developing information systems for cancer research

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


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


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


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


Cancer Registry

Clinical Trials

Pyxis

4

4

4

5

Cancer Epidemiology

5

5


5

Microarrays

2

3

Anatomic Path

Cytogenetics Lab


33

3

3Radiation Oncology

GeneSys SI: Architecture

Scheduling

Medical Records

Pharmacy

Lab Results1

1

11

Billing

1

Family History


Cancer Registry

Clinical Trials

Pyxis

4

4

4

5

Cancer Epidemiology

5

5


5

Microarrays

2

3

Anatomic Path

Cytogenetics Lab


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)


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)


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)


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)



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


n =1520

Other Diagnosis

Follicular Lymphoma


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

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

developing information systems for cancer research

Documents