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

PlexusOctober 3, 2012Karen A. Monsen, PhD, RN, FAAN

University of MinnesotaSchool of Nursing

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

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

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

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)

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

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

Surrogate for the Real Thing•intended identity: what is it a surrogate

for?•fidelity: how close is the surrogate to the

real thing? 

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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

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

Optimal Purpose of KR•“improve practice by reminding

practitioners about the inspirations that are the important sources of power” (p. 1)

Toward Understanding •representation and reasoning are

inextricably intertwined▫building knowledge content ▫building an intelligent reasoner

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.)

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

What is Interoperability?•Two systems that can understand and

exchange data▫Semantic interoperability (same meaning)▫Process interoperability (same processes of

care)

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

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

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

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

Example from the Real World•Omaha System Community

▫Clinical guidelines▫Data ▫Outcome evaluation▫Research

Identical Statistics…x1 y1 x2 y2 x3 y3 x4 y4

10.0

8.04

10.0

9.14

10.0

7.46 8.0 6.5

8

8.0 6.95 8.0 8.1

4 8.0 6.77 8.0 5.7

613.0

7.58

13.0

8.74

13.0

12.74 8.0 7.7

1

9.0 8.81 9.0 8.7

7 9.0 7.11 8.0 8.8

411.0

8.33

11.0

9.26

11.0

7.81 8.0 8.4

714.0

9.96

14.0

8.10

14.0

8.84 8.0 7.0

4

6.0 7.24 6.0 6.1

3 6.0 6.08 8.0 5.2

5

4.0 4.26 4.0 3.1

0 4.0 5.39

19.0

12.50

12.0

10.84

12.0

9.13

12.0

8.15 8.0 5.5

6

7.0 4.82 7.0 7.2

6 7.0 6.42 8.0 7.9

1

5.0 5.68 5.0 4.7

4 5.0 5.73 8.0 6.8

9

xi 9.0σ2xi 11.0

yi 7.5σ2yi 4.12ρxiyi 0.866

LS linear fit yi = 3 + 0.5xi

…Different Plots(x1, y1) (x2, y2)

(x3, y3) (x4, y4)

Anscombe’s quartet Anscombe, F. J. (1973). "Graphs in Statistical Analysis". American Statistician 27 (1): 17–21. JSTOR 2682899.

Sample• Family home visiting intervention

data from the Omaha System Data Warehouse

• • 218 clients • 14 PHNs• 6779 interventions

Sample

Key

Methods

Methods

Do PHNs Tailor Interventions?

Do PHNs Tailor Interventions?

PHN Signature Styles?

Data Quality Issue vs. Signature?

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)

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

Thank you!•mons0122@umn.edu