Quality Improvement Assessing Data Quality Lecture c This

Quality Improvement Assessing Data Quality Lecture c 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 IU 24 OC 000013. This material was updated in 2016 by Johns Hopkins University under Award Number 90 WT 0005. This work is licensed under the Creative Commons Attribution-Non. Commercial-Share. Alike 4. 0 International License. To view a copy of this license, visit http: //creativecommons. org/licenses/by-nc-sa/4. 0/.

Assessing Data Quality Learning Objectives — Lecture c • Discuss common causes of data insufficiency. • Describe how health information technology design can enhance data quality and improve quality and safety measure results. 2

Causes of Insufficient Data Quality — 1 • Systematic: – Unclear or ambiguous definitions. – Incomplete or unsuitable data. – Violations of data collection or processing protocols. – Poor screen/interface design. – Programming errors. – Lack of data quality checks. 3

Causes of Insufficient Data Quality — 2 • Random: – Inaccurate transcription or typing errors. – Data overload. – Motivational or turnover. 4

Data Quality Enhancement Opportunities 5

Best Practices: Prevention 6

More Best Practices: Prevention 7

Best Practices: Detection 8

Best Practices: Improvement Actions 9

HIT Solutions to Improve Data • Standardize terminology: – Better communication among professionals. – Improved patient care. – Enhanced data collection to evaluate outcomes. – Greater adherence to standards of care. – Enhanced assessment of professional competency. • Structured data vs. free text: – Narration. – Pick lists. – Checks. – Radio buttons. • Voice recognition as data capture mechanisms. 10

Possible Future HIT Solutions to Improve Data • Future possibilities – Natural language processing using machine learning. – Biometrics. 11

Assessing Data Quality Summary — Lecture c • Clinical data drive health care decisions. • Poor data quality have a significant negative impact on health care outcomes. • Data quality is multidimensional. • Insufficient data are linked to a number of systematic and random causes. • HIT professionals can use best practices to enhance data quality. 12

Assessing Data Quality References — Lecture c References Arts, D. , De Keizer, N. F. , & Scheffer, G. T. (2002). Defining and improving data quality in medical registries: A literature review, case study, and generic framework. J Am Med Inform Assoc, 9, 6: P 600– 11. 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 Thede, L. , & Schwiran, P. (2011 February 25). Informatics: The standardized nursing terminologies: A national survey of nurses’ experiences and attitudes. OJIN: The Online Journal of Issues in Nursing, 16, 2. Images Slide 5: Data quality enhancement opportunities. Courtesy Dr. Anna Maria Izquierdo. Porrera. Slide 6: Best practices: Prevention. Courtesy Dr. Anna Maria Izquierdo-Porrera. Slide 7: More best practices: Prevention. Courtesy Dr. Anna Maria Izquierdo-Porrera. Slide 8: Best practices: Detection. Courtesy Dr. Anna Maria Izquierdo-Porrera. Slide 9: Best practices: Improvement actions. Courtesy Dr. Anna Maria Izquierdo-Porrera. 13

Quality Improvement Assessing Data Quality Lecture c 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 IU 24 OC 000013. This material was updated in 2016 by Johns Hopkins University under Award Number 90 WT 0005. 14