Influencing Patient Safety in Rural Primary Care Clinics through Computerized Order Entry

1. Influencing Patient Safety in Rural Primary Care Clinics through Computerized Order Entry Matthew Samore, MD VA Salt Lake City Health Care System Professor of Internal Medicine Adjunct Professor of Biomedical Informatics University of Utah

2. Acknowledgments • Kim Bateman, MD • Frank Drews, PhD • Wu Xu, PhD • Brian Sauer, PhD • Shobha Phansalkar, PhD • Charlene Weir, PhD • Jonathan Nebeker, MD • Amyanne Wuthrich • Jose Benuzillo • Warren Pettey • Marjorie Carter • Marci Fjelstad • Rui Saito • Shuying Shen • Partners: University of Utah, Healthinsight, CaduRx • Funding: • INFORM study: AHRQ R01 HS15413

3. Outline • Background • Conceptual framework • Related work • Our active studies in patient safety • Patient safety and health information technology in rural settings: the INFORM study • Methods • Participating clinics • Health information technology • Measurements • Results to date • Summary and future directions

4. Conceptual framework • Natural vs. engineered systems • Natural systems • Living organisms • Weather systems • Engineered systems • Aircraft • Computers

5. Conceptual framework • Commonalities • Defining properties and functional capabilities are causal • Internal component relationships are deterministic • Potentially predictable relationship between internal components of system

6. Conceptual framework • Differences • Adaptability • How does system deal with change • Natural systems evolved • No spontaneous change of defining parameters • Adaptation as a result of unstable previous states, to meet the changing demands placed upon by environment • Technical systems • Intentional design and manufacturing to provide solution to a specific, practical problem • Suitable to perform a number of specific tasks

7. Conceptual framework • Differences • Transparency • Observers ability to observe and understand the algorithms that govern a systems operation • Natural systems • Non-transparent • Algorithms describing component interactions and state variables have to be deduced and are not fully understood • Models – describe and mimic behavior • Engineered systems • Designed for transparency • Relationships are planned and calculated during design process • Despite transparency have complex systems sometimes unpredicted and unanticipated emergent behavior • Transparency affects the ability to cope with changes of system

8. Conceptual framework • Differences • Linearity • Mathematical description of the relationship between output of system to its input • Natural systems • Often display non-linear behavior • Engineered systems designed to be linear • Makes it possible to solve linear problems analytically • Linear systems output can be predicted from input • Allows application of reductionistic approach

9. Conceptual framework • Differences • Predictability • Allows for anticipation of future state of system • Natural systems • More challenging to understand and modify • Partly due to adaptive and non-linear nature • Even simple natural systems difficult to predict • Engineered systems • Easier to understand and maintain • Higher complexity increases operator demand • Maintenance and prediction are possible

10. Conceptual framework • Clinical care delivery • Complex natural system that varies across myriad settings and care • Health information technology implementation • Engineered system meets natural system

11. Our active studies in patient safety • Medication management • Real-time detection of prescribing problems • Medicaid population (Nebeker, Xu, and Sauer) • VA health care system (Nebeker and Weir) • Clinical decision support systems in rural settings • Primary care (Samore and Bateman) • Nursing homes (Rubin) • Optimal laboratory monitoring intervals (Sauer) • Error-producing conditions in intensive care units, including interruptions and task ambiguity • Medical device problems • Use of observation (Drews and Samore)

12. Methods • INFORM study • Clinic-randomized trial to evaluate impact of computerized clinic order entry tool on clinical practice and office efficiency • Features of computerized clinic order entry tool • Web-based writing of outpatient orders, including prescriptions, immunizations, laboratory tests, X-ray studies, and work notes. • Accessible via any type of computer. • Electronic transmission of orders to pharmacies and other vendors or to local printers • Common access to records across providers who share patients while maintaining strict levels of confidentiality.

13. Methods: participating clinics • Rural primary care clinics with minimum of two providers were recruited • None had pre-existing electronic health records • Eighteen clinics randomly assigned to two groups • Early implementation (launch in year 1) • Delayed implementation (launch in year 2) • Post-launch, 2 clinics closed

14. Methods: participating clinics

15. Methods: health information technology user interface Fumbling for his recline button, Ted unwittingly instigates a disaster.

16. Methods: provider home page List of all refill requests your staff has submitted for your approval Pt: Search for a patient record

17. Methods: refill requests Select drug to review request and to edit, approve or deny Select patient name to review medication history

18. Methods: patient home page Active drug list Filters Status icons A brief tour: Status bar Navigation commands Special functions

19. Methods: drug favorites

20. Methods: drug interactions

21. Methods: mild drug interactions

22. Methods: measurements • Observation of office processes • Efficiency of refill process • Survey: • Theoretical framework of Information Technology Adoption Model (ITAM) • Focus groups • “Time trials” using scenarios • Comparison handwritten and electronic prescription writing • Time-to-complete task • Assessment of completeness, legibility, errors • Electronic prescriptions • Chart review (Fall, 2007)

23. Methods: experimental scenarios • Instructions for time trials • “For these scenarios, imagine that you’re with a real patient and work at your normal pace.” • Example: • “Next you see a regular patient, Abigail B. Cook. She is 74-year old with a history of rheumatoid arthritis. She also complains of pain when she urinates. The urine culture reveals that the she has a UTI due to E. coli that is resistant to fluoroquinolones. Prescribe an antibiotic to treat her UTI”

24. Results: impact on workflow • Medication refill system in one participating clinic before implementation of computerized clinic order entry

25. Results • Post-implementation of computerized clinic order entry

26. Results: survey • Response to one of the questions pertinent to patient safety: • “Integrating drug reference and drug interaction look-up with prescription writing is useful” • 57% (26 of 45) strongly agree • (answered 6 or 7 on 7 point Likert scale) • 9% (4 of 45) strongly disagree • (answered 1 or 2 on 7 point Likert scale)

27. Results: focus groups • Favorable: • “We see the interactions. That helps quite a bit. So, that has made a difference a couple of times. Essentially changed my mind…” • “One thing I liked, that there was a medicine that I don’t prescribe very often and didn’t know about the dose…”

28. Results: focus groups • Unfavorable • “it’s like getting those stupid things from the pharmacy…. It commented on too many menial, insignificant things that just waste your time…. Way too sensitive.” • “… the drug to drug reaction says “major” and you Look at other… look at ePocrates later and no reaction at all.”

29. Results: time trials

30. Results: drug interaction scenario • Frequency with which trimethoprim/sulfa was prescribed to elderly woman on methotrexate for rheumatoid arthritis • Handwritten • 11 of 13 instances • Computerized clinic order entry tool • 3 of 12 instances • Recommendation of the Multum database • “Generally avoid”

31. Results: electronic prescriptions • Analysis of instances of significant drug-interactions is in progress • Altogether: • 430,000 electronic prescriptions • 52,000 unique patients • Coumadin interactions • 7 patients received coumadin and amiodarone concomitantly • 29 patients received coumadin and NSAID (excluding celecoxib)

32. Summary and future directions • Implementation is clinical care system redesign • Measures of impact • “Perceived usefulness” versus “average effect” • “Experiment” versus “experience in the wild” • “Errors” versus “adverse events” • Transition to health information technology that enhances patient-centered care