1. Data Quality Improvement
Unit 11.3: Common Causes of Insufficient Data Quality & Design Recommendation
We have discussed the impact of poor data quality on quality measurement. We have defined ten different attributes that are used to define data quality, reviewed an example of each attribute in various clinical settings and highlighted key process recommendations to improve data quality. Let’s summarize what we’ve learned by reviewing common causes of insufficient data quality and review best practices that you can implement in your role to assure or improve the quality of health information.
Component 12/Unit 11
Health IT Workforce Curriculum Version 1.0/Fall 2010

2. At the end of this segment, the student will be able to:
Objectives
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
At the end of this segment, you will be able to discuss common causes of data insufficiency and describe how HIT design can enhance quality
Component 12/Unit 11
Health IT Workforce Curriculum Version 1.0/Fall 2010

3. 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
A case study of data quality in medical registries published by Arts, De Keizer, and Scheffer, in 2002, offers a good summary of many of the data quality issues previously presented in this module. In this article, they discuss common causes of insufficient data quality as either systematic or random. The systematic causes (or what is statistically referred to as Type I errors) are those that can be attributed to some bias or flaw in the measurement process that is not due to chance. Systematic causes, if not corrected, will cause repeated flaws or errors with a predictable pattern or a high degree of certainty. Some frequent systematic causes of insufficient data quality are unclear or ambiguous definitions; incomplete or unsuitable format; violations in the collection, processing or analysis; poor design in the tools or forms for data entry; and a lack of quality auditing or control processes.
Defining and improving data quality in medical registries: a literature review, case study, and generic framework. Danielle G T Arts, Nicolette F De Keizer, Gert-Jan Scheffer J Am Med Inform Assoc November 2002 (Vol. 9, Issue 6, Pages )
Component 12/Unit 11
Health IT Workforce Curriculum Version 1.0/Fall 2010

4. Causes of Insufficient Data Quality
Random
Inaccurate transcription or typing errors
Data overload
Motivational or turnover
A case study of data quality in medical registries published by Arts, De Keizer, and Scheffer, in 2002, offers a good summary of many of the data quality issues previously presented in this module. In this article, they discuss common causes of insufficient data quality as either systematic or random. The systematic causes (or what is statistically referred to as Type I errors) are those that can be attributed to some bias or flaw in the measurement process that is not due to chance. Systematic causes, if not corrected, will cause repeated flaws or errors with a predictable pattern or a high degree of certainty. Some frequent systematic causes of insufficient data quality are unclear or ambiguous definitions; incomplete or unsuitable format; violations in the collection, processing or analysis; poor design in the tools or forms for data entry; and a lack of quality auditing or control processes. Random causes occur with less predictability and can include inaccurate transcription or typing (as in free-text entries), sheer data overload and the possibility of ambiguity or selection of irrelevant data, or from inattention or poor understanding on the part of the individual completing the entry, analysis, or data warehousing procedures.
Defining and improving data quality in medical registries: a literature review, case study, and generic framework. Danielle G T Arts, Nicolette F De Keizer, Gert-Jan Scheffer J Am Med Inform Assoc November 2002 (Vol. 9, Issue 6, Pages )
Component 12/Unit 11
Health IT Workforce Curriculum Version 1.0/Fall 2010

5. Data Quality Enhancement Opportunities
Prevention
Create best possible quality through design
Detection
Inspect for potential or real quality issues
Improvement
Take action for ongoing quality improvement
Arts et al. examined planned and systematic procedures that take place before, during and after the data collection to identify causes of insufficient data quality. As an HIT professional, you will be in the best position to consider what can be done to prevent, detect, and facilitate improvement efforts. You will seek to create the best possible quality through the design of the application, the data collection process and subsequent reports or analyses. You will implement activities to detect potential or real flaws that can pose threats to data quality. And you will take corrective actions to improve data quality.
Let’s recap what some of those activities include under each of the three areas.
Component 12/Unit 11
Health IT Workforce Curriculum Version 1.0/Fall 2010

6. Best Practices: Prevention
Identify required data elements
Develop a data dictionary
Optimize data capture
Establish collection guidelines
The first step is to identify the required data elements for the task. A data dictionary with standard definitions and data formats will be essential to promoting data quality. You should seek terminology harmonization with accepted standards and avoid “local” naming conventions or glossaries. Data capture and completeness will improve when required data location is limited to fewer locations within the EHR. Optimizing the use of structured data fields over free-text will reduce the likelihood of missing data and improve data retrieval. While data extraction programs, such as natural language processing programs can be used to extract data, these programs work best when the variables are narrowly and consistently defined. Standard guidelines for data collection, analysis, and storage should also be documented and should note clear inclusion and exclusion criteria.
Component 12/Unit 11
Health IT Workforce Curriculum Version 1.0/Fall 2010

7. More Best Practices: Prevention
Control user access
Develop user-friendly screens
Create a data monitoring plan
Educate users
Privacy and security policies should govern the controlled access of the data and responsibility and accountability for data management must be delineated and observed. Attention must be paid to how clinical workflow and system design can affect data quality. The placement and design of structured data should facilitate the work of the clinician. Excessive navigation and requiring multiple “clicks” will decrease documentation compliance and promote the unintended use of free-text, or lead to missing or inaccurate data. The clinical specificity and number of option choices for structured data, if designed correctly, can facilitate data quality. Synonym and acronym recognition should be used wisely. It can speed data entry, but can also lead to inappropriate entries if the wrong choice is selected. Recognition and correction of data flaws requires a thoughtful monitoring plan. Priority data elements, such as those used for quality measurement, should be identified and targeted for monitoring and improvement as indicated. These data should be reviewed for data granularity, precision, currency, timeliness, completion, and accuracy. Data documentation quality can be improved through staff training and education. Content can include system use, screen navigation, use of references such as the data dictionary and collection guidelines and placement and completion of priority data items. Standard content and delivery methods should be identified to minimize localized work arounds. A plan for ongoing education of new users and new content or upgrades should be identified to prevent deterioration in the data quality due to lack of knowledge.
Component 12/Unit 11
Health IT Workforce Curriculum Version 1.0/Fall 2010

8. Best Practices: Detection
Automatic data checks
Conduct audits/data quality checks
Review collection protocols & report logic
Assess variability in data
Automatic domain or consistency checks, such as out-of-range data or inconsistencies between two data fields, at data entry, extraction or transfer can detect potential flaws or errors. Data errors undetectable through programming checks can still occur, such as incorrect patient location, and manual processes for auditing or data checking should be developed. Regular review of data collection protocols and report logic should be conducted with care to correct sources of ambiguity or a lack of currency with other changes in data definitions or catalog updates. Review of priority data items, such as frequency analysis or cross tabulations, can be conducted to detect flaws or unacceptable deviations in the data.
Component 12/Unit 11
Health IT Workforce Curriculum Version 1.0/Fall 2010

9. Best Practices: Improvement Actions
Provide regular reports to users
Correct inaccuracies & complete missing/incomplete data
Document & correct detected errors
Implement identified system changes
Users will continue to assume quality is present in the absence of data to inform them otherwise. Regular reports about data quality should be made available to them. Once inaccuracies or flaws in the data are known, corrections and edits to the data must be made. Documentation and correction of the flaws or errors is essential as and may provide justification for future design modification. As discussed in the beginning of this module, data are often shared across multiple interfaces and often used for application other than the originally intended purpose. Systematic study of the source of error and inadequacies should be conducted so current and future corrections can be made across all applicable systems.
Component 12/Unit 11
Health IT Workforce Curriculum Version 1.0/Fall 2010

10. Health IT Workforce Curriculum Version 1.0/Fall 2010
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
Clinical data are increasingly used to drive health care decisions. The data that are captured to document patient care are also used for billing, risk management, accreditation, quality, and health care research. Poor data quality can threaten patient safety and quality; decrease satisfaction; increase cost; and compromise strategic planning. Data quality is a complex, multi-dimensional concept and a number of attributes should be considered at all phases of HIT development and use. An HIT professional who is aware of the common systematic and/or random causes of data insufficiency can skillfully employ best practices in the areas of prevention, detection and quality improvement to enhance the overall quality, and usefulness, of healthcare data.
Component 12/Unit 11
Health IT Workforce Curriculum Version 1.0/Fall 2010