February 26, 2013: I. Sim APEX, etc. at UCSF Epi 206 — Medical Informatics Ida Sim, MD, PhD...

February 26, 2013: I. Sim APEX, etc. at UCSFEpi 206 — Medical Informatics

Ida Sim, MD, PhD

February 26, 2013

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

UCSF

Clinical Research Informatics

Copyright Ida Sim, 2013. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.

February 26, 2013: I. Sim Research InformaticsEpi 206 — Medical Informatics

Outline

• Systems for traditional clinical research• UCSF clinical research information systems

– REDCap– MyResearch– IDR Cohort Selection Tool– CELDAC

• Translational informatics

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

CLINICAL CARE RESEARCH

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

CTMSs

WELLNESS


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).


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

February 26, 2013: I. Sim Mobile and Internet-Based ResearchEpi 206 – Medical Informatics

Clients and Servers

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

Server

Client

February 26, 2013: I. Sim Mobile and Internet-Based ResearchEpi 206 – Medical Informatics

What Happens over the Cables

• Internet = network of networks– computers and cables all linked to one another and

talking to one another using protocols – supports lots of different internet protocols

• 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, smtp, https, etc. etc.– all sit on top of basic networking protocol TCP/IP

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

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


• Web is the internet traffic that uses http– servers send out information in HTML

• Hypertext Markup Language

– web browsers can decode HTML and display it• Health-specific protocols needed “on top of” http or

TCP/IP– a “grammar” for how to exchange health-related data

Internet vs. Web


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– supplanted by NHIN Direct?

• “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


Study 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)• SPIRIT standard protocol items for clinical trials

– http://annals.org/article.aspx?articleid=1556168


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 On...

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

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

• NIDA requires Tier 1 substance abuse measures– PROMIS http://nihpromis.org/'s assessment library– NCI library of Common Data Elements (CDEs)– CDISC SHARE: industry, NCI, FDA http://www.cdisc.org/cdisc-share

– AHRQ Registry of Patient Registries CDE outcome measures– RedCAP see demo

• …of case report forms (NCI, OpenClinica, etc.)

https://www.phenxtoolkit.org/

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

http://nihpromis.org/'s

http://www.assessmentcenter.net/documents/InstrumentLibrary.pdf

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

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

http://outcome.com/ropr.htm


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• Culture of sharing is still new


Outline





via iMedRIS

Funding Agency

MyResearch

Data analysis

Results reporting

On

cor C

RM

S

Protocol


IRB



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

Other Data Collection Tools

• Web-based– RedCAP– SurveyMonkey, Datstat, etc.

• Mobile– see http://ctsi.mobiledata.sgizmo.com/s3– UCSF has BAA with Qualtrics• See this for more pointers:

https://www.ctspedia.org/do/view/CTSpedia/SoftwareInterviewing



via iMedRIS

Funding Agency

MyResearch

Data analysis

Results reporting

On

cor C

RM

S

Protocol


IRB



Where Should Data Go?


REDCap

APEX


No More Lax Storage

• Storing Protected Health Information (PHI) on laptops, unsecured desktops is bad– mid 2000’s cancer registry

theft

• CA law: you can be fined up to $250,000 for PHI breach– $1 million MGH penalty– 57,000 Stanford patients

data lost in MD car

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 CHR criteria for secure data storage

• Works on PC, Mac with Leopard, Unix

• Free access to analytic software

• Need Internet connection to do your work (like Google Docs)

• http://myaccess.ucsf.edu – go to MyResearch


via iMedRIS

Funding Agency

MyResearch

Data analysis

Results reporting

On

cor C

RM

S

Protocol


IRB



Accessing Clinical Data


REDCap

APEX


MICU

FinanceResearch

QA

IntegratedData Repository

Internet

ADT Chem EHR XRay PBM Claims

• autofeed nightly, data stored securely with backup

Data from UCare to IDR

Real Time "IDR" Mining

• Pediatricians at Stanford admitted a 13 year-old with lupus, nephrosis, antiphospholipid antibodies, pancreatitis– anti-coagulate or not? no formal or informal evidence available

• Consulted STRIDE (Stanford version of "IDR") in real time, over 4 hours– full text searching identified 98 pedi lupus patients between

Oct. 2004 and July 2009– 10 developed thrombosis (per EHR), RR 14.7 with nephrosis,

RR 11.8 with pancreatitis

• Patient was anti-coagulated within 24 hours of admission


Frankovich, et al. NEJM 2011; 365(19):1758-9

UCSF CELDAC

Comparative Effectiveness Large Dataset Analysis Core (CELDAC), 80+ local and national health datasets


Other Integrated Data Sets

• ICPSR (Inter-University Consortium for Political and Social Research)– 500,000 files, 16

specialized collections– 206 files under Health

Care and Facilities• ICPSR has its own

terminology for cross-walking the data sets



Research Informatics at UCSF

• Oncore clinical research management system for large studies

• RedCAP available for secure surveys– web-based and mobile– standard variables increasingly available in a library

• Must keep all data/analyses in MyResearch environment– heavy penalties for data breaches

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

• CELDAC for large public datasets • Use of APEX for research is lower priority than clinical

roll-out


Outline




February 5, 2013: I. Sim OverviewMedical Informatics

Virtual Patient

Transactions

Raw data

Medical knowledge

Clinical research

transactions

Raw research

data

Dec

isio

n su

ppor

t

Med

ical

logi

c

CLINICAL CARE RESEARCH

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

WELLNESS

Biophysio-logical knowledge

Translational Bioinformatics


predictive, preventive, personalized, participatory

Informatics for P4 medicine

Sarkar, et al. JAMIA 2011; 18:354-7

Bioinformatics More "Advanced"

• Multitude of large-scale data– genome, phenome, metabolome, microbiome, etc.

• Established culture of data sharing– far less proprietary hoarding– common standards

• Gene Ontology, Minimum Information About a Microarray

Experiment (MIAME), Minimum Information About a

Proteomics Experiment (MIAPE), etc.

– large public datasets (GenBank, PharmGKB,

dbGAP, etc)


Need System/Complexity Sciences


• Bioinformatics data no longer the bottleneck; interpretation is

• Complexity is the study of complex adaptive systems– a collection of individual agents that can act in unpredictable

ways, and whose actions are interconnected so that one agent’s actions changes the context for other agents

– e.g., living cells, the brain, the immune system, the financial markets, ecosystems, and human populations

• Non-linear, self-organizing systems with feedback loops that cannot be understood simply by analyzing the individual components

From Data to Models

• Models– metabolic pathways, – multiscale models


Five Rings of Human Health

Schatz, BR, et al. Healthcare Infrastructure: Health Systems for Individuals and Populations. Springer, 2013.

Bio/Clinical Informatics at UCSF

• Nascent Institute for Computational Health Sciences (ICHS)• CTSI and other resources

– Cancer Informatics– UC BRAID BioBank and EngageUC

• standards for biosamples; obtaining, processing, sharing biospecimens

across all 5 UCs

– CTSI Consultation Services in Bioinformatics– UCSF Core services

• Training and classes– UCSF Bioinformatics PhD and Graduate Group– Translational Bioinformatics and Clinical Research Informatics

Summit, SF, March 18-22– Stanford Bioinformatics Graduate Certificate



Summary: 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)– moving towards modular component approach with standard data

elements (CDEs) and case report forms (CRFs)

• Translational bioinformatics going from data tsunami to need for models and complexity sciences

• Will EHRs, clinical informatics, translational bioinformatics, and digital health all come together?

February 5, 2013: I. Sim OverviewMedical Informatics

Next Classes

• New Clinical Research Paradigms

• Research Designs and Methods in the Digital Age

February 26, 2013: I. Sim APEX, etc. at UCSF Epi 206 — Medical Informatics Ida Sim, MD, PhD...

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Transcript of February 26, 2013: I. Sim APEX, etc. at UCSF Epi 206 — Medical Informatics Ida Sim, MD, PhD...