It's Not Going Away: Maximizing the benefits of the EHR for practice

1. It's Not Going Away: Maximizing the benefits of the EHR for practice Plexus October 3, 2012 Karen A. Monsen, PhD, RN, FAAN University of Minnesota School of Nursing

2. The Promise of the EHR • We envision a world wherein the EHR serves health care and improves patient health • We imagine fluid information exchange • We imagine being able to ask critical questions of EHR data • and get meaningful answers

3. The Reality! • EHR implementation is an ongoing nightmare • Data cemeteries and dead-ends abound • Important information is hidden or absent • We are spending extraordinary time and energy resources to nurse the computer • and still nurse the patient

4. But it’s NOT going away! • We need to find the solutions that will make the promise of the EHR our reality • sooner rather than later

5. The ROOT of the Problem • Chaos in Knowledge Representation • “If you cannot name it, you cannot teach it, research it, practice it, or put it into public policy.” – Norma M. Lang • (Nor can you put it in an EHR)

6. Knowledge Representation • conceptualization of an abstract notion or perspective communicated within a concrete platform (my definition) • R. Davis, H. Shrobe, and P. Szolovits. What is a Knowledge Representation? AI Magazine, 14(1):17-33, 1993 • Available at • http://groups.csail.mit.edu/medg/ftp/psz/k-rep.html

7. Language for Human Expression • Machines and People think Differently • (Actually, machines don’t think) • Therefore, to use machines to support clinician thinking • Teach clinicians to think like machines

8. Surrogate for the Real Thing • intended identity: what is it a surrogate for? • fidelity: how close is the surrogate to the real thing?  Dogs 4,970,318 people like this. Like

9. Mom and her Brothers

10. Canine

11. Surrogates are Always Imperfect • Any thing other than the thing itself is necessarily different from the thing itself • Errors in KR are inherent within KR • omission • generalization • Therefore incorrect reasoning and inferences are inevitable

12. Why is KR Necessary for Nursing? • Each EHR necessarily presents its view of what is important to attend to, and anything not easily seen in those terms may be ignored (p. 5) • Nursing worldview is often invisible within the EHR • Nurses are major EHR users

13. Optimal Purpose of KR • “improve practice by reminding practitioners about the inspirations that are the important sources of power” (p. 1)

14. Toward Understanding • representation and reasoning are inextricably intertwined • building knowledge content • building an intelligent reasoner

15. What are Standards? • Ways of agreeing on what we are saying so that EHRs and the people who use them reach shared understanding • Interface standards (Nanda, NIC, NOC, etc.) • Reference standards (SNOMED CT, etc.)

16. What is Semantic Equivalence? • Words or phrases with the same meaning • There is always more than one right way to talk about a health care concept • Pain • Discomfort • Alterations in comfort

17. What is Interoperability? • Two systems that can understand and exchange data • Semantic interoperability (same meaning) • Process interoperability (same processes of care)

18. EBP in EHRs • Clinical Practice Guidelines (CPGs) or other templates in EHRs can provide clinical decision support • Proprietary systems patent these guidelines • Big investment in time and money to develop clinical decision support based on CPGs • Redundant across all systems • Often patented/proprietary • Rarely expressed using standards

19. Research • 15 home care companies • 1 data standard (Omaha System) • Data mining study seeking hidden patterns in intervention data • 651,000 interventions • K means methods • Without agency ID – NO CLUSTERS FORMED

20. What Does This Mean? • Even when we do the same work for the same people, and use a standard, we are talking about our work differently • computer couldn’t make sense of the data despite millions of iterations of analysis

21. Take Home Message • To compare data across systems we must use standards in standard ways • CPGs in the public domain • Synthesis of the EBP literature • Semantic interoperability • Process interoperability

22. Example from the Real World • Omaha System Community • Clinical guidelines • Data • Outcome evaluation • Research

24. Identical Statistics…

25. …Different Plots (x1, y1) (x2, y2) (x3, y3) (x4, y4) Anscombe’s quartet Anscombe, F. J. (1973). "Graphs in Statistical Analysis". American Statistician27 (1): 17–21. JSTOR2682899.

26. Sample • Family home visiting intervention data from the Omaha System Data Warehouse • 218 clients • 14 PHNs • 6779 interventions

27. Sample

28. Key

29. Methods

30. Methods

31. Do PHNs Tailor Interventions?

32. Do PHNs Tailor Interventions?

33. PHN Signature Styles?

34. Data Quality Issue vs. Signature?

35. Preliminary Results • Differential use of case management by two PHNs (p < .001) • Higher proportion of surveillance vs. teaching, guidance, and counseling between two subgroups (p < .001)

36. It’s NOT going away! • The stakes are high • The rewards are great

37. Thank you! • mons0122@umn.edu