Clinical research and the electronic medical record: Interdisciplinary research agendas Michael G....

Clinical research and the electronic medical record:

Interdisciplinary research agendas

Michael G. Kahn MD, PhDBiomedical Informatics Core Director

Colorado Clinical and Translational Sciences Institute (CCTSI)Professor, Department of Pediatrics

University of Colorado

Director, Clinical InformaticsThe Children’s Hospital, [email protected]

mailto:[email protected]

Submission& ReportingEvidence-based

Review

NewResearchQuestions

StudySetupStudy Design

& Approval

Recruitment& Enrollment

StudyExecution

ClinicalPractice

PublicInformation

T1 Biomedical Research Investigator Initiated T1 T2 Translational ResearchIndustry Sponsored Commercialization

ClinicalTrial Data

BasicResearch Data

PilotStudies

RequiredData Sharing

OutcomesReporting

OutcomesResearch

Evidence-based Patient

Care and Policy

EMRData

A Lifecycle View of Clinical Research

The Promise of the Electronic Medical Record

• Merging prospective clinical research & evidence-based clinical care– A “front-end” focus

• Improving care one patient at a time (decision support)• Merging clinical care and clinical research data collection

• Clinically rich database for retrospective clinical research– A “back-end” focus

• Making discoveries across populations of patients• Improving care at the population / policy level

Grand Vision: Any clinical investigator can “belly up to the bar” for research-quality data

The Tale of A Trivial Data Request

• The original data request:

“For an upcoming grant application, how many patients were seen recently with neurofibromatosis-1 (NF-1) and scoliosis?”• “Recently seen” = an encounter of any type since 1/1/2008• NF-1: ICD-9 code starts with “237.7”• Scoliosis: ICD-9 code starts with “737.3”

• Result: N=15

The Tale of A Simple Data Query

• Drilling down:– This query required both diagnoses to be coded on

the same encounter (event).

N(Pt)

Encounter

Dx1 = NF-1

Dx2 = Scoliosis

1/1/2008 - today


• Second query:– NF-1 and Scoliosis diagnoses can be coded on

different encounters, both within time window– N= 28

N(Pt)

Encounter

Dx1 = NF-1

Dx2 = Scoliosis

1/1/2008 - today

Encounter


• Investigator still did not like the answer:– NF-1 is a life-long genetic illness– Scoliosis develops as a complication.– Therefore: NF-1 diagnosis at any time

Only scoliosis need to be “recently seen”– N= 47

N(Pt)

Encounter

Dx1 = NF-1

Dx2 = Scoliosis

Encounter

1/1/2008 - today

One Question Three temporal structures Three different answers

N = 15

N = 28

N = 47

Tale of a research query

• Use of C-Reactive Protein as a marker of clinical infection in the NICU

• First Temporal Structure:

No Abx

2+ days

CRP test

2 days 2 days

Abx Start

Days(Antibiotics)

Abx Stop


• This is not right!

No Abx

2+ days

CRP test

2 days 2 days

Abx Start

Days(Antibiotics)

Abx Stop

Abx Stop could occur during 2-day window for CRP test, as long as CRP test occurred before CRP test


• Does this capture the desired relationship?

Want to allow for Abx Stop to occur within the 2-day CRP window but only if after CRP test.

But do not want to require Abx Stop in the 2-day window

No Abx

2+ days

CRP test Abx Stop

2 days 2 days

Abx Start

Days(Antibiotics)


• What if I do want to constraint Abx Stop to the 2-day window? • What does that look like? Is the difference visually obvious?

No Abx

2+ days

CRP test Abx Stop

2 days 2 days

Abx Start

Days(Antibiotics)

No Abx

2+ days

CRP test Abx Stop

2 days 2 days

Abx Start

Days(Antibiotics)

Different temporal structures - Different answersDifferent Clinical Meanings/Interpretations

Representing Meaningful Temporal Relationships

• Three weeks prior to admission, a bright red patch appeared under the patient's eye.

• The patient developed a maculopapular rash that spread to her hands and then her knees the following day

• On admission, she began having fever to 40oC which resolved by HD #2

• She was discharged on HD #8

Original Assertions

6 Fever resolved

7

2

15

6

43

12

Red Patch appeared

Hospital Admission

3

4

Rash over Hands

Rash over Knees

5 Fevers

7 Hospital Discharge

• Explosive number of derived temporal concepts (full transitive closure)

• Not all of them are useful. But which ones?

Full Temporal Closure

6 Fever resolved

7

2

15

6

43

12

Red Patch appeared

Hospital Admission

3

4

Rash over Hands

Rash over Knees

5 Fevers

7 Hospital Discharge

• Explosive number of derived temporal concepts (full transitive closure)

• Not all of them are useful. But which ones?

Surgical cut time

Abx start time

Abx stop timeAbx redose time

Abx d/c time

43

21

5

76

8

Time Milestones Associated with Surgical Antibiotics Prophylaxis

• Eight (of 10) clinically-meaningful time intervals• Which ones are clinically relevant?• Which ones have recommendations?• Which ones can we extract?

Supporting Ad-Hoc Queries: Who is the User?

• Clinically-knowledgable but data-naive clinicians

• Goal: To ensure underlying temporal assumptions are explicit

• What type of user interface visual paradigm would support this type of interactive queries?– What meta-data support is

needed for clinically-meaningful derived temporal concepts

PatternFinder (Lam: University of Maryland)

From: Lam. Searching Electronic Health Records for Temporal Patterns. A Case Study with Azyxxi, 2008.http://www.cs.umd.edu/hcil/ehrviz-workshop/

www.cs.umd.edu/hcil/patternfinder

• Relational operators– “relative increase greater than X”– “relative increase greater than X%”– “relative decrease greater than X”– “relative decrease greater than X%”– “less than value in event X”– “equal to value in event X– “not equal to value in event X”

– “within X prior to (relative)”– “within X following (relative)”– “after X (relative)”– “before X (relative)”– “is equal to (relative)”

– “equal to value in event X”– “not equal to value in event X”

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Key Querying Features

From:Lam. Searching Electronic Health Records for Temporal Patterns. A Case Suty with Azyxxi, 2008.http://www.cs.umd.edu/hcil/ehrviz-workshop/

Patients with increasing dosages of Remeron followed by a heart attack within 180 daysFrom: Wang, Plaisant, Shneiderman. Workshop: Interactive Exploration of Electronic Health Records, 2008.

http://www.cs.umd.edu/hcil/ehrviz-workshop/

PatternFinder Interface

Patients with increasing dosages of Remeron followed by a heart attack within 180 days

SELECT P.*FROM Person P, Event E1, Event E2, Event E3, Event E4

WHERE P.PID = E1.PID AND P.PID = E2.PID AND P.PID = E3.PID AND P.PID = E4.PID AND E1.type = “Medication” AND E1.class = “Anti Depressant” AND E1.name = “Remeron" AND E2.type = “Medication” AND E2.class = “Anti Depressant” AND E2.name = “Remeron“ AND E3.type = “Medication” AND E3.class = “Anti Depressant” AND E3.name = “Remeron"

AND E2.value > E1.value AND E3.value >= E2.value AND E2.date > E1.date AND E3.date >= E2.date AND E4.type = “Visit” AND E4.class = “Hospital” AND E4.name = “Emergency" AND E4.value = "Heart Attack" AND E4.date >= E3.date AND 180 <= (E4.date – E3.date)

From: Wang, Plaisant, Shneiderman. Workshop: Interactive Exploration of Electronic Health Records, 2008.http://www.cs.umd.edu/hcil/ehrviz-workshop/


Result Set Visualization: Ball and Chain

LifeLines2: Align-Rank-Filterwww.cs.umd.edu/hcil/lifelines2


Health Services Research Temporal Templates?

Look-back Window

End of Observation Date

Observation Window

Index Event Date

Accrual Window

Maximum Follow-up Date

Patient-specific Dates

Time

Study-specific Dates

From A. Forster. The Ottawa Hospital-Data Request Form 2009

Data quality – Dirty Laundry

• Suppose the previous issues were solved and investigators can easily construct complex temporal and atemporal queries……

…..what is the quality of the results that come back?

Let’s assume the query interface issue is solved!Would this result be worrisome?

It’s tough being 6 years old…….

Should we be worried?

• No– Large numbers will swamp out effect of anomalous

data or use trimmed data– Simulation techniques are insensitive to small errors

• Yes– Public reporting could highlight data anomalies– Genomic associations look for small signals (small

differences in risks) amongst populations

Research Challenge

• Can we create a dynamic measure of data quality that is provided with the results of all queries?

• Query Results, quality measure

What would be the elements of QM?

Book cover images from Amazon.com

Measuring Data Quality

• Observed versus expected distributions• Outliers• Missing values• Performance on data validity

checks– Single attribute analysis– Double- / triple- / higher level attributes correlations– Physical / logical domain impossibilities

Defining data quality: The “Fit for Use” Model

• Borrowed from industrial quality frameworks– Juran (1951): “Fitness for Use”

• design, conformance, availability, safety, and field use

• Multiple adaptations by information science community– Not all adaptations are clearly specified– Not all adaptations are consistent– Not linked to measurement/assessment methods

37

How to measure data quality?

• Need to link conceptual framework with methods• Maydanchik: Five classes of data quality rules

– Attribute domain: validate individual values – Relational integrity: accurate relationships between

tables, records and fields across multiple tables– Historical: time-vary data– State-dependent: changes follow expected transitions– Dependency: follow real-world behaviors

39Maydanchik, A. (2007). Data quality assessment. Bradley Beach, NJ, Technics Publications.

Data Quality Assessment METHODS

• Five classes of data quality rules 30 assessment methods– Attribute domain rules (5 methods)

– Relational integrity: (4 methods)

– Historical: (9 methods)

– State-dependent: (7 methods)

– Dependency: (5 methods)

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Time and change assessments dominate!!

Dimension 1: Attribute domain constraints

41

Dimension 2: Relational integrity rules

42

Dimension 4: State-dependent rules

44

Dimension 5:Attribute dependency rules

45

Implementing the Framework in SAFTINet

• One of three AHRQ Distributed Research Network grants– SCANNER (UCSD)– SPAN (KPCO)

• Focused on safety net healthcare providers• Includes financial/clinical data integration

with Medicaid payments• Using Ohio State /TRIAD grid-technologies

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SAFTINet: Distributed research network

Grid Portal

Related DQ Work: Visualizing Data Quality

[email protected]

Clinical research and the electronic medical record: Interdisciplinary research agendas Michael G....

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