February 19, 2008: I. Sim Overview Medical Informatics Medical Informatics for Clinical Research Ida Sim, MD, PhD February 19, 2008 Division of General

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.


Outline

• Introduction

• What is Informatics

• Course Goals

• Overviews– clinical informatics– research informatics– the Big Picture

• Summary


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

http://ccti.ucsf.edu/


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


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


...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


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


Outline

• Introduction

• What is Informatics

• Course Goals

• Overviews– clinical informatics– research informatics– the Big Picture

• Summary


What are Computers For?

• Store

• Query and Retrieve

• Compute

• Report

• ...1’s and 0’s


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


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


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


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.


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


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


Outline

• Introduction• What is Informatics• Course Goals• Overviews

– clinical informatics– research informatics– the Big Picture

• Summary


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


Course Structure• 6 Lecture/Discussion Sessions

– PowerPoint file up 1+ days before lecture– class participation expected

• Assignments– 5 homeworks, no final exam

• Office “hours”: [email protected]– http://www.epibiostat.ucsf.edu/courses/schedule/

med_informatics.html


Outline



• 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)


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


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


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...)

http://www.cchit.org/


But No Good Choices?• Limited adoption of EHR

– 24% of outpatient clinics have an EHR [Jha, 2006]

– 13% California hospitals have EHR [CHCF, 2008]

– 4% hospitals have computerized physician order entry [Leapfrog, 2003]

• Case of market failure: a common good that the market is not distributing [Middleton, 2005]

– misaligned incentives

– systems are too expensive for many practices

– EHR products and companies come and go

– EHRs don’t clearly pay for themselves


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

• ...


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


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


“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


“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.


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


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 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 > 357,000 concepts)

• Versioning– new terms (e.g., SNP), defunct terms (e.g., dropsy), corrected concepts

(e.g., rabies not a psychiatric disorder)


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 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

Clinical / ResearchData Repository

Internet

ADT Chem EHR XRay PBM Claims

• Integrated historical data common to entire enterprise

Bring It All Together?


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

– research data repository• e.g., from “all” UCSF researchers

– integrated data repository (IDR)


EHR vs. CDR 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?

• CDR 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


CDR Informatics Challenges• Naming data• Exchanging data• Mapping similar data across different sources

– reconciling ICD-9, SNOMED, free text, etc. • Running huge databases (petabyte scale)• etc.


MICU

FinanceResearch

QA

Clinical / ResearchData 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)

• Who exchanges data with whom? privacy/security? rights? responsibilities?

– Regional Health Information Organization (RHIOs) governance structures proposed e.g., CalRHIO


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


Outline



• 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 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


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


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


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)


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)


Outline



• Summary

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Big Picture of Health Informatics

Virtual Patient

Transactions

Raw data

Medical knowledge

Clinical research

transactions

Raw research

data

Dec

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Med

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PATIENT CARE / WELLNES RESEARCH

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

Where clinicians want to stay

EHRs

CTMSs


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


Outline



• Summary


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


Next Classes

• Case Studies in Clinical Research Informatics– UCSF/CTSA’s Integrated Data Repository– CTSA Human Studies Metadata Repository

• EHRs

• Clinical research information systems

• Decision support, data mining

• Tying it all up

Documents

February 19, 2008: I. Sim Overview Medical Informatics Medical Informatics for Clinical Research Ida Sim, MD, PhD February 19, 2008 Division of General