february 26, 2008: i. sim clinical research informatics case studies medical informatics case...

February 26, 2008: I. Sim Clinical Research Informatics Case StudiesMedical Informatics

Case Studies in Clinical Research Informatics

Ida Sim, MD, PhD

February 26, 2008

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

UCSF

Copyright Ida Sim, 2008. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.


Outline

• Recap

• Clinical Research Informatics Overview

• Case studies– Integrated Data Repository– CTSA Human Studies Metadata

Repository

• Summary


Major Informatics Issues

• Naming data

• Exchanging data

• Reasoning with data and information to generate knowledge

• Secondary issues– user-centered design, organizational

change/QI, cost-benefits of health IT

Clinical Informatics Today

Clinic 2008

FrontDesk

Radiology

Claims

MedicalInformationBureau

Archive

Walgreens

Prescribing

Pharm BenefitManager

Benefits Check(RxHub)

HealthNetFormulary Check

B&TEligibility Authorization

Personal HealthRecord (PHR)

UCare

Electronic HealthRecord (EHR)

Specialist

Referral

ReferralAuthorization

Internet Intranet Phone/Paper/Fax

Lab

UniLab

(HL-7)


Challenge of Naming Data • The more coded your data, the more

expressive the vocabulary, the more computing you can do with the data– because the computer can “understand” more

• But coding costs time and effort– e.g., selecting billing codes

• And coding systems are hard to design, especially for changing fields like medical research


Outline

• Recap



Repository

• Summary


Clinical Research Informatics• Systems needed to support clinical research, just

like EHR supporting clinical care– study design and initiation

• protocol simulation, IRB submission, trial registration, etc.

– clinical trial management systems (CTMS)• case report forms, remote data capture, web-based surveys,

GCP compliance, study site management, etc.

– data management and discovery• analytic algorithms, visualization, modeling, etc.

– collaboration: wikis and beyond– reporting and data sharing

• publishing, trial results reporting, data repositories, etc.


Catch-up To Clinical Informatics• >80% of clinical research still using paper charts and forms

– $12 billion for paper-based trials vs. $2 b/yr for electronic trials industry

– transactional systems• enroll a patient, fill out case report form, report Adverse Event• NOT why this primary outcome, what is the design and why

• Naming data– beyond ICD-9...beyond even SNOMED...

• Exchanging data– clinical research is as fragmented as clinical care– need an “HL7” for exchanging research protocol and results

information

• Reasoning from data and information to knowledge


Health Informatics Big Picture

• Puts care and research together

• Separates data from the transactional systems used to collect that data– no hostage data!

• Shows need to capture computable knowledge, not just data

• Clear place for decision support

• Emphasizes user-centered design as glue

VirtualPatient

Transactions

Raw data

Medicalknowledge

Clinicalresearch

transactions

Rawresearch

data

DecisionsupportMedical logic

PATIENT CARE /WELLNES RESEARCH

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

Where clinicianswant to stay

EHRs

CTMSs


Lecture Progression

• Case studies in clinical research informatics

• EHRs• Clinical research IT

systems• Decision support• Translational

eScience/The Knowledge Layer

VirtualPatient

Transactions

Raw data

Medicalknowledge

Clinicalresearch

transactions

Rawresearch

data

DecisionsupportMedical logic

PATIENT CARE /WELLNES RESEARCH

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

Where clinicianswant to stay

EHRs

CTMSs


Outline

• Recap



Repository

• Summary

Data Spread out All Over

Clinic 2008

FrontDesk

Radiology

Claims

MedicalInformationBureau

Archive

Walgreens

Prescribing

Pharm BenefitManager

Benefits Check(RxHub)

HealthNetFormulary Check

B&TEligibility Authorization

Personal HealthRecord (PHR)

UCare

Electronic HealthRecord (EHR)

Specialist

Referral

ReferralAuthorization

Internet Intranet Phone/Paper/Fax

Lab

UniLab

(HL-7)


MICU

FinanceResearch

QA

Integrated Data Repository

Internet

ADT Chem EHR XRay PBM Claims

• Integrated historical data common to entire enterprise

Bring It All Together?


EHR vs. Clinical DR Queries

• EHR Queries

• What was Mr. Smith’s last potassium?

• Does he have an old CXR for comparison?

• What antihypertensives has he been on before?

• What did the neurology consult say about his epilepsy?

• Clinical Data Repository Queries

• What proportion of diabetics with AMI admissions were discharged on -blockers?

• What was the average Medicine length of stay in 2000 compared to 1995?

• What is the trend in use of head CTs in patients with migraine?


EHR/Data Repository Comparison

• Enterprise viewpoint more appropriate for QI and research

• Data repository cleans and aggregates data from multiple sources

Viewpoint Time Queries

EHR Patient Real-Time ClinicalData Repository Enterprise Historical Ad Hoc


IDR Informatics, etc. Challenges• Naming data: homework exercise using ICD-9 for

pneumonia• Exchanging data• Mapping similar data across different sources

– reconciling ICD-9, SNOMED, free text, etc. • Running huge databases (petabyte scale)• Data ownership, access rights, obligations

– governance agreements with data sources (e.g., hospital)– intellectual property protection (e.g., for star faculty)

• Privacy– patient opt-in vs. opt-out for being contacted for research

purposes


MICU

FinanceResearch

QA

IntegratedData 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 cochrane.uk 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 chart– e.g., SOAP, problem list, allergies, family history– not widely used, still controversial

MSH|…message header

PID|…patient identifier

<!-OBX…observation result>

OBX|1|ST|84295^NA||150|mmol/l|136-148|H||A|F|19850301<CR>


Data Interchange Challenges• Lots of complex data need to be sent to many

different groups for many different uses

• HL7 and DICOM widely used, but don’t address– the data naming issue (e.g., Na, sodium, serum

sodium)– exchange of other data, e.g.,

• clinical chart notes (CCD)

• microarray and gene expression data (MAGE/MIAMI)


MICU

FinanceResearch

QA

IntegratedData Repository

Internet

UCare PICIS Flowcast STOR IDXRad TSI

Where to Start?


Use Cases

• Technique in software and systems engineering to tease out functional requirements of a computer system– define user needs/requests– define what the system response should

be

• don’t worry about how the system works

• Includes goals, scenarios, actors, preconditions, exceptions, etc.


IDR Use Cases?

• Cohort identification– find all diabetics discharged in 2007 with

acute MI (who opt-in to research contact)• Geno-pheno correlation• Data mining across all clinical data for

– new associations to explore– quality improvement

• Identification of available biospecimens

IDR Source Systems Prioritization

UCareSTOR

LPPIEMR

PICIS

Worx CNeXT

FlowcastValue/Utility

Feasibility (based on availability, which can be influenced by either organizational or technical barriers or both)

TSI

CoPath

Misys

IDXradKaiserVA/

VistaSFGH/LCR

ED Apollo

Data in UCare:

All data

Some data

No data

axiUm

Jan. 2009Jan. 2010

Jan. 2011

Jan. 2012

IDR Phases and Target Systems

Phase IJan. 2009

UCarePICIS

Inpatient EMR and future outpatient EMRPeri-operative System

Phase IIJan. 2010

STOR WORX CoPath IDXRadApollo CNeXT Flowcast TSIED

Outpatient EMR and historic inpatient EMRCurrent pharmacy systemPathology RadiologyCardiology SystemUCSF Cancer RegistryScheduling and demographicsBilling systemEmergency Department System

Phase IIIJan. 2011

Langley-Porter Breast Care Center Urology Velos axiUmLCRMisysStudent Health

Separate outpatient EMRPatient/tumor tracking dbPatient tracking dbCTMS for Cancer CenterDentistry EMRSFGH EMR – Lifetime Clinical RecordClinical Labs systemSeparate EMR

Phase IVJan. 2012

VAMCKaiser

Vista EMREpic EMR


Implications of Use Cases

• Which data sources are important to capture? How clean must the data be?

• How far back should data be collected? How often must data be refreshed?

• How robust must the controlled vocabular(ies) be? What data interchange standard(s) should be used?

• Who owns the data? Who decides access? What can be done with the data? Who adjudicates disputes?

• How are experimental conditions described? (trials, observational studies, microarray experiments, etc)


Outline

• Recap



Repository

• Summary


Human Studies• One of most valuable sources of research data

– how many clinical studies are active at UCSF? how many are in pediatrics? in prevention?

– what are most common study outcomes in alcoholism trials? what are common eligibility criteria in asthma studies?

– how many Phase III trials does NHLBI fund in CTSA institutions? what’s being studied? what’s not?

• New requirement in FDA Amendments Act 2007 that all clinical trials phase II and above be registered in ClinicalTrials.gov– 20+ data about the trials (metadata=data about data) must

be reported, in text– to uniquely identify trials at inception to guard against

reporting bias / hiding of negative studies

Ida Sim, WHO, 2006

WHO Registration Data Set (1)WHO Registration Data Set (1)

1.1. Primary Register and Trial ID# (e.g., NCT)Primary Register and Trial ID# (e.g., NCT)

2.2. Date of Registration in Primary RegisterDate of Registration in Primary Register

3.3. Secondary ID#sSecondary ID#s

4.4. Source(s) of Monetary or Material SupportSource(s) of Monetary or Material Support

5.5. Primary SponsorPrimary Sponsor

6.6. Secondary Sponsor(s)Secondary Sponsor(s)

7.7. Contact for Public QueriesContact for Public Queries

8.8. Contact for Scientific QueriesContact for Scientific Queries

9.9. Public TitlePublic Title

10.10. Scientific TitleScientific Title

Ida Sim, WHO, 2006

WHO Registration Data Set (2)WHO Registration Data Set (2)

11.11. Countries of RecruitmentCountries of Recruitment

12.12. Health Condition(s) or Problem(s) StudiedHealth Condition(s) or Problem(s) Studied

13.13. Intervention(s)Intervention(s)

14.14. Key Inclusion & Exclusion CriteriaKey Inclusion & Exclusion Criteria

15.15. Study TypeStudy Type

16.16. Date of First EnrollmentDate of First Enrollment

17.17. Target Sample SizeTarget Sample Size

18.18. Recruitment StatusRecruitment Status

19.19. Primary Outcome(s)Primary Outcome(s)

20.20. Key Secondary Outcome(s)Key Secondary Outcome(s)


Beyond ClinicalTrials.gov• ClinicalTrials.gov extends WHO 20 metadata items. e.g.,

– study type => study design: Phase, randomization, etc.– satisfies FDA law, US Congress, public demand for

pharmaceutical sector accountability

• Is text reporting in ClinicalTrials.gov sufficient for CTSA use cases for clinical and translational research?– research characterization

• e.g., research purpose, clinical aims, enrolled population, research design, etc.

– research operations• e.g., facilitating IRB approval and trial registration, portfolio

management

– cross institutional• e.g., research inventory, benchmarking across CTSAs


GenomicsProteomicsPharmacogenomicsMetabolomics, etc.

Clinical trialsEpidemiologyMolecular Epi

Evidence-based practicePatient safetyQuality of care

Human Studies Metadata Repository

• ...to enable the “translation” in translational research

Basic Discovery

Clinical Research

Clinical Care

T1

Translation

T2

Translation

Bioinformatics

Medical Informatics

Human Studies


Human Studies Metadata

• Build a standardized repository of key descriptors (metadata) of all human studies conducted at CTSA institutions for CTSA priority use cases– Administrative data

• NCT number, primary sponsor, funder, public contact, PI,

public title, scientific title, date of first enrollment,

recruitment status

– Scientific data• health condition, key eligibility criteria, study type/study

design, sample size, interventions, primary outcomes,

key secondary outcomes, biospecimens collected


Standardizing Metadata Items

• Interventions, what are subfields?– intervention name (MeSH? CPT? SNOMED?)

– what else?• Difficult items

– eligibility criteria– study type / study design– interventions– outcomes– biospecimens collected


Current Status• CTSA Informatics national consortium project,

strong interest from NCRR, CTSA PIs, 12 participating CTSA institutions– define priority use cases– define metadata items and subfields based on use

cases– test and evaluate vocabulary and ontology

standards for representing items• e.g., SNOMED, RxNORM, Ontology of Clinical Research

(defn. of “study outcome”, allocation concealment), models of time, quantities, dates, etc.

• Get buy-in across all CTSA institutions, roll out


Outline

• Recap



Repository

• Summary


Summary• Clinical research informatics parallels, but is behind,

clinical care informatics• Research repository challenges discussed

– use cases to drive specification of functional requirements– naming data...and defining subfields

• “Garbage in garbage out” or “where is the information we have lost in data” – if raw data is wrong, incompatible, not computable– if information is not all right (e.g., out of context, incomplete)– if can’t get data out of source systems (technical, privacy,

intellectual property reasons)

february 26, 2008: i. sim clinical research informatics case studies medical informatics case...

Documents

clinical informatics

data clinical research

medical research slide

data management

data repositories

clinical care study

remote data capture

data sharing publishing