it's not going away: maximizing the benefits of the ehr for practice plexus october 3, 2012...
TRANSCRIPT
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?
Dogs4,970,318 people like this.Like
Mom and her Brothers
Canine
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
8.06.58
8.06.95
8.08.14
8.06.77
8.05.76
13.0
7.58
13.0
8.74
13.0
12.74
8.07.71
9.08.81
9.08.77
9.07.11
8.08.84
11.0
8.33
11.0
9.26
11.0
7.81
8.08.47
14.0
9.96
14.0
8.10
14.0
8.84
8.07.04
6.07.24
6.06.13
6.06.08
8.05.25
4.04.26
4.03.10
4.05.39
19.0
12.50
12.0
10.84
12.0
9.13
12.0
8.15
8.05.56
7.04.82
7.07.26
7.06.42
8.07.91
5.05.68
5.04.74
5.05.73
8.06.89
xi 9.0
σ2xi 11.0
yi 7.5
σ2yi 4.12
ρxiyi 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
