coordinators, All
• Which delivery channels should be used for provider notifications (for example, EHR inbox, in-platform task, email, SMS)? Options: EHR inbox, In-platform task, Email, SMS, Webhook
• Specify the required detection-to-notification timeliness for care gaps (for example, within 24 hours of encounter or nightly batch). Options: Within 24 hours, Nightly batch, Weekly, Custom
• What acceptance evidence will validate care gap detection accuracy and timeliness (for example, recall >=90% on sampled events and notification within SLA)?
• Who will manage provider opt-in preferences, templates, and escalation workflows for outreach?

Configure submission-ready reporting and exports

• Select the submission file formats required for external reporting and payer ingestion (for example, QRDA Category I, QRDA Category III, CSV, FHIR MeasureReport). Options: QRDA Category I, QRDA Category III, CSV, FHIR MeasureReport, Custom
• Do you require automated generation of payer- or regulator-specific submission packages and manifests? Options: Yes, No
• Which reporting period should the initial export cover for submission (for example, most recent calendar year, rolling 12 months)? Options: Most recent calendar year, Rolling 12 months, Custom period
• Which validation checks do you require prior to export (for example, QRDA schema validation, code set verification, value-range checks)? Options: QRDA schema validation, Code set verification, Value-range checks, Duplicate detection, Other
• Who will act as the authorized approver for final submission packages and attestations?
• Within what timeframe must final export and submission capabilities be in place to meet your first regulatory or payer deadline? Options: 2 weeks, 1 month, 2 months, Custom

Integrate with payer and regulatory submission endpoints

• List the external endpoints we must integrate with for submission (for example, state registry SFTP host, payer API base URL, CMS submission endpoint).
• Do payer endpoints require separate test and production credentials or certificates? Options: Separate test and production, Single credentials for both, Other
• Which transport protocols do your payers or registries support (for example, SFTP, HTTPS POST, SOAP, REST API)? Options: SFTP, HTTPS POST, SOAP, REST API, Other
• Specify any payer-specific transformation rules required for submission payloads (for example, payer-specific code mappings or custom metadata tags).
• Who is the payer or registry technical contact to coordinate endpoint testing and acceptance?
• Indicate the deadline for completing endpoint integration to meet your submission calendar.

Configure measure trend dashboards and alerts

• Which KPIs do you want on the measure trend dashboard (for example, numerator rate, denominator size, pass rate, monthly delta)?
• Are automated alerts required when a measure crosses a performance threshold (for example, drop >5 percentage points month over month)? Options: Yes, No
• Specify the threshold rules and alert severity levels you want configured (for example, warning at 3% drop, critical at 5% drop).
• Who should receive dashboard access and performance alerts (for example, quality director, measure owners, executive sponsor)?
• Which trend history retention window do you require for analytics (for example, 12 months, 36 months, full history)? Options: 12 months, 36 months, Full history, Custom
• Do you require benchmarking against peer national or regional averages in the dashboard? Options: Yes, No

• Master Services Agreement (MSA)
• Statement of Work (SOW)
• Subscription Agreement & Order Form
• Data Processing Agreement (DPA) / HIPAA Business Associate Addendum (BAA)
• Data Access Authorization
• Roles & Responsibilities Matrix (RACI)
• Acceptance Criteria & Validation Agreement
• Service Level Agreement (SLA)
• Change Order Agreement

• Go-live Health Check (weeks 1-4)
• First Measurement Review (weeks 4-10)
• Acceptance Gate Review (around day 90)
• Ongoing Quarterly Operational Review
• Annual Success Review

• Publish the quarter performance dashboard and trending analysis for stakeholder review.
• Produce a documented acceptance decision with pass/fail result for each criterion recorded in Solution Scope.
• Agree and document remediation items and resolution timelines for any failed criteria.
• Confirm the incumbent system's decommissioning status or approved read-only retention and data migration/archival completion.
• Publish the acceptance decision record with linked evidence and signatory confirmation.
• Create a remediation tracker with dates and validation steps for failed criteria.
• Deliver a checklist confirming incumbent decommissioning actions or read-only retention and data archive completion.
• Create tickets for agreed enhancements with planned implementation windows.
• Quarterly performance and trend review
• Confirm quarterly performance versus the targets recorded in Solution Scope for on-time submissions and care-gap closure.
• Reduce the high-severity ticket backlog and agree prioritized enhancement items with resolution windows.
• Agree validation cadence to detect and correct measure accuracy drift before it affects submissions.
• Re-confirm agreed success criteria and owners
• Schedule targeted training sessions or process refreshes to address recurring user errors.
• Year-to-date performance summary and trend analysis
• Confirm annual performance against targets recorded in Solution Scope for submission success and measure accuracy.
• Agree a prioritized list of measure definition iterations and a validation cadence for the next 12 months.
• Set expectations for ticketing SLAs and recurring validation checks to prevent accuracy regressions.
• Publish the year-end performance report with recommended measure updates and validation schedule.
• Create the annual roadmap of prioritized measure definition iterations with proposed timelines.
• Update ticketing SLAs or workflows where recurring delays or gaps were identified.
• Confirm the deployment and key integrations are operational and stable within the validated environment.
• Document the current adoption signals and identify any immediate adoption blockers with accountable owners and dates.
• Agree a remediation plan and timeline for high-priority defects to be resolved before first measurement.
• Deliver a deployment validation report including connectivity logs and endpoint health checks.
• List and prioritize open defects with resolution window and test plan.
• Provide a sample data extract for the measurement validation team to confirm data completeness.
• Present first-period outcomes for named measures
• Establish whether measure accuracy and abstraction time are trending toward the targets recorded in Solution Scope.
• Document the root causes for the top variances and the corrective actions required before the acceptance gate.
• Confirm the re-test plan and timeline to validate fixes prior to the acceptance decision.
• Produce and share the full comparison file of automated results versus manual benchmark samples.
• Implement agreed mapping or rule changes and schedule a validation re-run on the agreed date.
• Supply any missing clinical data extracts identified during root-cause analysis for vendor review.
• Restate acceptance criteria and numeric targets
• Deployment and integration validation
• Submission outcomes and exception review
• Present outcome data against each criterion and determine pass/fail
• Ticketing channel review
• Compare automated calculations to manual benchmarks
• Measure accuracy stability assessment
• Formal acceptance decision and signatory record
• Root-cause analysis for key gaps
• Measure accuracy drift and validation checks
• Early adoption signals and usage patterns
• Agree corrective actions and timeline to acceptance gate
• Open issues and blockers
• Prioritize enhancement and remediation backlog
• Remediation plan for any failed criteria
• Prioritize measure definition iterations and validation cadence
• Incumbent wind-down and data handover review
• Ticketing and SLA retrospective
• Confirm validation sample and re-test plan
• Agree immediate remediation actions
• Training and process adjustments