march 8, 2011: i. sim ehrs and research epi 206 — medical informatics ida sim, md, phd march 8,...

March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics

Ida Sim, MD, PhD

March 8, 2011

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

UCSF

Informatics for Clinical Research

Copyright Ida Sim, 2011. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.


Outline

• Systems for Traditional Clinical Research– sharing research meaning

• UCSF clinical research information systems– REDCap– MyResearch– IDR Cohort Selection Tool

• New Models of Clinical Research

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


Clinical Trial Management Systems

• Clinical Trial Management Systems (CTMS) are for running/managing a study– document management (protocol, case report forms)– finances, IRB– study calendar (what to do to whom when) and data

entry– data management and analysis– reporting


EHR vs. CTMS Contents

• EHR• Patient demographics• Chart notes

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

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

– interventions, design,

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


Just Like Need for EHR…

• Clinical trials are becoming very complex– avg 460 days (2002) to 780 days (2006)– avg # of participants: 1700 to 3400 over 30 yrs– # of study procedures: 70% increase to 85 procedures, from

2000 to 2005

• Fragmented, global industry– estimated 1100 organizations involved in clinical research in

2009 in US (Sponsors, CROs, SMOs, AHCs...)

– “43% big pharma FDA trials were conducted abroad... projecting as much as 65% within 3 years” [Tufts Outlook 2008]

• Can we afford to do this all in paper??

Tufts Center for the Study of Drug Development, “Growing Protocol Design Complexity Stresses Investigators, Volunteers,” Impact Report 10, no. 1 (January/February 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

?

Need to Interoperate Multiple Systems


Interoperation

• 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 over networks• 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


MICU

FinanceResearch

QA

Clinical / ResearchData Repository

Internet

ADT Chem EHR XRay PBM Claims

• How do the machines “talk” to each other?

Networking Basics


Internet = Network of Networks

itsa

medicine

ucsf.edu

nci.nih.gov

“the cloud”

myhome.com

Main Trunk Cables

local trunk cablethrough Berkeley

amazon.com

at homedial-in to itsa.ucsf.edu via modem

pacbell.net

aol.com

Internet Service Provider (ISP)via DSLor cable

LAN


• Protocol = grammar for machines talking to each other– e.g., hypertext transfer protocol http for web

• http://www.epibiostat.ucsf.edu/courses/schedule/med_informatics.html

– e.g., ftp file transfer protocol– all sit on top of basic networking protocol TCP/IP

• Health-specific protocols needed “on top of” http or TCP/IP– a “grammar” for how to exchange health-related data

What Happens Over the Cables

http://www.epibiostat.ucsf.edu/courses/schedule/med_informatics.html

http://www.epibiostat.ucsf.edu/courses/schedule/med_informatics.html


Health Data Interchange Protocols• HL7, “containers” for data packages, e.g., lab

• DICOM, “containers” for radiology studies– machine used, type of study, # of images, etc.

• CCD (Continuity of Care Document) for EHR data interchange (official standard under Meaningful Use)– e.g., problem list, allergies, family history

• “Containers” do not address the data naming issue– e.g., Na, sodium, serum sodium -- need to standardize to a SNOMED code

MSH|…message headerPID|…patient identifier<!-OBX…observation result>OBX|1|ST|84295^NA||150|mmol/l|136-148|H||A|F|19850301<CR>


Interoperation Over the Stack

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

Syn

tact

icS

em

ant

ic a

nd

Wo

rkflo

w


Sharing Research Meaning


ClinicalBilling

Physical Networking


Standard Vocabulary

PracticeManagement

Systems





Systems

Clinical StudyData Models

Syn

tact

icS

em

ant

ic a

nd

Wo

rkflo

w


Computable Protocol

• Study protocol is core essence of a research study– the investigational plan, including the actions to be

undertaken, the measurements, and the analysis

procedures to be followed– is not the same as the study protocol document (i.e.,

the Word or PDF file)• If this protocol is made computable, and

standardized, then all clinical studies can be described in the same way so that clinical research systems can interoperate


Components of a Study Protocol• Who

– participants: eligibility criteria, recruitment, followup– investigators: PI, sponsors, advisors, etc.

• What– interventions or exposures: experimental, control– study outcomes: primary, secondary, baseline

• When– dates of enrollment, timing of assessments

• Where– study sites

• Why– background, objective, hypothesis

• How– analytic approach, study monitoring, outcomes adjudication, etc.


Computable Protocol


ClinicalBilling

Physical Networking


Standard Vocabulary

PracticeManagement

Systems





Systems

Computable Protocol

Syn

tact

icS

em

ant

ic a

nd

Wo

rkflo

w


Need Standardization To...

• Common computable study protocol– the study plan: e.g., eligibility criteria, treatment, outcomes

• Ontology of Clinical Research1, SDTM, BRIDG, etc. etc.

• Common variables (aka common data elements, CDEs)– see Clarke M, Trials 2007,e.g.,

• “menopause” definition to standardize enrolled population • common outcomes for data pooling, meta-analysis (e.g., “MI”)

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

• e.g., SNOMED, NCI Thesaurus

• Common interchange standards– e.g., CDISC (“HL7 for clinical research”)

1http://rctbank.ucsf.edu/home/ocre


Sharing and Standardization

• …of research variables– NCI caDSR (Data Standards Repository)

• library of Common Data Elements (CDEs) that you and others can define https://cdebrowser.nci.nih.gov/CDEBrowser/

– CDISC SHARE: industry, NCI, FDA http://www.cdisc.org/cdisc-share

• enables precise and standardized data element definitions that can be used within applications and across studies to improve biomedical research and its link with healthcare

– PhenX Toolkit https://www.phenxtoolkit.org/

• a catalog of high-priority measures (e.g., MI) for genome-wide association studies (GWAS) and other studies

– AHRQ Registry of Patient Registries CDE outcome measures• …of case report forms (NCI, OpenClinica, etc.)

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

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

http://www.cdisc.org/cdisc-share

http://www.cdisc.org/cdisc-share

https://www.phenxtoolkit.org/

https://www.phenxtoolkit.org/

https://www.phenxtoolkit.org/index.php?pageLink=browse.protocoldetails&id=40801


Summary: Sharing 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 a common computable protocol model– need to be very precise, research needs change as

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


Outline



• New Models of Clinical Research


Clinic 2008

FrontDesk

Radiology


Walgreens



HealthNet

B&T

UCare

Specialist



Lab

UniLab

(HL-7) IRB Funding Agency

MyResearch

Data analysis

Results reporting

On

cor C

RM

S?

Protocol




UCSF Research Info Systems

Integrated Data Repository

REDCap

APEX


REDCap

• Web-based tool for building study databases and defining data entry forms– https://redcap.ucsfopenresearch.org/– https://redcap.ucsfopenresearch.org/index.php?ac

tion=training– is HIPAA-compliant (unlike Survey Monkey)

• Available to you for free

https://redcap.ucsfopenresearch.org/

https://redcap.ucsfopenresearch.org/index.php?action=training

https://redcap.ucsfopenresearch.org/index.php?action=training


Clinic 2008

FrontDesk

Radiology


Walgreens



HealthNet

B&T

UCare

Specialist



Lab

UniLab


MyResearch

Data analysis

Results reporting

On

cor C

RM

S?

Protocol




Where Should REDCap Data Go?


REDCap

APEX


No More Lax Storage

• Storing Protected Health Information (PHI) on laptops, unsecured desktops is bad

– VA example, cancer

registry theft• CA law: you can be

fined up to $250,000 for PHI breach

– recent $1 million

MGH penalty

PI #2

PI #1


• PHI stored in FISMA level secure database

• 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

• Works on PC, Mac with Leopard, Unix• Gives you free access to SAS, SPSS, Microsoft

Access/Project, etc.• http://oaais.ucsf.edu/OAAIS/networking/research

_data/1034-DSY.html– 500 users signed up, 90 monthly users

http://oaais.ucsf.edu/OAAIS/networking/research_data/1034-DSY.html

http://oaais.ucsf.edu/OAAIS/networking/research_data/1034-DSY.html


Clinic 2008

FrontDesk

Radiology


Walgreens



HealthNet

B&T

UCare

Specialist



Lab

UniLab


MyResearch

Data analysis

Results reporting

On

cor C

RM

S?

Protocol




Accessing Clinical Data


REDCap

Epic


MICU

FinanceResearch

QA

IntegratedData Repository

Internet

ADT Chem EHR XRay PBM Claims

• autofeed nightly, data stored securely with backup

Data from UCare to IDR


UCSF IDR for Cohort Selection• All UCare data since July 1, 2005 roll-out of UCare

– 2.875 million records (not all unique)– 5 Million encounter records -- manual refresh, awaiting

switch to APEX– Includes inpatient data, Dentistry, beginning to get billing

data, no STOR/VA/Kaiser/THREDS data yet; • Cohort Selection Tool is new tool to find potential patients

– you need a MyResearch account• http://its.ucsf.edu/main/networking/research_data/1034-

DSY.html

– IDR training available: http://its.ucsf.edu/main/5466-

DSY.html

IDR User InterfaceCohort Selection Tool Demo


Current Limitations

• Search option limitations– Race: only Asian, Black, Native American, Other are

searchable in UCare (no “Hispanic”)– Can search by specific ICD9 code under “GE Centricity”– “ICD9 Diagnoses” lists diagnoses in English (maps ICD9 to

a proprietary Harvard ontology)• Other limitations

– Diagnoses include both primary and secondary– Queries are for entire time period since 2005

• I.e., can’t ask for only 2003-2005– Data is whatever comes out of UCare, errors exist (e.g.

married children under 10)• Beware!


Research Informatics at UCSF

• RedCAP available for secure surveys• Must keep all data/analyses in MyResearch

environment– heavy penalties for data breaches

• Beta version of Cohort Selection Tool for identifying UCare patient data

• Use of APEX for research is lower priority than clinical roll-out


Summary: (Traditional) Research Informatics

• 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– moving towards modular component approach with

• standard data elements (CDEs) and case report forms (CRFs)• common computable protocol models (OCRe) and interchange

exchange standards (CDISC)• Still very very early in design and use of clinical research

information systems


Outline



• New IT-enabled Approaches to Clinical Research


Learning Healthcare System

• Ideal US health care systems is– “a Learning Healthcare System that is designed to

generate and apply the best evidence for the collaborative health care choices of each patient and provider; to drive the process of discovery as a natural outgrowth of patient care” (IOM Evidence-Based Medicine Roundtable)

• IT/informatics necessary to make this happen


Current Research Approach• Studies are expensive, difficult to conduct, 30-40% of

studies never accrue enough patients– estimated 2 million pts needed/yr for US-based trials– will be worse with personalized medicine

• Studies take years to answer limited questions in limited populations

• Study designs and results are heterogenous, limiting ability to pool findings or make summary interpretations

• Research questions don’t address combination treatments (e.g., ACEI and amlodipine)

• Research questions don’t track with front-line clinical needs– no good data on mid- to long-term efficacy or effectiveness of

antidepressants• Overall lack of generalizability, relevance, and sustainability


What “Learning” is Needed?

• Therapeutic efficacy and effectiveness– desired benefit in ideal conditions?– desired benefit in usual conditions?

• Increasing therapeutic precision and adherence– using treatment markers, experience

• How to promote and sustain behavior change• What are individual predictors of worsening (e.g.,

depression, IBD, asthma)• What are prevalence, natural hx, etc. of even rare

diseases?• How to enable patients, families, communities, and

clinicians to maintain wellness and manage chronic illness together?

• etc.


Getting Better Therapeutic Evidence

• For evidence on whether an intervention works, use PICO(S) – Population: women, children, ethnic groups– Intervention: combinations, generics, behaviors– Control: current state of the art, not placebo– Outcomes: patient-centered, e.g., symptoms, side

effects, need to be standardized– (Setting): outpatient, community, not tertiary care

• How can IT/informatics enable more research in these areas?

Anti-depressant Efficacy

In 2005, 27 million Americans were prescribed anti-depressants1

“…data often come from short-term (6- to 12-week) efficacy trials that cannot show whether treatments are effective over the medium- and long-term”2

1Olfson, et al. Arch Gen Psych 2009;66(8):848-8562APA Depression Guideline 2010

Learning Healthcare System

RCT of long-term comparative effectiveness

of antidepressants in primary care

We invite you to participate in a study on the effectiveness of …

Xing Xu 10/4/20107/21/19327/21/19327/21/1932427 King Rd. SF

Prozac 20 mg, 1 tab PO daily, #30

Consent to Being Contacted for Studies

Yes

APEX

Watch this YouTube video for informed

consent…real-time chat for questions…secure sign-up for enrollment

Or at a website

Every Clinic is a Study Site

RandomizationePharmacy

AntiD Study

Masked drug(s)

Dec 13, 2009

Guilt

Child care

Worse after school drop-off

AT&T

Data Collection App

Wakemate Sleep Monitor

ePharmacy

AntiD Study

e-coupon

Study DB

Dec 13, 2009

Guilt

Child care

Worst after school drop-off

AT&T

covariates Ano

nym

izat

ion

APEX

Large-Scale Research

In 2005, 27 million Americans were prescribed anti-depressants1

“…data often come from short-term (6- to 12-week) efficacy trials that cannot show whether treatments are effective over the medium- and long-term”2

Since 2005, # of subjects in all antidepressant drug trials worldwide total <100,000 (<0.4% of 27 million)

If only 1 out of 250 antidepressant patients in the US enrolls, would exceed total number of participants in all antidepressant trials worldwide in last 5 years

1Olfson, et al. Arch Gen Psych 2009;66(8):848-8562APA Depression Guideline 2010


New Ways to Use IT for…

• Behavior change– goal setting, reminders, gaming, social computing

• Therapeutic efficacy / comparative effectiveness– collaborative clinical trials, large-scale distributed

trials, secondary analysis of pooled EHR data, N-of-1 studies

• Improving patient-centered outcomes– symptom monitoring, trigger discovery, sensors

• Real-time trend detection– flu symptoms (GoogleFlu), asthma from Icelandic

ash, bioterrorism


Clinical Research Informatics Summary

• Parallel IT/informatics needs between clinical care and clinical research– complex workflow, fragmented systems – need semantic and syntactic interoperation– need for standardization of research variables for

evidence synthesis (e.g. CER, “big science”)• Informatics for traditional clinical research still

quite immature• What new models of clinical research can

informatics enable?

march 8, 2011: i. sim ehrs and research epi 206 — medical informatics ida sim, md, phd march 8,...

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