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HITSC Implementation Workgroup July 28, 2014. - 1 - Massachusetts eHealth Collaborative © MAeHC. All rights reserved. Quality Data Measurement Is An End-to-End.

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Presentation on theme: "HITSC Implementation Workgroup July 28, 2014. - 1 - Massachusetts eHealth Collaborative © MAeHC. All rights reserved. Quality Data Measurement Is An End-to-End."— Presentation transcript:

1 HITSC Implementation Workgroup July 28, 2014

2 - 1 - Massachusetts eHealth Collaborative © MAeHC. All rights reserved. Quality Data Measurement Is An End-to-End Process TransportValidation and analysisUser access Clinical source system Documentation & extraction RX Labs Vitals Problems Patient Provider Payer etc Remediation and Improvement HIE Web services TCPIP SFTP Other Electronic reporting MU, PQRS, AQC, etc Data management Report viewing Case tracking Data extraction Queries Pre-defined data marts Management Info System User information Utilization analysis Other

3 - 2 - Massachusetts eHealth Collaborative © MAeHC. All rights reserved. QDC Analytics Measure Inventory PQRS Pioneer ACO GovernmentCommercialPCMH MU Stage 1 and 2 EP and EH

4 - 3 - Massachusetts eHealth Collaborative © MAeHC. All rights reserved. Customer Examples

5 - 4 - Massachusetts eHealth Collaborative © MAeHC. All rights reserved. Data Sources ClinicalClaims

6 - 5 - Massachusetts eHealth Collaborative © MAeHC. All rights reserved. CCDA has been workable, but it is cumbersome From a technical perspective, it’s an unwieldy container, but these issues can be overcome CCDA architecture provides framework for standardized document-representation structured data Biggest issue is wide implementation variation across EHR vendors Two types of variation Data availability -One CCDA does not fit all needs -Some clinical information not available in CCDAs with certain vendors due to timing of CCDA generation in workflow ·e.g, Labs and E&M codes not included in CCDAs that get generated at end of visits -Data introduced for MU Stage 2 CQMs not available or not standardized among vendors (e.g., patient communication codes, devices applied on patients, reasons for not performing intervention, etc) Semantic normalization -Some fields are rough but often can be remediated (problems, medications, vitals, labs) vendor-by-vendor -Other fields are often not available or non-standardized across systems (historical procedure information, social history for codes on smoking status, cessation counseling, etc)

7 - 6 - Massachusetts eHealth Collaborative © MAeHC. All rights reserved. Recommendations Availability of standardized templates and implementation guides for high frequency and high value use cases ambulatory visit, inpatient visit, ED visit, specialist referral, nursing home TOC, HIE data aggregation, quality data aggregation, etc Current work in CCDA templates and FHIR profiles is working on this, but needs to be aggressively accelerated and made available Certification testing focused more specifically on implementation of CCDA to support data availability and semantic normalization for high priority use cases

8 Information www.maehc.org Micky Tripathi, PhD MPP President & CEO mtripathi@maehc.org 781-434-7906 www.maehc.org mtripathi@maehc.org


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