Approaches to Queries for Cohort Identification
Transcript of Approaches to Queries for Cohort Identification
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ApproachestoQueriesforCohortIdentification
PaulHarris,PhDProfessor,BiomedicalInformatics
Patients
CommunityFaculty/Staff
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Faculty/Staff
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OR…
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291Publications
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Step 1: Potential volunteers (or their parents/caretakers) self-register to indicate a willingness to be contacted for research studies.
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Potential volunteers (or their parents/caretakers) self-register to indicate a willingness to be contacted for research studies.
Poweredby
Potential volunteers (or their parents/caretakers) self-register to indicate a willingness to be contacted for research studies.
Poweredby
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Registered researchers search database for individuals based on study inclusion criteria and geographical location (Only De-Id Information)
Researchers send IRB approved recruitment message to ‘matched’ volunteers.
Volunteers make final choice to share identifiable information for direct contact.
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Researchers contact interested volunteers and follow normal study consent procedures.
152Publications
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?
It’sEarly… NoFormalEvaluationYetforEnrollment,ButPeopleareClicking
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• AnInformaticsplatformdesignedtocontinuouslyengagepatientsandofferopportunitiestoparticipateinresearch
• Acohortof~20,000Vanderbiltpatientsthathaveoptedintobecontacted directly bye-mailtoparticipateinresearchortoprovideinputonresearchideas
• Aconvenientandefficientpanelofpatientrepresentativesforwhichwehavemedicalrecorddata
• Uses:survey,clinical,interventionalresearchandstakeholderengagementtoguideresearchefforts
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MRAVCohortAuthenticated(MHAV)individualscanusethislinktoprovideinformationabouthowtheywishtobecontactedforrecruitment(e.g.e-mail,phone,onlybymydoctor,etc)
0100020003000400050006000
18-29
30-39
40-49
50-59
60-69
70-79
80-89
90-99
MRAVCohortAgeDistribution
MRAVCohortTop5 Conditions
HypertensionNOS
BenignHypertension
DiabetesUncomplTypeII
HyperlipidemiaNEC/NOS
AllergicRhinitisNOS
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• ObtainIRBapprovalforuseofMRAVrecruitmenttoolandemailcontactlanguage
• SubmitaMyResearchAccessRequest
• Reviewedforparticipantburdenandavailabilityofprogrammers
• Self-ServiceIdentificationthroughRDDiscoverInterface
• - or- submitaResearchDerivativeRequesttoidentifyeligiblepatientsbasedonstudycriteria,ifapplicable(IDASCore,$120perprogramminghour)
• Onceapproved,DataCoordinatingCenterCoreprogrammerssendemailnotificationstoparticipantsincludingapprovedlanguage($82perprogramminghour)
HowtoRecruitPatientsFromTheMRAVCohort
ExpressionofInterest/Pre-ScreeningSurvey
ExpressionofInterest/Pre-ScreeningSurvey
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• Enablesmoreefficientscreeningandrecruitingofpatientsversustraditionalmethodsofmanuallyreviewingupcomingappointmentlistsandcross-referencingEMR
• Basedonalistofupcomingappointmentsinapredeterminedsetofclinics,SubjectLocatorsearchespatient’sEMRviatheresearchdatawarehouseforcommonlyused,discreteinclusion/exclusioncriteriatosignificantlynarrowdownthenumberofpatientsthatrequirescreening
– CriteriaincludesIC9/10andCPTcodes,demographics,vitals,keywords,medications
Snapshot–PilotStudies
Nephrology• Examined: 2598• Candidates: 96
(reduction- 96%)CleftPalate• Examined: 2490• Candidates: 27
(reduction- 99%)
Cardiology(2studies)(reduction- 95%)
StartingHereàFilteringCriteria
…
ReviewList
ClinicsofInterest
StudyWorkQueue(DailyReview)
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BioVUØ LinksDNAextractedfromdiscardedblood
samplestode-identifiedEMRØ ~177kDNAsamples,over20k pediatricØ 74 BioVUprojectsapprovedtodate
SyntheticDerivativeØ Researchtooltoenablestudieswith
de-identifiedclinicaldataØ Contains2.5millionrecords;highlydetailed
longitudinalclinicaldatafor~1million
ResearchDerivativeØ IdentifiedclinicaldatawarehouseØ Tools(e.g.SubjectLocator)Ø Services(FeeforService)Ø NewREDCap extractiontoolkit
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•Recommendations for clinical data representation to support phenotyping• 1. Structure clinical data into queryable forms.• 2. Recommend use of a common data model, but also support customization for
the variability and availability of EHR data among sites.
•Recommendations for phenotype representation models• 3. Support both human-readable and computable representations.• 4. Implement set operations and relational algebra.• 5. Represent phenotype criteria with structured rules.• 6. Support defining temporal relations between events.• 7. Use standardized terminologies, ontologies, and facilitate reuse of value sets.• 8. Define representations for text searching and natural language processing.• 9. Provide interfaces for external software algorithms.• 10. Maintain backward compatibility.
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PARTNERS
Visionandpurpose
Ourgoalistopositivelyimpacthumanhealthbyimprovingparticipantenrollmentandretentioninmulti-centerclinicaltrials.
Achievingthisgoalwillrequiresophisticatedinformatics-basedrecruitmenttools andnovelengagementapproachestoacceleraterecruitmentandretention.
Participant Trial
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KeyPrinciples• RespectingCTSAautonomyanddiversity• Afocusonminorityandunderservedpopulations• Makingthemostofelectronichealthrecords• Preservingadiseaseneutralapproach• Focusoncostefficiency• Respectingandreturningvaluetoparticipants• Buildonbestpractice(avoidreinventingthewheel)• Evidencebased….Whatworks?(testbed)• Finiteresources– scalability/tools• Homeforrecruitmentexperts(across+beyondCTSA)
EHR-Based Site Feasibility
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EHR-Based Feasibility
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EHR-Based Feasibility
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Same,butDifferent
EHR-Based Feasibility
EHR-Based Feasibility
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Non-Fed Fed
EHR-Based Feasibility: Federated
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RelatedResultsFederated Non-Federated
Counts TriNetXCountsDifferencebetweencounts
TriNetX(CTSASites)
Differencebetweencounts
Count1 237,770 172,500
Count2 233,310 2.28% 168,710 2.19%
Count3 214,630 8.00% 157,650 6.56%
Count4 181,630 15.0% 129,660 17.75%
Vanderbilt Differencebetweencounts RIC Differencebetween
counts
9,731 39,571
9,505 2.32% 38,319 3.16%
9,106 4.20% 37,132 3.10
7,302 19.81% 7,302 16.76%
ThechangeinthecountsisvirtuallythesamebetweenFederatedandNon-Federated
+NeedFederatedtodosophisticatedsensitivityanalysisofinclusion/exclusionrules
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We’reworkingonjoining…
We’reworkingonleveraging…
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We’reworkingonjoining…
We’reworkingonleveraging…
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Recentofferings(videoavailable)
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