Pitfalls and realities of working with Big Data
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This document summarizes Karim Keshavjee's presentation on designing a solution to enable standardized data collection across electronic medical records (EMRs) and the transmission of that data to a central repository. The presentation outlines the problem, lessons learned from previous related projects, stakeholder engagement activities, the proposed solution design, key barriers addressed by the design, and advantages of the approach. The proposed design features clinical data collection forms that are evidence-based, can be incorporated into any EMR instantly, and retrieve data via a standard server to facilitate research using big data.
Pitfalls and realities of working with Big Data
- 1. Karim Keshavjee MD, MBA, CCFP, CPHIMS NAPCRG PBRN Jun 30, 2014 Bethesda, Maryland 1
- 2. I am a provider of commercial services that may be alluded to in this CME activity I do not intend to discuss an unapproved or investigative use of a commercial product or device in my presentation I will not be discussing any use of products used on patients 2
- 3. “You have to take life as it happens, but you should try to make it happen the way you want to take it” 3
- 4. The Problem Experience with Solutions/Lessons Learned Stakeholder Engagement Solution Design Brief Data Collection Architecture Key Barriers and resolution Advantages Conclusion
- 5. >Physician use of EMR|EHR in North America has increased substantially in the last 4 years Current EMRs are not able to Capture standardized data across EMRs Transmit data to a central repository Present guideline recommendations at point of care 5 1National Physician Survey 2013
- 6. Requires structured data Ability to conduct many small experiments rapidly (Amazon phenomenon) Greatest benefit lies in speeding up feedback cycle between research findings and bedside application 6
- 7. Review of previous projects, experiences, lessons learned Stakeholder Analysis Identified 8 distinct groups Stakeholder interviews N = 90, 8-12 per stakeholder group Iterative process of asking about problems and designing solutions 7
- 8. 8 N=90
- 9. • Data collection projects are costly • EMR vendors not able to focus on data projects • Not scalable to multiple diseases • Difficult updating to new evidence 9
- 10. 10 Highly Successful High Blood Pressure Management Initiative Now used in 40 Clinics across Ontario
- 11. 11 RA Form Used by 60 Rheumatologists In Ontario
- 12. Design clinical forms Usability tested (once) (with researchers, policy makers, patients and providers) Evidence-based incorporated into EMRs|EHRs instantly or almost instantly? Independent of the vendor 12
- 13. • Vendors include browser window in EMR • Provider selects template the usual way • EMR gets the form from a website • Form data provided to EMR using XML 13
- 14. 14 Standard Data Forms Authoring System EMR 1 EMR 2 EMR 3 Forms Server Research Database
- 15. 15 Standard Data Forms Authoring System EMR 1 EMR 2 EMR 3 Forms and Knowledge Server Research Database Clinical Knowledge Server
- 16. Privacy –Privacy Architecture Governance –Governance infrastructure Balancing needs of various stakeholders Usability for clinicians vs. structured/coded for research 16
- 17. Faster updates to forms and evidence Faster time to data collection and research Less onus on vendor A/B testing of forms Ability to deliver decision support into form 17
- 18. Make research easier, faster and cheaper BUILD IN REB approvals and Privacy Patient involvement Usability testing A/B testing and forms feedback eHealth and mHealthTesting 18
- 19. Big data pitfalls can only be solved by new designs, not by accepting the limitations of current EHRs New designs need to balance the needs of multiple stakeholders to be successful New designs need easy data capture at the point of care, guideline recommendations in real-time, analyze provider and patients behaviors quickly, reject hypotheses daily 19
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