clinical research and the electronic medical record: interdisciplinary research agendas
DESCRIPTION
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 PresentationTRANSCRIPT
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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]
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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
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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
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Grand Vision: Any clinical investigator can “belly up to the bar” for research-quality data
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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
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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
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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
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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
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One Question Three temporal structures Three different answers
N = 15
N = 28
N = 47
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10
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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
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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
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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)
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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)
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Different temporal structures - Different answersDifferent Clinical Meanings/Interpretations
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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
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Original Assertions
6 Fever resolved
7
2
15
6
43
12
Red Patch appeared
Hospital Admission
34
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?
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Full Temporal Closure
6 Fever resolved
7
2
15
6
43
12
Red Patch appeared
Hospital Admission
34
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?
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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?
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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
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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
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• 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/
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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
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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/
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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
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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/
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From: Wang, Plaisant, Shneiderman. Workshop: Interactive Exploration of Electronic Health Records, 2008.http://www.cs.umd.edu/hcil/ehrviz-workshop/
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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
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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?
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Let’s assume the query interface issue is solved!Would this result be worrisome?
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It’s tough being 6 years old…….
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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
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Research Challenge
• Can we create a dynamic measure of data quality that is provided with the results of all queries?
• Query Results, quality measure
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What would be the elements of QM?
Book cover images from Amazon.com
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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
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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.
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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!!
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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
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Related DQ Work: Visualizing Data Quality
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Related DQ Work: Visualizing Data Quality