ELECTRONIC HEALTH RECORDS AND DIAGNOSTIC ERRORS: FROM CHALLENGES TO OPPORTUNITIES

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1 ELECTRONIC HEALTH RECORDS AND DIAGNOSTIC ERRORS: FROM CHALLENGES TO OPPORTUNITIES HARDEEP SINGH, MD, MPH HOUSTON VA CENTER FOR INNOVATIONS IN QUALITY, EFFECTIVENESS & SAFETY MICHAEL E. DEBAKEY VA MEDICAL CENTER BAYLOR COLLEGE OF MEDICINE 1 Center for Innovations in Quality, Effectiveness and Safety 2 From Encounter to Policy: An Integrated Approach to Improving Quality of Care Multidisciplinary Team Reducing diagnostic errors Improving health ITrelated patient safety 3 Dean Daniel Traber Ashley Shailaja Roosan Medical Informatics Physician/ Health IT Social Work/ Qualitative Research Psychologist/ Analyst Sociologist/ Qualitative Research Medical Informatics Viraj Viral Elise Jessica Janet Donna Daniel Physician/ Health IT Physician/ Health IT Project Coordinator Research Coordinator Research Coordinator Project Coordinator Human Factors Downloaded from 1

2 Learning Objectives 4 Discuss types of patient safety risks and challenges involving diagnosis that can occur in electronic health record (EHR)-enabled settings Identify conceptual frameworks and potential areas for solutions for mitigating diagnostic safety risks in EHR-enabled health care 5 6 Downloaded from 2

3 8 Diagnosis # cases % Pulmonary embolism % Poisoning, ADR, overdose % Lung cancer % Colorectal cancer % Acute coronary syndrome % Breast cancer % Stroke % Congestive heart failure % Fracture % Abscess % Pneumonia % Aortic aneurysm/dissection 9 1.5% Appendicitis 9 1.5% Depression 9 1.5% Schiff et al 2009 Current Landscape & Why Little Progress Basic Science at the confluence of cognitive science, informatics, human factors, social science, & the art of medicine Experts still debating definition of diagnosis lack of standards for most diagnosis concepts Operational definitions of diagnostic error harder & especially with evolving diagnosis Uncertainty at play; not always black & white Singh H Jt Comm J Qual Saf 2014; Zwaan & Singh Diagnosis 2015 Downloaded from 3

4 Safety Begins with Measurement 10 We cannot improve what we cannot measure! We cannot measure what we cannot define! IOM Definition of Diagnostic Error 11 The failure to a) establish an accurate and timely explanation of the patient s health problem(s) or b) communicate that explanation to the patient 12 Defining Preventable Diagnostic Harm MISSED OPPORTUNITIES A HARM (from delayed or wrong treatment/test) B C NO MISSED OPPORTUNITIES D Missed opportunities in diagnosis due to system and/or cognitive factors Preventable diagnostic harm Delayed/wrong diagnosis associated with patient harm but no clear evidence of missed opportunities Delayed/wrong diagnosis but no clear evidence of missed opportunities Adapted from Singh Jt Comm J Qual Patient Saf 2014 Downloaded from 4

5 of 8 IOM Goals With EHR Relevance GOAL 1 Facilitate more effective teamwork in the diagnostic process among health care professionals, patients, and their families GOAL 3 Ensure that health information technologies support patients and health care professionals in the diagnostic process GOAL 4 Develop and deploy approaches to identify, learn from, and reduce diagnostic errors and near misses in clinical practice But First: The Technology Hype Cycle 15 Downloaded from 5

6 Our Research Shows Emerging Risks 16 Outpatient diagnostic errors: 1 in 20 US adults annually Common diseases missed despite clear evidence to suggest diagnosis (or need to pursue diagnostic evaluation) Failure to elicit or act upon key history/exam finding or red flags Time spent with patients vs. the EHR? Does the EHR constrain clinical reasoning? Singh et al BMJQS 2014; Singh et al JAMA IM 2013; Singh et al JCO 2012 The Famous Ebola Misdiagnosis 17 Temperature of 103 but no fever Travel history in nurse s EHR notes not seen by the doc Day 1-Blame Nurse Day 2-Blame EHR Day 3-None of the above Few lessons learned Upadhyay D, et al. Diagnosis 2014 Emerging Risks - #2 18 Disappearing differential diagnosis in midst of other required documentation Docs often don t seek help when they most need it Implications for decision support (computer or human) Singh et al JAMA IM 2013; Meyer et al JAMA Intern Med 2013 Downloaded from 6

7 Diagnostic Accuracy and Confidence Physicians assessed 4 clinical vignettes (2 easy & 2 difficult) based on real-world cases Goals Assess how diagnostic accuracy is aligned with perception of confidence in that accuracy Meyer et al JAMA Intern Med 2013 Diagnostic Accuracy vs. Confidence 20 Emerging Risks - #3 21 Data display and comprehension Downloaded from 7

8 22 Patient Perspectives-Portal Usability 23 The lab results are not organized in any logical order like by date. I have trouble finding the newest result. And the graphs. They are just wrong. When I log in, I can see the new labs. But once I ve viewed them already, it moves them to somewhere else. I couldn t figure out where to go to find them. Not user friendly. Giardina et al J of Pt Exp Downloaded from 8

9 Emerging Risks # 4 25 Overlooking documented critical information in EHR Communication breakdowns persist despite EHRs Communication of Test Results 26 Evaluation of 1,163 outpatient abnormal lab & 1,196 abnormal imaging test result alerts 7% abnormal labs lacked timely follow-up 8% abnormal imaging lacked timely follow-up Why abnormal test results continue to get missed in health IT-based settings Singh et al Am J Med 2010 & Singh et al Archives of Int Med 2009 Ambiguous Responsibility a Huge Issue 27 Downloaded from 9

10 28 And More Digital Data Is on the Way 29 Smartphone Wearables Patients can now continuously monitor their data real-time and send it to their docs Multiple Socio-Technical Issues 30 Issue Software Content Usability Workflow Providers Organizational Examples no functionality for saving, tracking, and retrieving alerts too many unnecessary alerts poor signal to noise ratio on screen surrogate feature to forward alerts when providers out of office not used properly lack of knowledge/training policies for follow-up ambiguous Singh et al JAMA Int Med 2013 Downloaded from 10

11 How can Health IT Support Diagnosis? 31 Health IT must be safe Health IT must be used safely We must use health IT to improve diagnostic safety Leveraging health IT to identify delayed, wrong or incorrect diagnosis before patient harm Sittig & Singh N Engl J Med Nov 8;367(19): Singh Sittig BMJ Qual Saf. doi: /bmjqs Big Data Safety Net 33 EHR-based triggers that look for follow-up actions on clues (or red flags) to detect delays prospectively Basic versions: + hemoccult or microcytic anemia with no subsequent colonoscopy in 60 days suspicious chest-x ray with no follow-up CT scan in 30 days Murphy, Singh et al BMJQS 2013; Radiology 2015; Chest 2016 Downloaded from 11

12 34 Must Evaluate How We Are Doing 35 ONC-sponsored Safety Assurance Factors for EHR Resilience (SAFER) Guides Proactive risk assessment and guidance 1 st draft of best practices and knowledge Self-assessment; not meant to be regulatory Focused on high-risk areas including Test results & Communication Nine guides all freely available Singh et al BMC Med Inf 2013 Downloaded from 12

13 Opportunities for Patient Engagement 37 Need Novel Ways to Help Patients 38 The result was abnormal but I didn t realize it. There s a comment section but the doctor never leaves a comment. My triglycerides are high. Ok, what does that mean? What am I supposed to do? I had to figure out the sodium was low. There s a problem with low sodium, what can I do? I m not a doctor. I hope they ll call if it s problematic. Giardina et al J of Pt Exp Improving Diagnosis Needs a Socio-Technical Approach Sittig Singh QSHC 2010 Downloaded from 13

14 Health IT Innovations to Support Diagnosis 40 Dimension Software Content Usability Examples Better health IT tools/functions Smarter alerts & diagnostic decision support Better user-interfaces; signal to noise ratio Need More Than Just Health IT 41 Dimension Workflow People Organization External rules Evaluation & Measurement Examples Time interacting with patients Culture change Policies for closed-loop test results follow-up National entity for shared learning Data to separate reality vs. hype; Triggers & ONC SAFER Guides Thank you and Acknowledgements 42 Funding Agencies: Department of Veterans Affairs National Institute of Health Agency for Health Care Research & Quality ONC for SAFER Guides Multidisciplinary team at VA Health Services Research Center for Innovation Web: Downloaded from 14