February 21, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Ida Sim, MD, PhD February 21, 2012 Division of General Internal Medicine, and

February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics

Ida Sim, MD, PhD

February 21, 2012

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

UCSF

Electronic Health Records for Clinical Research

Copyright Ida Sim, 2011. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.


Summary of Last Class

• Informatics crucial for making sense of complex data, and crucial for promise of translational research

• Key informatics challenges– naming data– exchanging data– reasoning to knowledge, capturing knowledge

• Challenges occur in parallel for clinical care and clinical research

February 14, 2012: 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


• Policy Context• EHR Features Affecting Research

– functionality and adoption– naming data– getting data out

• Personal Health Records• What Now for PHRs?• Summary

Outline


Promotion of EHR Adoption

• “Stimulus” Recovery Act (2009) directed $19 billion to health IT

• $17.2 billion through Medicare/Medicaid payments for “meaningful use” of EHRs– if MD/clinic/hospital achieves meaningful use by

2011 or 2012, can receive up to $44K over 5 years

(starting in 2011)– phased out if meaningful use starts after 2014

• Medicare fees to be reduced for “non-EHR physician users” starting 2015


Meaningful Use

• Stage 1 (2011) , basic functions, e.g., – capture vital signs, demographics, active meds, allergies,

up-to-date problem lists, smoking status– one clinical decision support rule and track compliance– computer provider order entry (CPOE) (>30% of pts)– electronic prescribing (of >40% of prescriptions)– capability of exchanging key clinical information– report clinical quality measure to CMS or states– provide patients with clinical summaries of encounter

• Stage 2 (2013) and Stage 3 (2015) – was to ramp up all of above, increase pt-facing services– on hold as of 2011, Stage 1 over-reach?


Certified EHRs

• Certified Health IT Product List at http://onc-chpl.force.com/ehrcert– ambulatory practice

• 1169 products (was 269 in 2011)• (Epic products from 2008,2009,2010 listed separately)

– inpatient • 571 products (was 101 in 2011)• GE Centricity (aka UCare) certified, but we dropped

them due to problems with CPOE

• Epic is market dominant– 33-44% of U.S. population has at least one

account in Epic


Rising Office-Based EHR Adoption

CDC NAMC Survey, 12/2/2011 http://thehealthcareblog.com/blog/2011/12/02/2011-ehr-adoption-rates/

Rising Hospital EHR Adoption

• From 16% in 2009 to 35% in 2011 (AHA survey, Feb 2012)

• Incentivized by Meaningful Use payments– 85% of hospitals intend to secure MU payments– CMS has paid out $3 billion in HITECH incentives

to 2000 hospitals and 41,000 providers (Sibelius, Feb 17,

2012)



Health IT Expenditures

• Catching up under-investment in IT– in early 2000s, only 2.5% of gross revenue on IT [Gartner

Group, 2003] vs. ~8% of gross revenue in banking, 2% in securities

• UCSF spent $50 mil+ on UCare; over $100m expected total on Epic


Costs (Outpatient)

• 60% of US docs work in practices with 10 or fewer MDs

• Initial costs ~$30K/MD for basic-function EHR1

– 3-person practice total costs $124K to $225K2

– 10-25% lost productivity during roll-out (6 months +)

• Ongoing costs ~$15K annually per MD3

• > 1/2 of costs are for hardware and software– other half for “complementary innovations”

1 http://www.informationweek.com/news/healthcare/EMR/232600025, MGMA survey2 http://www.pwc.com/us/en/healthcare/publications/rock-and-a-hard-place.jhtml3 http://content.healthaffairs.org/content/30/3/481.short


Complementary Innovations

• Everything you need to do to make the purchased “out of the box” EHR work in your organization

• Customization of– installation: interfaces to existing (legacy) systems– user interfaces– condition-specific templates (e.g., for headache, DM)

• Workflow redesign• New quality improvement programs

– e.g., clinical pathways• Organizational change

– appoint, train, and pay physician EHR leaders/ champions


Benefits

• Tangible (range $0 to $14,000 per MD)– reduction in dictation costs, medical records staff (for chart

pulls, etc), duplicate lab tests• HITECH incentive payments for meeting Meaningful Use

– up to $44K per MD over 5 years (but retroactively) – avoidance of penalties after 2014

• Accountable Care Organization (ACO) rule finalized Oct. 2011, makes tangible previously intangible benefits– quality of care– improvement in care coordination– service improvement– customer satisfaction


Overall Cost Savings?

• Obama administration estimate of savings, cited to support HITECH Act – ~$80b to $200b

• “As currently implemented, hospital computing might modestly improve process measures of quality but does not reduce administrative or overall costs.”1

– annual survey of 4000 hospitals from 2003 to 2007– linked to Medicare Cost Reports and quality data

from Dartmouth Health Atlas1 Himmelstein, et al. AJM (2010) 123:40-46


Cost/Benefit Equation

• Costs are substantial, benefits vary widely• Extent of benefits dependent on many factors, but

especially on the nature and extent of complementary innovations

• But complementary innovations – are costly

• often require new or extra staffing

– are difficult to implement• involve organizational change and changing physician behavior

– challenge the intellectual capital of the practice• managerial, financial, organizational change, quality improvement

• Bottom line: EHRs are not a “sure-fire” investment



– functionality and adoption– naming data – getting data out

• Personal Health Records• What Now for EHRs?• Summary

Outline


• Retrospective cohort study of outpatients• Compare 5 year rate for congestive heart failure for

diabetics treated with a glitazone vs. not– find diabetics– find whether treated with a glitazone– for these patients, find all subsequent cases of congestive

heart failure – analyze at 5 years

• adjust for age, sex, severity of diabetes, previous CHF,

other meds, etc., etc.

Outcomes Research Project


• Diabetes diagnosis– chart, HgbA1C, meds taken, problem list...

• Glitazone usage– orders, pharmacy

• Potential confounders– age, sex, severity, other meds, etc.

Health System Minnesota: 50 paper, 50 computer

200,000 lives, 460 physicians



Types of Data Needed


Community-Based Research

• For generalizability, and where chronic conditions are, you want to analyze EHR data from community practices

• Which EHRs products should you work with? • Which practices should you approach for

participation?


Which EHRs?

• Should be an ONC-certified EHR that meets (some) Meaningful Use criteria

• Should provide needed functionality for study protocol– patient demographics– problem list– medication list – clinical documents and notes

• The more structured and coded the data, the better


Which Practices?

• Adoption curve– what % of docs using the system? where are they

on adoption curve? (takes 6+ months for initial roll-out, 1-2 years for comfortable use)

• Which functionality being used?– most EHR purchasers do not use all available

functionality (e.g., guidelines support)• Is there a physician champion?

– your best liaison to the practice’s EHR• Consider a practice-based research network for

outpatient/community clinics


• Policy context• EHR Features Affecting Research



Outline


How Structured is the Data?

• Structured data does not equal coded data


How Coded is the Coded Data?

• Availability of coding does not mean coding is used!• e.g., Problem List

– “more than 80% of patients have at least one entry in structured

data” (MU Stage 1)– to what vocabulary? who does the coding? gamed”?

Malignant neoplasm of colon, unspecified site


• A term is a designation of a concept or an object in a specific vocabulary

• e.g., English blood = German blut – standardization enables predictable, accurate search and

retrieval

• “Controlled vocabularies” range from simple lists of terms to rich descriptions of knowledge– terminologies: list of terms corresponding to concrete (e.g.,

heart) and abstract concepts (e.g., hypertension) – ontologies: includes concepts, their definitions, various types

of relationships among the concepts, and axioms• data (e.g., lisinopril), information (e.g., lisinopril IS-A ACEI)• knowledge (e.g., ACEIs lower blood pressure)

Standardization of Clinical Terms


Notable Clinical Vocabularies


Terminology Features (e.g, ICD-9)

• 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• 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)


Terminology Features (cont.)

• Unambiguousness– each concept clearly defined (e.g.,

immunocompromise)• Non-redundancy

– each concept has only one corresponding code • Consistency

– each code has only one meaning in all situations • Concept permanence

– meaning never changes, even with new versions


ICD-9 Concept Coverage

• How well would ICD-9 do in capturing a medical chart?

• Inpatient and outpatient charts from 4 medical centers abstracted into 3061 concepts [Chute, 96]

– diagnoses, modifiers, findings, treatments and procedures, other

• Matching: 0=no match, 1=partial, 2=complete– 1.60 for diagnoses– 0.77 overall– ICD-9 augmented with CPT: overall 0.82


ICD-9 Coding Accuracy • VBAC uterine rupture rate

– 665.0 and 665.1 ICD-9 discharge codes used in study (NEJM 2001;345:3-8)

– letter to editor: in 9 years of Massachusetts data• 716 patients with 665.0 and 665.1 discharged• reviewed 709 charts• 363 (51.2%) had actual uterine rupture

– others had incidental extensions of C-section incision, or were incorrectly coded or typed

• 674.1 (dehiscence of the uterine wound) used to code another 197 ruptures (or 35% of confirmed cases of uterine rupture)

• i.e., sensitivity 65%, specificity 51.2%


SNOMED-CT “Ontology”• To “help structure and computerize the medical

record, reducing the variability in the way data is captured, encoded and used for clinical care of patients and medical research”– 311,000 unique health care concepts– 800,000 descriptions– over 1.36 million relationships between concepts, e.g.,

• Diabetes Mellitus IS_A disorder of glucose regulation• Finger PART_OF hand


SNOMED-CT Structure• Formally constructed vocabulary/knowledge map

– 18 high-level hierarchies • e.g. finding, organism, substance, body structure, event, social

context

– each concept can be described by many attributes • e.g., finding site = lung, associated-morphology = inflammation

– encodes “knowledge”• pneumonia is an infection of the lung by an organism

– can “post-coordinate” terms to increase expressive power• pneumonia: finding-site=lung ; finding-site=lower lobe;

laterality=right; causative agent=pneumococcus;• http://bioportal.nci.nih.gov/ncbo/faces/pages/quick_search.xhtml


SNOMED-CT Status

• Best semantic coverage of all existing vocabs

• de facto standard for EHR clinical vocabulary– owned by newly created International Healthcare

Terminology Standards Development Organization

(Danish, with 9 founding countries)– site-licensed (i.e., free) in U.S., as a founding country


Coding Barriers

• Poor inter-coder reliability– 3 docs, 5 opthalmology cases, 242 concepts, 2 SNOMED-

CT browsers [Chiang M, 2006]

• reliability between coders (exact term match): 44% and 53%• reliability within same coder: 45% over 2 browsers

• Automatic coding into ICD-9, etc. – precision (true pos) 0.88, recall (sens) 0.9 [Goldstein, 2007]

– experts precision 0.6 to 0.9, recall 0.7 - 0.9– still a major Natural Language Processing (NLP) research

challenge in general, let alone with typical clinical notes

ICD-9 Going Away…

• ICD-10 to be required as of Oct. 2013 for all HIPPA-covered institutions

• Example– W5803XA Crushed by alligator, initial encounter– W5803XD Crushed by alligator, subsequent encounter


AMA, 2010 http://www.ama-assn.org/ama1/pub/upload/mm/399/icd10-icd9-differences-fact-sheet.pdf


EHR for Research Summary

• Variable adoption of EHRs limits benefit to clinical research

• Not automatically going to help clinical research– if all unstructured free text, won’t help much at all

• the more structured it is (i.e., more defined fields), the better– if just coded sporadically in ICD-9

• problem with gamed codes, poor semantic coverage• ICD-10 transition will be very challenging

– very, very few EHRs coded in SNOMED• some clinical concepts still not well covered• SNOMED is essentially unusable by front-line clinicians • general automated coding still some time away, but may be an

option for constrained domains (e.g., path, radiology reports)


• Policy context• EHR Features Affecting Research



Outline


• Retrospective cohort study of outpatients• Compare 5 year rate for congestive heart failure for

diabetics treated with a glitazone vs. not– find diabetics– find whether treated with a glitazone– for these patients, find all subsequent cases of congestive

heart failure – analyze at 5 years

• adjust for age, sex, severity of diabetes, previous CHF,

other meds, etc., etc.

Outcomes Research Project


• Diabetes diagnosis– chart, HgbA1C, meds taken, problem list...

• Glitazone usage– orders, pharmacy

• Potential confounders– age, sex, severity, other meds, etc.





Types of Data Needed


Getting Data Out

• Cohort identification– how many potentially eligible patients at UCSF?

• Data extraction– extract particular data items for particular

patients?– cannot “go to APEX” to pull out data for outcomes

research• APEX built for treating one patient at a time• backend database (Clarity) is a relational database, but

data schema is proprietary


MICU

FinanceResearch

QA

Clinical / ResearchData Repository

Internet

ADT Chem EHR XRay PBM Claims

• Integrated historical data common to entire enterprise

Repository Solution

ReplicaSource Systems

IDR & My Research – Big Picture

Audit DB /IDR

Data Warehouse

End User Tools

Cognos BI

Data Warehousing Business Intelligence

Cohort Selection

Tool (i2b2), SAS,

STATA,SPSS,Alias.ti,

Enterprise Architect

UCare

PICIS

CancerRegistry

MisysIDXrad

Apollo

Worx

CTMS

STOR

MAR

Flowcast

TSI

CoPath

Kaiser

VA

ED

Epic

Extract,

Transfer

Proxy process and

Load

Axium

Siemens Radiology

Transplant

Terminal Servers

SGD Web top

Alfresco

REDCap

Epic

LPPIEMR

UCare

Will be replaced by Epic

Will have interfaces to bring data into Epic

SFGH

PICIS

CancerRegistry

CTMS?

TSI

Kaiser

VA

Axium

Transplant?

LPPIEMR

SFGH

Security

Red = Currently Integrated


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 2010 compared to 2005?

• 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


EHRs: The Way Forward

• EHRs ensure– availability, accessibility, legibility, some degree of

record completeness• Large volume reliable extraction of data will require

– manual review, and/or– custom-designed automated information extraction

methods, or– data repositories

• Will discuss more in Mar 6 class on clinical research informatics





Outline


PHRs

• Aims of PHR– give patients better access to their own data,

enable self-stewardship/correction of data, free reliance on lost charts, self-management of chronic diseases, empowerment, etc.

• What patients really want– communication with their doctor– prescription renewals– appointment scheduling and referrals– lab results– information and support to take own care


Types of PHRs

• Patient portal to physician-owned EHR (e.g., Epic’s MyChart)

• Independent sites for patients to do data entry

• Giant file cabinets in the sky– employer or health plan-based portals, e.g.,

• Dossia: Intel, Walmart, AT&T, etc.• Indivo: open source “Personally Controlled Health Record”

(a “Quicken for health care”)

– Microsoft HealthVault– Google Health discontinued Jan 2012


Issues• Privacy

– ARRA extends HIPAA protection to PHRs• Security

– is password-based security adequate? For banks/credit cards, etc. there are legal limits to damages and liability

– what laws can "undo”/restitute disclosure of sensitive health data?

• Data stewardship– accuracy/completeness of data being entered

• Personal control: will it be overwhelming? – what granularity (diagnosis, lab value, note)?– change over time? context (emergency, psych)?– delegation of control?


Issues (cont)

• Equity and health disparities– digital divide across income, language, cultural disparities

• Value– "Metcalfe's law”: the value only appears when enough

people and institutions start to use the system (e.g., fax

machine, HealthVault and hospitals)

Healthcare Information Access Roles

ProviderPatient

Payer Society

Primary care

Specialists

AncillariesImmediate

FamilyExtended

Family

Community Support

FriendsLegally Authorized

Reps

Admin.

Staff

Claims Processors

Subcontractors

Clearinghouses

Insurers

Public Health

State Licensure

Boards

Law Enforcement

Internal QA

External accreditation

orgs

Clinical Trials

Sponsors

Fraud Detection

Medical Information

Bureau

Business Consultants

National Security

Bioterrorism Detection

EHR, PHR, X-HR

Virtual Patient

Transactions

Raw data

Medical knowledge

Clinical research

transactions

Raw research

data

Dec

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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.

EHRs

PHR

. .

Patient

Pat

ient

Tra

nsac

tions




• Personal Health Records• What Now for EHRs? • Summary

Outline


Current State of EHRs

• HITECH driving adoption of yesterday’s fundamentally mis-conceived technology – lots of activity, churn, money, effort spent to meet

Meaningful Use – level of data exchange being mandated is unlikely

to improve care quality, decrease cost

• ACO era starting to align incentives– to drive and reward use of data for care, not billing– to magnify role of patient and teams– to diminish role of hospitals– upends business roles, business models

What’s Wrong with X-HR?

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.

EHRs

PHR

. .

Patient


Breaking Out of X-HR


Free the Data!

• To serve patient and research needs, need comprehensive data about each patient

• Non-starter for everyone to use the same system, or to collect all data about a patient in one place

• So data must be made exchangeable– questions must go to the data, not vice versa– transaction systems must layer on top of data, in

modular, substitutable ways • e.g., Word, Pages, Google Docs, etc for text documents

– privacy and context “metadata” must travel with the

data


Broad Questions• How do we “free the data”?

– “a danger that EHR adoption during early stages of meaningful use may

exacerbate the problem of incompatible legacy systems” (Presidential Council of Advisors

in Science and Technology, Health IT, 2011)

• Will “free” data add up to Big Data or a meaningless jumble?

• How to get better designed commercial systems? • What kinds of health IT implementation are

appropriate now?• How to ensure that privacy concerns don’t erect

insurmountable barriers to research?• What are the roles of other data owners (patient,

insurance, public health…)?





Outline


• Major barriers still exist to EHR adoption

• EHR does not always = easier clinical research

• Coding is critical– standardized, coded data trumps free text

• especially important for research• but most controlled vocabularies have insufficient clinical

coverage and are difficult to use– automated methods possible in restricted or custom situations

• In the midst of huge changes in health and health IT – “meaningful use” is driving EHR products and adoption– business models are changing throughout the industry– poised for disruptive change: contrast your APEX vs. iPad

experience

Take-Home Points


Next Class

Documents

February 21, 2012: I. Sim EHRs and Research Epi 206 — Medical Informatics Ida Sim, MD, PhD February 21, 2012 Division of General Internal Medicine, and