1. Data Provenance Community Meeting July 31 st, 2014

2. Meeting Etiquette Click on the “chat” bubble at the top of the meeting window to send a chat. 2 Please mute your phone when you are not speaking to prevent background noise. – All meetings are recorded. Please do not put your phone on hold. – Hang up and dial back in to prevent hold music. Please announce your name before speaking Use the “Chat” feature to ask questions or share comments. – Send chats to “All Participants” so they can be addressed publicly in the chat, or discussed in the meeting (as appropriate).

3. Agenda Topic Time Allotted General Announcements2 minutes Use Case Discussion40 minutes Tiger Team Report Out5 minutes DPROV Initiative Process Review10 minutes Next Steps/Questions3 minutes 3

4. Next meetings: HL7 DProv Joint Working Session: Cancelled All Hands: Thursday August 7 th, 2014 – 2:30-3:30 pm ET http://wiki.siframework.org/Data+Provenance+Initiative All meeting materials (including this presentation) can be found on the Past Meetings page: http://wiki.siframework.org/Data+Provenance+Past+Meetings General Announcements 4

5. S&I Framework Phases outlined for Data Provenance PhasePlanned Activities Pre-Discovery  Development of Initiative Synopsis  Development of Initiative Charter  Definition of Goals & Initiative Outcomes Discovery  Creation/Validation of Use Cases, User Stories & Functional Requirements  Identification of interoperability gaps, barriers, obstacles and costs  Review of Candidate Standards Implementation  Creation of aligned specification  Documentation of relevant specifications and reference implementations such as guides, design documents, etc.  Development of testing tools and reference implementation tools Pilot  Validation of aligned specifications, testing tools, and reference implementation tools  Revision of documentation and tools Evaluation  Measurement of initiative success against goals and outcomes  Identification of best practices and lessons learned from pilots for wider scale deployment  Identification of hard and soft policy tools that could be considered for wider scale deployments We are Here 5

6. Data Provenance –Use Case (Discovery) Ahsin Azim– Use Case Lead Presha Patel – Use Case Lead 6

7. Proposed Use Case & Functional Requirements Development Timeline 7 Week Target Date (2014) All Hands WG Meeting Tasks Review & Comments from Community via Wiki page due following Tuesday by 8 P.M. Eastern 16/12 Use Case Kick-Off & UC Process Overview Introduce: In/Out of Scope & Assumptions Review: In/Out of Scope & Assumptions 26/19 Review: In/Out of Scope & Assumptions Introduce: Context Diagram & User Stories Review: Context Diagram & User Stories 36/26Review: Context Diagram & User StoriesReview: Continue Review of User Stories 47/3 Review: Finalize User Stories Introduce: Pre/Post Conditions Review: Pre/Post Conditions 57/10 Review: Finalize User Stories Introduce: Pre/Post Conditions Review: Pre/Post Conditions 67/17 Review: Pre/Post Conditions Introduce: Actors & Roles, and Activity Diagram/Base Flow Review: Actors & Roles and Activity Diagram/Base Flow 77/31 Review: Actors & Roles, and Activity Diagram/Base Flow Introduce: Functional Requirements & Sequence Diagram Data Requirements Review: Functional Requirements & Sequence Diagram, and Data Requirements 88/7 Review: Functional Requirements, Sequence Diagram and Data Requirements Introduce: Risks & Issues Review: Risks & Issues 98/14 Review: Risks and Issues Begin End-to-End Review End-to-End Review by community 108/21End-to-End Comments Review & dispositionEnd-to-End Review ends 118/28Finalize End-to-End Review Comments & Begin ConsensusBegin casting consensus vote 129/4Consensus Vote*Conclude consensus voting

8. Sections for Review 8 Today we will be reviewing: 1.Pre/Post Conditions Introduce: 1.Actors & Roles 2.Activity Diagrams 3.Base Flows Double click the icon to open up the Word Document with the sections for review

9. Draft Use Case Information Interchange per scenario 9 End Point (EHR) End Point (EHR) Data Source (EHR, Lab, Other) Assembler (EHR, HIE, other systems) Assembler (EHR, HIE, other systems) Data Source (EHR, Lab, Other) Transmitter ONLY (HIE, other systems) Transmitter ONLY (HIE, other systems) Scenario 1 Scenario 2 Scenario 3 Data Source (EHR, Lab, Other) Data Source (EHR, Lab, Other) Pre-step – Creation of the data and associated provenance information

10. Based on the Context Diagram, we can break up our workflows into 3 different scenarios: 1.Data Source  End Point 2.Data Source  Transmitter  End Point 3.Data Source  Assembler  End Point Note – For each of the above, there is a pre-step associated with creation of the data and associated provenance information Draft Definitions: Data Source – Health IT System where data is created (the true source) Transmitter – A system that serves as a pass through connecting two or more systems Assembler– A system that extracts, composes and transforms data from different patient records End Point – System that receives the data Note: In this context, when say data we are referring to an atomic data element (a piece of information) 10 Scenarios

11. Scenario 1: Data Source  End Point User Story 1: A patient arrives at the ophthalmologist’s office for her annual eye exam. The ophthalmologist conducts an eye exam and captures all of the data from that visit in his EHR. The ophthalmologist electronically sends the information back to the patient’s PCP (where all data in the report sent was created by the ophthalmologist). User Story 2: A patient wishes to transmit the Summary of Care Document she downloaded from her PCP to her Specialist. Rather than downloading and sending it herself, she requests that the PCP transmits a copy of the document on her behalf to her Specialist. PCP is the only author of the Summary of Care Document and also the sender of the information to the Specialist. The Specialist understands from the document’s provenance that it is authentic, reliable, and trustworthy. Note: Provenance for the request made to the PCP is not in scope for this user story. 11 User Stories – Scenario 1

12. Scenario 2: Data Source  Transmitter  End Point User Story 1 (no alteration in exchange): While training for a marathon, a patient fractures his foot. The patient’s PCP conducts a foot exam and captures all of the data from that visit in his EHR. The PCP also calls in a referral for the patient to an orthopedic specialist for further treatment. After the PCP calls in the referral, the summary of care information is made available to the specialist, by passing through a transmitter, before being received by the orthopedic specialist’s system. The orthopedic specialist receives the summary of care with provenance information and an indication that the data passed through a transmitter. 12 User Stories – Scenario 2

13. Scenario 3: Data Source  Assembler  End Point Note: A community of providers have established a data use agreement that allows them to upload data to an HIE repository. When data is sent to the repository, the provenance information is also included. User Story 1: A patient is rushed to the Emergency Department due to a car accident. The physician wants to obtain the patient’s summary record as part of the delivery of care. The physician queries the HIE repository and receives a summary record from the past six months. The data received includes the provenance information from the originating sources and also information that identifies the assembler and the actions they have taken. User Story 2: A patient with diabetes goes to Lab A to have his blood drawn. Lab A sends the lab results in a standard lab format to the PCP’s EHR with provenance information attached. Upon reviewing the lab results, the PCP decides to refer the diabetic patient to a specialist for consultation. The PCP electronically sends a referral to the specialist. The referral document includes relevant data originating in the PCP's EHR along with provenance information from Lab A that is transformed into a representation that is compatible with the referral document. 13 User Stories – Scenario 3

14. Scenario 3: Data Source  Assembler  End Point User Story 3: A PCP tethered PHR enables patient to download and transmit Summary of Care records that includes provenance information to anyone she chooses. Patient downloads full Summary of Care Document, disaggregates the medications, problems, and vital signs in the document and then copies these into her PHR along with medications, problems and vital signs added previously. Patient then sends selected medications, vitals, and problems from PHR to her Fitness Trainer App in a mobile device friendly format using different terminology for expressing vital sign measures. The patient authorizes the Fitness Trainer App to access the patient’s information and put into a format that is recognizable by the Fitness Trainer App client. The Fitness Trainer App user (could be patient, physical therapist, etc.) receives provenance information showing that the information received has been assembled by the patient and that it was authored by various other clinical staff. Alternative to User Story 3: Prior to visiting her ophthalmologist, a patient uploads clinical information into her PHR from several providers that she receives care from. She also enters information into the PHR on her health. The patient then sends a summary of care report from her PHR to her ophthalmologist which includes the self-reported data along with clinical data from her other providers. The ophthalmologist receives the report with provenance information and an indication that it was assembled by the PHR. 14 User Stories – Scenario 3 (cont.)

15. Preconditions Where it exists, the assembling software, is integrated into systems such as EHRs, PHRs, and HIEs – indicating the type of information for a receiver to use as provenance for calculating reliability, and the organization or person responsible for deploying it There exists an Access Control System that allow the assembler to perform necessary tasks for predecessor artifacts and newly assembled artifacts All systems generating or consuming any artifact are capable of persisting the security labels received and data segmentation based the security labels assigned by the artifact generator, which may be an assembler 15 Post Conditions Receiving system has incorporated provenance information into its system and association of the provenance information to the source data is persisted Sending and receiving systems have recorded the transactions in their security audit records Pre/Post Conditions

16. A look ahead: Data Provenance Next Week 16 August 7 th, 2014 – All Hands Community Meeting (2:30-3:30) – Review Actors & Roles, Activity Diagrams and Base Flows Provide your comments on the bottom of this page http://wiki.siframework.org/Data+Provenance+Use+Cases http://wiki.siframework.org/Data+Provenance+Use+Cases

17. HL7 DProv Joint Working Session Bob Yencha – Subject Matter Expert Kathleen Connor – Subject Matter Expert Ioana Singureanu – Subject Matter Expert Neelima Chennamaraja – Subject Matter Expert Johnathan Coleman- Initiative Coordinator 17

18. Tiger Team Report Created entry-level templates to provide complete coverage at all levels of CDA to the extent allowable by the base specification Updates to front matter based on community feedback On track for on-time submission to HL7

19. S&I Data Provenance Initiative Presentation to the Data Provenance Community July 31st, 2014 Johnathan Coleman Initiative Coordinator – Data Provenance ONC/OCPO/OST (CTR)

20. Challenge While there are several existing efforts to address data provenance, no authoritative specification, standard, or model for provenance has been universally adopted to-date within the context of HIT. The variability in how HIEs, EHRs, and PHRs currently capture, retain, and display provenance is problematic for the interoperable exchange, integration, and interpretation of health data. 20 Background

21. Initiative Purpose and Goals To establish a standardized way for capturing, retaining, and exchanging the provenance of health information (including inbound, system generated, and outbound provenance). The initiative will: – Establish guidance for handling data provenance in content standards, including the level to which provenance should be applied – Establish the minimum set of provenance data elements and vocabulary – Standardize the provenance capabilities to enable interoperability 21 Background

22. Initiative Activities: S&I Framework PhasePlanned Activities Pre-Discovery  Development of Initiative Synopsis  Development of Initiative Charter  Definition of Goals & Initiative Outcomes Discovery  Creation/Validation of Use Cases, User Stories & Functional Requirements  Identification of interoperability gaps, barriers, obstacles and costs  Review of Candidate Standards Implementation  Creation of aligned specification  Documentation of relevant specifications and reference implementations such as guides, design documents, etc.  Development of testing tools and reference implementation tools Pilot  Validation of aligned specifications, testing tools, and reference implementation tools  Revision of documentation and tools Evaluation  Measurement of initiative success against goals and outcomes  Identification of best practices and lessons learned from pilots for wider scale deployment  Identification of hard and soft policy tools that could be considered for wider scale deployments We are Here 22

23. 23 End Point (EHR) End Point (EHR) Data Source (EHR, Lab, Other) Data Source (EHR, Lab, Other) Transmitter ONLY (HIE, other systems) Transmitter ONLY (HIE, other systems) Scenario 1 Scenario 2 Scenario 3 Data Source (EHR, Lab, Other) Data Source (EHR, Lab, Other) Pre-step : Creation of the data and associated provenance information Assembler (EHR, HIE, other systems) Assembler (EHR, HIE, other systems) Initiative Activities: Use Case Scenarios (DRAFT)

24. Initiative Activities Data Provenance Tiger Team Established as a subgroup of the S&I Initiative. Tasked with working with SDOs to accelerate the S&I Framework harmonization phase by: – Evaluating current provenance requirements in CDA and various HL7 specifications. – Supporting development of a harmonized Implementation Guide (IG) for Provenance in a CDA. Meets weekly as part of a joint S&I/HL7 working meeting. Reports on progress to HL7 Community Based Collaborative Care (CBCC) WG meetings and the S&I Data Provenance Initiative meetings. 24

25. Data Provenance Tiger Team Accomplishments: Proposed the Data Provenance project in HL7 and received approval from: – US Realm Task Force – Community Based Collaborative Care (CBCC) (sponsoring WG) – Structured Documents Work Group (SDWG) (co-sponsor) – Domain Experts Steering Division (DESD) – Technical Steering Committee (TSC) Supporting development of HL7 Provenance Specification: – HL7 Implementation Guide for CDA® Release 2: Data Provenance, Release 1 – Targeted for September 2014 ballot as DSTU – Working with other HL7 workgroups on vocabulary harmonization 25 Initiative Activities

26. Conclusion Expected Outcomes: Technical specifications which enable provenance information to be conveyed in a standardized way, from creation to exchange and integration across multiple health information systems. Ultimately these standards will improve the confidence in the integrity of health information, facilitating greater trust in healthcare data and its use in clinical care, interventions, analysis, decision making and clinical research, and others. Community Participation: S&I Data Provenance Initiative: Thursdays 2:30-3:30 pm ET – http://wiki.siframework.org/Data+Provenance+Initiative http://wiki.siframework.org/Data+Provenance+Initiative HL7/S&I D-PROV Working Sessions: Mondays 3:00-4:00pm ET – https://www3.gotomeeting.com/register/868770014 https://www3.gotomeeting.com/register/868770014 26

27. Support Team and Questions Please feel free to reach out to any member of the Data Provenance Support Team: Initiative Coordinator: Johnathan Coleman: jc@securityrs.comjc@securityrs.com OCPO Sponsor: Julie Chua: julie.chua@hhs.govjulie.chua@hhs.gov OST Sponsor: Mera Choi: mera.choi@hhs.govmera.choi@hhs.gov Subject Matter Experts: Kathleen Conner: klc@securityrs.com and Bob Yencha: bobyencha@maine.rr.comklc@securityrs.combobyencha@maine.rr.com Support Team: – Project Management: Jamie Parker: jamie.parker@esacinc.comjamie.parker@esacinc.com – Use Case Development: Presha Patel: presha.patel@accenture.com and Ahsin Azim: ahsin.azim@accenturefederal.compresha.patel@accenture.comahsin.azim@accenturefederal.com – Harmonization: Rita Torkzadeh: rtorkzadeh@jbsinternational.comrtorkzadeh@jbsinternational.com – Standards Development Support: Amanda Nash: amanda.j.nash@accenturefederal.com amanda.j.nash@accenturefederal.com – Support: Lynette Elliott: lynette.elliott@esacinc.com and Apurva Dharia: apurva.dharia@esacinc.comlynette.elliott@esacinc.comapurva.dharia@esacinc.com 27