February 19, 2008: I. Sim Overview Medical Informatics Medical Informatics for Clinical Research Ida Sim, MD, PhD February 19, 2008 Division of General Internal Medicine, and Center 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.
58
Embed
February 19, 2008: I. Sim Overview Medical Informatics Medical Informatics for Clinical Research Ida Sim, MD, PhD February 19, 2008 Division of General.
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
February 19, 2008: I. Sim OverviewMedical Informatics
Medical Informatics for Clinical Research
Ida Sim, MD, PhD
February 19, 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.
February 19, 2008: I. Sim OverviewMedical Informatics
Outline
• Introduction
• What is Informatics
• Course Goals
• Overviews– clinical informatics– research informatics– the Big Picture
• Summary
February 19, 2008: I. Sim OverviewMedical Informatics
Introduction: Ida Sim, MD, PhD• Position
– Associate Professor, General Internal Medicine– Director, Center for Clinical and Translational
Informatics (ccti.ucsf.edu)
• Research areas– informatics and policy for clinical trial registration
and reporting– computer-assisted evidence-based practice– informatics for clinical research– economics of health information technology
February 19, 2008: I. Sim OverviewMedical Informatics
Health Care Quality
• Doing the right thing– based on scientific evidence
• right – without error
• to the right people– e.g., blood pressure meds by ethnicity
• at the right time– beta-blockers at hospital discharge for
heart attacks
February 19, 2008: I. Sim OverviewMedical Informatics
Doing the Right Thing...• Cusp of a “new medicine”
– genomics revolution– personalized medicine
• Human genome findings will need to be translated into population and clinical medicine
• But research findings are often not translated to practice – many examples of care that diverges from
best evidence
February 19, 2008: I. Sim OverviewMedical Informatics
...Right
• Poor safety– a “747” in deaths from medical errors every
day To Err is Human, Institute of Medicine (IOM), 2000
• Poor quality– “Between the health care we have and the
care we could have lies not just a gap, but a chasm.” Crossing the Quality Chasm, IOM, 2001
February 19, 2008: I. Sim OverviewMedical Informatics
EHR/Informatics to the Rescue? • To improve and transform health care
– “Within the next 10 years, electronic health records will ensure that complete health care information is available for most Americans at the time and place of care, no matter where it originates” President Bush, State of the Union speech, Jan. 2004
• To help clinical research– “Frankly, one of the biggest attractions to LastWord
(aka UCare) is going to be a boon to clinical research. Information will be accessible in a much more uniform and complete way.” ex-SOM Dean Haile Debas, UCSF Daybreak, 2001
February 19, 2008: I. Sim OverviewMedical Informatics
Outline
• Introduction
• What is Informatics
• Course Goals
• Overviews– clinical informatics– research informatics– the Big Picture
• Summary
February 19, 2008: I. Sim OverviewMedical Informatics
What are Computers For?
• Store
• Query and Retrieve
• Compute
• Report
• ...1’s and 0’s
February 19, 2008: I. Sim OverviewMedical Informatics
Informatics is ...• The use of computers to understand and
manage complexity – Store, Query and Retrieve, Compute, and Report
complex data, information, knowledge– how can 1’s and 0’s stand in for complex data,
information, and knowledge?• Biomedical informaticians focus on the storage,
retrieval and optimum use of data, information and knowledge for problem solving and decision making in biomedicine
February 19, 2008: I. Sim OverviewMedical Informatics
Biomedical Informatics
T1
Translation
T2
TranslationGenomicsProteomicsPharmacogenomicsMetabolomics, etc.
Clinical trialsEpidemiologyMolecular Epi
Evidence-based practicePatient safetyQuality of care
Basic Discovery
Clinical Research
Clinical Care
Bioinformatics
Medical Informatics
February 19, 2008: I. Sim OverviewMedical Informatics
Informatics is not IT
• Information technology (IT) uses today’s technology to meet today’s operational needs for– storing: building and maintaining databases– querying and retrieving: SQL, transactions– computing: linear regressions, financial
forecasts– reporting: UCare lab results reporting
February 19, 2008: I. Sim OverviewMedical Informatics
Informatics is not IT (cont.)• Informatics is using computers to understand and
manage complexity within biomedicine – basic biomedical informatics:
• foundational theories and methods for knowledge representation, computational reasoning,
• draws on computer science, philosophy, linguistics, math...
– applied• developing, using, and evaluating end-user systems for
problem solving and decision making in biomedicine
• draws on QI, sociology, psychology, human-centered computing, evaluation sciences, etc.
February 19, 2008: I. Sim OverviewMedical Informatics
GenomicsProteomicsPharmacogenomicsMetabolomics, etc.
Clinical trialsEpidemiologyMolecular Epi
Evidence-based practicePatient safetyQuality of care
Informatics & Translation
• Informatics enables transfer and analysis of data, information, and knowledge across spectrum of clinical research to care
• ...enables the “translation” in translational research
Basic Discovery
Clinical Research
Clinical Care
T1
Translation
T2
Translation
Bioinformatics
Medical Informatics
February 19, 2008: I. Sim OverviewMedical Informatics
Why Important to You?• “Old” days
– build your own database, analyze it, publish• “New” days
– you want/need to bring together lots of data • of different types (numbers, text, images)
• from different sources (microarrays, charts, claims)
– you want/need analytic methods and models beyond statistics
– you need wide collaboration with other PIs, labs, health systems
• Querying across home-grown databases is not possible; in a networked world, informatics is key
February 19, 2008: I. Sim OverviewMedical Informatics
Outline
• Introduction• What is Informatics• Course Goals• Overviews
– clinical informatics– research informatics– the Big Picture
• Summary
February 19, 2008: I. Sim OverviewMedical Informatics
Course Goals
• Be familiar with core concepts in medical informatics: vocabularies, interchange standards, decision support systems
• Understand the current state of health information technology use for patient care and clinical research
• Understand the major informatics issues in clinical and translational research
• Be alert to informatics issues in grant proposals and what grant reviewers will be looking for
February 19, 2008: I. Sim OverviewMedical Informatics
Course Structure• 6 Lecture/Discussion Sessions
– PowerPoint file up 1+ days before lecture– class participation expected
February 19, 2008: I. Sim OverviewMedical Informatics
EHRs vs. PHRs
• Electronic health/medical records, owned by health care institution– e.g., UCare (our name for the GE Centricity
product), Epic, Cerner, etc.
• vs. Personal Health Records (PHR), owned by the patient– e.g., HealtheVet, Microsoft HealthVault
February 19, 2008: I. Sim OverviewMedical Informatics
8 Types of EHR FunctionalityViewing Electronic viewing of chart notes, problem and medication lists, discharge
summaries, laboratory results, and radiology results.
Documentation Entry of visit note and other information into the EMR, whether throughdictation or direct keyboard entry.
Order Entry Electronic physician order entry of drug prescriptions, laboratorytests, radiology studies, or referrals.
Care Planningand Management
Managing patients in disease management programs, such as for asthma orcongestive heart failure
Patient-Directed Patient education materials; web-based education modules, self-diagnosisalgorithms, patient-viewing of EMR data, and e-mail with care providers
Billing and OtherAdministrative
Determination of insurance eligibility, assistance with visit level coding,management and tracking of referrals.
PerformanceReporting
Quality and utilization reporting to both internal and external audiences
Messaging E-mail or other messaging system among providers and staff within theorganization, or to external organizations
February 19, 2008: I. Sim OverviewMedical Informatics
Lots of Choice
• Certification Committtee for Health Information Technology http://www.cchit.org/ helps sort wheat from chaff – functionality: what does EHR do– interoperability: what other systems EHR “talks to”– security
• Started certifying products in 2006
– Ambulatory: 99 certified EHRs so far (~50% of companies)
– Inpatient: 9 certified EHRs so far (not GE Centricity yet...)
February 19, 2008: I. Sim OverviewMedical Informatics
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)
• Who exchanges data with whom? privacy/security? rights? responsibilities?
– Regional Health Information Organization (RHIOs) governance structures proposed e.g., CalRHIO
February 19, 2008: I. Sim OverviewMedical Informatics
Summary of Clinical Informatics
• Health IT is complex, fragmented, frequently incompatible, and EHRs still not widely used– free text is hard to datamine, standard
vocabularies are hard to build, use, maintain– health-specific “grammars” (e.g., HL7) needed for
exchanging clinical data • Data repositories clean and aggregate data from
multiple sources– if data coding isn’t standardized across data
sources, aggregation may not be possible or meaningful
February 19, 2008: I. Sim OverviewMedical Informatics
Outline
• Introduction• What is Informatics• Course Goals• Overviews
– clinical informatics– research informatics– the Big Picture
• Summary
February 19, 2008: I. Sim OverviewMedical Informatics
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.
February 19, 2008: I. Sim OverviewMedical Informatics
Catch-up To Clinical Informatics• >80% of clinical research still using paper charts
and forms– $12 billion for paper-based trials vs. $2 billion/year
for electronic trials industry• Naming data
– e.g., NCI’s Common Data Elements (e.g., definition of menopause for breast cancer studies)
• Exchanging data– e.g., CDISC (HL7, FDA, NCI standards for
regulated research interchange)• Reasoning from data and information to
knowledge
February 19, 2008: I. Sim OverviewMedical Informatics
D-I-K...Wisdom• Data
– raw observations/objective facts, “discrete, atomistic, tiny packets with no inherent structure or necessary inter-relationships”
• Information– data with meaning, formed data, processed data
• Knowledge– tacit / not codifiable (e.g. “expertise”, clinical sense)– vs. explicit / codifiable (e.g. guideline)– useful for predicting future, guiding future action
February 19, 2008: I. Sim OverviewMedical Informatics
D-I-K Example• Data
– HgbA1C value 10.1%
• Information– that value is above the normal range
• Knowledge– high HgbA1C occurs in diabetes mellitus and
predicts higher long-term risk for cardiovascular complications
• There’s also process knowlege, i.e., how to do things
February 19, 2008: I. Sim OverviewMedical Informatics
Large-scale Knowledge Discovery• Garbage in garbage out
– if raw data is wrong, incompatible, not computable– if information is wrong (e.g., out of context)– if can’t get data out of source systems (technical, privacy,
intellectual property reasons)
• Many methods for data mining– statistics (classical, bayesian)– neural networks, bayes nets, clustering, classification, etc,
• Lots of informatics research work needed in– algorithms for biomedical discovery– how to represent complex knowledge (e.g., systems
biology, clinical trial results, how to diagnose)
February 19, 2008: I. Sim OverviewMedical Informatics
CTSA Informatics
• One of main cross-CTSA Steering Committees (others include Education, Community Engagement, “Translational”)
• Informatics plans were critical for getting a CTSA
• Working on national consortial activities– UCSF leads on 2 active projects (IDR and
Human Studies Repository)
February 19, 2008: I. Sim OverviewMedical Informatics
Outline
• Introduction• What is Informatics• Course Goals• Overviews
– clinical informatics– research informatics– the Big Picture
• Summary
54
Draft
for N
RC com
mitt
ee re
port,
do
not c
ite o
r circ
ulat
e
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.
Where clinicians want to stay
EHRs
CTMSs
February 19, 2008: I. Sim OverviewMedical Informatics
Big Picture Take-Home Points
• Puts care and research together
• Separates data from the transactional systems used to collect that 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
February 19, 2008: I. Sim OverviewMedical Informatics
Outline
• Introduction• What is Informatics• Course Goals• Overviews
– clinical informatics– research informatics– the Big Picture
• Summary
February 19, 2008: I. Sim OverviewMedical Informatics
Summary• Key informatics challenges
– naming data– exchanging data– reasoning to knowledge, capturing knowledge
• Challenges occur in parallel for clinical care and clinical research
• Informatics is not IT, not desktop support
• Informatics crucial for managing complexity of modern clinical care and research, and crucial for promise of translational research
February 19, 2008: I. Sim OverviewMedical Informatics
Next Classes
• Case Studies in Clinical Research Informatics– UCSF/CTSA’s Integrated Data Repository– CTSA Human Studies Metadata Repository