Quality Improvement

1. Quality Improvement Assessing Data Quality Lecture a This material (Comp 12 Unit 9) was developed by Johns Hopkins University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC000013. This material was updated in 2016 by Johns Hopkins University under Award Number 90WT0005. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/.

2. Assessing Data QualityLearning Objectives — Lecture a • Understand the different purposes of data. • Discuss the impact of poor data quality on quality measurement. • Identify 10 attributes of data quality and key process recommendations.

3. Data and Health Care – American Health Information Management Association (AHIMA)

4. Quality Improvement (QI) vs. Research

5. Impact of Poor Data Quality • Poor data attributes can lead to: • Threats to quality and safety. • Patient and staff dissatisfaction. • Increased operational cost. • Less effective decision making. • Reduced ability to make and execute organizational strategy.

6. Data Quality Management (DQM) Model

7. AHIMA DQM: Application • Application’s purpose, the question to be answered, or the aim for collecting the data is clear. • Boundaries or limitations of data collected are known and communicated. • Complete data are collected for the application. • Value of the data is the same across applications and systems. • The application is of value and is appropriate for the intent. • Timely data are available. AHIMA. (2012 July). Data quality management model (Updated).Journal of AHIMA, 83,7: 62–67.

8. AHIMA DQM: Collection — 1 • Education and training are effective and timely. • Communication of data definitions is timely and appropriate. • Data source provides most accurate, most timely, and least costly data. • Data collection is standardized. • Data standards exist. • Updates and changes are communicated appropriately and on a timely basis. • Data definitions are clear and concise. AHIMA. (2012 July). Data quality management model (Updated). Journal of AHIMA, 83, 7: 62–67.

9. AHIMA DQM: Collection — 2 • Data are collected at the appropriate level of detail or granularity. • Acceptable values or value ranges for each data element are defined; edits are determined. • The data collection instrument is validated. • Quality (i.e., accuracy) is routinely monitored. • Meaningful use is achieved via the evaluation of EHR data. AHIMA. (2012 July). Data quality management model (Updated). Journal of AHIMA, 83, 7: 62–67.

10. AHIMA DQM: Warehousing/Interoperability — 1 • Appropriate edits are in place. • Data ownership is established. • Guidelines for access to data and/or systems are in place. • Data inventory is maintained. • Relationships of data owners, data collectors, and data end users are managed. • Appropriate conversion tables are in place. • Systems, tables, and databases are updated appropriately. AHIMA. (2012 July). Data quality management model (Updated). Journal of AHIMA, 83, 7: 62–67.

11. AHIMA DQM: Warehousing/Interoperability — 2 • Current data are available. • Data and application journals (data definitions, data ownership, policies, data sources, etc.) are appropriately archived, purged, and retained. • Data are warehoused at the appropriate level of detail or granularity. • Appropriate retention schedules are established. • Data are available on a timely basis. • Health information exchange is achieved as a result of interoperability of EHRs. AHIMA. (2012 July). Data quality management model (Updated). Journal of AHIMA, 83, 7: 62–67.

12. AHIMA DQM: Analysis • Algorithms, formulas, and translation systems are valid and accurate. • Complete and current data is available. • Data impacting the application are analyzed in context. • Data are analyzed under reproducible circumstances. • Appropriate data comparisons, relationships, and linkages are displayed. • Data are analyzed at the appropriate level of detail or granularity. AHIMA. (2012 July). Data quality management model (Updated). Journal of AHIMA, 83, 7: 62–67.

13. Data Quality Attributes • Definition • Accuracy • Accessibility • Comprehensiveness • Consistency • Currency • Timeliness • Granularity • Precision • Relevancy

14. Definitions of Data — 1 • Key process recommendations: • Develop a data dictionary. • Ensure metadata with standard definitions, normal and abnormal values, and variable properties are included in the data. • Create data governance policies and procedures. • Assign responsibility for oversight of the management of the data.

15. Definitions of Data — 2 • Provide definitions so current and future users will know what the data mean. • Include clear meaning and acceptable values for each element (AHIMA, 2006). • Example: • An organization is preparing to participate in a nationally recognized surveillance system for public reporting of hospital-acquired infections. • First, they review the definitions for all variables to be submitted to assure they match the definitions required by the database.

16. Accuracy of Data — 1 • Correct values. • Valid. • Attached to the correct patient record (AHIMA, 2006). • Example: • The patient’s health insurance information must be accurate for billing purposes. • If a reference table is available with the codes of all preapproved insurance providers, an automated process can be put into place to verify data accuracy. • Other insurer codes can be entered manually after preapproval.

17. Accuracy of Data — 2 • Key process recommendations: • Establish policies/procedures and provide guidance to ensure integrity, validity, and reliability of the data (AHIMA, 2006).

18. Accessibility of Data — 1 • Easily obtainable. • Legal to access with strong protections and built-in controls (AHIMA, 2006). • Example: • A quality improvement team at a home care agency needs demographic data to complete a clinical outcome analysis for their congestive heart failure patients. • They complete the required forms and are granted time-limited access to designated tables in the agency’s data warehouse that contain the needed demographics.

19. Accessibility of Data — 2 • Key process recommendations: • Gain consensus on defined minimum amount of data to be accessible to participants to support their mission/objectives. • Provide protection controls with traceable audit capability.

20. Comprehensiveness of Data — 1 • Required data items are included. • The entire scope of required data are collected. • Intentional limitations are documented (AHIMA, 2006). • Example: • Hospitals are at risk for nonpayment if a patient develops a pressure ulcer in their care. • Prompts can be written to require a detailed assessment to determine if any pressure areas were present on admission. • This assessment includes the location, size, and extent of tissue damage.

21. Comprehensiveness of Data — 2 • Key process recommendations: • Establish guidelines for the most recent and comprehensive data required for the participants’ mission/objectives (AHIMA, 2006).

22. Assessing Data QualitySummary — Lecture a • The 10 attributes of data quality are: • Definition. • Accuracy. • Accessibility. • Comprehensiveness. • Consistency. • Currency. • Timeliness. • Granularity. • Precision. • Relevancy.

23. Assessing Data QualityReferences — Lecture a — 1 References American Health Information Management Association (AHIMA). Available from: http://Ahima.org AHIMA. (2012 July). Data quality management model (Updated). Journal of AHIMA, 83,7:62–67. HL7 Standards. Available from: http://www.hl7standards.com/blog/2009/09/17/what-is-hqmf-health-quality-measures-format/ Kasprak, J. (2010 October 12). OLR backgrounder: Electronic health records and “Meaningful Use.” Available from: http://www.cga.ct.gov/2010/rpt/2010-R-0402.htm National Healthcare Safety Network. HITECH legislation. Available from: http://www.cdc.gov/nhsn/ Solberg, Mosser, McDonald. (1997). Journal of Quality Improvement. Charts, Tables, Figures 9.1 Table: Quality improvement (QI) vs. research. Adapted by Dr. Anna Maria Izquierdo-Porrera from Solberg et al. (1997).

24. Assessing Data QualityReferences —Lecture a — 2 Images Slide 3: Data and health care (AHIMA). Courtesy Dr. Anna Maria Izquierdo-Porrera. Slide 6: Data quality management model. Courtesy Dr. Anna Maria Izquierdo-Porrera.

25. Quality ImprovementAssessing Data QualityLecture a This material (Comp 12 Unit 9) was developed by Johns Hopkins University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC000013. This material was updated in 2016 by Johns Hopkins University under Award Number 90WT0005.