1. Clinical Quality Framework cqframework.info All Hands Meeting February 11, 2016 11am-12:30pm ET

2. 2 Logistics As a reminder, please mute your phone when you are not talking to the group. When speaking, please say your name before making your comment. You can ask questions by unmuting or by using the “Chat” feature on the web meeting. To find the “Chat” feature, look for the “Chat” bubble at the top of the meeting window. From S&I Framework to Participants: Could you please explain how the terminologies are used in this instance? All Panelists CQF Wiki: cqframework.info 2 Send your “chat” to All Panelists in order to ensure the comments are addressed publicly. This meeting is being recorded. Should you need to take another call, please leave the meeting and rejoin (i.e., please do not put the meeting line on hold).

3. Agenda TopicPresenter WelcomeSwapna Bhatia, Project Administrative Support DIGITizE/IOM Pilot on Genomics-Based CDS Sandy Aronson, DIGITizE Co-chair Sarah Beachy, DIGITiZE Director FHIR-Based CQF HarmonizationBryn Rhodes, CQF Subject Matter Expert FHIR-Based CQF Connect-a-Thon – May 2016 Bryn Rhodes, CQF Subject Matter Expert CQL-based HQMF Readability PilotBryn Rhodes, CQF Subject Matter Expert Managing Episode of Care for eCQM and CDS – Seeking Input and Experience Floyd Eisenberg, CQF Co-Coordinator Next StepsKen Kawamoto, CQF Co-Coordinator Questions and DiscussionKen Kawamoto, CQF Co-Coordinator CQF Wiki: cqframework.info 3

4. Welcome Announcements, Meeting Schedules, Agendas, Minutes, Reference Materials, Use Cases, Project Charter, and General Information are posted on cqframework.info cqframework.info Clinical Quality Framework (CQF) All Hands meetings are held bi-weekly on Thursdays from 11am to 12:30pm ET https://siframework1.webex.com/siframework1/onstage/g.php?t=a&d=666535029 Dial In: +1-650-479-3208 Access code: 666 535 029 CQF Data Model meetings are held weekly on Wednesdays from 1 to 2pm ET https://meetings.webex.com/collabs/#/meetings/detail?uuid=M8UL81KQZZKHCW46R28OYGI NQM-8ENJ&rnd=47738.082690 https://meetings.webex.com/collabs/#/meetings/detail?uuid=M8UL81KQZZKHCW46R28OYGI NQM-8ENJ&rnd=47738.082690 Dial In: +1-770-657-9270 Participant passcode: 217663 CDS-on-FHIR/CQF Office Hours meetings are held weekly on Wednesdays from 11am to 12pm ET https://global.gotomeeting.com/meeting/join/554237525 Dial In: +1-770-657-9270 Participant passcode: 6870541 CQF Wiki: cqframework.info 4

5. Displaying and Integrating Genetic Information Through the EHR Action Collaborative (DIGITizE AC)

6. Can We Deploy Health Information Technology that Safely Brings the Benefits of Genetics to Far More Patients? How Quickly Can We Do So? Can We Deploy Health Information Technology that Safely Brings the Benefits of Genetics to Far More Patients?

7. Can We Create Inter-institutional Foundational Health Information Technology Infrastructure that Increases the Power of Genetics That will be helpful now but also stand the test of time? Can We Create Inter-institutional Foundational Health Information Technology Infrastructure that Increases the Power of Genetics

8. Strategy Assemble Stakeholders Identify areas of agreement Transform into an inter-institutional project coordination group

9. Stackholders Government Providers Laboratories Vendors Patients Representatives Standards Organizations

10. Membership Sandy Aronson, Partners HealthCare J.D. Nolen, Cerner Mark Adams, Good Start Genetics Gil Alterovitz, Harvard Medical School Brian Anderson, athenahealth Jane Atkinson, NIDCR Larry Babb, Partners HealthCare Dixie Baker, Martin, Blanck and Associates Gillian Bell, Moffitt Cancer Center Chris Chute, Johns Hopkins University Chris Coffin, Invitae Mauricio De Castro, U.S. Air Force Carol Edgington, McKesson Laurel Estabrooks, Soft Computer Corporation Robert Freimuth, Mayo Clinic Geoff Ginsburg, Duke University Jennifer Hall, University of Minnesota Stephanie Hallam, Good Start Genetics Heather Halvorson, U.S. Air Force Gillian Hooker, NextGxDx Stan Huff, Intermountain Healthcare Kristen Janes, Kaiser Permanente Andrew Kasarskis, Mount Sinai School of Medicine Anthony Kerlavage, NCI Deborah Lange-Kuitse, McKesson Debra Leonard, University of Vermont Steve Lincoln, Invitae Ira Lubin, CDC Elaine Lyon, ARUP Laboratories John Mattison, Kaiser Permanente Larry Meyer, VA Blackford Middleton, Vanderbilt University Doug Moeller, McKesson Scott Moss, Epic James O'Leary, Genetic Alliance Erin Payne, Northrop Grumman Brian Pech, Kaiser Permanente Teji Rakhra-Burris, Duke University Priyadarshini Ravindran, Allscripts Mary Relling, St. Jude Children's Research Hospital Wendy Rubinstein, NCBI Hoda Sayed-Friel, Meditech Megan Schmidt, Sunquest Information Systems Jud Schneider, NextGxDx Sam Shekar, Northrop Grumman Brian Shirts, University of Washington Brad Strock, Epic Jeff Struewing, NHGRI Charles Tuchinda, First Databank Deepak Voora, Duke University Michael Watson, ACMG Scott Weiss, Partners HealthCare Jon White, ONC Bob Wildin, NHGRI Ken Wiley, NHGRI Marc Williams, Geisinger Grant Wood, Intermountain Healthcare

11. Identify Areas of Agreement Framework for Increasing Support for Genetics in the EHR Ecosystem PGx Use Case Patterns Germline Use Case Patterns Somatic Use Case Patterns

12. Objective Learn how to work together while producing near term benefit for patients Simple use cases are good for this

13. Framework for Increasing Support for Genetics in the EHR Ecosystem Don’t Boil the Ocean Initial PGx Use Case Types PGx Use Case Patterns Germline Use Case Patterns Somatic Use Case Patterns Specific Example Boil some initial cups while standing on firm ground

14. Abacavir – HLA-B57:01 Approximately 6% of European ancestry patients are hypersensitive to Abacavir Hypersensitivity can produce life threatening reaction Genetic test can predict hypersensitivity Martin et al, 2012 CPIC Guidelines

15. Thiopurine - TPMT Metabolic effect Prescribing too high a dose places patient at risk for myelosuppression Test is required to accurately dose Reilling et al, 2011 CPIC Guideline

16. Key Pharmacogenomic Use Cases Types #Use Case Types 1Incorporating Genetic Results into EHR User Interfaces 2Adding genetic tests in order sets 3Clinical Decision Support (CDS) identifies when a test should be ordered (pre-test alert*) 4CDS identifes when a drug order is inconsistent with a test result (post-order alert*) * Note pre and post order status refers to the status of the test order as opposed to the drug order

17. Project Coordination

18. Example of Cross Institutional Dependencies A use case calls for providers to implement a CDS rule that requires data from the EHR ecosystem To supply the required data EHR ecosystem vendors need to receive data from lab system vendors To instantiate the required data flows lab and EHR ecosystem vendors need better defined standards Standards organizations need feedback from lab and provider organizations to produce needed refinements The Action Collaborative has the breadth of membership required to manage these types of issues

19. Interdependency Labs Providers Data

20. Interdependency Labs Providers EHR Vendors LIS Vendors Supporting Vendors Data Interoperability and functionality

21. Interdependency Labs Providers EHR Vendors LIS Vendors Supporting Vendors Data Cooperation / Interfaces Interoperability and functionality

22. Interdependency Labs Providers EHR Vendors LIS Vendors Supporting Vendors Standards & Ontology Organizations Data Cooperation / Interfaces Standards and Ontologies Interoperability and functionality

23. Interdependency Labs Providers EHR Vendors LIS Vendors Supporting Vendors Standards & Ontology Organizations Data Cooperation / Interfaces Standards and Ontologies Input Interoperability and functionality

24. Interdependency Labs Providers EHR Vendors LIS Vendors Supporting Vendors Standards & Ontology Organizations Gov Agencies Data Cooperation / Interfaces Standards and Ontologies Input Interoperability and functionality Proof of what is possible/helpful

25. Interdependency Labs Providers EHR Vendors LIS Vendors Supporting Vendors Standards & Ontology Organizations Gov Agencies Data Cooperation / Interfaces Standards and Ontologies Input Interoperability and functionality Proof of what is possible/helpful Funding / Reimbursement Environment that Makes this Possible

26. The Good News Labs Providers EHR Vendors LIS Vendors Supporting Vendors Standards & Ontology Organizations Gov Agencies Cooperation / Interfaces Patients

27. Lucky Choice in Baseline Rules Not dependent on structured variant transfer Warn every time potentially desirable Existing standards work

28. Implementation Guide Rational LOINC Transfer Codes Suggested Rules

29. Where to Next Pilots! More Use Cases – Internal: FH – CSER Led: Lynch Syndrome Things the community feels we can help with

30. FHIR-Based CQF Harmonization First-class resources have been defined – Still have some polishing/documentation to do FHIR-Infrastructure Alignment is complete – ModuleMetadata type is committed – FHIR-I is submitting proposals for alignment of conformance resources Incorporating CDS/eCQM Ballot Comments is in progress – Reconciled spreadsheets are on Google docs – Anyone that can/wants to help is welcome to join the CQF calls where we will be coordinating the effort and reviewing progress

31. FHIR-Based CQF Connect-a-Thon May 2016 Proposal has been submitted Scenarios from the January Connect-a-Thon were brought forward Additional Scenarios: – Knowledge Management Repository service and client – Measure Evaluation results – SOA Integration Scenario Intended to model a complete clinical quality spike – Includes artifact distribution, ingestion, and evaluation for decision support and quality measurement Event Publish/Subscribe Service (EPS) Unified Communication Service (UCS)

32. CQL-Based HQMF Readability Pilot Objective – Supplement the CQL-Based HQMF IG with additional material to inform knowledge artifact development with CQL Areas of Focus – Display of CQL Population Criteria within the HQMF – Usability/Readability of the resulting HQMF – Consistent authoring of CQL – Accessible Documentation for CQL-Based Artifacts 32

33. Areas of Active Discussion Reordering criteria expressions – top-down vs bottom up Utilizing HTML Functionality – Linking, expand/collapse, fly-out options UX Review/508 Review Criteria Sections CQL Style Guide CQL Documentation Availability Common Library Usage 33

34. Reordering and Indenting of Criteria CQL expressions have been typically developed “bottom-up” – Starting from the data elements and value sets – Construct intermediate expressions to represent conceptual components of the criteria – Combine these intermediates to produce the overall criteria This approach makes sense from an authoring perspective, but can be confusing to someone coming at the measure from the criteria sections Reorder the expressions “top-down” – Starting from the top-level population criteria expressions – Display the component expressions involved as they are encountered, and indented below the referencing expression 34

35. Utilization of HTML UX/Usability Review/508 Review Several options here – Add hyper-links to the expressions to allow to be easily navigated Doesn’t work very well with native HTML functionality, the navigation is abrupt and often unclear what’s happening – Add expand/collapse functionality to help organize the criteria expressions The solution is functional, but it doesn’t really add much because it just hides the criteria. For very large measures, this may be useful, but in general, probably not – Add fly-outs to enable discovery without having to navigate This could be done in pure HTML with the “alt-text”, but it would rely on browser- specific behavior, and could potentially introduce some 508 compliance issues Adding java-script would enable some much more sophisticated functionality Specific coloring for valueset references Hyperlinks for valueset references to the data elements section 35

36. Criteria Sections Follow HQMF Guidance in ensuring all relevant criteria are displayed for a measure, even if the criteria is empty 36 Measure Score Initial Population DenominatorDenominator Exclusion Denominator Exception NumeratorNumerator Exclusion Measure Population Measure Population Exclusion ProportionRequired Optional RequiredOptionalN/A RatioRequired OptionalN/ARequiredOptionalN/A Continuous Variable RequiredN/A RequiredOptional CohortRequiredN/A

37. CQL Style Guide Work with Measure Authors to define a CQL Style Guide Specify conventions for – White-space usage – Naming – Casing of identifiers – Indenting style – Expression organization (e.g. when is a block “too big”) Identify and establish guidance for common patterns Potentially develop tooling to support automatic formatting (a la GoFormat) 37

38. CQL Documentation Availability Increase accessibility of relevant CQL documentation from the measure itself – Could use hyper-links to an online CQL Reference – Could embed documentation for CQL operations used within the measure As flyouts or a “documentation” section at the end of the measure 38

39. Common Library Usage/Documentation Work with artifact developers to establish common libraries As with any knowledge asset reuse effort, this would require – Governance How are the assets developed, tested, approved, published, and maintained? – Visibility How do artifact developers find relevant assets? – Guidance How do artifact developers know how to use the assets? Documentation accessibility from the resulting artifacts 39

40. Next Steps Develop more complex/representative measures – Complex Logic – Multiple Populations – Composite Measures – Stratification – Risk Adjustment Gather feedback from different venues 40

41. Defining Episode of Care Definition: A series of temporally contiguous healthcare services related to the treatment of a given spell of illness or provided in response to a specific request by the patient or other relevant entity. 1 41 1 National Quality Forum (NQF). Measurement Framework: Evaluating Efficiency Across Patient-Focused Episodes of Care. Washington, DC: NQF; 2009.

42. Defining Episode of Care 2 42 2 National Quality Forum (NQF). Evaluating Episode Groupers: A Report from the National Quality Forum. Washington, DC: NQF; 2014. (p. 8)

43. Episode of Care – Claims-based 3 43 3 National Quality Forum (NQF). Evaluating Episode Groupers: A Report from the National Quality Forum. Washington, DC: NQF; 2014. (p. 10)

44. Seeking Field Experience with Clinical Data Determining the “start” and “end” of clinical encounters within an episode – Examples: – Within a hospital facility: ED —› Observation Status —› Inpatient Outpatient Surgery —› Observation —› Admission Admission —› ICU —› Step-down Unit —› MedSurg – Cross settings of care – Examples: Ambulatory office —› Physical Therapy —› ED —› Ambulatory office Tracking care for a specific health concern over time and location – Admission – Discharge times – Arrival – Departure times 44

45. Episode of Care: Observation Use Case Observation care is a well-defined set of specific, clinically appropriate services, which include ongoing short term treatment, assessment, and reassessment before a decision can be made regarding whether patients will require further treatment as hospital inpatients or if they are able to be discharged from the hospital. Observation services are commonly ordered for patients who present to the emergency department and who then require a significant period of treatment or monitoring in order to make a decision concerning their admission or discharge. 45 Medicare Benefit Policy Manual. (Chapter 6, Section 20.6) https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/downloads/bp102c06.pdf

46. Episode of Care: Observation Use Case Seeking input on how EHRs handle transition from Emergency Department to Observation Status to Admission within a single episode of care Purpose – to evaluate the single episode from facility arrival time to treatment to facility departure time 46 Single Episode of Care – 1 Facility Emergency Department Observation Status Hospital Admission

47. Episode of Care: Observation Use Case Method to Determine the Following Times 47 ED Arrival Time Departure Time Observation Arrival Time Departure Time Hospital Admission Arrival Time Departure Time Admission Time Discharge Time Admission Time Discharge Time Admission Time Discharge Time

48. Next Steps Engage in workgroups (www.cqframework.info)www.cqframework.info Pilots –Co-Coordinator, Ken KawamotoCo-Coordinator, Ken Kawamoto Standards Development –Subject Matter Expert, Bryn RhodesSubject Matter Expert, Bryn Rhodes Join us for the upcoming Clinical Quality Framework All Hands meeting: 2/25 CQF Wiki: cqframework.info 48

49. Questions and Discussion NameE-Mail Ken Kawamoto, Co-Coordinatorkensaku.kawamoto@utah.edu Floyd Eisenberg, Co-Coordinatorfloyd.eisenberg@esacinc.com Swapna Bhatia, Initiative Supportswapna.bhatia@esacinc.com cqframework.info CQF Wiki: cqframework.info 49