February 14, 2012: I. Sim Overview Medical Informatics Medical Informatics for Clinical Research Ida Sim, MD, PhD February 14, 2012 Division of General

February 14, 2012: I. Sim OverviewMedical Informatics

Medical Informatics for Clinical Research

Ida Sim, MD, PhD

February 14, 2012

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

UCSF

Copyright Ida Sim, 2012. 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

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

Informatics (ccti.ucsf.edu)– Co-Founder, Open mHealth.org

• Research areas– knowledge systems for clinical research (e.g., trial

registration and reporting, trial design)– computer-assisted evidence-based practice– health information technology policy– mobile health

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”

– $1000 genome is coming– the “exposome” will be assessed– expectations of hyper-personalized care

• Findings 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, 2004

– ARRA stimulus bill provided “$19 billion to accelerate adoption of Health Information Technology 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, 2009 (http://www.speaker.gov/newsroom/legislation?id=0273#health)

– “It’s about a patient who can have face-to-face video chats with her doctor”…“Veterans can now download their electronic medical records with a click of the mouse.” President Obama, State of the Union, 2011

http://www.speaker.gov/newsroom/legislation?id=0273#health


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

– “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


...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)


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 not IT

• Information technology (IT) primarily concerned with technology; informatics with information

• IT focuses on storing, accessing, and exchanging bits and bytes– server machines, server availability, storage capacity – building and maintaining databases– security and privacy– network connectivity and infrastructure (e.g., network

speeds)


Informatics is ...

• The use of computers to make sense of data– analyzing biomedical problems to determine what

data is needed– how that data should be obtained, organized,

analyzed, and presented – to researchers, clinicians, patients, and students

for problem solving • How can 1’s and 0’s stand in for complex data,

information, and knowledge in biomedicine?


GenomicsProteomicsPharmacogenomicsMetabolomics, etc.

Clinical trialsEpidemiologyMolecular Epi

Evidence-based practicePatient safetyQuality of careExposome

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/Self Care

T1

Translation

T2

Translation

Bioinformatics

Medical Informatics


Why Important to You?• “Old” days

– collect your own data, analyze it, publish• “New” days

– you want/need to bring together lots of data • different types (numbers, text, images)

• different sources (microarrays, EHRs, claims, Facebook)

– you need wide collaboration with other PIs, labs, health systems

– you want/need to use health IT to deliver interventions to or collect data from patients

• In a networked world with more data than we can make sense of, 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, decision support systems

• Understand the current state of health information technology for patient care and clinical research

• Understand the major informatics issues in clinical and translational research

• Learn about and use UCSF resources for data access and informatics, to be successful at grant proposals, etc.


Course Structure• 6 Lecture/Discussion Sessions

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

• Assignments– 3 short homeworks– one 3-4 page project proposal (50% of grade)

• email project idea to me by March 1

• 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/quality improvement, cost-benefits of health IT

Clinical Informatics Today

Clinic

FrontDesk

Radiology

Claims

MedicalInformationBureau

Archive

Walgreens

Prescribing

Pharm BenefitManager

Benefits Check(RxHub)

HealthNetFormulary Check

B&TEligibility Authorization

APEX

Electronic HealthRecord (EHR)

Specialist

Referral

ReferralAuthorization

Internet Intranet Phone/Paper/Fax

Lab

UniLab

(HL-7)

Epic MyChart


EHRs vs. PHRs

• Electronic health/medical records, owned by health care institution– e.g., APEX (our name for the Epic product),

GE Centricity (aka UCare), Cerner, etc.

• vs. Personal Health Records (PHR) for viewing by the patient– owned by pt: e.g., Microsoft HealthVault,

Google Health (RIP)– as part of EHR: e.g., MyChart, HealtheVet


8 Types of EHR Functionality


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 “Mine-able”• 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

MeSH Example

• http://www.nlm.nih.gov/mesh/MBrowser.html– navigate from tree top

• Terms are arranged in a “tree”– “parent” terms have a broader meaning– “child” terms have a narrower meaning

• PubMed automatically “explodes” your search term to include articles having any child terms– http://www.nlm.nih.gov/bsd/disted/pubmedtutorial/

glossary.html (see “automatic explosion”)

http://www.nlm.nih.gov/mesh/MBrowser.html

http://www.nlm.nih.gov/bsd/disted/pubmedtutorial/glossary.html

http://www.nlm.nih.gov/bsd/disted/pubmedtutorial/glossary.html


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

• 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)


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.


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

• ...

Data Spread Out All Over

Clinic 2011

FrontDesk

Radiology

Claims

MedicalInformationBureau

Archive

Walgreens

Prescribing

Pharm BenefitManager

Benefits Check(RxHub)

HealthNetFormulary Check

B&TEligibility Authorization

APEX

Electronic HealthRecord (EHR)

Specialist

Referral

ReferralAuthorization

Internet Intranet Phone/Paper/Fax

Lab

UniLab

(HL-7)

Epic MyChart


MICU

FinanceResearch

QA

IntegratedData Repository (IDR)

Internet

ADT Chem APEX XRay PBM Claims

• Integrated historical data common to entire enterprise

Repositories to the Rescue?


Promise of Data Repositories

• Data warehouse / data repository– for business intelligence, data mining, knowledge

discovery

• UCSF’s main “warehouse” is the Integrated Data Repository (IDR)– data from APEX (go live is Feb 9)– from “all” UCSF researchers and from Moffitt,

Kaiser, SFGH, etc.


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• 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• Need systems 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., common definition of menopause for breast cancer studies

• Reasoning from data to 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), machine learning, 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)


Outline



• Summary

45

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


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


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

• EHRs

• Clinical decision support systems

• Clinical research methods

• Methods for Internet-based research

• Tying it all up

Documents

February 14, 2012: I. Sim Overview Medical Informatics Medical Informatics for Clinical Research Ida Sim, MD, PhD February 14, 2012 Division of General