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March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Ida Sim, MD, PhD
March 8, 2005
Division of General Internal Medicine, and Program in Biological and Medical Informatics
UCSF
Clinical Research Informatics
Copyright Ida Sim, 2005. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Guest Lecture Next Week
• Paul Tang, MD Chief Information Officer, Palo Alto Medical Foundation– Tuesday, March 15, 8:45 to 10:15 am– state of the art electronic medical record
• same one that Kaiser is spending $2.8 billion on
– the promise and the reality
• Last class lecture at 1-2:30 Tuesday March 15
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Outline
• “Critical Crossroads”• Biomedical informatics base for clinical
research– design and execution– management and analysis– application to clinical care
• Summary
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Crossroads for Clinical Research
• 1st translation– from basic science to clinical science
• 2nd translation– from clinical science to clinical practice
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Clinical Research in America• seen “…for what it is—a fragmented cottage industry
constituted of multiple stakeholders, some of whose funding is derived from governmental sources and others from industry, with no overarching vision, no cohesive organizational framework, and at times not even a common forum for dialogue or active collaboration. The current poorly articulated and highly compartmentalized components of the existing nonsystem are inefficient and often redundant. Hence, they diminish effectiveness and increase costs of translating basic research to patient care while often not contributing materially to its safety or efficiency.
• Most importantly, this existing but outdated infrastructure, put in place nearly 40 years ago to support a much narrower and less professional spectrum of clinical research activities, is currently functioning on overload. The US capacity to translate basic science into improved health care for its population is rapidly being exceeded by the burgeoning scientific opportun-ities at hand. Basically, the ‘clinical research grid’ is failing."
(Crowley, et al, JAMA 2004; 291:1120-1126), Institute of Medicine
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Properties of Ideal Research Grid?• Integrates research design, execution, reporting, and analysis• Integrated with clinical care systems
– for capturing data from routine care– for fielding research decision support modules at point of care
• to increase generalizability (e.g., effectiveness and pragmatic trials) • to allow for greater sample size for geno-phenotype correlations
• Allows reuse and sharing of – information sytems (e.g., data collection forms, decision support,
security features, adverse events reporting, etc)– protocols and protocol elements (e.g., eligibility rule for menopause)– data (e.g., for pooling and meta-analysis, for data discovery)
• Integrated with decision support systems for applying results to clinical care
• Secure, flexible, robust, maintanable, cheap, portable…
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
National Clinical Research Enterprise
• IOM suggested a public-private partnership to– set and
maintain national standards
– coordinate and fund sustainable infrastructure
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
NCRE Objective
• Continuously improving national clinical research infrastructure to sustain safe and efficient transfer of basic research to practice
• Agenda– increasing public understanding, confidence,
participation in clinical research– adequately trained, diverse workforce– improving the US biomedical informatics base for
clinical investigation– funding this innovative clinical research enterprise
• Lots of activity in 2003, nothing since…
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Outline
• “Critical Crossroads”• Biomedical informatics base for clinical
research– design and execution– management and analysis– application to clinical care
• Summary
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Biomedical Informatics Base — 3 Spheres
Administrative Clinical Care Research
ClinicalBilling
Physical Networking
Standard Communications Protocols (e.g., HL-7)
Standard Vocabulary
PracticeManagement
Systems
ElectronicMedicalRecord
??
Medical BusinessData Model
Clinical CareData Model
??
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Industry is the Innovator
• RCTs was a $3.6 billion business in 2000 (C. Scott, 7/00)
– in 1988, 95% of RCTs conducted by academics– now, over 80% conducted by industry
• Ergo, much of the technology innovation in clinical research execution is going on in industry– Applied Clinical Trials software directory
• http://www.actmagazine.com/appliedclinicaltrials/article/articleDetail.jsp?id=145986
• NIH just getting into this with Roadmap initiative– National Electronic Clinical Trials Research Network
(NECTAR)– NCI caBIO also very involved in clinical trial informatics
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
The Clinical Trial Cycle (per NCI)
New Ideas
Protocol & Funding
Findings
Approval &Preparation
Design Trial
Activate TrialConductTrial
UtilizeResults •trial simulators
•trial costing•protocol authoring
•IRB approval•CRF design
•data management•remote data entry•GCP compliance
•data analysis•reporting
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Infrastructure for Clinical Trials
New Ideas
Protocol & Funding
Findings
Approval &Preparation
Design Trial
Activate TrialConductTrial
UtilizeResults •trial simulators
•trial costing•protocol authoring
•IRB approval•CRF design
•data management•remote data entry•GCP compliance
•data analysis•reporting
• a few companies• many companies
• FDA electronic submission standards
• a few companies
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Research and Care Together
for patientcare & policy
for clinical researchNew Ideas
Protocol & Funding
Findings
Approval &Preparation
DesignStudy
ActivateStudy
ConductStudy
UtilizeResults for basic research
ClinicalCare
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Joint Infrastructure for Care and Research
Administrative Clinical Care Research
ClinicalBilling
Physical Networking
Standard Communications Protocols (e.g., HL-7)
Standard Vocabulary
PracticeManagement
Systems
ElectronicMedicalRecord
Clinical Research Management
Systems
Medical BusinessData Model
Clinical CareData Model
Clinical StudyData Models
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Outline
• “Critical Crossroads”• Biomedical informatics base for clinical
research– design and execution– management and analysis– application to clinical care
• Summary
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Running a Study• You are planning a study on infant jaundice...• What relevant studies have been completed on this
topic?• What ongoing studies should you know about?• You’re interested in running your study over the
web as much as possible– what types of study activities can be done over the
web?– how good is the technology for these activities?
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Relevant Trials: Completed/Ongoing• Published
– Medline– Cochrane Controlled Trials Register
• ~328,000 records of controlled trials• manual logging of CCTs by hand searching journals• accessible from UCSF machines (IP address) only
• Non-profit/government– www.clinicaltrials.gov
• 12,600 trials, ~3000 open• NIH-supported and some commercial cancer and AIDS trials
– www.cancer.gov/clinicaltrials– www.actis.org
• AIDS Clinical Trials Information Service
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Trial Registries, cont.
• Commercial: mostly for patient recruitment– www.centerwatch.com– www.ClinicalTrialFinder.com– www.controlled-trials.com– www.clinicaltrials.com– etc., etc., etc.
• Meta-registries– www.trialscentral.org (from Cochrane people)
• pointers to hundreds of clinical trial registries, by disease– metaRegister of Controlled Trials
• >14,000 commercial and ongoing trials
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Case: Clinical Research Informatics• You are planning on a study on infant jaundice...• What relevant studies have been completed on this
topic?• What ongoing studies should you know about?• You’re interested in running your study over the
web as much as possible.– what types of study activities can be done over the
web?– how good is the technology for these activities?
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Is the Promise Realized Yet?
• “The future appears bright for web-based clinical trials.” – Marks, et al, Paradigm shifts in clinical trials enabled
by information technology. Statist Med 2001• “Valid reasons exist for the slow growth of
technology adoptions in clinical trial activities”… “These efficiency gains are only possible as attitudes change regarding moving from old inefficient manual processes to more automated processes”– Marks, Validating electronic source data in clinical
trials, Control Clin Trials 2004
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Clinical Study Tasks
• Project website• Subject recruitment• Eligibility determination• Protocol and forms
– distribution– online data entry
• Randomization• Data collection
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Project Website
• Project website of e-text pages– GISSI website GISSI-x summaries, references
• http://wwwcardio.marionegri.it/trials.htm
– HERS main results revised tables from JAMA report• http://www.keeptrack.ucsf.edu/hers2/HERSfindat.htm
• Requirements– web server computer
• use a web hosting service (see http://www.cnet.com/)• or have a web server program (e.g., Apache)
– pages of material• produce these using a web editor (FrontPage, Dreamweaver)
• or use Word (save as HTML file)
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
JIFE Client/Server Model
itsa
jaundice
ucsf.edu
KaiserOakland
KaiserSanta Clara
Internet
at homedial-in to itsa.ucsf.edu via modem
pacbell.net
aol.com
LAN
KaiserSan Diego
• The “jaundice.ucsf.edu” computer hasweb server software. It “serves” web pagesin response to http commands such ashttp://jaundice.ucsf.edu/project-home.html
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
• Personnel– webmaster: handles the machine stuff– web designer: produces text & graphical content
• Status: easily doable today• More difficult if wish to export static views of
Access database to web– e.g., go to website that shows latest contents of a data
table– requires more advanced database/web programming
Project Website (cont.)
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Exporting Access Data to Website• Static views of Access database on web
– ASP program tells server how to make web pages of data in the database– web server must be able to “serve” ASP pages
Access JIFE DB
Internet Information Server 4.0
KaiserOakland
KaiserSanta Clara
Internet
KaiserSan Diego
Active Server Pages (ASP)
jaundice.ucsf.edu
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Automated Eligibility Determination
• Study enrollment is big bottleneck• Eligible patients: patients whose characteristics
match with eligibility criteria• For computerized matching, need to have computer-
interpretable descriptions of– patient characteristics– the eligibility criteria
Match Eligible Patients
EHR
Eligibility Rule
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Eligibility Example• Eligibility criterion: women who are 2 or fewer
years post-menopause, as defined in NCI’s Common Data Elements set
• Allowed values:Above categories not applicable AND Age < 50Above categories not applicable AND Age >=50Post (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)
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
EHR Data Needed
• Gender• Age• Time since LMP, whether
– 6 or fewer months, or 12 or more months
• Past surgical history– bilateral ovariectomy and/or hysterectomy
• Therapy– LH-RH analogs, or– estrogen replacement
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Computer-Interpretable Eligibility Rule
• Logical rules– (Prior bilateral ovariectomy) OR (>12 mo since LMP AND no prior hysterectomy)– first order logic is the best representation model for this
• Temporal constraints – greater than 12 months since LMP...– representing time requires second-order logic
• Can do simple cases with database rules and triggers– new, “temporal” SQL allows time-based queries on
intervals, timepoints, etc.
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Promising, but...
• Coding of eligibility rules is difficult• Richly detailed, coded EHR not widely available
or well integrated• At present, can only expect computer to suggest
potential subjects. EHR can– prompt MD in real-time to refer patient to study, or– periodically batch notify MD of eligible patients, or– send letter of solicitation to patients
• Similar problems bedevil automated identification of guideline eligibility
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Protocol and Forms Distribution• Allows for centralized forms management and storage
through a project website• If expecting users to download, print, fill out and fax form
back– need protocol and forms in electronic format (e.g.,Word
or PDF)– for forms not available in electronic form
• scan it using a scanner ($100-$4000)– makes an image of the page (e.g., .gif or .jpeg)
• optical character recognition (OCR) scanning– convert scanned text into an editable document
(e.g., Word)• Status: easily doable today
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
JIFE Forms Download
itsa
jaundice
ucsf.edu
KaiserOakland
KaiserSanta Clara
Internet
at homedial-in to itsa.ucsf.edu via modem
pacbell.net
aol.com
LAN
KaiserSan Diego
• “jaundice.ucsf.edu” “serves” forms such ashttp://jaundice.ucsf.edu/case-form.pdf for printing out
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Protocol and Forms Distribution
• If expecting users to enter data online over the web– need someone to design the forms and build them to be
served over the web• e.g., using Access Visual Basic• more advanced: use combination of Access and FrontPage
– need security mechanisms (e.g., user login)– need data validation checks built into forms entry– data forms must send data to a database
• needs ODBC (Open DataBase Connectivity) drivers, etc.
• Status: doable with more advanced programming
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Infant Jaundice Online Forms
itsa
jaundice
ucsf.edu
KaiserOakland
KaiserSanta Clara
Internet
at homedial-in to itsa.ucsf.edu via modem
pacbell.net
aol.com
LAN
KaiserSan Diego
• “jaundice.ucsf.edu” “serves” online entry formssuch as http://jaundice.ucsf.edu/case-form.asp.Users enter data, which get checked at the clientside, and data is sent back to “jaundice.ucsf.edu.”
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
• Requirements– a web-based data collection form to collect patient information– programs to verify eligibility and randomize patient– program to generate a response to the enroller
– security, privacy, and backup provisions• Some commercial systems do this for you• Status: doable with some programming
Web-based Randomization
Project Central Enroller
patient info
randomization results
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Electronic Data Capture• Fax• Voice
– most systems ~ 95-99% accurate for restricted domains– some systems need to train computer to your voice
• Handhelds/tablet computers– need docking station for downloading– wireless PDA/tablet computer can’t be far from a receiver
node• radiofrequency: slow, prone to interference• infrared: requires line of sight between PDA and node
– must be HIPAA-compliant transmission• Remote physiologic monitoring
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Issues in Remote Data Capture
• Managing copies (local and central copies?)• Concurrent updates
– what if 2 people want to update same record?
• Merging data• Security and privacy
– HIPAA makes this all much more complicated
• System downtime• System response time• Workflow issues
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Design and Execution Summary
• Project website– easily doable today
• Automated eligibility– partially doable today; fundamental problems unsolved
• Protocol and forms distribution– easily doable today
• Online data entry– doable with more programming
• Randomization– doable with some programming
• Remote data capture– doable, HIPAA making this trickier, more expensive
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Trials on the Web Summary
• Held back by paper-based processes and– FDA requirements (for electronic documents not data)– lack of mobile hardware– attitude (e.g., on data validation of Case Report Forms)
• Solutions available, but not integrated• NIH NECTAR initiative just starting• UCSF considering an Informatics Center with
centralized services
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Outline
• “Critical Crossroads”• Biomedical informatics base for clinical
research– design and execution– management and analysis– application to clinical care
• Summary
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Case Study: RCTs
• How many Phase III RCTs does NIH fund?• What percentage of depression RCTs are placebo-
controlled?• What percentage of patients in CHF trials have
renal failure?• Which acute MI trials report 5 year mortality?• Is industry funding correlated with positive
outcomes?• What percentage of trials report outcome results
selectively?
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
NIH Data Reporting Requirement• As of May, 2003
– all grants over $500,000 direct required to “report all data publicly”
– variation among institutes on how to implement this
• no standard format
• What use is “the data” if the study design is poorly reported, not computable, or not available?
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Summary on Management/Analysis• Systematic under-investment in informatics for clinical
research results management and analysis– need common data structures for various study types (e.g., RCTs,
systematic reviews, guidelines)– need standardized research variables (e.g., NCI Common Data
Elements)– need standardized coding of variables and covariates
• Contrast to rise of computational biology/bioinformatics science– DNA sequences: GenBank; metabolic pathways: BioCyc– pharmacogenetics, pharmacogenomics: PharmGKB– protein sequence: UniProt; protein structure: PDB, etc.
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Outline
• “Critical Crossroads”• Biomedical informatics base for clinical
research– design and execution– management and analysis– application to clinical care
• Summary
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
2nd Translation, to Clinical Care
• 2 approaches using information technology– “firehose” approach
• if 1 article/guideline is good, 1000 is better…
– decision support to present evidence-based action options
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
IT for Translation• “Non-thinking” information technology approaches
– evidence-based medicine resources• ACP Journal Club, Clinical Evidence, PubMed, Up-to-Date• Cochrane systematic reviews
– guidelines (over 3500 guidelines)• www.guidelines.gov
– quality measures (301 measures)• www.qualitymeasures.ahrq.gov
• Clinical decision support systems– … in which the characteristics of an individual patient
are matched to a computerized clinical knowledge base for patient-specific recommendations
– need both coded EHR and coded biomedical knowledge
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Computable Biomedical Knowledge Bases
• Pharmacogenetics/genomics: PharmGKB• SNPs: SNP Consortium• Randomized trials
– Trial Bank Project (http://rctbank.ucsf.edu/Presenter/)• computable database of RCT design, execution, results
• Physiological models– Archimedes: diabetes
• No computable repositories of – clinical guidelines– systematic reviews, decision/cost-effectiveness models– “textbook” information
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Shared Knowledge Bases (KBs)• Should share the primary knowledge (e.g., RCT results)!
– open access knowledge repository– each system can build its own rule set based on shared
evidence– KB updating and maintenance also shared
• E.g., Global Trial Bank partnership w/ Public Library of Science– computable RCT protocols and results tied in to trial registration
(e.g., clinicaltrials.gov)– peer reviewed by PLoS Clinical Trials– open access knowledge base and journal, with an open peer
review/discussion forum– decision support systems/EHRs can directly query GTB
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Summary: Application to Care• Currently at first generation
– static web/e-text information resources• humans have to retrieve and read articles
– individual decision support systems have expensive, hard-to-maintain, hand-coded knowledge bases
• To move to next generation, need– widely deployed EHRs, reasonably coded in SNOMED– shared computable biomedical knowledge bases– a better theory of when and how to translate evidence
to care
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Outline
• “Critical Crossroads”• Biomedical informatics base for clinical
research– design and execution– management and analysis– application to clinical care
• Summary
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Joint Infrastructure for Care and Research
Administrative Clinical Care Research
ClinicalBilling
Physical Networking
Standard Communications Protocols (e.g., HL-7)
Standard Vocabulary
PracticeManagement
Systems
EHRExecutionAnalysis
Medical BusinessData Model
Clinical CareData Model
Clinical StudyData Models
Application to Care
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Summary
• Clinical research grid is failing– concerted action is required
• Need to integrate solutions across practice management, clinical care, clinical research
• Informatics is a critical component of solution– design and execution: e.g., NECTAR initiative– computable knowledge bases of clinical research
studies: very, very early in this– application to clinical care: need computable EHR and
biomedical knowledge
March 8, 2005: I. Sim Research InformaticsEpi 206 – Medical Informatics
Teaching Points• Many aspects of clinical research can now be done
electronically, but in fragmented fashion– limitations include reliance on paper, lack of wide EHR
adoption, lack of standard coding
• Research needs to be more integrated with care– for sample size reasons for geno-pheno correlations– for effectiveness, pragmatic trials
• Informatics infrastructure needs to be shared• The dialogue is just beginning…
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