The Changing Face of Data Quality Within VHA

1. The Changing Face of Data Quality Within VHA Presented by: Elizabeth Franchi, Director, HDI, Data Quality Program Sara Temlitz, Business Product Manager, HDI, Data Quality Program Jackie Houston, Identity Management Data Quality (IMDQ) Program Manager

2. Topics of Discussion Provide an overview of the Data Quality Program Discuss the overall structure, goals and strategies of the program Examine the primary roles and responsibilities Review the DQ role within VHA Review the existing and new programs in development within the Data Quality Program Identity Management Stewardship Program Data Stewardship Program Business Product Management Discuss the overall business benefits of implementing the Data Quality Program throughout VHA enterprises Business impacts Planned activities to achieve improved data quality and reduced patient safety risk Enhancement of the electronic medical record and the interoperability with business partners and ultimately care to patientsProvide an overview of the Data Quality Program Discuss the overall structure, goals and strategies of the program Examine the primary roles and responsibilities Review the DQ role within VHA Review the existing and new programs in development within the Data Quality Program Identity Management Stewardship Program Data Stewardship Program Business Product Management Discuss the overall business benefits of implementing the Data Quality Program throughout VHA enterprises Business impacts Planned activities to achieve improved data quality and reduced patient safety risk Enhancement of the electronic medical record and the interoperability with business partners and ultimately care to patients

3. Data Quality Program Overview

4. DQ Program: Mission Mission “Manage and implement a data quality framework to improve and ensure comprehensive and accurate VHA data for clinicians, administrators, veterans, sharing partners and business stakeholders.”

5. DQ Program: Overview The Veterans Health Administration (VHA) Data Quality Program: Is part of the Office of Information (OI) Health Data and Informatics Office (HDI) and Provides, facilitates and supports strategic direction for data management Provides VHA with operational support to improve the quality of data required to provide and manage health care information Includes several key components to help formalize and establish accountability for management of VHA data

6. DQ Program Guiding Principles Information is an enterprise-wide business asset and resource; Decision-making and business planning are based on information which is derived from data; Technical and business strategy are connected; Technology plays a supportive role in the data quality process; Data governance and stewardship programs formalize accountability and management responsibilities for data; Management of data is a method to achieve the data quality needed to support the delivery of health care. appropriate people represent business processes and are involved in the decisions that affect them; appropriate people represent business processes and are involved in the decisions that affect them;

7. Data Quality – Roles & Responsibilities Develop and manage programs for continuous data quality improvements Provide guidance and consultation to Lines of Business Manage data quality activities in support of VHA’s business VHA’s data steward for Patient Identity Data ensuring the availability of the longitudinal health record across our enterprise and with other business partners, like DoD Support VHA EHR integrity within the MPI and other applications Subject matter expert for selected national VHA reference tables Serves as key member of domain action teams and other VHA committees

8. OI HDI Data Quality Program Overview

9. DQ Program: Structure

10. Data Quality Functional Areas These are the major functional areas that must be performed to achieve high quality data within VHA. The data quality functional areas leverage both the emerging functional framework known as the Data Management Body of Knowledge (DMBOK) Functional Framework[1] and the Federal Data Quality Guide Draft. Both business and technical organizations across VHA and VA have the responsibility for performing these functions. The Data Quality Program’s primary focus is to help establish and coordinate activities, and identify and develop best practices, requirements, and processes within each of these areas. [1] Refer to http://www.dama.org/public/pages/index.cfm?pageid=795 These are the major functional areas that must be performed to achieve high quality data within VHA. The data quality functional areas leverage both the emerging functional framework known as the Data Management Body of Knowledge (DMBOK) Functional Framework[1] and the Federal Data Quality Guide Draft. Both business and technical organizations across VHA and VA have the responsibility for performing these functions. The Data Quality Program’s primary focus is to help establish and coordinate activities, and identify and develop best practices, requirements, and processes within each of these areas.

11. DQ Program Business Benefits

13. DQ Goals and strategies are intended to by dynamic and will be reviewed annually to ensure congruence with VHA strategic direction. DQ Goals and strategies are intended to by dynamic and will be reviewed annually to ensure congruence with VHA strategic direction.

14. DQ Strategies Flow from Goals A complete listing of Data Quality Strategies linked to DQ goals can be found in the DQ Plan.A complete listing of Data Quality Strategies linked to DQ goals can be found in the DQ Plan.

15. Systematic Activities support a Proactive Approach to Managing DQ As an established program with responsibility for stewardship of patient identity data, the IdM program examples illustrate a systemic approach to managing data. As an established program with responsibility for stewardship of patient identity data, the IdM program examples illustrate a systemic approach to managing data.

16. Data Quality Program Activities

17. VHA’s Electronic Health Record VHA is recognized as a leader in the world of electronic health records “VHA’s integrated health information system, including its framework for using performance measures to improve quality, is considered one of the best in the nation.” Institute of Medicine (IOM) Report, “Leadership by Example: Coordinating Government Roles in Improving Health Care Quality (2002)” Data Quality must be looked at in the context or providing a robust Electronic Health Record to meet business needs.Data Quality must be looked at in the context or providing a robust Electronic Health Record to meet business needs.

18. VHA’s Electronic Health Record features: More than 100 separate applications that support day-to-day activities of healthcare operations, including: Registration/enrollment/eligibility ; Provider systems; and Management and financial systems Through the Computerized Patient Record System (CPRS): Delivers an integrated electronic health record covering all aspects of patient care and treatment; and Provides access to remote data through Remote Data Views, Inter-facility Consults, VistAWeb

19. Business Benefits of the electronic health record Improves quality and safety of healthcare to veterans; Seamlessly provides health records and images when needed/authorized; and Enhances interoperability among VA and other business stakeholders VHA implemented an electronic Master Patient Index (MPI) to facilitate the sharing of a patient centered longitudinal health record within VHA and with external business partners such as DoD 16 million unique patients on VHA’s MPI 16 million unique patients on VHA’s MPI

20. VHA Data Quality Efforts VHA’s Master Patient Index Uniquely identifies patients; Assigns a unique identifier (the Integration Control Number or ICN) Holds roughly 15 million unique patients Part of data quality efforts are to: Ensure the availability of the electronic health record for patient care and related activities Manage the unique identifiers and patient identity traits Maintain record correlations across VHA systems like VistA, and Enable external linkages with DoD and other business partners based on the ASTM Standard, ASTM E1714-00 for Assignment of Universal Healthcare Identifiers based on the ASTM Standard, ASTM E1714-00 for Assignment of Universal Healthcare Identifiers

21. DQ Program: Clinical Data Quality

22. Clinical Data Quality Program Emerging program within Data Quality Provides framework to identify and support activities to resolve clinical data quality issues within VHA Collaborate in the development of overall data quality metrics and goals for prioritized data quality opportunities Identify problem areas for audit and analysis for data verification and correction Monitor and resolve data quality integrity issues, conduct studies and other analyses

23. DQ Program: Program Analysis

24. Program Analysis Program Objectives Provide support to program areas within DQ Assist in the development of overall data quality metrics and goals for prioritized data quality opportunities Provide audit and analysis for data verification and correction Monitor and resolve data quality integrity issues, conduct studies and other analysis

25. What is Data Quality Analytics? Applies techniques to: Analyze the quality of data, Determine its ability to be used for its intended purpose, and Recommend corrective action when needed Example techniques include: Data profiling Process assessments, e.g., Healthcare Failure Mode and Effect Analysis (HFMEAs) Root Cause Analysis (RCAs) Healthcare Failure Mode & Effect Analysis (HFMEA): A prospective assessment that identifies and improves steps in a process thereby reasonably ensuring a safe and clinically desirable outcome. A systematic approach to identify and prevent product and process problems before they occur. Root Cause Analysis (RCA) A process for identifying the basic or contributing causal factors associated with a patient safety incident.  RCA’s are designed to identify what happened, how an event occurred, and why it occurred.  A RCA is not used when dealing with intentional unsafe acts.  The RCA will be used for both individual and aggregated reviews that identify trends or patterns not noticeable in individual reviews. (informal definition)  A process for identifying the basic or contributing causal factors associated with an…(name your event)Healthcare Failure Mode & Effect Analysis (HFMEA): A prospective assessment that identifies and improves steps in a process thereby reasonably ensuring a safe and clinically desirable outcome. A systematic approach to identify and prevent product and process problems before they occur. Root Cause Analysis (RCA) A process for identifying the basic or contributing causal factors associated with a patient safety incident.  RCA’s are designed to identify what happened, how an event occurred, and why it occurred.  A RCA is not used when dealing with intentional unsafe acts.  The RCA will be used for both individual and aggregated reviews that identify trends or patterns not noticeable in individual reviews. (informal definition)  A process for identifying the basic or contributing causal factors associated with an…(name your event)

26. What is Data Profiling? Assessment of data: Content/values within databases The data’s defined characteristics (or metadata), e.g., data type (a date field must include month, day and year) Profiling activity may be initiated: To analyze a reported issue (assertion testing) To proactively discover data characteristics and anomalies

27. Data Profiling Benefits Enables a better understanding of the data’s quality and ability to be used for its intended business purpose Identifies problematic areas and enables recommendations to be developed and implemented to improve inconsistent, inaccurate, and/or incomplete data Enables data descriptions and business rules to be verified and improved e.g., a date can be imprecise or must be precise (month, day and year required). A vital sign must have an associated service location.e.g., a date can be imprecise or must be precise (month, day and year required). A vital sign must have an associated service location.

28. Recent DQ Analytics Activity Profiling patient identity traits (e.g., Mother’s Maiden Name) Comparing metadata (dictionaries, models, Primary View) Running queries against Corporate Data Warehouse (CDW) data from the VistA Patient file to: Determine if the data meets the metadata description Identify other data anomalies Results will be used to improve Primary View business rules, e.g, Identification of additional blank equivalents for Mother’s Maiden Name Over 25 millions record in the CDW Patient File: 25,147,912 over 16 million # of Records with a MMN entry:– 63.7% (April 2008) Over 25 millions record in the CDW Patient File: 25,147,912 over 16 million # of Records with a MMN entry:– 63.7% (April 2008)

29. Recent DQ Analytics Activity Initiating a HFMEA for Date of Death Erroneous Dates of Death cause cancellation of medications, future appointments, etc. Incorrect Dates of Death lead to data integrity errors, such as: Vitals exist for after DOD Encounters exist after DOD Omission of Dates of Death cause business processes to continue, such as: Appointment letters sent to deceased patients Medications delivered for deceased patients Appointment slots not available for waiting patients

30. DQ Program: Identity Management

31. Identity Management Stewardship The Identity Management Stewardship Program is responsible for the business stewardship of identity data as part of the electronic health record within VHA. A major component of this program is the Identity Management Data Quality (IMDQ) team, which provides specific stewardship for the data required to link patient identities to their related information in VHA and other systems. The team is responsible for the data integrity and business analytics support of VHA’s Master Patient Index (MPI), the unique identifier system for all patient records within VHA.

32. IMDQ Team Responsibilities Defines business rules and processes governing identity management to ensure accuracy and integrity of VHA’s longitudinal health record Monitors and resolves data integrity issues and conflicts on the MPI and local VistA systems Resolution of duplicates Mismatches and catastrophic edits Ensure the quality of data Improve process and method of entering patient record data Facilitate information dissemination and training Improve the understanding of the MPI and identity management and it’s direct relation to the patient record

33. Identity Management Activities Management of Catastrophic Edits and other data quality issues Support of Master Patient Index (MPI) development Enhancements to existing software, including Primary View Implementation Re-hosting to Administrative Data Repository (ADR) Assistance for implementation of Person Service Identity Management (PSIM) IMDQ Toolkit development Non-person enumeration Enrollment System Redesign implementation Other HeV application development support

34. Catastrophic Edits I think the best way for me to emphasize the critical role data quality has to plan in ensuring that Electronic Health Records perform as they are supposed to, is to focus on what happens when changes to a patient record that result in the record being changed to that of another patient. OR when Different patients’ records are merged using the Duplicate Record Merge software, resulting in a change in the patient’s identity The record starts out as Jane Q. Doe and then the identity fields on the electronic health record are changed to those of John Q. Doe. I think the best way for me to emphasize the critical role data quality has to plan in ensuring that Electronic Health Records perform as they are supposed to, is to focus on what happens when changes to a patient record that result in the record being changed to that of another patient. OR when Different patients’ records are merged using the Duplicate Record Merge software, resulting in a change in the patient’s identity The record starts out as Jane Q. Doe and then the identity fields on the electronic health record are changed to those of John Q. Doe.

35. Catastrophic Edits to Identity Effects of Catastrophic Edits Incorrect association of patient medical record data to a different patient’s record Intermingling of two different patients’ records Actions taken to reduce Catastrophic Edits Changes to VistA software to warn of potential catastrophic edits Distribution of notifications of occurrences to local management Tracking of resolution to ensure medical records are restored National staff within the Identity Management Data Quality group monitor alerts about potential problems daily Patient safety risk is high Treatment may be administered to wrong patient May not have complete or accurate information about patients, including allergies, current medications Electronic health record may become fragmented or have conflicting data

36. DQ Program: Data Stewardship

37. Data Stewardship Objectives Develop an effective data stewardship program for the organization, leveraging existing activities; Coordinate/support business and technical stewardship efforts for VHA business community Work with Line-of-Business (LOB) Data Stewards and subject matter experts and ensure that they have necessary knowledge, training, and support to fulfill stewardship responsibilities; Lead/coordinate stewardship activities for the domains defined in enterprise information models and by LOB managers; and Collaborate with DoD, Indian Health Service, business partners, VA technical teams and external data architecture and standards organizations. * Emerging program; not yet fully staffed

38. DQ Program: Business Products

39. Business Product Management The Business Product Management Program ensures that business stakeholder data quality requirements are identified and communicated through appropriate processes, and monitors progress to ensure business needs are met. This Program serves as a liaison between business stakeholder groups, technical communities and the Data Quality Program in issues related to data quality, including those involving identity management.

40. Business Product Management Program Provides expertise to business stewards on: Business requirements development, Enterprise requirements management process, and IT development process Works with stewards and other stakeholders to: Define detailed business requirements specifications for both legacy VistA and HealtheVet applications Develop and obtain approval of overarching data quality requirements Collaborate with ESMs, architects, developers and other technical staff to develop formal business models and technical specifications Participate in design validation and user acceptance testing The Business Product Management Manager will plan and implement data quality analysis, improvement and compliance efforts as they relate to the development of requirements; provide data quality requirements, and coordinate data quality review and input thought the software design and development lifecycles to ensure that business needs are met. The Business Product Management Program will be supported by a Business Product Manager, Business Product Analyst and 4 Business Analysts, who will work closely with stakeholders and technical teams to develop, define and document requirements, and participate in modeling, design specification/validation and product acceptance efforts. The Business Product Management Manager will plan and implement data quality analysis, improvement and compliance efforts as they relate to the development of requirements; provide data quality requirements, and coordinate data quality review and input thought the software design and development lifecycles to ensure that business needs are met. The Business Product Management Program will be supported by a Business Product Manager, Business Product Analyst and 4 Business Analysts, who will work closely with stakeholders and technical teams to develop, define and document requirements, and participate in modeling, design specification/validation and product acceptance efforts.

41. Enterprise DQ Requirements Develop Requirements for Enterprise Repository Integrate DQ requirements into the development lifecycle Inception Elaboration Construction Transition Provide guidance and support to development and program staff

42. Summary The VHA Data Quality Program continues to develop and expand its role in improving data for all aspect of healthcare within VHA and with other business partners High quality data is essential for the provision of quality healthcare for our patients and for other business needs Collaboration with others within our organization is critical in ensuring data quality

43. Resources Data Quality http://vaww.vhaco.va.gov/dataquality Identity Management Data Quality http://vista.med.va.gov/mpi_dqmt/ Administrative Data Quality Council http://vaww.vhaco.va.gov/dataquality/adq.htm Data Consortium http://datacon.vssc.med.va.gov/default.aspx

44. Contacts Beth Franchi, Director, VHA Data Quality Program(414) 389-4191Elizabeth.Franchi2@va.govSara Temlitz, Business Product Manager(414) 389-4192Sara.Temlitz@va.govJackie Houston, Identity Management Data Quality Program Manager(205) 554-3449Jackie.Houston@va.gov