David Blevins 28 July, 2014 Ontologies in Medical Care Data Integration and Reuse Challenges Ontologies in Medical Care: Data Integration and Reuse Challenges

1 Quick Introductions  Ontologies in medicine  Practical uses  Uses in big data

2Ontologies in Medical Care: Data Integration and Reuse Challenges Ontologies in Medical Care  Background –Standards  Implementation –Standards Compliance –Regulatory Compliance –Feasibility –Case studies  Remarks

3 Messaging Standards  HL7 –HL7 v2.3 and v2.5 required under Meaningful Use (started in 1989) –HL7 v3 (started 1995, uses ISO Reference Information model)  Purpose: electronically transmit medical requests and information between distributed systems Ontologies in Medical Care: Data Integration and Reuse Challenges

4 HL7 Samples  Lab result  Procedure result (note)  Lab Result Corrections Ontologies in Medical Care: Data Integration and Reuse Challenges © Copyright 2008 by Health Level Seven

5 Continuity of Care Documents  Two standards: –Continuity of Care Record (CCR) –Clinical Document Architecture (CDA)  Purpose: Provide a vendor-neutral copy of a structured and well formatted patient health record that can be electronically transmitted and understood by medical record systems Ontologies in Medical Care: Data Integration and Reuse Challenges

6 Sample CDA excerpt Ontologies in Medical Care: Data Integration and Reuse Challenges

7 Sample CCR Spec Ontologies in Medical Care: Data Integration and Reuse Challenges Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA , United States

8 Other Standards  SNOMED  LOINC  CPT  ICD –ICD-9 –ICD-10  Purpose: Provide standard vocabularies for documenting patient procedures, diagnoses, lab results, and various other medical details Ontologies in Medical Care: Data Integration and Reuse Challenges

9 Ontologies in Medical Care  Background –Standards  Implementation –Standards Compliance –Regulatory Compliance –Feasibility –Case studies  Remarks

10 A Quick Note Before We Proceed  Medical data is ideal for big data analysis: high volume, high variety data with high contextual meaning  Massive challenges in spite of mature standards –Data Silos (no cooperation between vendors) –Failure to comply with standards Ontologies in Medical Care: Data Integration and Reuse Challenges

11 HL7 is a series of tubes, treated like a truck  Making the standard perform activities it was never meant to handle  Example: Device interfaces Ontologies in Medical Care: Data Integration and Reuse Challenges

12 Burn your dictionaries  Users can enter custom vocabularies, rendering the LOINC/CPT/ICD dictionaries useless  Labs use different service compendiums Ontologies in Medical Care: Data Integration and Reuse Challenges

13Ontologies in Medical Care: Data Integration and Reuse Challenges Standards and “Standards”  HL7 variety  Testing Variety  Inconsistent implementation

14Ontologies in Medical Care: Data Integration and Reuse Challenges Ontologies in Medical Care  Background –Standards  Implementation –Standards Compliance –Regulatory Compliance –Feasibility –Case studies  Remarks

15 Meaningful Use  MU 1 –13 required core objectives –Selection of 5 objectives from a list of 9 optional objectives –Clinical Quality Measures  MU 2 –17 required core objectives –Selection of 3 objectives from a list of 6 optional objectives –Comprehensive CCD  MU 3 –Ongoing development. Planned finalization in 2016 Ontologies in Medical Care: Data Integration and Reuse Challenges

16 Meaningful Use certification  Certified third parties –Non-governmental (Industry) groups permitted to certify compliance –The private sector handles certification of functionality  What happens when something breaks? –What about compliance-critical components, like clinical quality measures? Ontologies in Medical Care: Data Integration and Reuse Challenges

17Ontologies in Medical Care: Data Integration and Reuse Challenges Ontologies in Medical Care  Background –Standards  Implementation –Standards Compliance –Regulatory Compliance –Feasibility –Case studies  Remarks

18 Feasibility  Acquisitions –Are the new owners maintaining documentation? –Are the new owners concerned about quality, or merely being compliant?  Resources (monetary, human) –Is there enough money to implement changes? –Are there enough competent staff to implement changes?  Outsourcing (Stakeholder engagement, quality, support) –Does the contractor care about quality? –Is the contractor competent? –Does the contractor support their work? If they do, in what ways? Ontologies in Medical Care: Data Integration and Reuse Challenges

19Ontologies in Medical Care: Data Integration and Reuse Challenges Ontologies in Medical Care  Background –Standards  Implementation –Standards Compliance –Regulatory Compliance –Feasibility –Case studies  Remarks

20 Immunization Registries  Background –Timeline: 1 year –Impetus: regulatory compliance –Result: “Probably ongoing” –Problem: Vendor support Ontologies in Medical Care: Data Integration and Reuse Challenges

21 Practice Management Systems  Background –Products: may be vendor supplied, or third party. May or may not be intended to work with a specific system –Messages: ADTs (Admit Discharge Transfer) and DFTs (Detailed Financial Transactions) Ontologies in Medical Care: Data Integration and Reuse Challenges Medscape EHR Report 2014 Copyright Medscape 2014

22 Data Migrations  Background –Legislation: right to your data –Vendor approach: have your data (be careful what you wish for) Ontologies in Medical Care: Data Integration and Reuse Challenges Medscape EHR Report 2014 Copyright Medscape 2014

23Ontologies in Medical Care: Data Integration and Reuse Challenges Ontologies in Medical Care  Background –Standards  Implementation –Standards Compliance –Regulatory Compliance –Feasibility –Case studies  Remarks