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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
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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
2
3
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)
• …
Vojtech Huser Beyond Cohort Discovery 5
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
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LimitsLimits
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1
2
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)
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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
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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)
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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
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LimitsLimits
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1
2
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
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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
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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
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