Upload
susanna-mcgee
View
219
Download
0
Embed Size (px)
Citation preview
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.
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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).
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
Trial Design
Protocol
Funding Agency
Site 1 Site 2 Site 3
Site Management Organization (SMO)
Study DB
Data analysis
Results reporting
Contract R
esearch Organ
ization
(CR
O)
SponsorsAcademic PIs
?
Need to Interoperate Multiple Systems
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
MICU
FinanceResearch
QA
Clinical / ResearchData Repository
Internet
ADT Chem EHR XRay PBM Claims
• How do the machines “talk” to each other?
Networking Basics
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• 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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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>
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
Sharing Research Meaning
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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.
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
Computable Protocol
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
Computable Protocol
Syn
tact
icS
em
ant
ic a
nd
Wo
rkflo
w
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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.)
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
MyResearch
Data analysis
Results reporting
On
cor C
RM
S?
Protocol
Trial DesignSponsorsAcademic PIs
Site 1 Site 2 Site 3
Site Management Organization (SMO)
UCSF Research Info Systems
Integrated Data Repository
REDCap
APEX
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
MyResearch
Data analysis
Results reporting
On
cor C
RM
S?
Protocol
Trial DesignSponsorsAcademic PIs
Site 1 Site 2 Site 3
Site Management Organization (SMO)
Where Should REDCap Data Go?
Integrated Data Repository
REDCap
APEX
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• 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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
MyResearch
Data analysis
Results reporting
On
cor C
RM
S?
Protocol
Trial DesignSponsorsAcademic PIs
Site 1 Site 2 Site 3
Site Management Organization (SMO)
Accessing Clinical Data
Integrated Data Repository
REDCap
Epic
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
MICU
FinanceResearch
QA
IntegratedData Repository
Internet
ADT Chem EHR XRay PBM Claims
• autofeed nightly, data stored securely with backup
Data from UCare to IDR
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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!
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
Outline
• Systems for Traditional Clinical Research– sharing research meaning
• UCSF clinical research information systems– REDCap– MyResearch– IDR Cohort Selection Tool
• New IT-enabled Approaches to Clinical Research
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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.
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
March 8, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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?