UCSF Informatics Day 2014 - Mark Pletcher, "Making EHR Data Useful for the Learning Healthcare System"

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UCSF Informatics Day 2014 - Mark Pletcher, "Making EHR Data Useful for the Learning Healthcare System"

  1. 1. Making EHR Data Useful: The Learning Healthcare System UCSF Informatics Day, June 10, 2014 Mark J. Pletcher, MD MPH Dept of Epidemiology and Biostatistics
  2. 2. EHR Data  Learn • The Learning Healthcare System – Concept, IOM vision, PCORI vision – Barriers • Getting started at UCSF • Randomized quality improvement – Rationale, example – How to make this easy at UCSF
  3. 3. Learning Healthcare System • Institute of Medicine concept – A system of healthcare delivery that “drives the process of discovery as a natural outgrowth of patient care” – Electronic Health Records are a major facilitator 2007: The Learning Healthcare System: Workshop Summary 2012: Best Care at Lower Cost: The Path to Continuously Learning Health Care in America
  4. 4. Learning Healthcare System • Major barriers – Mixing research with clinical care • Can slow down clinical care • Ethical considerations – Clinical care is not delivered at random • We treat some and don’t treat others for good reasons (confounding by indication) • We don’t measure things systematically (selection bias and measurement bias) – EHR systems • Not designed for research data collection • Different across institutions
  5. 5. Learning Healthcare System • PCORI’s answer: PCORnet – National network geared for comparative effectiveness research – Clinical Data Research Networks • Gather common data elements from EHRs – Patient-Powered Research Networks • Gather engaged patients excited to do research – Support very large and low-cost research studies – Phase I: Just started!
  6. 6. Getting started at UCSF • Why we need to get started here: – PCORnet needs help! – Pilot data for PCORnet studies – For many studies, 1 center is enough – Local issues may trump – We can get “deep” into our EHR data – Improving care HERE
  7. 7. Randomized quality improvement • Quality improvement – Doing things we THINK will improve care – Evidence-based…but not entirely – The law of unintended consequences – Show that it works  disseminate more broadly • Randomized controlled trials  better evidence • Randomized quality improvement (RQI) trials – Disseminate, but cautiously (to a randomly selected subset) – Make sure it’s working
  8. 8. Randomized quality improvement • Example: The Statin Guidelines Project • New Statin Guidelines are: – VERY evidence-based – VERY controversial
  9. 9. Randomized quality improvement • Randomized dissemination of statin guidelines – Find people who “should” be on a statin – Randomly assign them to • Usual care • Encouragement to take a statin – Compare outcomes • Statin use • Statin side effects and quality of life • CHD outcomes (pilot for PCORnet)
  10. 10. Randomized quality improvement • Let’s make this easy at UCSF – Build infrastructure • Data systems, regulatory processes, expertise – Identify EHR-measurable outcomes • Engage patients, providers, administrators • Which are important, which can be improved • Start measuring them – Find providers with good ideas for how to improve • Invite, support, partner – Randomly disseminate and evaluate with RQI Trial
  11. 11. Summary • Let’s turn UCSF into a Learning Healthcare System – Do quality improvement, but “cautiously” – Invest in infrastructure so we can study as we go – Start doing it!
  12. 12. Thanks for listening! • Questions?
  13. 13. Barriers to getting started at UCSF • Why we “can’t” just start doing this at UCSF… – Wait for PCORnet to figure this out – Small sample size – UCSF is not a “closed” system – Infrastructure investment – Ethical issues
  14. 14. Ethical issues • Randomized dissemination – Do we need informed consent? – Is it EVER ethical to randomize without consent? – Is it more ethical to do “cluster-randomization”? – Different approaches to informed consent • Informational, opt out, after-the-fact, etc Faden 2013: An ethics framework for a learning health care system Pletcher 2014: Informed consent in randomized quality improvement trials: A critical barrier for learning health systems
  15. 15. The Health eHeart Study • Online platform for engaging patients – Cardiovascular cohort study – Leverage emerging technology – Platform to support trials and other studies • Health eHeart participants are consented to participate in research – Answer surveys – Special data collection – Special interventions (sensors, social networks, etc)

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