beyond cohort discovery amia (biomedical and health informatics)

Going Beyond Cohort Discovery: Current Limitations, Advanced Methodologies and Future Trends

Going Beyond Cohort Discovery: Current Limitations, Advanced Methodologies and Future Trends

Vojtech Huser Vikrant Deshmukh

Adam Wilcox Henry Lowe

AMIA summit, 2012

PanelistPanelist

• Vojtech Huser, Panel Moderator– Assistant Clinical Investigator, Laboratory for Informatics

Development, National Institutes of Health, Clinical Center

• Vikrant Deshmukh– Senior Data Warehouse Architect, U of Utah Hospital

• Adam Wilcox– Assistant Professor at Columbia University and Director of the Clinical

Databases, Information Services, New York Presbyterian Hospital

• Henry Lowe– Director of the Center for Clinical Informatics, Stanford University

Vojtech Huser Beyond Cohort Discovery 2

Background Background

• Growth of data warehousing• CTSA IDR surveys in 2007,2008,2010

• Increasing number of integrated sources• Improved researcher’s access to data– IRB policies, user-friendly query tools– Now an exception of a projects does not use an

IDR• emergence of established platforms – i2b2, BI tools


TopicsTopics

• So you have your cohort estimate: now what?

1.self-service search tools2.human-expert-assisted search3.prospective, computer-assisted

cohort recruitment


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1. Self-service query tools overview1. Self-service query tools overview

• i2b2 (1.4 -> 1.6), SHRINE • FURTHER (U of Utah)• STRIDE (Stanford)• Business intelligence based systems

• (Business Objects, Cognos, MS, Pentaho, others)

• BTRIS (NIH)• DEDUCE (Duke) (+DISCERN)

• StarBrite (Vanderbilt) (SD)

• …


FactorsFactors

• User factor– Zero training system

• vs. intro video vs. advanced training– User not versed in terminologies– What is out there in the IDR?

• granularity, source systems, IDR vs. EHR)– Level of support (helpdesk)

• System factor– Underlying warehouse (data model, search engine)– User interface metaphor


LimitsLimits


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Next stepsNext steps

• Cohort estimation– Feasibility review vs. full IRB approval

• Results provide answer to the question• Data extraction

• Further manual review (narrowing the cohort)• for retrospective study

– Limited in-tool support, link to further systems

• More complex query (human assisted)– May not be an option (not supported, too expensive)


Challenges (self service query tools)Challenges (self service query tools)

• Temporal logic• Ability to chain queries (Q1 output is input for Q2)• Knowledge representation

– SQL query + schema– Phenotype modeling (HealthFlow, PheKB, eMerge)– Eligibility criteria of clinical trials (OCRe, Arden Syntax)

• Import/Export – within single platform– sharing among users within organization

• New inferred concepts in IDR• Search query elements, variables (interim constructs)

– inter-element parameter passing • Depression, PHQ-9 score, therapy, 50% reduction in initial score


2. Human-assisted search2. Human-assisted search

• DEFINITION: Ability of data requestors to ask for custom queries performed by human analyst

• Level of integration– No such option– Designated analyst (within department)– Data warehouse analyst (differing availability)– Institutionalized entity with charge-back model

• Response time, request submission (web-form with ticket # vs. informal request)


FactorsFactors

• Data+Requestor Data+Analyst+Requestor– Misunderstanding– Quality control (is this exactly the criteria requestor needed)

• Not restricted by tool metaphor or UI• Mode of communication platform

• Web-form, emails, face-to-face meetings, iteration• Ability to review the “code” (in addition to results)

– Power requestor

• Cycle time (interim results)• Week vs. month, interactive results

• Ability to import search definition from self-service tool


LimitsLimits


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3. prospective computer-assisted cohort recruitment3. prospective computer-assisted cohort recruitment• DEFINITION: informatics solution facilitating

recruitment (eligibility criteria) (CTA)• Data warehouse based alerts vs. EHR system

alerts• Different execution paradigm– Single patient (single event processing)– Population (retrospective queries)

• Ability to reuse the same retrospective logic– Reuse each search element criterion– Flip a switch


Example 1: Human-assisted search (Marshfield Clinic)Example 1: Human-assisted search (Marshfield Clinic)• Flagship modality for providing access to IDR• Request tracking since 2001, centralized team, pool of analyst (funded

projects)– research team, administrative team

• Parameters: – report title, reason, data criteria (attachment), desired columns, output (SAS,

Excel), report frequency – link to similar prior requests

• Query code/results is archived (code re-use)• between 2001-2008: 5507 queries

– Use by various stakeholders– Compliance perspective– Research use (complex queries, analyst time, lines of resulting code)

• Focus groups used for re-design of the request workflow– Analyst provides warehouse knowledge (what more can I ask?)– Multiple iterations, face-to-face meetings


Example 2: NIH Clinical CenterExample 2: NIH Clinical Center• 1. self service query tool:

– BTRIS platform– Flagship modality

• 2. human-assisted search– Limited degree (no elaborate financial model)

– Can also be limited to assistance with the query tool– Provided to inform improvements within the self-service platform

• 3. prospective recruitment– Not fully applicable to Clinical Center as research hospital

• Not within EHR– In development phase around the IDR

• Studies compete for participants– Database of healthy volunteers

• ability to search on 12 parameters• generate a list of phone numbers


beyond cohort discovery amia (biomedical and health informatics)

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