New Informatics Capabilities for Scholarly Projects & Disease Registries

New Informatics Capabilities for Scholarly Projects & Disease Registries

Russ Waitman, PhD(plus Kahlia Ford, Dongsheng Zhu, and Dan Connolly)

Associate Professor, Director Medical Informatics Department of Biostatistics

December 8, 2010

Outline

New tools: Redcap (easy); HERON (hard) Existing Velos capability in CRIS Redcap Demo HERON/i2b2 discussion

CTSA Oversight Process Demo Milestones

General Discussion

Clinical Research Information Systems KUMC has purchased Velos eResearch and calls it “CRIS”

Define Studies, Assign Patients to Studies Design and Capture data on electronic Case Report Forms

(CRFs) – ideally in real time. Capture Adverse Events, Reports, Export Data for analysis. Options: Samples, Financials, Regulatory IRB

Other Approaches OnCore by Forte Research Systems – more expensive,

highly customized for Cancer Centers…. Ferrari to Velos’ Audi.

RedCap by Paul Harris at Vanderbilt University – “free but not open source”, capabilities growing. Think Hyundai

CRIS Intro Screen

CRIS: sample e Case Report Form

CRIS: Document Adverse Events

Redcap process For all needs: Register your project with us so we can make sure we don't

screw up and drop the ball. Our first customers: OSARM and Urology. Urology had the opportunity to re-

use data dictionaries developed by Vanderbilt Urology for Prostatectomy and Cystectomy

Check out the cool training materials under videos at  http://www.project-redcap.org/

Check out what other people have done that you can modify/steal in the library. After you register your project, a CRIS team member, likely Kahlia Ford will get

in touch with you. We'll set you up and give you access to production. We also have a test

environment which I will use for this demo.  http://bmidev1.kumc.edu/redcap/ It uses the same username and password as everyone's email. There's a survey module which we haven't played with yet but will be

supporting if there's interest.

Redcap Disclaimer Works if PI takes responsibility for data

Scalability: informatics provides consultation and responsibility for technical integrity; not your dictionary.

Existing for clinical trials: CRIS/Velos Multiple years of experience CRIS team builds for you with biostats review Ideal for defined trials/grants Budget for CRIS team and biostats explicity

• Administrative bottlenecks• Poor integration of translational resources• Delay in the completion of clinical studies• Difficulties in human subject recruitment• Little investment in methodologic research• Insufficient bi-directional information flow• Increasingly complex resources needed• Inadequate models of human disease• Reduced financial margins • Difficulty recruiting, training, mentoring scientists

CTSA Background: NIH Goal to Reduce Barriers to Research

NIH CTSAs: Home for Clinical and Translational Science

Trial Design

Advanced Degree-Granting

Programs

Participant& CommunityInvolvement

RegulatorySupport

Biostatistics

ClinicalResources

BiomedicalInformatics

ClinicalResearch

Ethics

CTSAHOME

NIH

OtherInstitutions

Industry

Dan Masys: http://courses.mbl.edu/mi/2009/presentations_fall/masys.ppt

Gap!

KUMC CTSA Specific Aims1. Provide a HICTR portal for investigators to access clinical and

translational research resources, track usage and outcomes, and provide informatics consultative services.

2. Create a platform, HERON (Healthcare Enterprise Repository for Ontological Narration), to integrate clinical and biomedical data for translational research.

3. Advance medical innovation by linking biological tissues to clinical phenotype and the pharmacokinetic and pharmacodynamic data generated by research cores in phase I and II clinical trials (addressing T1 translational research).

4. Leverage an active, engaged statewide telemedicine and Health Information Exchange (HIE) effort to enable community based translational research (addressing T2 translational research).

Aim #2: Create a data “fishing” platform

Develop business agreements, policies, data use agreements and oversight.

Implement open source NIH funded (i.e. i2b2) initiatives for accessing data.

Transform data into information using the NLM UMLS Metathesaurus as our vocabulary source.

Link clinical data sources to enhance their research utility.

Develop business agreements, policies, data use agreements and oversight. September 6, 2010 the hospital, clinics and

university signed a master data sharing agreement to create the repository. Four Uses: After signing a system access agreement, cohort identification

queries and view-only access is allowed but logged and audited Requests for de-identified patient data, while not deemed human

subjects research, are reviewed. Identified data requests require approval by the Institutional

Review Board prior to data request review. Medical informatics will generate the data set for the investigator.

Contact information from the HICTR Participant Registry have their study request and contact letters reviewed by the Participant and Clinical Interactions Resources Program

Constructing a Research Repository: Ethical and Regulatory Concerns Who “owns” the data? Doctor, Clinic/Hospital, Insurer,

State, Researcher… perhaps the Patient? Perception/reality is often the organization that paid for the system

owns the data. My opinion: we are custodians of the data, each role has rights

and responsibilities Regulatory Sources:

Health Insurance Portability and Accountability Act (HIPAA) Human Subjects Research

Research depends on Trust which depends on Ethical Behavior and Competence

Goals: Protect Patient Privacy (preserve Anonymity), Growing Topic: Quanitifying Re-identification risk.

Re-identification Risk ExampleWill the released columns in combination with publicly available data re-identify individuals?

What if the released columns were combined with other items which “may be known”?

Sensitive columns, diagnoses or very unique individuals?

New measures to quantify re-identification risk.

Reference: Benitez K, Malin B. Evaluating re-identification risks with respect to the HIPAA privacy rule. J Am Med Inform Assoc. 2010 Mar-Apr;17(2):169-77.

Constructing a Repository: Understanding Source Systems, Example CPOE

Generic Interface

Engine (GIE)

LaboratorySystem

PharmacySystem

WizOrderServer

WizOrderClient

MainframeDB2

RxDB

HL7Lab DB

TemporaryData queue (TDQ)

InternalFormat

HL7

SQL

SQL

SQL

Repackages and Routes

Print SubSystem

document

KnowledgeBase, Files

SQLOrderables, Orderset DB

Drug DB

SQL

SQL

Most Clinical Systems focus on transaction processing for workflow automation

Constructing a Repository: Understanding Differing Data Models used by Systems

http://www.cs.pitt.edu/~chang/156/14hier.html

http://www.ibm.com/developerworks/library/x-matters8/index.html Star Schemas: Data Warehouses

Hierarchical databases (MUMPS), still very common in Clinical systems (VA VISTA, Epic, Meditech)

Relational databases (Oracle, Access), dominant in business and clinical systems (Cerner, McKesson)

Murphy SN, Weber G, Mendis M, Gainer V, Chueh HC, Churchill S, Kohane I. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inform Assoc. 2010 Mar-Apr;17(2):124-30.

HERON: Repository Architecture

Workflow: System Access

Workflow & Oversight: Request Data

Implement NIH funded (i.e. i2b2) initiatives for accessing data.

i2b2: Count Cohorts

i2b2: Patient Count in Lower Left

i2b2: Ask for Patient Sets

i2b2: Analyze Demographics Plugin

i2b2: Demographics Plugin Result

i2b2: View Timeline

i2b2: Timeline Results

Transform data into information using standard vocabularies and ontologies

Source terminology Completed planned Notes

Demographics: i2b2 April 2010 Using i2b2 hierarchy. Restricted search criteria to geographic regions (> 20,000 persons) instead of individual zipcodes

Diagnoses: ICD9 April 2010 Using i2b2 hierarchyProcedures: CPT June 2010 UMLS extract scripts developed with UTHSC at Houston

Lab terms: LOINC November 2010 Plan to use i2b2 hierarchyMedication ontologies: NDF-RT December 2010 Physiologic effect, mechanism of action, pharmacokinetics, and

related diseases.Nursing Observations July 2010- NDNQI pressure ulcers mapped to SNOMED CT to evaluate

automated extraction of self reported activity. (Drs. Dunton and Warren.)

Pathology: SNOMED CT February 2011 Providing coded pathology results and patient diagnosis is a critical objective for defining cancer study cohorts in Aim 3.

Clinical narrative 2012 As hospital restructures clinical narrative documentation to use EPIC’s SmartData (CUI) concepts, will determine appropriate standard.

National Center for Biological Ontology

2013 In support of Aim 3 focus on bridging clinical and bioinformatics to advance novel methods.

What we’ve done and current plan 4 milestones: Statistics, Alpha, Beta, 1.0

NightHeronStats: data obtained in October added crude statistics to CTSA submission

Alpha: complete as of Tuesday Full production environment process in place. Get data, store, transform, deidentify, i2b2 access. Epic only:

Demographics, Diagnoses (pat_enc), Meds (dispenses; using Epic hierarchy), Labs (final, top 500 mapped to LOINC)

Validation is a work in progress Uncovering i2b2 “bugs” in web client

Current Plan continued Beta: target January

Implement System Access Agreement Process Add Problem List data (diagnoses) Add vital signs, nursing observations for informatics

research Understand “age” Auditing, logging to central service Start validation with Hospital Prototype billing data ICD9/CPT from IDX clinic billing

system

Current and Future Plan HERON 1.0: target Spring

Implement Data Use Agreement, download data mechanism Ready for widespread use Add data: IDX integration with Epic, other “key” results Improve medication representation Provider/service representation and search?

HERON 1.X and 2.0: Summer? Monthly updates More data and features (Path, Rad, Micro) Wire up EMR intervention data for informatics research