This lecture discusses assessing data quality and improving it through health information technology (HIT). It identifies common causes of insufficient data quality, such as unclear definitions, incomplete data, and programming errors. Both systematic and random issues can negatively impact data quality. The lecture outlines best practices for preventing, detecting, and improving data quality issues. Standardizing terminology, structuring data entry, and utilizing technologies like voice recognition can enhance data quality. Overall, high quality clinical data is important for healthcare decisions, and HIT professionals can implement strategies to enhance data quality.
The 10th Annual Utah Health Services Research Conference: Data Quality in Multi-Site Health Services and Comparative Effectiveness Research: Lessons from PHIS+ By: Ram Gouripeddi
Health Services Research Conference: March 16, 2015
Patient Centered Research Methods Core, University of Utah, CCTS
Researchers and care providers wanted to have access to all of the patients` vitals signs (temperature, blood pressure, heart rate, and respiratory rate) but most of this data wasn?t recorded, only a few readings a day were posted to the patients Electronic Medical Record (EMR). The EMR isn`t meant to store such volume of data, let alone to perform any data mining on it. This session will describe the architecture of the solution that was implemented to collect these vital signs automatically from Bedside Medical Devices (BDMI), and store them into a temporary storage, then load them into a Hadoop cluster. The session will also cover how the team married this vital signs data in the HDFS (Hadoop File System) with the rest of the EMR data for our Principles Investigators (PI) in our research institute to search for correlations between administered medications, diagnosis, and vital signs readings. The session will describe the reasons behind the design decisions that were made, such as using a Cloud Hadoop cluster versus on-premises while maintaining HIPAA.
The 10th Annual Utah Health Services Research Conference: Data Quality in Multi-Site Health Services and Comparative Effectiveness Research: Lessons from PHIS+ By: Ram Gouripeddi
Health Services Research Conference: March 16, 2015
Patient Centered Research Methods Core, University of Utah, CCTS
Researchers and care providers wanted to have access to all of the patients` vitals signs (temperature, blood pressure, heart rate, and respiratory rate) but most of this data wasn?t recorded, only a few readings a day were posted to the patients Electronic Medical Record (EMR). The EMR isn`t meant to store such volume of data, let alone to perform any data mining on it. This session will describe the architecture of the solution that was implemented to collect these vital signs automatically from Bedside Medical Devices (BDMI), and store them into a temporary storage, then load them into a Hadoop cluster. The session will also cover how the team married this vital signs data in the HDFS (Hadoop File System) with the rest of the EMR data for our Principles Investigators (PI) in our research institute to search for correlations between administered medications, diagnosis, and vital signs readings. The session will describe the reasons behind the design decisions that were made, such as using a Cloud Hadoop cluster versus on-premises while maintaining HIPAA.
Journal for Clinical Studies: Close Cooperation Between Data Management and B...KCR
Every clinical trial is a source of multidimensional data, analyzed to answer questions on safety, efficacy and others. Invalid or incomplete data may lead to invalid conclusions and wrong decision. KCR’s Biostatistician, Adrian Olszewski, highlights the importance of cooperation between data management and biostatistics to improve data quality by introducing both statistical knowledge and the ability to create specialized, programmatic tools and advanced queries giving a good foundation for deeper and faster data investigations. Read more in the article published in the October Issue of Journal for Clinical Studies (p. 42-46).
iHT² Health IT Summit Beverly Hills – Case Study "The EHR & Quality: The Current Evidence" Abha Agrawal, MD, FACP, COO & VP of Medical Affairs, Norwegian American Hospital
Case Study "The EHR & Quality: The Current Evidence"
∙ Understand where EHRs have demonstrated evidence based quality improvement
∙ Learn what areas for improvement exist to improve quality and physician productivity
∙ Discuss how results can be driven across diverse care settings and systems
∙ Identify unintended consequences of HIT
Paper presented at the 2012 MLA Quad Chapter meeting in Baltimore, MD, Oct. 13-16. Discusses i2b2 and how it could be used in medical education. And suggests other data if i2b2 not available in your hospital.
Prof Mendel Singer Big Data Meets Public Health and Medicine 2018 12-22mjbinstitute
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The information in ISR’s Electronic Media Use in Academic Medical Center Patient Recruitment report sheds light on the benefits, challenges and strategies of electronic media use for clinical trial recruitment. We are currently in an era where Facebook, Twitter, and Google searches link all of us to a larger global community. Clinical sites are actively navigating how to apply these resources to connect with patients around the world. The report will help academic medical centers, site personnel and CRO/sponsors understand which channels and strategies will best help accomplish their recruitment goals, and which ones can be developed for increased success in this realm.
Presenting precisionFDA for the first time at the Precision Medicine Coalition in Washington, DC on February 24, 2016
Any views or opinions expressed here do not necessarily represent the views of the FDA, HHS, or any other entity of the United States government. Furthermore, the use of any product names, trade names, images, or commercial sources is for identification purposes only, and does not imply endorsement or government sanction by the U.S. Department of Health and Human Services.
A presentation given at the Duke Margollis Health Policy meeting in 2015 and providing insights into the current challenges related to EHR data quality. Proposes a new approach - OneSource.
The Imperative of Linking Clinical and Financial Data to Improve Outcomes - H...Health Catalyst
Quality and cost improvements require the intelligent use of financial and clinical data coupled with education for multi-disciplinary teams who are driving process improvements. Once a data warehouse is established, healthcare organizations need to set up multi-disciplinary clinical, financial, and IT specialist teams to make the best use of the data. Sometimes, financial involvement is minimized or even excluded for a number of reasons that can turn out to be counterproductive. However, including financial measurements and participation up front can help enhance the recognized value and sustainability of quality improvement or waste reduction efforts. the In this session you will learn keys to success and real-life examples of linking clinical, financial and patient satisfaction data via multi-disciplinary teams that produce impressive results.
Journal for Clinical Studies: Close Cooperation Between Data Management and B...KCR
Every clinical trial is a source of multidimensional data, analyzed to answer questions on safety, efficacy and others. Invalid or incomplete data may lead to invalid conclusions and wrong decision. KCR’s Biostatistician, Adrian Olszewski, highlights the importance of cooperation between data management and biostatistics to improve data quality by introducing both statistical knowledge and the ability to create specialized, programmatic tools and advanced queries giving a good foundation for deeper and faster data investigations. Read more in the article published in the October Issue of Journal for Clinical Studies (p. 42-46).
iHT² Health IT Summit Beverly Hills – Case Study "The EHR & Quality: The Current Evidence" Abha Agrawal, MD, FACP, COO & VP of Medical Affairs, Norwegian American Hospital
Case Study "The EHR & Quality: The Current Evidence"
∙ Understand where EHRs have demonstrated evidence based quality improvement
∙ Learn what areas for improvement exist to improve quality and physician productivity
∙ Discuss how results can be driven across diverse care settings and systems
∙ Identify unintended consequences of HIT
Paper presented at the 2012 MLA Quad Chapter meeting in Baltimore, MD, Oct. 13-16. Discusses i2b2 and how it could be used in medical education. And suggests other data if i2b2 not available in your hospital.
Prof Mendel Singer Big Data Meets Public Health and Medicine 2018 12-22mjbinstitute
Presentation by Prof. Mendel Singer of Case Western Reserve University, on the issue of "big data" in health care and policy research. Presented at the Myers-JDC-Brookdale Institute in Jerusalem.
The information in ISR’s Electronic Media Use in Academic Medical Center Patient Recruitment report sheds light on the benefits, challenges and strategies of electronic media use for clinical trial recruitment. We are currently in an era where Facebook, Twitter, and Google searches link all of us to a larger global community. Clinical sites are actively navigating how to apply these resources to connect with patients around the world. The report will help academic medical centers, site personnel and CRO/sponsors understand which channels and strategies will best help accomplish their recruitment goals, and which ones can be developed for increased success in this realm.
Presenting precisionFDA for the first time at the Precision Medicine Coalition in Washington, DC on February 24, 2016
Any views or opinions expressed here do not necessarily represent the views of the FDA, HHS, or any other entity of the United States government. Furthermore, the use of any product names, trade names, images, or commercial sources is for identification purposes only, and does not imply endorsement or government sanction by the U.S. Department of Health and Human Services.
A presentation given at the Duke Margollis Health Policy meeting in 2015 and providing insights into the current challenges related to EHR data quality. Proposes a new approach - OneSource.
The Imperative of Linking Clinical and Financial Data to Improve Outcomes - H...Health Catalyst
Quality and cost improvements require the intelligent use of financial and clinical data coupled with education for multi-disciplinary teams who are driving process improvements. Once a data warehouse is established, healthcare organizations need to set up multi-disciplinary clinical, financial, and IT specialist teams to make the best use of the data. Sometimes, financial involvement is minimized or even excluded for a number of reasons that can turn out to be counterproductive. However, including financial measurements and participation up front can help enhance the recognized value and sustainability of quality improvement or waste reduction efforts. the In this session you will learn keys to success and real-life examples of linking clinical, financial and patient satisfaction data via multi-disciplinary teams that produce impressive results.
Understand how the HSCIC are continuing to improve data quality:
- Good quality data is, and has always been, a key part of improving services.
- It supports informed decision making.
- Those decisions can affect a single individual or the whole of the health and social care system.
This presentation was delivered at EHI Live 2013.
Data science and the use of big data in healthcare delivery could revolutionize the field by decreasing costs and vastly improving efficiency and outcomes. There is an abundance of healthcare data in Canada, but it is mostly siloed and difficult to access due to privacy and security challenges. This session will offer insights into best practices for healthcare analytics programs, as well as use cases that demonstrate the potential benefits that can be realized through this work.
An overview of clinical healthcare data analytics from the perspective of an interventional cardiology registry. This was initially presented as part of a workshop at the University of Illinois College of Computer Science on April 20, 2017.
As per EU MDR, Post Marketing Clinical Follow-up (PMCF) is a continuous process where device manufacturers need to proactively collect and evaluate clinical data of the device when it is used as per the intended purpose. EU MDR gives more emphasize on PMCF data to confirm the safety and performance of the device throughout its expected lifetime, ensure continued acceptability of identified risks and detect emerging risks based on factual evidence.
DAMA Webinar - Big and Little Data QualityDATAVERSITY
While technological innovation brings constant change to the data landscape, many organizations still struggle with the basics: ensuring they have reliable, high quality data. In health care, the promise of insight to be gained through analytics is dependent on ensuring the interactions between providers and patients are recorded accurately and completely. While traditional health care data is dependent on person-to-person contact, new technologies are emerging that change how health care is delivered and how health care data is captured, stored, accessed and used. Using health care as a lens through which to understand the emergence of big data, this presentation will ask the audience to think about data in old and new ways in order to gain insight about how to improve the quality of data, regardless of size.
Health IT Summit in Chicago 2014 – “The EHR & Quality: The Current Evidence” with Abha Agrawal, MD, FACP, COO & VP of Medical Affairs, Norwegian American Hospital
Abha Agrawal, MD, FACP
COO & VP of Medical Affairs
Norwegian American Hospital
iHT2 case studies and presentations illustrate challenges, successes and various factors in the outcomes of numerous types of health IT implementations. They are interactive and dynamic sessions providing opportunity for dialogue, debate and exchanging ideas and best practices. This session will be presented by a thought leader in the provider, payer or government space.
Navigating Challenges: Mental Health, Legislation, and the Prison System in B...Guillermo Rivera
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CRISPR-Cas9, a revolutionary gene-editing tool, holds immense potential to reshape medicine, agriculture, and our understanding of life. But like any powerful tool, it comes with ethical considerations.
Unveiling CRISPR: This naturally occurring bacterial defense system (crRNA & Cas9 protein) fights viruses. Scientists repurposed it for precise gene editing (correction, deletion, insertion) by targeting specific DNA sequences.
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Gene Therapy: Correcting genetic diseases like cystic fibrosis.
Agriculture: Engineering crops resistant to pests and harsh environments.
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International Collaboration: Clear guidelines are needed for research and human trials.
Public Education: Open discussions ensure informed decisions about CRISPR.
Prioritize Safety and Ethics: Safety and ethical principles must be paramount.
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the IUA Administrative Board and General Assembly meeting
Lecture 9C
1. 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 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 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
3. 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
4. Causes of Insufficient
Data Quality — 2
• Random:
– Inaccurate transcription or typing errors.
– Data overload.
– Motivational or turnover.
4
10. 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
11. Possible Future HIT Solutions
to Improve Data
• Future possibilities
– Natural language processing using machine
learning.
– Biometrics.
11
12. 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
13. 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:P600–11.
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
14. 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 IU24OC000013.
This material was updated in 2016 by Johns
Hopkins University under Award Number
90WT0005.
14
Editor's Notes
Welcome to Quality Improvement: Assessing Data Quality. This is Lecture c.
The Objectives for Assessing Data Quality are to:
Discuss common causes of data insufficiency.
Describe how health information technology design can enhance data quality and improve quality and safety measure results.
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 unit.
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 uncertainty.
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.
Inattention or poor understanding on the part of the individual completing the entry, analysis, or data warehousing procedures.
The team 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.
Identify the required data elements for the task. A data dictionary with standard definitions and data formats will be essential to promoting data quality. Seek terminology harmonization with accepted standards and avoid “local” naming conventions or glossaries. Data capture and completeness will improve when the 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.
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 and, wherever feasible, incorporate clinical decision support and electronic clinical quality measures (eCQM). 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 elements. Standard content and delivery methods should be identified to minimize localized workarounds. 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.
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, such as incorrect patient location, which can be undetectable through programming checks, can still occur. 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.
Users will continue to assume that quality is present in the absence of data to inform them otherwise. Regular reports about data quality should be made available to them. Validation of eCQM results by themselves can serve as a data quality check. 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 and may provide justification for future design modification. As discussed in the beginning of this unit , data are often shared across multiple interfaces and often used for applications 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.
HIT solutions offer a number of opportunities to improve the quality of data.
Standardization of terminology has multiple effects in the improvement of data quality.
In their study, Thede et al., described some of the benefits of standardization in the nursing field as including:
Better communication among health care providers.
Improved patient care.
Enhanced data collection to evaluate outcomes.
Greater adherence to standards of care.
And facilitation of assessment of competency.
Some advantages can be assigned to the use of standardized terminology by other professionals.
Another significant aspect of the improvement of data quality relies on the use of structured data fields. Structured data fields are fields that contain information that has a pre-defined data model. This allows this data to fit into relational tables, thus permitting the easy extraction into reports. The use of structured data fields is somewhat challenging, particularly for clinicians. During their training and their previous experience with paper charts, clinicians have used a narrative form to document the ailments of their patients. This “storytelling” format is difficult to translate into the more dry, structured data format and requires some retraining for all clinicians. However, in the current EMRs, there are areas where clinicians can document in free-text or “storytelling” format and yet preserve the integrity of the structured data fields, establishing a balance that preserves data quality and the needs for narrative documentation. Structured data fields can be accomplished through pick lists, check buttons, or radio buttons.
Finally, there is another data capture option based on voice recognition that will enable the user to capture more comprehensive data. However, the majority of this data capture, albeit comprehensive, happens in a free-text format.
Additional options, although not currently in wide use, can enhance the quality of data collected through an HIT solution. The use of natural-language processing machines capable of learning can eventually prove a valuable tool to enhance quality of data. The use of biometric tools that enter the measurements directly into the EHR can be an additional tool for data quality improvement.
This concludes Lecture c of Assessing 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, clinical decision support, patient safety/risk management, accreditation, quality measures, and health care research.
Poor data quality can threaten both patient safety and quality of care, decrease satisfaction, increase cost, and compromise strategic planning. Data quality is a complex, multidimensional 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 health care data.
