Quality Improvement Data Quality Improvement Lecture a This

Quality Improvement Data Quality Improvement Lecture a This material (Comp 12_Unit 11 a) 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 IU 24 OC 000013.

Data Quality Improvement Learning Objective─Lecture a • Understand the different purposes of data. • Discuss the impact of poor data quality on quality measurement. • Identify ten attributes of data quality and key process recommendations. Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 2 Lecture a

Data and Healthcare (AHIMA) Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 3 Lecture a

QI vs. Research Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 4 Lecture a

Health Quality Measures Format • Support MU as described by the American Recovery and Reinvestment Act (ARRA). Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 5 Lecture a

Impact of Poor Data Quality Poor data attributes can lead to error • Threats to quality and safety • Patient and staff dissatisfaction • Increased operational cost • Less effective decision-making • Reduced ability to make and execute strategy Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 6 Lecture a

Data Quality Management (DQM) Model Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 7 Lecture a

Data Quality Attributes • • • Definition Accuracy Accessibility Comprehensiveness Consistency Health IT Workforce Curriculum Version 3. 0/Fall 2012 • • • Currency Timeliness Granularity Precision Relevancy Quality Improvement Data Quality Improvement 8 Lecture a

Definitions of Data Key process recommendations: Develop a data dictionary that represents agreement of all users on the standard definitions, normal and abnormal values, variable properties, etc. , that are included in the data. Responsibility for oversight of the management of the data must be assigned. Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 9 Lecture a

Definitions of Data • Definitions should be provided so current and future users will know what the data mean • Each element should have clear meaning and acceptable values (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. Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 10 Lecture a

Accuracy of Data • 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 pre-approved insurance providers, an automated process can be put into place to verify data accuracy. Other insurer codes can be entered manually after pre-approval. Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 11 Lecture a

Accuracy of Data • Key process recommendations: • Establish policies/procedures and provide guidance to ensure integrity, validity and reliability of the data. (AHIMA, 2006) Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 12 Lecture a

Accessibility of Data • 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 Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 13 Lecture a

Accessibility of Data • 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. Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 14 Lecture a

Comprehensiveness of Data • 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 non-payment 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. Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 15 Lecture a

Comprehensiveness of Data • Key process recommendations: • Establish guidelines for the most recent and comprehensive data required for the participants mission/objectives. (AHIMA, 2006) Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 16 Lecture a

Data Quality Improvement Summary─Lecture a The ten attributes of data quality are: • Definition • Accuracy • Accessibility • Comprehensiveness • Consistency • Currency • Timeliness • Granularity • Precision • Relevancy Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 17 Lecture a

Data Quality Improvement References─Lecture a References • American Health Information Management Association (AHIMA). Available from: http: //Ahima. org • HITECH legislation. National Health Safety Network Available: http: //www. cdc. gov/nhsn/ • HL 7 http: //www. hl 7 standards. com/blog/2009/09/17/what-is-hqmf-health-quality-measures-format/ • OLR Backgrounder: Electronic Health Records and “Meaningful Use” October 12 2010. Available from: http: //www. cga. ct. gov/2010/rpt/2010 -R-0402. htm • Solberg, Mosser, Mc Donald. Journal of Quality Improvement. 1997 Charts, Tables, Figures Table 11_1. QI. Vs Research. Adapted by Dr. Anna Maria Izquierdo-Porrera from Solberg et al (1997) Images Slide 3: Data and Healthcare. Courtesy Dr. Anna Maria Izquierdo-Porrera. AHIMA Slide 5: Health Quality Measure Format (HQMF). Courtesy Dr. Anna Maria Izquierdo-Porrera Slide 7: Data Quality Management Model. Courtesy Dr. Anna Maria Izquierdo-Porrera Health IT Workforce Curriculum Version 3. 0/Fall 2012 Quality Improvement Data Quality Improvement 18 Lecture a