February 24, 2009: I. Sim EHRs and Research Epi 206 — Medical Informatics Ida Sim, MD, PhD February 24, 2009 Division of General Internal Medicine, and

February 24, 2009: I. Sim EHRs and ResearchEpi 206 — Medical Informatics

Ida Sim, MD, PhD

February 24, 2009

Division of General Internal Medicine, and Center for Clinical and Translational Informatics

UCSF

Informatics for Clinical Research

Copyright Ida Sim, 2009. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.


Outline

• Clinical Research Systems Overview• Interoperation (aka naming data) • Major clinical research information systems at

UCSF– MyResearch– Integrated Data Repository– Clinical Research Management Systems

• Summary

Big Picture of Health Informatics

Virtual Patient

Transactions

Raw data

Medical knowledge

Clinical research

transactions

Raw research

data

Dec

isio

n su

ppor

t

Med

ical

logi

c

PATIENT CARE / WELLNES RESEARCH

Workflow modeling and support, usability, cognitive support, computer-supported cooperative work (CSCW), etc.

CRMSs


Clinic 2008

FrontDesk

Radiology

MedicalInformationBureau

Walgreens

Pharm BenefitManager

Benefits Check(RxHub)

HealthNet

B&T

UCare

Specialist

ReferralAuthorization

Internet Intranet Phone/Paper/Fax

Lab

UniLab

(HL-7)

IRB Funding Agency

Study DB

Data analysis

Results reporting

Contract R

esearch O

rganization (C

RO

)

Protocol

Trial DesignSponsorsAcademic PIs

?Site 1 Site 2 Site 3

Site Management Organization (SMO)

Clinical Research Today

• >80% on paper


Market Facts

• In early 90s, 80% trials done in academia, now 20% – about 10% of UCSF studies are pharma-related

• Huge business– Big Pharma: $15.9 b on clinical trials, out of $43 b total R&D

[2006 PhRMA Industry Profile]

– NIH budget $28.8 billion 2008

• Fragmented, global industry– estimated 1200 organizations involved in clinical research in

2004 in US (Sponsors, CROs, SMOs, AHCs...)– CROs $14b market in 2006– “43% big pharma FDA trials were conducted abroad...

projecting as much as 65% within 3 years” [Tufts Outlook 2008]


Clinic 2008

FrontDesk

Radiology


Walgreens



HealthNet

B&T

UCare

Specialist



Lab

UniLab

(HL-7)

IRB

Trial Design

Protocol

Funding Agency

Site 1 Site 2 Site 3


Study DB

Data analysis

Results reporting

Contract R

esearch Organ

ization

(CR

O)

SponsorsAcademic PIs

?

Want “Interoperability”


Outline



• Summary


Interoperability

• Ability of two or more systems or components to exchange information and to use the information that has been exchanged [IEEE Standard Computer Dictionary, 1990]

– syntactic: grammar, composition of what is said• e.g., using an exchange protocol• e.g., HL7, DICOM, XML Document Type Definition (DTD)

– semantic: meaning of what is said• e.g., using a controlled vocabulary aka dictionary• e.g., SNOMED, ICD-9


2 Routes to Interoperability

• Single standard system approach– one end-to-end system from trial inception to end– used by all PIs, industry, CROs, worldwide

• Modular, component-based, interfaces approach– define common terms, models, interchange

protocols– provide software components for assembly – provide way to “certify” compatibility of systems


1 Route to Interoperability

• Single system approach stunts innovation– no single CRMS, no single EHR for US

• Modular component approach– taken by Office of the National Coordinator for

EHRs and clinical care– taken by National Cancer Institute (caBIG

program) and CTSA for CRMSs and clinical

research


Biomedical IT “Stacks”

Administrative Clinical Care Research

ClinicalBilling

Physical Networking

Communications Protocols (e.g., HL-7)

Standard Vocabulary

PracticeManagement

Systems

Medical BusinessData Model

ElectronicMedicalRecord

Clinical CareData Model

Clinical Res. Management

Systems

Clinical StudyData Models


EHR vs. CRMS Contents

• EHR• Patient demographics• Chart notes

– problem list• Visit and assessment• Lab and other orders• Lab and other results• Clinical decision-making• Discharge summary

• CRMS• Title, NCT #, IRB #• Protocol document

– interventions, design,

outcomes, etc.• Study assessment• Outcomes assessment• Case report forms• Data analysis• Trial reporting/publication


CTMS Software Components

Clinical Trial Management Infrastructure

Adverse Event Reporting

Study Calendar

Routine Data Exchange Financial Billing

Structured Protocol

Representation

Laboratory Interfaces

Clinical Trials DatabaseSystems

Interoperation

CRF Standardization

InvestigatorAnd

Site Credentialing

14

CTMS Workspace Goals

• Facilitate the planning and instantiation of clinical trials,

(and monitoring of trials once they are instantiated)

• Facilitate the conduct of clinical trials

• Facilitate the reporting and sharing of clinical trial data

to existing/ new destinations

• Achieve interoperability

• Increase the ability of systems to access and use the

data and functionality of other systems

• Facilitate the integration of new sources and destinations

of data


Critical Components

• Terminologies/vocabularies– base terms used to describe biomedical concepts

• e.g., SNOMED, NCI Thesaurus

• Common Data Elements– clinicallly-agreed upon data items for research

• e.g., “menopause” defined a certain way

• Common data model of study protocol– study information: e.g., eligibility criteria, treatment, outcomes

• CTOM, SDTM, BRIDG, OCRe, etc. etc.

• Common interchange standards– e.g., CDISC (“HL7 for clinical research”)– so design, monitoring, reporting systems, etc. can talk


Data Elements

• To ensure sharable data, research data variables should be standardized– standardized in naming

• terms should be from a controlled vocabulary (e.g., SNOMED, NCI Thesaurus)

– standardized to be common across studies• e.g., menopause with full clinical definition, to be used in

all breast cancer trials

• Both can be standardized through common electronic Case Report Forms (CRFs)

Use “Add” button to list multiple symptoms

SNOMED Concept has been added to formRichesson R, 2007

Standardized Naming SNOMED-CT Browser for Research

Implementation Status• > 300 electronic case report forms (CRFs)

• > 26,000 questions

• ~ 400 SNOMED CT and RxNorm browser contexts– 75% represent current findings (e.g., context is physical exam,

clinical assessment forms) and 25% represent historical findings (e.g., medical history context).

• 200 investigators and research staff have been trained on this tool

– Compliance is high (up to 90%)

– Satisfaction seems high (few complaints)

Richesson R, 2007


Standardization Across Studies

• Interventional studies fundamentally done to demonstrate differences between interventions, by types of patients [Clarke M, Trials 2007]

– common outcome measures necessary for pooling/meta-analysis

• e.g., 5-year cancer free survival, common asthma measures– also common eligibility criteria, e.g., Post-menopause

• post (Prior bilateral ovariectomy, OR >12 mo since LMP with no prior hysterectomy and not currently receiving therapy with LH-RH analogs [eg. Zolades])

• post (Prior bilateral ovariectomy, OR >12 mo since LMP with no prior hysterectomy)

• pre (<6 mo since LMP AND no prior bilateral ovariectomy, AND not on estrogen replacement)

• above categories not applicable AND Age >=50


“Phenotype” Standardization

• A study outcome can be considered a clinical phenotype– a clinically observable manifestation of a person’s

genetic makeup and environmental influences• Standardizing outcomes standardizes phenotype

for large-scale geno-pheno correlation


NCI Approach in Cancer

• NCI caDSR (Data Standards Repository)– library of Common Data Elements (CDEs) that

others have defined– you can define new CDEs using terms from NCI

Thesaurus

• Let’s go search...– https://cdebrowser.nci.nih.gov/CDEBrowser/

https://cdebrowser.nci.nih.gov/CDEBrowser/


Case Report Forms (CRF)

• Why reinvent the wheel for common forms?• NCI has a Forms Builder

– “shop” for CDEs, put them into your cart– insert CDEs into your case report form

• can download as HTML, XML, Excel, Word, PDF

• NCI now building library of forms – e.g., Demographics Case Report Form built from CDEs

• PDF, Word, etc. or directly to CTMS for direct data entry

• Grant proposals to NCI are beginning to be reviewed on use of CDEs


Summary: Interoperation of Research Data

• Interoperation = meaningful exchange of data among computers– syntactic: how things are said, the grammar– semantic: what is said, the meaning

• Semantic standardization a greater challenge in research than clinical care– need to be very precise, research needs change as

knowledge grows, researchers very individualistic• Moving towards standardized, coded variables


Outline



• Summary


Clinic 2008

FrontDesk

Radiology


Walgreens



HealthNet

B&T

UCare

Specialist



Lab

UniLab

(HL-7) IRB Funding Agency

MyResearch

Data analysis

Results reporting

Velo

s,Qu

esg

en,O

nc

or C

RM

S

Protocol




UCSF Research Info Systems

Integrated Data Repository


Problems with Data Security

• PIs storing Protected Health Information (PHI) data on laptops, unsecured desktops

– thefts, losses– VA, cancer registry

• New CA law increases penalties for breaches

• MyResearch is ultra-secure storage for your PHI data

IRB Funding Agency

MyResearch

Data analysis

Results reporting

Velo

s,Qu

esg

en,O

nc

or C

RM

S

Protocol





No More Lax Storage

PI #2

PI #1

MyResearch

Secure location with backup

SAS, R

Firewall

Pixels only

Secure Global Desktop


• PHI stored in FISMA level secure database system

• Data never physically leaves MyResearch

• Your browser is a “dumb” window onto the MyResearch computer– SAS, etc. runs on data

on MyResearch– you see pixels only, no

local caching on your

computer

MyResearch

MyResearch

Secure location with backup

SAS, R

Firewall

Pixels only

Secure Global Desktop


Using MyResearch

• Satisfies soon-to-be required CHR criteria for secure data storage– if you don’t use MyResearch, must show you are

as secure (i.e., FISMA level, very difficult)• Works on PC, Mac with Leopard, Unix• Is brand new, still has bugs • Will cost ~$400/yr/PI starting ~July• http://oaais.ucsf.edu/OAAIS/networking/

research_data/1034-DSY.html


Outline



• Summary


• Retrospective cohort study of outpatients• Compare 5 year rate for congestive heart failure for

diabetics treated with a glitazone vs. not– find diabetics– find whether treated with a glitazone– for these patients, find all subsequent cases of congestive

heart failure – analyze at 5 years

• adjust for age, sex, severity of diabetes, previous CHF,

other meds, etc., etc.

Outcomes Research Project


• Diabetes diagnosis– chart, HgbA1C, meds taken, problem list...

• Glitazone usage– orders, pharmacy

• Potential confounders– age, sex, severity, other meds, etc.

Health System Minnesota: 50 paper, 50 computer

200,000 lives, 460 physicians

Health System Minnesota: 50 paper, 50 computer

200,000 lives, 460 physicians

Types of Data Needed


Using UCare for Research

• Cannot easily query UCare directly– no user interface for group-level queries– may reduce response time for clinical care– lots of important data (e.g., STOR outpatient data)

not in UCare• Solution is to copy UCare data to IDR

– autofeed nightly, data stored securely with backup– supports ad hoc group-level queries, e.g., cohort

identification• how many potentially eligible patients in UCare?


MICU

FinanceResearch

QA

IntegratedData Repository

Internet

ADT Chem EHR XRay PBM Claims

• Integrated historical data common to entire enterprise

Integrated Data Repository


i2b2 Demo

• UCSF IDR will be built using the i2b2 software suite from Harvard Partners

• Demo of i2b2 query interface to over 5000 anonymized real records from Partners

– https://38.99.4.62:8443/i2b2/


UCSF IDR Status

• Current focus on bringing up IDR content– public datasets (e.g., NHANES)– individual PI data (for sharing just with your group,

by request, or with everyone)– negotiating with Med Center on UCare data

• Semantic standardization still problematic– need to map source data to standardized terms

(e.g. asthma)– need data models of clinical research, (e.g.,

primary outcome, Ontology of Clinical Research)


IDR and UCSF Research Culture

• Have established policies on who owns what data when and when to share under what circumstances– e.g., what if you put data in then leave UCSF?

• In 3 years, UCSF will revisit policy on IDR use– may well require all PIs to deposit final research

data into IDR for sharing


Outline



• Summary


CRMSs

• Clinical Research Management Systems are for running/managing a study– document management (protocol, CRFs)– finances, IRB– study calendar (what to do to whom when) and

data entry– data management and analysis– reporting

• Most systems designed for supporting interventional clinical trials (CTMS)


E.g., UCSF Cancer Center

• Need to file reports to NCI to continue as an NCI-designated Cancer Center– e.g., trials initiated, pt demographics

• Bought Velos bought Velos eResearch in 2003 for reporting– required all Cancer Center investigators to use

Velos


Velos Use at Cancer Center

• Study summary data entered as free text– administrative (PI, contact info, etc)– protocol (treatments, outcomes, etc)– IRB-related

• submitted protocol, approval status, current protocol• tied to Center’s protocol review system, but not CHR

• Patient data– PI can define case report forms, data stored in Velos

• basic report functions• export to SAS or Excel for analysis

– data hosting and analysis only started in 2007, 4 years after purchase


Velos Functions Not Used

• Not used for finances, study calendar• Not used for selecting, using, or reusing NCI’s

Common Data Elements or CDE-based case report forms


How Did it Go?

• Initial very rocky implementation • Major weakness is “easy to get data in, hard to get

data out”– underlying data schema and access are opaque, by

design– less reponsive/customizable to reporting and analysis

needs• Duke has 3 Velos implementations that don’t talk to

each other

• Cancer Center now switching from Velos to Oncor– even as many CTSA institutions move to Velos


CTMS Policy at UCSF

• Currently– Cancer Center on Oncor– rest of campus on smattering of other systems

• e.g., Quesgen, Immune Tolerance Network/Stanford

• Longer term– plans to develop a Clinical Research Management

“Core” supporting a few coordinated CTMSs across campus

• You’re still pretty much on your own right now...


Summary• Clinical research fragmented, global, essentially

separate from clinical care• Clinical research informatics ongoing in two worlds

– most still paper, commercial CTMSs mostly document centered (PDFs) rather than data or concept-centered

– movement towards modular component world with • standard data elements (CDEs) and case report forms (CRFs)• common clinical study data models and interchange exchange

standards (CDISC)

• UCSF offering secure data storage and access to UCare data via IDR

• Still very very early in design and use of clinical research information systems

Documents

February 24, 2009: I. Sim EHRs and Research Epi 206 — Medical Informatics Ida Sim, MD, PhD February 24, 2009 Division of General Internal Medicine, and