Upload
amos-mclaughlin
View
218
Download
0
Tags:
Embed Size (px)
Citation preview
February 17, 2009: I. Sim OverviewMedical Informatics
Medical Informatics for Clinical Research
Ida Sim, MD, PhD
February 17, 2009
Division of General Internal Medicine, andCenter for Clinical and Translational Informatics
UCSF
Copyright Ida Sim, 2009. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.
February 17, 2009: I. Sim OverviewMedical Informatics
Outline
• Introduction
• What is Informatics
• Course Goals
• Overviews– clinical informatics– research informatics– the Big Picture
• Summary
February 17, 2009: 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– knowledge systems for clinical research (e.g.,
trial registration and reporting, trial design)– computer-assisted evidence-based practice– economics and policy of health information
technology
February 17, 2009: 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 17, 2009: 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 17, 2009: 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 17, 2009: 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
– Stimulus bill “provides $19 billion to accelerate adoption of Health Information Technology (HIT) systems by doctors and hospitals, in order to modernize the health care system, save billions of dollars, reduce medical errors and improve quality” American Recovery and Reinvestment Act fact sheet, Nancy Pelosi, Feb, 2009 (http://www.speaker.gov/newsroom/legislation?id=0273#health)
February 17, 2009: I. Sim OverviewMedical Informatics
EHR/Informatics to the Rescue
• 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
– About today's biomedical research enterprise...“At no other time has the need for a robust, bidirectional information flow between basic and translational scientists been so necessary.” ex NIH Director, Elias Zerhouni, 2008
February 17, 2009: I. Sim OverviewMedical Informatics
...or Maybe Not
• “Current efforts aimed at the nationwide deployment of health care IT will not be sufficient to achieve the vision of 21st century health care, and may even set back the cause if these efforts continue wholly without change from their present course.” National
Academies Report ‘Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions’, Jan 2009 (http://www.nap.edu/catalog.php?record_id=12572)
February 17, 2009: I. Sim OverviewMedical Informatics
Outline
• Introduction
• What is Informatics
• Course Goals
• Overviews– clinical informatics– research informatics– the Big Picture
• Summary
February 17, 2009: I. Sim OverviewMedical Informatics
What are Computers For?
• Store
• Query and Retrieve
• Compute
• Report
• ...1’s and 0’s
February 17, 2009: 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?• Informatics focuses on the storage, retrieval and
optimum use of data, information and knowledge for problem solving and decision making in biomedicine
February 17, 2009: 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 17, 2009: 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 17, 2009: 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 and reasoning, i.e., “artificial intelligence”
• 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 17, 2009: 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 17, 2009: 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 17, 2009: I. Sim OverviewMedical Informatics
Outline
• Introduction• What is Informatics• Course Goals• Overviews
– clinical informatics– research informatics– the Big Picture
• Summary
February 17, 2009: I. Sim OverviewMedical Informatics
Course Goals
• Be familiar with core concepts in medical informatics: vocabularies, 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 17, 2009: I. Sim OverviewMedical Informatics
Course Structure• 6 Lecture/Discussion Sessions
– PowerPoint file up 1+ days before lecture– class participation expected
• Assignments– 4 homeworks, no final exam
• Office “hours”: [email protected]– http://www.epibiostat.ucsf.edu/courses/schedule/
med_informatics.html
February 17, 2009: I. Sim OverviewMedical Informatics
Outline
• Introduction• What is Informatics• Course Goals• Overviews
– clinical informatics– research informatics– the Big Picture
• Summary
February 17, 2009: I. Sim OverviewMedical Informatics
Major Informatics Issues
• Naming data
• Exchanging data
• Reasoning with data and information to generate knowledge
• Secondary issues– user-centered design, organizational
change/quality improvement, cost-benefits of health IT
Clinical Informatics Today
Clinic 2009
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)
February 17, 2009: 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,
Google Health
February 17, 2009: 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 17, 2009: 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: 20 EHRs meet 2008 critieria
– Inpatient: 1 EHR meets 2008 criteria, 14 met 2007 criteria (GE Centricity had premarket conditional certification for 2007)
February 17, 2009: I. Sim OverviewMedical Informatics
But No Good Choices?• Limited adoption of EHR, e.g., California [CHCF, 2008]
– 13% of medical practices use EHR• nationally, 24% of outpatient clinics have an EHR [Jha, 2006]
– 37% individual MDs use EHR vs 28% nationally
– 25% MDs write prescriptions and order refills electronically• Case of market failure: a common good that the
market is not distributing [Kleinke, 2005]
– misaligned incentives
– systems are too expensive for many practices
– EHR products and companies come and go
– EHRs don’t clearly pay for themselves
February 17, 2009: I. Sim OverviewMedical Informatics
EHR Informatics Challenges
• Difficult to use, poor user-interface design
• Naming data– data isn’t coded, isn’t “mine-able”
• Systems don’t talk to each other (e.g., to pharmacy, to lab)
• Not built to support research
• ...
February 17, 2009: I. Sim OverviewMedical Informatics
Free Text is not “Mineable”
• e.g., want to retrieve all pneumonia admissions
• Computers cannot read free text– “Mrs. Jones has a left bilobar pneumonia” = ???– DGIM tried to use STOR to pull out CHF patients
for QI but free text terms used were too varied
• For EHRs to “understand” the clinical content– need to code concepts into standardized terms – e.g., ICD-9 486.0 Pneumonia, org unspecified
February 17, 2009: I. Sim OverviewMedical Informatics
Naming Data• Computers can help us
– store, retrieve, query, compute, and report data • For this to happen, we must describe/name the
data in such a way that the computer– “understands” the data– can manipulate the data
• e.g., sort them, graph them, add numbers, perform analyses
– can retrieve the data for later use• The computer’s ability to manage data depends
on how well the data is described
February 17, 2009: I. Sim OverviewMedical Informatics
“Naming” Data: To Humans
• To describe a thought for another human to understand, we use
– symbols (words) with shared meaning• e.g., English, Chinese, Urdu words; IM lingo
– a system for codifying meaning using those words• e.g, English grammar, mathematical notation
• We must also make the coded message concrete
– e.g., skywriting “I LUV U”, drawing graph on beach
– and persistent• text on paper, an oil painting, lecture on YouTube
24142 1083.9 96
February 17, 2009: I. Sim OverviewMedical Informatics
“Naming” Data: To Computers• Computers need to be talked to also!• To describe a thought for computers to understand, use
– a controlled vocabulary for a domain, like a dictionary• e.g., ICD-9, SNOMED
– a data model that stores the “words” together in a standard format
• e.g., relational data model
– an interchange protocol, like a grammar, that codifies the meaning of “words” sent between computers
• e.g., HTTP or FTP
• Make the thoughts concrete and persistent by storing as 1’s and 0’s on hard disks, etc.
February 17, 2009: I. Sim OverviewMedical Informatics
Notable Clinical Vocabularies
Vocabulary Name Dom ain Use
SNOMED Standa rdized Nome nclatur e of Huma n and Ve t Me dicine
Clinical Medicine
EMR Docume ntation
MeSH Medical Subje ct Heading Biomedica l Indexing
Bibliographic Retrieval
ICD-9 International Classif icatio n of Diseases
Diseases Billing
CPT Curren t P rocedura l Te rminology
Medical Procedu res
Billing
DSM-IV Diagnosti c and S tatis tical Manual of Mental Diso rders
Pys chiat ry Billing, Nosolo gy
LOINC Logical Obse rvation Ide ntifier Name s and Codes
Labs Lab s yste ms , Billing
February 17, 2009: I. Sim OverviewMedical Informatics
Problems of Controlled Vocabs• Coverage
– is the idea (e.g., SNP) included?
• Granularity / specificity– do you need left heart failure? subendocardial myocardial infarction?
• Synonomy– cervical: does this mean related to the neck or the cervix?
• Relationships between terms– lisinopril IS-A ACE-inhibitor; see http://icd9cm.chrisendres.com/index.php?
action=child&recordid=2851
• Atomic concepts vs. “post-coordinated” concepts– left heart failure vs. left + heart failure;
• Usability– can you find the “right” code (SNOMED CT has > 300,000 concepts)
• Versioning– new terms (e.g., SNP), defunct terms (e.g., dropsy), corrected concepts
(e.g., rabies not a psychiatric disorder)
February 17, 2009: I. Sim OverviewMedical Informatics
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
• How to make coding easier/cheaper?– pay someone other than doctor– automatic coding from text, voice recognition,
etc.
Data Spread out All Over
Clinic 2009
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)
February 17, 2009: I. Sim OverviewMedical Informatics
EHR Informatics Challenges
• Difficult to use, poor user-interface design
• Naming data– data isn’t coded, isn’t “mine-able”
• Systems don’t talk to each other (e.g., to pharmacy, to lab)
• Not built to support research
• ...
February 17, 2009: I. Sim OverviewMedical Informatics
MICU
FinanceResearch
QA
Clinical / ResearchData Repository
Internet
ADT Chem EHR XRay PBM Claims
• Integrated historical data common to entire enterprise
Bring It All Together?
February 17, 2009: I. Sim OverviewMedical Informatics
Repositories to the Rescue?
• Data warehouse / data repository– for business intelligence, data mining, knowledge
discovery
• Different kinds of biomedical data repositories– clinical data repository (CDR)
• e.g., UCSF Hospital
– integrated data repository (IDR)• e.g., from “all” UCSF researchers and from Moffitt, Kaiser,
SFGH, etc.
February 17, 2009: I. Sim OverviewMedical Informatics
EHR vs. IDR 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?
• IDR 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?
February 17, 2009: I. Sim OverviewMedical Informatics
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 Clinical
Data Repository Enterprise Historical Ad Hoc
February 17, 2009: I. Sim OverviewMedical Informatics
MICU
FinanceResearch
QA
Clinical / ResearchData Repository
Internet
ADT Chem EHR XRay PBM Claims
• How do the machines “talk” to each other?
Networking Basics
February 17, 2009: I. Sim OverviewMedical Informatics
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
February 17, 2009: I. Sim OverviewMedical 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
February 17, 2009: I. Sim OverviewMedical 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 chart– e.g., problem list, allergies, family history
• “Containers” do address the data naming issue– e.g., Na, sodium, serum sodium
MSH|…message header
PID|…patient identifier
<!-OBX…observation result>
OBX|1|ST|84295^NA||150|mmol/l|136-148|H||A|F|19850301<CR>
February 17, 2009: 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 17, 2009: I. Sim OverviewMedical Informatics
Outline
• Introduction• What is Informatics• Course Goals• Overviews
– clinical informatics– research informatics– the Big Picture
• Summary
February 17, 2009: 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 17, 2009: 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., common definition of menopause for breast cancer studies
• Exchanging data– e.g., CDISC “grammar” for exchanging research
data• Reasoning from data to information to knowledge
February 17, 2009: 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 17, 2009: 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 17, 2009: 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 17, 2009: 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 Database)
February 17, 2009: I. Sim OverviewMedical Informatics
Outline
• Introduction• What is Informatics• Course Goals• Overviews
– clinical informatics– research informatics– the Big Picture
• Summary
55
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 17, 2009: 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 17, 2009: I. Sim OverviewMedical Informatics
Outline
• Introduction• What is Informatics• Course Goals• Overviews
– clinical informatics– research informatics– the Big Picture
• Summary
February 17, 2009: 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 17, 2009: I. Sim OverviewMedical Informatics
Next Classes
• EHRs
• Clinical research information systems
• Methods for Internet-based research
• Decision support, data mining
• Tying it all up
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