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Developing Information Systems for Cancer Research. Christopher Flowers, MD, MSc Assistant Professor Medical Director, Oncology Data Center Bone Marrow and Stem Cell Transplant Center Winship Cancer Institute Emory University. Health Care Data Integration Medical Intelligence Applications. - PowerPoint PPT Presentation
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Developing Information Systems for Developing Information Systems for Cancer ResearchCancer Research
Christopher Flowers, MD, MScChristopher Flowers, MD, MScAssistant ProfessorAssistant Professor
Medical Director, Oncology Data CenterMedical Director, Oncology Data CenterBone Marrow and Stem Cell Transplant CenterBone Marrow and Stem Cell Transplant Center
Winship Cancer InstituteWinship Cancer InstituteEmory UniversityEmory University
Health Care Data Integration
Medical Intelligence Applications
PatientDemographics
GSI DB
INTEGRATEDHEALTH CARE
DATA
Pharmacy
Financial /Billing System
Lab Results
O.R. Surgery &Materials Sys.
CancerRegistry
TranscribedNotes
Tissue Bank
Any OtherLegacy, CurrentSource System
MEDICALINTELLIGENCE
CASE REPORT
TISSUE BANK
GENETIC
SYSTEMADMINISTRATION
EXTRACTTRANSFORM
& LOAD(ETL)
REGULARDATA
UPDATE& REFRESH
HEALTH CARESOURCE SYSTEMS
NUTEC SERVICESPROCESSES
GENESYS SIDATABASE
GENESYS SISOFTWAREMODULES
JDBC
ODBC
ASCII
HL7
DTS
HEALTH CARE DATA INTEGRATION & MEDICAL INTELLIGENCE
What Data are available? What Data are available? Patient Genomics Patient Genomics
– Microarrays and Gene ChipMicroarrays and Gene Chip– Analysis ResultsAnalysis Results– Quality ValuesQuality Values
Hospital Patient ManagementHospital Patient Management– Patient Demographics Patient Demographics
» Inpatient, Outpatient, Patient TypesInpatient, Outpatient, Patient Types» Location, Physician, Visits Location, Physician, Visits
Hospital Patient Accounting Hospital Patient Accounting – Financial DataFinancial Data
» Patient charges Patient charges » Payments and CollectionsPayments and Collections
– Summarized Financial Visit DataSummarized Financial Visit Data– Charge DescriptionCharge Description
Pharmacy Pharmacy – Orders, Drugs, MedicationOrders, Drugs, Medication– FormularyFormulary– Drug InteractionsDrug Interactions– CostsCosts
Medical Records Medical Records – Procedures & Diagnosis (CPT4 & ICD9) Procedures & Diagnosis (CPT4 & ICD9) – Visit, AbstractVisit, Abstract– PhysicianPhysician– Admit Diagnosis, Admit Source and TypeAdmit Diagnosis, Admit Source and Type– RDRG/DRGRDRG/DRG
What Data are available?What Data are available?
Clinic Patient Accounting Clinic Patient Accounting – Patient Registration; Demographics, Insurance (FSC), Employer, CasePatient Registration; Demographics, Insurance (FSC), Employer, Case– ProviderProvider– General Ledger General Ledger – Financial Data & InvoicesFinancial Data & Invoices
• Laboratory Results – Lab Orders, General Results and Micro– Clinic and Hospital Patients
What Data are available?What Data are available?
Radiation Oncology Radiation Oncology – Treatment PlansTreatment Plans
Clinical Trials Clinical Trials – StudiesStudies– Patient DemographicsPatient Demographics– Pathology Pathology
Cancer Registry Cancer Registry – Patient Demographics and abstractPatient Demographics and abstract– Pathology, Treatment Plans and Discharge SummaryPathology, Treatment Plans and Discharge Summary– Progress Notes, Radiology results, ChargesProgress Notes, Radiology results, Charges
What Data are available?What Data are available?
Patient Chart InformationPatient Chart Information– Physician NotesPhysician Notes– Radiology ReportsRadiology Reports– HLAHLA– Cancer Anatomic PathCancer Anatomic Path– Lab Test ResultsLab Test Results
Other (Forms entry)Other (Forms entry)– IBMTR/ABMTR FormIBMTR/ABMTR Form– Acute Myelogenous FormAcute Myelogenous Form– Patient Profile FormPatient Profile Form– Informed ConsentInformed Consent
What Data are available?What Data are available?
Analysis of Search Algorithms for Analysis of Search Algorithms for Oncologic Disease Identification Oncologic Disease Identification
Using GeneSys SIUsing GeneSys SIMichael Graiser, PhD1, Ashley Hilliard1, Rochelle Victor1,
Ragini Kudchadkar, MD1, Leroy Hill1, Michael S. Keehan, PhD2,Jonathan Simons, MD1, Christopher Flowers, MD1
1 Winship Cancer Institute, Department of Hematology and Oncology, Emory University School of Medicine, Atlanta, GA (http://www.winshipcancerinstitute.org)
2 NuTec Health Systems, Atlanta, GA* (email: [email protected])
* Emory University has a financial interest in NuTec Health Systems, which designed and built GeneSys SI. Emory may financially benefit from this interest if NuTec is successful in marketing GeneSys SI. This project may produce income for Emory’s charitable purposes and for NuTec’s commercial purposes.
Development of GeneSys SIDevelopment of GeneSys SI
● Collaborative effort between Emory’s Winship Cancer Collaborative effort between Emory’s Winship Cancer Institute and NuTec Health SystemsInstitute and NuTec Health Systems
● Web-based query tool and genomic analysis tools Web-based query tool and genomic analysis tools designed with a team of Emory oncologists and designed with a team of Emory oncologists and research investigatorsresearch investigators
● August, 2002 – 175,000 Emory patients identified by August, 2002 – 175,000 Emory patients identified by cancer diagnosis loaded into GeneSys SIcancer diagnosis loaded into GeneSys SI● New patients added by individual patient consentNew patients added by individual patient consent
● Ongoing efforts to add new sources of dataOngoing efforts to add new sources of data● Tissue BankingTissue Banking● Genomic toolsGenomic tools
GeneSys SI ModulesHealth Care Applications
APPLICATIONS USERS
Principal InvestigatorResearcherPhysician
Health Care AdministratorFinancial Administrator
Cost Controller
Principal InvestigatorResearcher
Health Care Administrator
Principal InvestigatorResearcherPhysician
Clinical Research Outcomes Research
Health Care Data Mining Personalized Medicine
Quality Assurance Materials Cost Analysis
Labor Cost Analysis
Case Report Forms Patient Surveys Exit Interviews
Patient Consenting
Genetic Research Match Clinical Outcome
Match Phenotype Personalized Medicine
GENESYS SI SOFTWARE HEALTH CARE INSTITUTION
Forms BuildingPatient Data EntryForms/Data ImportQuery Tool (GSI DB)
CASE REPORT
Microarray QuantificationMicroarray AnalysisSNiP AnalysisPublic DB Search
GENETIC
TOOLSMODULES
MEDICALINTELLIGENCE
Multi DB QueryData Mining & AnalysisMulti DB ReportingRegional MapSurvival AnalysisChart Search & ValidationLab Views & Graphics
Principal InvestigatorProcurement Administrator
Procurement Assistant
Protocol Management Tissue Harvest Request
Archived Tissue Request Procurement Operations
Protocol Reg. & Admin.Tissue RequestsTissue ArchiveTissue Bank Administration
TISSUE BANK
System AdministratorDB AdministratorSecurity Service
Add Database to GSI Configure GSI
Monitor GSI Enforce Security Policies
Relation ManagementElement ConfigurationResource/DB ManagementSecurity Policy Management
SYSTEMADMINISTRATION
GENESYS SI DATABASEINTEGRATED HEALTH CARE DATA
SEAMLESS
INTEGRATION
GeneSys SI
Gene Expression Information
Clinical Information
Sequence
Information
External Databases
Linked patient-level dataLinked patient-level data● PathologyPathology● Cancer RegistryCancer Registry● Laboratory ResultsLaboratory Results● Radiology ResultsRadiology Results● Medication utilizationMedication utilization● Clinical outcomesClinical outcomes● GenomicsGenomics
Scheduling
Medical Records
Pharmacy
Lab Results1
1
11
Billing
1
Family History
Occupational Exposure
Cancer Registry
Clinical Trials
Pyxis
4
4
4
5
Cancer Epidemiology
5
5
Tissue Banking(under construction)
5
Microarrays
2
3
Anatomic Path
Cytogenetics Lab
Physician NotesRadiology Reports
33
3
3Radiation Oncology
GeneSys SI: Architecture
Scheduling
Medical Records
Pharmacy
Lab Results1
1
11
Billing
1
Family History
Occupational Exposure
Cancer Registry
Clinical Trials
Pyxis
4
4
4
5
Cancer Epidemiology
5
5
Tissue Banking(under construction)
5
Microarrays
2
3
Anatomic Path
Cytogenetics Lab
Physician NotesRadiology Reports
33
3
3Radiation Oncology
GeneSys SI: Architecture
Scheduling
Medical Records
Pharmacy
Lab Results1
1
11
Billing
1
Family History
Occupational Exposure
Cancer Registry
Clinical Trials
Pyxis
4
4
4
5
Cancer Epidemiology
5
5
Tissue Banking(under construction)
5
Microarrays
2
3
Anatomic Path
Cytogenetics Lab
Physician NotesRadiology Reports
33
3
3Radiation Oncology
Investigator D
efined
Forms Data
Public Databases
Genetic Protein
GeneSys SI contains information on patients who have visited Emory University Hospital, Crawford Long Hospital, or The Emory Clinic and have received an oncology diagnosis. Benign neoplasms are also included.
Database Population
Numbers
• Total patients 175,748
• Newly consented 551
• By ICD9 & ICD10
Data currently available in GeneSys SIData currently available in GeneSys SI DATA SOURCE ENTRY DATE HISTORY (YEARS)DATA SOURCE ENTRY DATE HISTORY (YEARS)
Emory Data WarehouseEmory Data WarehouseHospital administrative (HealthQuest)Hospital administrative (HealthQuest)Clinic administrative (IDX)Clinic administrative (IDX)Medical RecordsMedical RecordsClinical LabsClinical LabsHospital PharmacyHospital PharmacyClinic PhamacyClinic Phamacy
September, 1995September, 1995September, 1994September, 199419871987January, 2001 January, 2001 January, 1998January, 1998April, 2002April, 2002
9910101717 33 66 22
Cancer RegistryCancer RegistryEmory HositalEmory HositalCrawford Long HospitalCrawford Long Hospital
1977197719811981
27272323
Clinical TrialsClinical Trials 19811981 2121
Electronic Medical RecordElectronic Medical RecordPowerChartPowerChart 19911991 1313
Radiation OncologyRadiation OncologyThe Emory ClinicThe Emory ClinicCrawford Long HospitalCrawford Long Hospital
1994199420012001
101033
FormsFormsInformed ConsentInformed Consent
July, 2003July, 2003 1 1
GenomicsGenomics TBDTBD N/AN/A
Linked Oncology DatabaseLinked Oncology Database
Useful for:Useful for:● Retrospective clinical outcomes researchRetrospective clinical outcomes research● Clinical trials planningClinical trials planning● Cost effectiveness analysesCost effectiveness analyses● Storage of unique clinical dataStorage of unique clinical data● Linking to public genomic and proteomic databasesLinking to public genomic and proteomic databases
● PharmacogenomicsPharmacogenomics
Limitations of linked heterogeneous databasesLimitations of linked heterogeneous databases
● Reliance on patient identifiers such as SSN to linkReliance on patient identifiers such as SSN to link● data entry errors, missing data, business practicesdata entry errors, missing data, business practices
● Patchwork of different databases not intended for Patchwork of different databases not intended for research purposesresearch purposes
● Reliance upon coded outcomes (e.g. ICD-9 codes)Reliance upon coded outcomes (e.g. ICD-9 codes)● frequently assigned by personnel unfamiliar with patient, frequently assigned by personnel unfamiliar with patient,
disease, or proceduredisease, or procedure
● Multiple sources for the same dataMultiple sources for the same data● diagnosis, treatment, DOB, DOE, other demographics diagnosis, treatment, DOB, DOE, other demographics
Breitfeld et.al. J Clin Epi, 2001.Breitfeld et.al. J Clin Epi, 2001.Earle et al. Med Care, 2002.Earle et al. Med Care, 2002.Verstraeten et.al. Verstraeten et.al. Expert Rev. VaccinesExpert Rev. Vaccines, 2003., 2003.
Research ObjectivesResearch Objectives
● Develop query algorithms to identify pts with a Develop query algorithms to identify pts with a histological diagnosishistological diagnosis● Follicular lymphomaFollicular lymphoma
● Examine sensitivity and specificity of query Examine sensitivity and specificity of query algorithmsalgorithms
● Develop query strategies for identifying pts with Develop query strategies for identifying pts with other diseases of interestother diseases of interest
10 Leading Cancer Sites by Gender, US, 200510 Leading Cancer Sites by Gender, US, 2005
32%32% BreastBreast
12%12% Lung & bronchusLung & bronchus
11%11% Colon & rectumColon & rectum
6%6% Uterine corpusUterine corpus
4%4% Non-Hodgkin’s lymphoma Non-Hodgkin’s lymphoma
4%4% Melanoma of skinMelanoma of skin
3%3% OvaryOvary
3%3% ThyroidThyroid
2%2% Urinary bladderUrinary bladder
2%2% PancreasPancreas
20%20% All other sitesAll other sites
Men710,040
Women662,870
ProstateProstate 33%33%
Lung & bronchusLung & bronchus 13%13%
Colon & rectumColon & rectum 11%11%
Urinary bladderUrinary bladder 7%7%
Melanoma of skin Melanoma of skin 5%5%
Non-Hodgkin’s lymphoma Non-Hodgkin’s lymphoma 4%4%
Leukemia Leukemia 3%3%
Kidney Kidney 3%3%
Oral cavity Oral cavity 3%3%
PancreasPancreas 2%2%
All other sitesAll other sites 17%17%*Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder.
American Cancer Society, 2005.
Lymph Node
Courtesy of Thomas Grogan, MD.Courtesy of Thomas Grogan, MD.Courtesy of Thomas Grogan, MD.Courtesy of Thomas Grogan, MD.
MedulaMedula
Primary FolliclePrimary Follicle
Marginal ZoneMarginal ZoneAfferent LymphaticVesselAfferent LymphaticVessel
Mantle ZoneMantle Zone
Germinal CenterGerminal Center
SecondaryFollicleSecondaryFollicle
PostcapillaryVenulePostcapillaryVenule
ArteryArtery
EfferentLymphatic VesselEfferentLymphatic Vessel
MedullarySinusMedullarySinus
MedullaryCordMedullaryCord
SubcapsularSinusSubcapsularSinus
CortexCortex
WHO NHL ClassificationB-cell Precursor B-cell neoplasms
− B-acute lymphoblastic leukemia (B-ALL)
− Lymphoblastic lymphoma (LBL)
Peripheral B-cell neoplasms− B-cell chronic lymphocytic leukemia/small
lymphocytic lymphoma
− B-cell prolymphocytic leukemia
− Lymphoplasmacytic lymphoma/immunocytoma
− Mantle cell lymphoma
− Follicular lymphoma
− Extranodal marginal zone B-cell lymphoma of MALT type
− Nodal marginal zone B-cell lymphoma
− Splenic marginal zone lymphoma
− Hairy cell leukemia
− Plasmacytoma/plasma cell myeloma
− Diffuse large B-cell lymphoma
− Burkitt’s lymphoma
T-cell/NK-cell Precursor T-cell neoplasm
− Precursor T-acute lymphoblastic leukemia (T-ALL)
− Lymphoblastic lymphoma (LBL)
Peripheral T-cell/NK-cell neoplasms− T-cell chronic lymphocytic leukemia/prolymphocytic
leukemia
− T-cell granular lymphocytic leukemia
− Mycosis fungoides/Sézary syndrome
− Peripheral T-cell lymphoma not otherwise characterized
− Hepatosplenic gamma/delta T-cell lymphoma
− Angioimmunoblastic T-cell lymphoma
− Extranodal T-/NK-cell lymphoma, nasal type
− Enteropathy-type intestinal T-cell lymphoma
− Adult T-cell lymphoma/leukemia (HTLV1+)
− Anaplastic large cell lymphoma, primary systemic type
− Anaplastic large cell lymphoma, primary cutaneous type
− Aggressive NK-cell leukemia
Fisher et al. In: DeVita et al, eds. Cancer: Principles and Practice of Oncology. 2005:1967.Jaffe et al, eds. World Health Organization Classification of Tumours. 2001.
WHO NHL ClassificationB-cell Precursor B-cell neoplasms
− B-acute lymphoblastic leukemia (B-ALL)
− Lymphoblastic lymphoma (LBL)
Peripheral B-cell neoplasms− B-cell chronic lymphocytic leukemia/small
lymphocytic lymphoma
− B-cell prolymphocytic leukemia
− Lymphoplasmacytic lymphoma/immunocytoma
− Mantle cell lymphoma
− Follicular lymphoma− Extranodal marginal zone B-cell lymphoma of
MALT type
− Nodal marginal zone B-cell lymphoma
− Splenic marginal zone lymphoma
− Hairy cell leukemia
− Plasmacytoma/plasma cell myeloma
− Diffuse large B-cell lymphoma
− Burkitt’s lymphoma
T-cell/NK-cell Precursor T-cell neoplasm
− Precursor T-acute lymphoblastic leukemia (T-ALL)
− Lymphoblastic lymphoma (LBL)
Peripheral T-cell/NK-cell neoplasms− T-cell chronic lymphocytic leukemia/prolymphocytic
leukemia
− T-cell granular lymphocytic leukemia
− Mycosis fungoides/Sézary syndrome
− Peripheral T-cell lymphoma not otherwise characterized
− Hepatosplenic gamma/delta T-cell lymphoma
− Angioimmunoblastic T-cell lymphoma
− Extranodal T-/NK-cell lymphoma, nasal type
− Enteropathy-type intestinal T-cell lymphoma
− Adult T-cell lymphoma/leukemia (HTLV1+)
− Anaplastic large cell lymphoma, primary systemic type
− Anaplastic large cell lymphoma, primary cutaneous type
− Aggressive NK-cell leukemia
Fisher et al. In: DeVita et al, eds. Cancer: Principles and Practice of Oncology. 2005:1967.Jaffe et al, eds. World Health Organization Classification of Tumours. 2001.
MethodsMethods
● Selected disease for initial query algorithm study Selected disease for initial query algorithm study (follicular lymphoma - FL)(follicular lymphoma - FL)
● Developed and ran queries for FL using all available Developed and ran queries for FL using all available sources for diagnosissources for diagnosis● Clinic & Hospital ICD9 codes, Cancer Registry histology Clinic & Hospital ICD9 codes, Cancer Registry histology
codes, Medical record text reports: chart, pathologycodes, Medical record text reports: chart, pathology
● Verified diagnosis for each patientVerified diagnosis for each patient● pathology reportspathology reports● other chart reportsother chart reports
● For each query calculated specificity and sensitivityFor each query calculated specificity and sensitivity
GeneSys SI queries to find follicular lymphoma patientsGeneSys SI queries to find follicular lymphoma patients
QUERYQUERY SOURCESOURCE CRITERIACRITERIA
QCQC Cancer Registry NHL patients Cancer Registry NHL patients NHL between 1985-2002NHL between 1985-2002
Q1Q1 Cancer Registry, histology (ICD-0)Cancer Registry, histology (ICD-0) 9690, 9691, 9695, 96989690, 9691, 9695, 9698
Q2Q2 Text search - pathology reportsText search - pathology reports ““follicular” near “lymphoma”follicular” near “lymphoma”
Q3Q3 Text search - pathology reportsText search - pathology reports ““follicular lymphoma”follicular lymphoma”
Q4Q4 Text search - all medical recordsText search - all medical records ““follicular” near “lymphoma”follicular” near “lymphoma”
Q5Q5 Text search - all medical recordsText search - all medical records ““follicular lymphoma”follicular lymphoma”
Q6Q6 Clinic ICD-9 diagnosis codesClinic ICD-9 diagnosis codes 202.0, 202.00, 202.01, 202.02, 202.0, 202.00, 202.01, 202.02, 202.03, 202.04, 202.05, 202.06, 202.03, 202.04, 202.05, 202.06, 202.07, 202.08202.07, 202.08
Q7Q7 Hospital ICD-9 diagnosis codesHospital ICD-9 diagnosis codes (same ICD9 codes)(same ICD9 codes)
Q8Q8 Query 2 + 6Query 2 + 6 (criteria for query 2 OR 6)(criteria for query 2 OR 6)
Q9Q9 Query 4 + 6Query 4 + 6 (criteria for query 4 OR 6)(criteria for query 4 OR 6)
Q10Q10 Query 1 + 2Query 1 + 2 (criteria for query 1 OR 2)(criteria for query 1 OR 2)
Patients found with follicular lymphoma queriesPatients found with follicular lymphoma queries
QUERYQUERY SOURCESOURCE PATIENTS RESULTSPATIENTS RESULTS
QCQC Cancer Registry NHL patients Cancer Registry NHL patients 425425
Q1Q1 Cancer Registry, histology (ICD-0)Cancer Registry, histology (ICD-0) 242242
Q2Q2 Text search 1 – pathology reportsText search 1 – pathology reports 406406
Q3Q3 Text search 2 – pathology reportsText search 2 – pathology reports 126126
Q4Q4 Text search 1 – all medical records Text search 1 – all medical records 531531
Q5Q5 Text search 2 – all medical recordsText search 2 – all medical records 193193
Q6Q6 Clinic ICD-9 codesClinic ICD-9 codes 901901
Q7Q7 Hospital ICD-9 codesHospital ICD-9 codes 288288
Q8Q8 Query 2 + 6Query 2 + 6 11371137
Q9Q9 Query 4 + 6Query 4 + 6 12331233
Q10Q10 Query 1 + 2Query 1 + 2 498498
Schematic Diagram of Query OutcomesSchematic Diagram of Query Outcomes
Q6Q6
Q4
Q4
QCQC
Q2Q2
Q7 Q7Q1Q1
Q5Q5
Q3
Q3
Schematic Diagram of Query OutcomesSchematic Diagram of Query Outcomes
n =1520
Other Diagnosis
Follicular Lymphoma
Schematic Diagram of Query OutcomesSchematic Diagram of Query Outcomes
n =1520Q1
Other Diagnosis
Follicular Lymphoma
RESULTS – Analysis of follicular lymphoma cases RESULTS – Analysis of follicular lymphoma cases
Purple=Path verified Purple=Path verified Red =Chart verifiedRed =Chart verified White=Total verifiedWhite=Total verified
Query# #Pat #True Pos #False Pos #True Neg #False NegQuery# #Pat #True Pos #False Pos #True Neg #False Neg
Q1Q1 242242 151151++4444=195=195 2323++2424=47=47 765765++303303=1068=1068 145145++6565=210=210
Q2Q2 406406 269269++1919=288=288 102102++1616=118=118 686686++311311=997=997 2727++9090=117=117
Q3Q3 126126 9696++66=102=102 2121++33=24=24 767767++324324=1091=1091 200200++103103=303=303
Q4Q4 531531 279279++9494=373=373 131131++2727=158=158 657657++300300=957=957 1717++1515=32=32
Q5Q5 193193 123123++3636=159=159 2828++66=34=34 760760++321321=1081=1081 173173++7373=246=246
Q6Q6 901901 143143++3535=178=178 490490++233233=723=723 298298++9494=392=392 153153++7474=227=227
Q7Q7 288288 106106++3131=137=137 101101++5050=151=151 687687++277277=964=964 190190++7878=268=268
Q8Q8 11371137 280280++4343=323=323 569569++245245=814=814 219219++8282=301=301 1616++6666=82=82
Q9Q9 12331233 286286++102102=388=388 591591++254254=845=845 197197++7373=270=270 1010++77=17=17
Q10Q10 498498 285285++5252=337=337 123123++3838=161=161 665665++289289=954=954 1111++77=68=68
Query#Query## Case
IdentifiedSensitivity
PathSpecificity
PathSensitivityAll Notes
SpecificityAll Notes
Q1Q1 195 51% 97% 48% 96%
Q2Q2 288 91% 87% 71% 89%
Q3Q3 102 32% 97% 25% 98%
Q4Q4 373 94% 83% 92% 86%
Q5Q5 159 42% 96% 39% 97%
Q6Q6 178 48% 38% 44% 35%
Q7Q7 137 36% 87% 34% 86%
Q8Q8 323 95% 28% 80% 27%
Q9Q9 388 97% 25% 96% 24%
Q10Q10 337 96% 84% 48% 86%
* For sensitivity and specificity calculations, numbers of true and false negatives were based on the total population of patients unique to these queries (1520 pts; 1084 pt w/ path) and not the entire patient population in GeneSys SI.
Results: Algorithms Sensitivity & SpecificityResults: Algorithms Sensitivity & Specificity
Query#Query## Case
IdentifiedSensitivity
PathSpecificity
PathSensitivityAll Notes
SpecificityAll Notes
Q1Q1 195 51% 97% 48% 96%
Q2Q2 288 91% 87% 71% 89%
Q3Q3 102 32% 97% 25% 98%
Q4Q4 373 94% 83% 92% 86%
Q5Q5 159 42% 96% 39% 97%
Q6Q6 178 48% 38% 44% 35%
Q7Q7 137 36% 87% 34% 86%
Q8Q8 323 95% 28% 80% 27%
Q9Q9 388 97% 25% 96% 24%
Q10Q10 337 96% 84% 48% 86%
* For sensitivity and specificity calculations, numbers of true and false negatives were based on the total population of patients unique to these queries (1520 pts; 1084 pt w/ path) and not the entire patient population in GeneSys SI.
Results: Algorithms Sensitivity & SpecificityResults: Algorithms Sensitivity & Specificity
Query#Query## Case
IdentifiedSensitivity
PathSpecificity
PathSensitivityAll Notes
SpecificityAll Notes
Q1Q1 195 51% 97% 48% 96%
Q2Q2 288 91% 87% 71% 89%
Q3Q3 102 32% 97% 25% 98%
Q4Q4 373 94% 83% 92% 86%
Q5Q5 159 42% 96% 39% 97%
Q6Q6 178 48% 38% 44% 35%
Q7Q7 137 36% 87% 34% 86%
Q8Q8 323 95% 28% 80% 27%
Q9Q9 388 97% 25% 96% 24%
Q10Q10 337 96% 84% 48% 86%
* For sensitivity and specificity calculations, numbers of true and false negatives were based on the total population of patients unique to these queries (1520 pts; 1084 pt w/ path) and not the entire patient population in GeneSys SI.
Results: Algorithms Sensitivity & SpecificityResults: Algorithms Sensitivity & Specificity
Query#Query## Case
IdentifiedSensitivity
PathSpecificity
PathSensitivityAll Notes
SpecificityAll Notes
Q1Q1 195 51% 97% 48% 96%
Q2Q2 288 91% 87% 71% 89%
Q3Q3 102 32% 97% 25% 98%
Q4Q4 373 94% 83% 92% 86%
Q5Q5 159 42% 96% 39% 97%
Q6Q6 178 48% 38% 44% 35%
Q7Q7 137 36% 87% 34% 86%
Q8Q8 323 95% 28% 80% 27%
Q9Q9 388 97% 25% 96% 24%
Q10Q10 337 96% 84% 48% 86%
* For sensitivity and specificity calculations, numbers of true and false negatives were based on the total population of patients unique to these queries (1520 pts; 1084 pt w/ path) and not the entire patient population in GeneSys SI.
Results: Algorithms Sensitivity & SpecificityResults: Algorithms Sensitivity & Specificity
ROC Plot for Search AlgorithmsROC Plot for Search Algorithms
Q6
Q7
Q8Q9
Q4Q2
Q3
Q10
Q5Q1
0%10%20%30%40%50%60%70%80%90%
100%
0% 20% 40% 60% 80% 100%
1 - Specificity
Sens
itivi
ty
● Highest SensitivityHighest Sensitivity● Free Text search w/ near algorithmFree Text search w/ near algorithm● Combination queriesCombination queries
● Highest SpecificityHighest Specificity● Cancer Registry code, Free Text query “follicular Cancer Registry code, Free Text query “follicular
lymphoma”lymphoma”
● Limiting search to pathology reports improves Limiting search to pathology reports improves specificityspecificity
● Best Overall PerformanceBest Overall Performance● Free Text query “follicular lymphoma” +/- Cancer Free Text query “follicular lymphoma” +/- Cancer
Registry codeRegistry code
ConclusionsConclusions
● Use query results for outcomes research Use query results for outcomes research on FL (n=405)on FL (n=405)
● Test query algorithms for:Test query algorithms for:● other Non-Hodgkin’s lymphomaother Non-Hodgkin’s lymphoma● Breast ca., prostate ca., colorectal ca.Breast ca., prostate ca., colorectal ca.
● Develop and test query algorithms for Develop and test query algorithms for treatments and outcomestreatments and outcomes
● Modify the query engine and interface to Modify the query engine and interface to automate algorithmsautomate algorithms
Future DirectionsFuture Directions
Winship Cancer InstituteWinship Cancer InstituteOncology InformaticsOncology Informatics
● Leroy HillLeroy Hill● Michael Graiser, PhDMichael Graiser, PhD● Rochelle VictorRochelle Victor● Ragini Kudchadkar, MDRagini Kudchadkar, MD● Susan Moore MD, MPHSusan Moore MD, MPH
●Bonita Feinstein RNBonita Feinstein RN●Ashley HilliardAshley Hilliard●James YangJames Yang●John TumehJohn Tumeh●Simone ParkerSimone Parker
Potential ProjectsPotential Projects
● Cancer Outcomes ResearchCancer Outcomes Research● Genomic Discovery / Pharmacogenomics Genomic Discovery / Pharmacogenomics ● Clinical Trials SupportClinical Trials Support● Medical InformaticsMedical Informatics
Cancer Outcomes ResearchCancer Outcomes Research
● Examining Treatment Strategies & Outcomes for Examining Treatment Strategies & Outcomes for Fludarabine Refractory CLLFludarabine Refractory CLL
● The influence of Comorbidity on Outcome in patients The influence of Comorbidity on Outcome in patients undergoing Allogeneic Transplantationundergoing Allogeneic Transplantation Other Cancer TreatmentsOther Cancer Treatments
● Examining Treatment Strategies & Outcomes for Examining Treatment Strategies & Outcomes for Relapsed Follicular LymphomaRelapsed Follicular Lymphoma
● Management of Squamous Cell Cancer of the Anus Management of Squamous Cell Cancer of the Anus (Reducing Surgical Morbidity)(Reducing Surgical Morbidity)
● Examining Regimen-Related ToxicityExamining Regimen-Related Toxicity
PharmacogenomicsPharmacogenomics
● Provide utilization data for cost-effectiveness Provide utilization data for cost-effectiveness studiesstudies
● Provide resources to support observational Provide resources to support observational studies and clinical trials in studies and clinical trials in pharmacogenomicspharmacogenomics
● Resource for developing algorithms for Resource for developing algorithms for pattern recognitionpattern recognition
Clinical Trials SupportClinical Trials Support
● Screening algorithms for identifying patients Screening algorithms for identifying patients eligible for clinical trialseligible for clinical trials
● Identify populations that would permit clinical Identify populations that would permit clinical trial investigationtrial investigation
● Data resource for monitoring trial outcomesData resource for monitoring trial outcomes Regimen-related toxicityRegimen-related toxicity Treatment ResponseTreatment Response SurvivalSurvival
Medical InformaticsMedical Informatics
● Advanced database search algorithmsAdvanced database search algorithms Pattern RecognitionPattern Recognition Neural NetworksNeural Networks Bayesian NetworksBayesian Networks Hierarchical Statistical ModelsHierarchical Statistical Models
caCORE
Enterprise Vocabulary
Common Data Elements
Biomedical Objects
Scientific ApplicationsScientific Applications
Common Data Elements (CDEs)
Data descriptors or “metadata” for cancer researchPrecisely defining the questions and answers What question are you asking, exactly? What are the possible answers, and what do they
mean?
Ongoing projects covering various domains Clinical Trials Imaging Biomarkers Genomics
caBIO Overview
Software industry design paradigms Unified Modeling Language (UML)
representations of biomedical “objects” Java 2 Enterprise Edition “n-tier” system
architecture Broad coverage of biomedicine (but not comprehensive yet): Genomics Gene expression Model systems for cancer Human clinical trialsData “on-tap” via application programming interfaces
Cancer Clinical Database Application SystemCancer Clinical Database Application SystemWeb Form Generation Web Form Generation
Web form input fields for Cancer Chemotherapy
Configurable column attributes for the Cancer Chemotherapy form