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

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Clinical research and the electronic medical record interdisciplinary research agendas

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]


A lifecycle view of clinical research

T1 Biomedical Research

Investigator Initiated T1  T2 Translational Research

Industry Sponsored Commercialization

Basic

Research Data

Pilot

Studies

New

Research

Questions

Study

Setup

Study Design

& Approval

Outcomes

Research

Clinical

Practice

Clinical

Trial Data

Recruitment

& Enrollment

Evidence-based Patient Care and Policy

EMR

Data

Submission

& Reporting

Evidence-based

Review

Study

Execution

Required

Data Sharing

Outcomes

Reporting

Public

Information

A Lifecycle View of Clinical Research


The promise of the electronic medical record

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

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


The tale of a trivial data request

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

    N(Pt)

    Dx1 = NF-1

    Encounter

    Dx2 = Scoliosis

    1/1/2008 - today

    The Tale of A Simple Data Query

    • Drilling down:

      • This query required both diagnoses to be coded on the same encounter (event).


    The tale of a simple data query1

    N(Pt)

    Dx1 = NF-1

    Encounter

    Encounter

    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


    The tale of a simple data query2

    N(Pt)

    Dx1 = NF-1

    Encounter

    Encounter

    Dx2 = Scoliosis

    1/1/2008 - today

    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


    One question three temporal structures three different answers

    One Question  Three temporal structures Three different answers

    N = 15

    N = 28

    N = 47


    Tale of a research query

    Days(Antibiotics)

    Abx Start

    Abx Stop

    CRP test

    No Abx

    2+ days

    2 days

    2 days

    Tale of a research query

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

    • First Temporal Structure:


    Tale of a research query1

    Days(Antibiotics)

    Abx Start

    Abx Stop

    CRP test

    No Abx

    2+ days

    2 days

    2 days

    Tale of a research query

    • This is not right!

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


    Tale of a research query2

    Days(Antibiotics)

    CRP test  Abx Stop

    No Abx

    2+ days

    2 days

    2 days

    Abx Start

    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


    Tale of a research query3

    Days(Antibiotics)

    CRP test  Abx Stop

    No Abx

    2+ days

    2 days

    2 days

    Abx Start

    Abx Start

    Days(Antibiotics)

    CRP test  Abx Stop

    No Abx

    2+ days

    2 days

    2 days

    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?


    Different temporal structures different answers different clinical meanings interpretations

    Different temporal structures - Different answersDifferent Clinical Meanings/Interpretations


    Representing meaningful temporal relationships

    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

    Original Assertions

    7

    6

    1

    1

    Red Patch appeared

    5

    2

    Hospital Admission

    3

    Rash over Hands

    2

    4

    Rash over Knees

    5

    Fevers

    6

    Fever resolved

    7

    Hospital Discharge

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

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

    3

    4


    Full temporal closure

    Full Temporal Closure

    7

    6

    1

    1

    Red Patch appeared

    5

    2

    Hospital Admission

    3

    Rash over Hands

    2

    4

    Rash over Knees

    5

    Fevers

    6

    Fever resolved

    7

    Hospital Discharge

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

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

    3

    4


    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?

    1

    2

    3

    4

    5

    6

    7

    8

    Abx d/c time

    Surgical cut time

    Abx redose time

    Abx stop time

    Abx start time

    Time Milestones Associated with Surgical Antibiotics Prophylaxis


    Supporting ad hoc queries who is the user

    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-meaningfulderived temporal concepts


    Patternfinder lam university of maryland

    PatternFinder (Lam: University of Maryland)

    www.cs.umd.edu/hcil/patternfinder

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


    Key querying features

    Key Querying Features

    • 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”

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

    22


    Patternfinder interface

    PatternFinder Interface

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

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


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

    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

    Result Set Visualization: Ball and Chain

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


    Lifelines2 align rank filter

    LifeLines2: Align-Rank-Filter

    www.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/


    Clinical research and the electronic medical record interdisciplinary research agendas

    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

    Study-specific Dates

    Maximum Follow-up Date

    Accrual Window

    Time

    Look-back Window

    Observation Window

    End of Observation Date

    Index Event Date

    Patient-specific Dates

    Health Services Research Temporal Templates?

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


    Data quality dirty laundry

    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

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


    It s tough being 6 years old

    It’s tough being 6 years old…….


    Should we be worried

    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

    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

    What would be the elements of QM?

    Book cover images from Amazon.com


    Measuring data quality

    Measuring Data Quality

    • Observed versus expected distributions

    • Outliers

    • Missing values

    • Performance on data validitychecks

      • Single attribute analysis

      • Double- / triple- / higher level attributes correlations

      • Physical / logical domain impossibilities


    Defining data quality the fit for use model

    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


    How to measure data quality

    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

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


    Data quality assessment methods

    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)

    Time and change assessments dominate!!


    Dimension 1 attribute domain constraints

    Dimension 1: Attribute domain constraints


    Dimension 2 relational integrity rules

    Dimension 2: Relational integrity rules


    Dimension 4 state dependent rules

    Dimension 4: State-dependent rules


    Dimension 5 attribute dependency rules

    Dimension 5:Attribute dependency rules


    Implementing the framework in saftinet

    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


    Saftinet distributed research network

    SAFTINet: Distributed research network

    Grid Portal


    Related dq work visualizing data quality

    Related DQ Work: Visualizing Data Quality


    Related dq work visualizing data quality1

    Related DQ Work: Visualizing Data Quality


    Clinical research and the electronic medical record interdisciplinary research agendas

    [email protected]


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