Clinical research and the electronic medical record: Interdisciplinary research agendas
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Clinical research and the electronic medical record: Interdisciplinary research agendas Michael G. Kahn MD, PhD Biomedical Informatics Core Director Colorado Clinical and Translational Sciences Institute (CCTSI) Professor, Department of Pediatrics University of Colorado Director, Clinical Informatics The Children’s Hospital, Denver [email protected]
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Clinical research and the electronic medical record: Interdisciplinary research agendas. Michael G. Kahn MD, PhD Biomedical Informatics Core Director Colorado Clinical and Translational Sciences Institute (CCTSI) Professor , Department of Pediatrics University of Colorado - PowerPoint PPT Presentation
Transcript of Clinical research and the electronic medical record: Interdisciplinary research agendas
Clinical research and the electronic medical record:
Interdisciplinary research agendasMichael G. Kahn MD, PhD
Biomedical Informatics Core DirectorColorado Clinical and Translational Sciences Institute (CCTSI)
Professor, Department of PediatricsUniversity of Colorado
Director, Clinical InformaticsThe Children’s Hospital, [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)
EncounterDx1 = NF-1
Dx2 = Scoliosis
1/1/2008 - today
The Tale of A Simple Data Query
• Second query:– NF-1 and Scoliosis diagnoses can be coded on
different encounters, both within time window– N= 28
N(Pt)
EncounterDx1 = NF-1
Dx2 = Scoliosis
1/1/2008 - today
Encounter
The Tale of A Simple Data Query
• 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)
EncounterDx1 = NF-1
Dx2 = ScoliosisEncounter
1/1/2008 - today
One Question Three temporal structures Three different answers
N = 15
N = 28
N = 47
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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
Tale of a research query
• 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
Tale of a research query
• 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)
Tale of a research query
• 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
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12
Red Patch appeared
Hospital Admission
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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
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12
Red Patch appeared
Hospital Admission
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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
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5
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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/
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
From: Wang, Plaisant, Shneiderman. Workshop: Interactive Exploration of Electronic Health Records, 2008.http://www.cs.umd.edu/hcil/ehrviz-workshop/
From: Wang, Plaisant, Shneiderman. Workshop: Interactive Exploration of Electronic Health Records, 2008.http://www.cs.umd.edu/hcil/ehrviz-workshop/
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
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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
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Dimension 2: Relational integrity rules
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Dimension 4: State-dependent rules
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Dimension 5:Attribute dependency rules
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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
Related DQ Work: Visualizing Data Quality
