february 13, 2007: i. sim overview medical informatics medical informatics for clinical research ida...

February 13, 2007: I. Sim OverviewMedical Informatics

Medical Informatics for Clinical Research

Ida Sim, MD, PhD

February 13, 2007

Division of General Internal Medicine, andCenter for Clinical and Translational Informatics

UCSF

Copyright Ida Sim, 2007. 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

• Functional Overview of Informatics


Introduction: Ida Sim, MD, PhD• Position

– Associate Professor, General Internal Medicine– Director, Center for Clinical and Translational

Informatics

• Research areas– informatics and policy of clinical trial registration

and reporting– computer-assisted evidence-based practice– informatics for clinical research– economics of health information technology


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• Institute of Medicine (IOM) report on

medical errors (2000)– a “747” in deaths from medical errors every day

• And quality problems are endemic– Institute of Medicine (IOM) 2001 report

• “Health care today harms too frequently and

routinely fails to deliver its potential benefits…

Between the health care we have and the care

we could have lies not just a gap, but a chasm.”


EHR to the Rescue? • To improve and transform health care

– personalized, guideline-based, error-free, 24/7 decision support

– “A nationwide effort is needed to build a technology-based information infrastructure that would lead to the elimination of most handwritten clinical data within the next 10 years…” (IOM “Chasm” report, 2001)

• 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


Yet...• Limited adoption of EHR

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

– <10% hospitals have EHR [Leapfrog, 2003]

– only 4% hospitals have computerized physician order entry (CPOE) [Leapfrog, 2003]

• >80% of clinical research still using paper charts and forms

• Persistent under-investment in IT– only 2.5% of gross revenue on IT [Gartner Group, 2003]

– vs. ~10% in comparable information-intensive sectors (e.g., banking)


National Policy Response• Office of the National Coordinator for Health

Information Technology (ONC for HIT), started 2004– inform clinical practice

• i.e., increase EHR use

– interconnect clinicians • through computers that “talk” to each other

• via the National Health Information Network (NHIN)

– personalize care• i.e. consumer-centric care, not “personalized” medicine

– improve population health• public health surveillance, “accelerate scientific discovery”

• Government to catalyze private-sector solutions


IT for Clinical Research?• Attention is secondary to IT for clinical care

– ONC for HIT minor attention to clinical research– UCSF: UCare for clinical care first...

• Focused attention from– electronic clinical trials industry

• $2 billion/year (vs. $12 billion for paper-based trials)

– NIH, e.g., through Roadmap program• Clinical and Translational Science Awards (CTSA)• National Electronic Clinical Trials and Research Network

(NECTAR)• National Centers for Biomedical Computing, NCI, etc.


NECTAR • Major barriers in clinical trials of chronic diseases

– recruitment in target population (ie community)– costs and logistics of running trials in small

geographically dispersed practices

• NECTAR vision– all US clinics on web are potential study sites– all patients in the US are potential subjects– informatics and other technologies to enhance

• recruitment, protocol administration

• standardized data reporting and data sharing

• cross-study analyses, communication, and collaboration


GenomicsProteomicsPharmacogenomicsMetabolomics, etc.

Clinical trialsEpidemiologyMolecular Epi

Evidence-based practicePatient safetyQuality of care

Care and Research Coupled

• “Geno” - “pheno” correlations require bridging bench, bedside, clinic, and community

• Informatics is part of that bridge

Basic Discovery

Clinical Research

Clinical Care

T1

Translation

T2

Translation

Bioinformatics

Medical Informatics


Outline

• Introduction


• Course Goals



What are Computers For?

• Store

• Query and Retrieve

• Compute

• Report


Informatics is Not Programming• The use of computation to understand and

manage complexity – Store, Query and Retrieve, Compute, and Report

complex ... stuff• Informatics is not clinical computing/IT

– clinical computing/IT uses today’s technology to meet operational needs

– informatics seeks computational advances that will lead to tomorrow’s solutions for clinical science and practice


NIH Definition of Informatics• Includes database design, graphical interfaces,

querying approaches, data retrieval, data visualization and manipulation, data integration through the development of integrated analytical tools, and tools for electronic collaboration, as well as computational and mathematical research including the development of structural, functional, integrative, and analytical models and simulations– http://grants.nih.gov/grants/guide/pa-files/PAR-06-

411.html

http://grants.nih.gov/grants/guide/pa-files/PAR-06-411.html

http://grants.nih.gov/grants/guide/pa-files/PAR-06-411.html


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


• Course Goals



Course Goals

• Be familiar with core concepts in medical informatics: vocabularies, interchange standards, decision support systems

• Understand how computers are used to manage information in health care and clinical research

• Learn how information technology is being used to improve clinical care and research

• Understand how the new NIH emphasis on informatics impacts clinical research


Outcomes

• You will be able to recognize an informatics problem when confronted with one

• You will know when you need informatics assistance in your grant (i.e., consult or collaboration)

• You will have an answer when your colleagues say “why can’t computers just ...”


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

• Introduction


• Course Goals

• Functional Overview of Informatics– Store– Query and Retrieve– Compute– Report


CDSS Running Example• Hypertension treatment Clinical Decision Support

System (CDSS)– Clinic has an EHR

– During patient visit, CDSS notes that BP and trend is too high.

– CDSS checks patient’s Cr, diabetes status, cardiac status, current meds and allergies and recommends drug therapy change according to JNC VII guidelines and insurance coverage.

– Presents e-prescription for MD to verify. If verified, order is sent directly to pharmacy and medication list updated.


Major Tasks of this CDSS• EHR has to “know”

– BP trend– patient is diabetic, has micro-albuminuria

• Apply the guideline– BP is “too high” for diabetic– diabetics with micro-albuminuria should be on an ACE

inhibitor• E-prescribing

– patient not yet on ACE inhibitor– benazapril is covered ACEI by pt insurance plan– given pts. age and weight, start at 5 mg PO QD– generate prescription and e-mail to pharmacy


Outline

• Introduction


• Course Goals



Data Storage• Computers can help us

– store, retrieve, query, compute, and report data • For this to happen, we must describe 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


• Types of data and detail sufficient for– real-time clinical care

• notes, orders, labs, prescriptions, xray (reports)...

– administation• demographic, billing, provider IDs...

• What about quality improvement and research?– standardized data collection, symptom scales, etc– e.g., examples?

What Data Should EHRs Store?


How Should EHRs Store Data?

• Computers cannot read. For EHRs to “understand” the clinical content– need to code concepts into standardized terms – a standardized vocabulary acts like a dictionary

• DGIM tried to use STOR to pull out all CHF patients for quality improvement program but terms used were too varied

• Free text vs. coded entries– e.g., how to guarantee that all pneumonia

admissions will be retrieved if asked for?– “Mrs. Jones has a left bilobar pneumonia”– Pneumonia: Yes, Location: Left


“Describing” 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 audiotape

24142 1083.9 96


“Describing” 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 VocabulariesVocabulary Name Domain Use

SNOMED Standardized Nomenclatureof Human and Ve t Medicine

ClinicalMedicine

EMRDocumentation

MeSH Medical Subject Heading BiomedicalIndexing

BibliographicRetrieval

ICD-9 International Classificationof Diseases

Diseases Billing

CPT Current ProceduralTerminology

MedicalProcedures

Billing

DSM-IV Diagnostic and StatisticalManual of Mental Disorders

Pyschiatry Billing,Nosology

LOINC Logical ObservationIdentifier Names and Codes

Labs Lab systems,Billing


Problems of Controlled Vocabs• Coverage

– is the idea (e.g., SNP) included?

• Granularity– do you need left heart failure?

• Synonomy– cervical: neck or cervix related?

• Relationships between terms– lisinopril IS-A ACE-inhibitor

• Size and “post-coordination”– left heart failure vs. left + heart failure

• Usability– can’t find the right code

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


Controlled Vocab for CDSS• Hypertension treatment CDSS

– During patient visit, CDSS notes that BP and trend is too high. It checks patient’s Cr, diabetes status, cardiac status, current meds and allergies and recommends drug therapy change according to JNC VII guidelines and insurance coverage. Presents e-prescription for MD to verify. If verified, order is sent directly to pharmacy and medication list updated.

• How are BP readings labelled? Systolic and diastolic?• ICD-9 for hypertension (401.1), diabetes (250.00) at each

encounter? any encounter? what if only “HTN” written in problem list? Who codes the ICD-9? Why?

• Medications, from EHR, PHR, or pharmacy– “benaz” or benazepril or Lotensin– is lisinopril an ACEI?


Tradeoffs of Coding• 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.


Teaching Point: Storing Data

• The more coded your data, the more the computer can do with it

• But – controlled vocabularies are hard to build

and maintain– standardized coding is difficult and

expensive


Outline

• Introduction


• Course Goals



• HTN CDSS example– During visit, CDSS notes that BP and trend is too high. It

checks patient’s Cr, HgbA1C, cardiac status, current meds and allergies and recommends drug therapy change according to JNC VII guidelines and insurance coverage. Presents e-prescription for MD to verify. If verified, order is sent directly to pharmacy and medication list updated.

• Inefficient to query multiple databases: EHR, claims, pharmacy, ADT, lab, xray, etc

• May also wish to query data collected as part of clinical research – data from research protocols, GCRCs, etc.

Beyond the EHREHR

EHRPharmacy

Admin

Query, Retrieval, Data Interchange

Clinic 2007

FrontDesk

Radiology

Claims

MedicalInformationBureau

Archive

Walgreens

Prescribing

Pharm BenefitManager

Benefits Check(RxHub)

HealthNetFormulary Check

B&TEligibility Authorization

Personal HealthRecord

UCare

Electronic MedicalRecord

Specialist

Referral

ReferralAuthorization

Internet Intranet Phone/Paper/Fax

Lab

UniLab

(HL-7)


MICU

FinanceResearch

QA

Data Repository

Internet

ADT Chem EHR XRay PBM Claims

• Integrated historical data common to entire enterprise

A Clinical Data Repository


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

• Health-specific protocols needed “on top of” http or tcp/ip– e.g., Health Level 7 (HL-7), DICOM (xrays)

What Happens Over the Cables

MSH|…message header

PID|…patient identifier

<!-OBX…observation result>

OBX|1|ST|84295^NA||150|mmol/l|136-148|H||A|F|19850301<CR>


MICU

FinanceResearch

QA

Data Repository

Internet

ADT Chem EHR XRay PBM Claims

• Integrated historical data common to entire enterprise

What is “Data Warehouse” For?


Types of Queries

• Clinical care

• 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?

• Quality Improvement• 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– need standardization of codes, definitions,

and data schemas

Viewpoint Time Queries

EHR Patient Real-Time ClinicalData Repository Enterprise Historical Ad Hoc


UCSF “RDE” Example

• Will all databases use the same vocabulary?• Is using the same vocabulary enough?

Project 1

DB 1

Project 2

DB 2

Project 3

DB 3

Project 4

DB 4

•Breast CA (not DCIS)•Menopause

•Osteoporosis (Heel US)•Menopause

•Osteoporosis (DXA)•Menopause

•Breast CA (DCIS ok)•Alzheimers (path)

xrays/CT/MRImicroarray data

RDE

Data mining/Display ToolsRadiologySTOR


Teaching Point: Query/Retrieve

• Data warehouses clean and aggregate data from multiple sources– health-specific “grammars” (e.g., HL7) are

used for sending clinical data over the network

• If data coding isn’t standardized across data sources, aggregation may not be meaningful


Outline

• Introduction


• Course Goals



Some General Thinking Tasks

• Finding things (Google)

• Individual knowledge discovery (ie learning)

• New knowledge discovery (ie research)

• Applying knowledge to action

• Artistic creation

• ...

= clinical research

= clinical care


Knowledge Discovery

• From Data -> Information -> Knowledge


D-I-K...Wisdom• Data

– raw observations/objective facts, “discrete, atomistic, tiny packets (with) no inherent structure or necessary (inter)relationship”

• Information– data with meaning, formed data, processed data

• Knowledge– tacit: no codifiable (eg “expertise”, clinical sense)– vs. explicit: codifiable (eg guideline)– useful for predicting future, guiding future action


Knowledge Discovery

• Many methods for “data mining” – statistics (classical, bayesian)– neural networks, bayes nets, clustering, classification, etc, etc.– natural language processing, information extraction, etc.

• Methods vary at at incorporating prior knowledge, causal knowledge, types of data that can be handled, etc.


Comparing Methods (1)

To loan or not to loan...

Linear regression Linear classification


Comparing Methods (2)

Clustering

Non-linear classifiers

Neural Network Nearest Neighbor


Some General Thinking Tasks

• Finding things (Google)

• Individual knowledge discovery (ie learning)

• New knowledge discovery (ie research)

• Applying knowledge to action

• Artistic creation

• ...

= clinical research

= clinical care


Knowledge to Action• Decision support

– given starting conditions and a defined set of action choices, recommend or rank action choices for user(s)

• Requires some “thinking” to recommend or rank– strictly deterministic (if-then) thinking– fuzzy and probabilistic thinking

• in starting data or reasoning procedure

• outcomes (e.g. prob. of adverse reaction)

– often thinking about concepts, not numbers• symbolic vs. quantitative computing


Decision Support “Thinking”• Strictly deterministic

– first-order logic rule-based systems– adhoc rule-based systems (non-mathemetical reasoning

about probability)• e.g., if high WBC AND cough AND fever AND abn. CXR

then likelihood of pneumonia is 4 out of 5

• Probabilistic/fuzzy– bayesian networks

• formal probabilistic reasoning, extension of decision analysis

– neural networks– fuzzy logic, case-based reasoning, etc., or hybrids


HTN CDSS Example• EHR has to “know”

– BP trend– patient is diabetic, has micro-albuminuria

• Apply the guideline– BP is “too high” for diabetic– diabetics with micro-albuminuria should be on an ACE

inhibitor

• E-prescribing– patient not yet on ACE inhibitor– benazapril is covered ACEI under insurance plan– given pts. age and weight, start at 5 mg PO QD– generate prescription and e-mail to pharmacy


Rule-Based Approach• Forward chaining/reasoning (data-driven)

– start with data, execute applicable rules, see if new conclusions trigger other rules, and so on

– example• if DX = DIABETES AND SBP > 130 => GIVE-DM-BP-MED

• if GIVE-DM-BP-MED AND MICROALBUMINURIA => GIVE-ACEI

• if GIVE-ACEI AND NOT-TAKING (ACEI) => CHECK-ACEI-COVERAGE

• IF NOT-ALLERGIC (ACEI) AND INSURANCE-COVERED (ACEI) => PRESCRIBE (ACEI)

– use if sparse data


Discussion• Reasoning features

– includes both symbolic information (has diabetes) and quantitative information (SBP < 130)

– deterministic: if-then construct– value-based: assumes that BP drug is

worth marginal benefit– could be multi-user

• Some problems with rule-based systems?


Broad Comparisons

• Knowledge Discovery• Clinical research• More quantitative• Values-free• Probabilistic and

deterministic

• Knowledge to Action• Clinical care• More symbolic• Values-dependent• Deterministic and

probabilistic• Multiple users

• Comparative features apply to human and computer reasoning – but implies use of different computing techniques


Teaching Point: Compute• What is a computational problem?

– knowledge discovery: when you have data and want knowledge

– applying knowledge to action: how to figure out what to do next

• Informatics principles include– specifying the problem abstractly (e.g., D-I-K,

symbolic? probabilistic? values? domain? users?)– meaningful data coding, aggregation, interchange– choosing the best method (not just statistics)– efficient algorithms to achieve solution


Outline

• Introduction


• Course Goals



Visualizing Complex Information

• http://biocyc.org/ECOLI/new-image?type=OVERVIEW– the more coded and structured the underlying

information, the more powerful and flexible the visualization can be

• Best information exploration interfaces incorporate– cognitive psychology

• error mitigation techniques

– human-computer interaction principles• principle of “5-7 item” limit of human attention

– engineering design and aesthetics• “affordances,” “usability”

http://biocyc.org/ECOLI/new-image?type=OVERVIEW


Summary• Computers help us

– store, query and retrieve, compute, and report on– data, information, and knowledge

• Biomedical informatics is using computers to manage the complexity in biomedicine– complexity of concepts (e.g., findings, diagnoses)– complexity of relationships among concepts (e.g.,

disease etiology, treatment predictors)– complexity of care system (e.g., EHRs, guidelines)

• Since clinical research getting more complex, informatics has increasing role to play...


Teaching Points• Store

– standardized coding is critical but difficult• Query and retrieve

– coding must be standardized across databases for data repositories

• Compute– formal approach to specifying computational

problems and selecting best methods• Report

– information exploration design draws from many fields

february 13, 2007: i. sim overview medical informatics medical informatics for clinical research ida...

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