OHDSI MEETINGS THIS WEEK
OHDSI Community Call - Tuesday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M59X2V1U61WC9ASID2Z5N3UT95-D1JL&rnd=811649.98682211121
NLP workgroup meeting - Wednesday at 2pm EThttps://global.gotomeeting.com/join/707196421
Population-Level Estimation (Eastern hemisphere) workgroup meeting - Wednesday at 3pm Hong Kong timehttps://meetings.webex.com/collabs/meetings/join?uuid=M6WE9AOKFETH2VEFPVCZWWBIT0-D1JL
Architecture Working Group - Thursday at 10am EThttps://jjconferencing.webex.com/mw3100/mywebex/default.do?service=1&siteurl=jjconferencing&nomenu=true&main_url=%2Fmc3100%2Fe.do%3Fsiteurl%3Djjconferencing%26AT%3DMI%26EventID%3D610982452%26UID%3D501476547%26Host%3DQUhTSwAAAAQu4P1o9qm71JJ1Zj4-uvZbjQttsCinu71JCRxBAHAXnzjjRAiTspTzU9ojLmjMF4CcTBWw4zn1dqYPTWu5vJ9_0%26FrameSet%3D2%26MTID%3Dm3e1ceeca56f1e94c9fcf1ae98c10e02e
GIS working group meeting - Next Monday (June 18th) at 10am ET
Simple, modern video meetings for the global workforce. Join from anywhere, including your desktop, browser, mobile device, or video room device.
ANNOUNCEMENTS
2018 OHDSI Symposium - REGISTER NOW https://www.ohdsi.org/symposium-registration-2/ https://www.ohdsi.org/tutorial-workshops/
2018 OHDSI Symposium - CALL FOR PARTICIPATION https://www.ohdsi.org/collaborator-showcase/
China Hack-a-thon - Materials from the 2018 China Hack-a-thon are now online: https://www.ohdsi.org/past-events/
2018 China Symposium https://www.ohdsi.org/events/2018-china-symposium/
Moving Study Materials to Guthub Monday, June 18th . If you have study materials on the OHDSI wiki, please move them here: https://github.com/OHDSI/StudyProtocolSandbox https://github.com/OHDSI/StudyProtocols
The word ‘happy’ would lose its meaning if it were not balanced by sadness.
COMMUNITY PUBLICATIONS
Empowering genomic medicine by establishing critical sequencing result data flows: the eMERGE example
Harmonizing Outcomes for Genomic Medicine: Comparison of eMERGE Outcomes to ClinGen Outcome/Intervention Pairs
Reporting Data Quality Assessment Results: Identifying Individual and Organizational Barriers and Solutions.
T Callahan, J Barnard, L Helmkamp, J Maertens and M Kahn,
EGEMS (Washington, DC) , Sep 2017 04
Electronic health record (EHR) data are known to have significant data quality issues, yet the practice and frequency of assessing EHR data is unknown. We sought to understand current practices and attitudes towards reporting data quality assessment (DQA) results by data professionals.The project was conducted in four Phases: (1) examined current DQA practices among informatics/CER stakeholders via engagement meeting (07/2014); (2) characterized organizations conducting DQA by interviewing key personnel and data management professionals (07-08/2014); (3) developed and administered an anonymous survey to data professionals (03-06/2015); and (4) validated survey results during a follow-up informatics/CER stakeholder engagement meeting (06/2016).The first engagement meeting identified the theme of unintended consequences as a primary barrier to DQA. Interviewees were predominantly medical groups serving distributed networks with formalized DQAs. Consistent with the interviews, most survey (N=111) respondents utilized DQA processes/programs. A lack of resources and clear definitions of how to judge the quality of a dataset were the most commonly cited individual barriers. Vague quality action plans/expectations and data owners not trained in problem identification and problem-solving skills were the most commonly cited organizational barriers. Solutions included allocating resources for DQA, establishing standards and guidelines, and changing organizational culture.Several barriers affecting DQA and reporting were identified. Community alignment towards systematic DQA and reporting is needed to overcome these barriers.Understanding barriers and solutions to DQA reporting is vital for establishing trust in the secondary use of EHR data for quality improvement and the pursuit of personalized medicine.
A Data Quality Assessment Guideline for Electronic Health Record Data Reuse.
NG Weiskopf, S Bakken, G Hripcsak and C Weng,
EGEMS (Washington, DC) , Sep 2017 04
We describe the formulation, development, and initial expert review of 3x3 Data Quality Assessment (DQA), a dynamic, evidence-based guideline to enable electronic health record (EHR) data quality assessment and reporting for clinical research.3x3 DQA was developed through the triangulation results from three studies: a review of the literature on EHR data quality assessment, a quantitative study of EHR data completeness, and a set of interviews with clinical researchers. Following initial development, the guideline was reviewed by a panel of EHR data quality experts.The guideline embraces the task-dependent nature of data quality and data quality assessment. The core framework includes three constructs of data quality: complete, correct, and current data. These constructs are operationalized according to the three primary dimensions of EHR data: patients, variables, and time. Each of the nine operationalized constructs maps to a methodological recommendation for EHR data quality assessment. The initial expert response to the framework was positive, but improvements are required.The initial version of 3x3 DQA promises to enable explicit guideline-based best practices for EHR data quality assessment and reporting. Future work will focus on increasing clarity on how and when 3x3 DQA should be used during the research process, improving the feasibility and ease-of-use of recommendation execution, and clarifying the process for users to determine which operationalized constructs and recommendations are relevant for a given dataset and study.
A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks.
TJ Callahan, AE Bauck, D Bertoch, J Brown, R Khare, PB Ryan, J Staab, MN Zozus and MG Kahn,
EGEMS (Washington, DC) , Jun 2017 12
To compare rule-based data quality (DQ) assessment approaches across multiple national clinical data sharing organizations.Six organizations with established data quality assessment (DQA) programs provided documentation or source code describing current DQ checks. DQ checks were mapped to the categories within the data verification context of the harmonized DQA terminology. To ensure all DQ checks were consistently mapped, conventions were developed and four iterations of mapping performed. Difficult-to-map DQ checks were discussed with research team members until consensus was achieved.Participating organizations provided 11,026 DQ checks, of which 99.97 percent were successfully mapped to a DQA category. Of the mapped DQ checks (N=11,023), 214 (1.94 percent) mapped to multiple DQA categories. The majority of DQ checks mapped to Atemporal Plausibility (49.60 percent), Value Conformance (17.84 percent), and Atemporal Completeness (12.98 percent) categories.Using the common DQA terminology, near-complete (99.97 percent) coverage across a wide range of DQA programs and specifications was reached. Comparing the distributions of mapped DQ checks revealed important differences between participating organizations. This variation may be related to the organization's stakeholder requirements, primary analytical focus, or maturity of their DQA program. Not within scope, mapping checks within the data validation context of the terminology may provide additional insights into DQA practice differences.A common DQA terminology provides a means to help organizations and researchers understand the coverage of their current DQA efforts as well as highlight potential areas for additional DQA development. Sharing DQ checks between organizations could help expand the scope of DQA across clinical data networks.
Constructing an Open-Access Bio-Signal Repository from Intensive Care Units.
D Kim, S Lee, TY Kim, S Jin, J Park, J Ko, RW Park and D Yoon,
Studies in health technology and informatics , 2017
Bio-signals can be crucial evidence in detecting urgent clinical events. However, until now, access to this data was limited. We aim to construct and provide a new open bio-signal repository with data gathered from more than 40 intensive care unit (ICU) beds. For doing so, we completed the interfacing system with the patient monitors at the target beds and plan to expand this data set to more than 100 ICU beds. Once completed, we plan to publicly open the data to catalyze interesting clinical-event detection research.
Factors Influencing Progress of Health Information Exchange Organizations in the United States.
LM Overhage, J Covich-Bordenick, X Li and JM Overhage,
Studies in health technology and informatics , 2017
Progress is being made toward improved healthcare interoperability in the United States, but exchange between electronic health records alone is insufficient. Using data from the eHealth Initiative's Annual Survey of Health Information Exchange, we developed models of HIE financial and operational progress. Our analysis suggests that organizations that focus on enabling exchange thorugh education and policy need to be considered separately from those focused on the actual exchange. The associations between characteristics and progress in data exchanging HIEs suggest that diversity of participants as both originators and receivers of data and breadth of data are important underlying success factors.
