Unit 11d: Common Causes of Insufficient Data Quality & Design Recommendation Data Quality Improvement This material was developed by Johns Hopkins University,

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Presentation transcript:

Unit 11d: Common Causes of Insufficient Data Quality & Design Recommendation Data Quality Improvement This material 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 IU24OC

At the end of this segment, the student will be able to: Discuss common causes of data insufficiency Describe how health information technology (HIT) design can enhance quality Objectives Component 12/Unit 112 Health IT Workforce Curriculum Version 2.0/Spring 2011

Causes of Insufficient Data Quality 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 Component 12/Unit 113 Health IT Workforce Curriculum Version 2.0/Spring 2011

Causes of Insufficient Data Quality Random Inaccurate transcription or typing errors Data overload Motivational or turnover Component 12/Unit 114 Health IT Workforce Curriculum Version 2.0/Spring 2011

Data Quality Enhancement Opportunities Improvement Take action for ongoing quality improvement Detection Inspect for potential or real quality issues Prevention Create best possible quality through design Component 12/Unit 115 Health IT Workforce Curriculum Version 2.0/Spring 2011

Best Practices: Prevention Identify required data elementsDevelop a data dictionaryOptimize data captureEstablish collection guidelines Component 12/Unit 116 Health IT Workforce Curriculum Version 2.0/Spring 2011

More Best Practices: Prevention Control user access Develop user-friendly screens Create a data monitoring plan Educate users Component 12/Unit 117 Health IT Workforce Curriculum Version 2.0/Spring 2011

Best Practices: Detection Automatic data checks Conduct audits/data quality checks Review collection protocols & report logic Assess variability in data Component 12/Unit 118 Health IT Workforce Curriculum Version 2.0/Spring 2011

Best Practices: Improvement Actions Provide regular reports to users Correct inaccuracies & complete missing/incomplete data Document & correct detected errors Implement identified system changes Component 12/Unit 119 Health IT Workforce Curriculum Version 2.0/Spring 2011

Summary Clinical data drive health care decisions Poor data quality have a significant negative impact on healthcare outcomes Data quality is multi-dimensional Insufficient data are linked to a number of systematic and random causes HIT professionals can use best practices enhance data quality Component 12/Unit 1110 Health IT Workforce Curriculum Version 2.0/Spring 2011

References Arts D, De Keizer NF, Bosman R, De Jonge E, Joore J. Training in data definitions improves quality of intensive care data. Critical Care. 7: Arts D, De Keizer NF, Scheffer GT. Defining and improving data quality in medical registries: a literature review, case study, and generic framework. J Am Med Inform Assoc 9;6: P Chappell K, Newman C. Potential tenfold drug overdoses on a neonatal unit. Arch Dis Child Fetal Neonatal Ed OLR Backgrounder: Electronic Health Records and “Meaningful Use” October Available from: Component 12/Unit 1111 Health IT Workforce Curriculum Version 2.0/Spring 2011