February 22, 2011: I. Sim EHRs and Research Epi 206 — Medical Informatics Ida Sim, MD, PhD February 22, 2011 Division of General Internal Medicine, and

February 22, 2011: I. Sim EHRs and ResearchEpi 206 — Medical Informatics

Ida Sim, MD, PhD

February 22, 2011

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

• 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• Informatics crucial for making sense of complex

data, and crucial for promise of translational research


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

PATIENT CARE /WELLNES RESEARCH

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

Where clinicianswant to stay

EHRs

CRMSs


Class Progression

• Today– EHRs

• Decision support systems• Clinical research

informatics• Methods for Internet-

based research• Tying it all up

VirtualPatient

Transactions

Raw data

Medicalknowledge

Clinicalresearch

transactions

Rawresearch

data

PATIENT CARE /WELLNES RESEARCH

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

Where clinicianswant to stay

EHRs

CRMSs


• Recovery Act and Health IT• Use, Costs, and Benefits of EHRs• EHR Features Affecting Research

– functionality and adoption– naming data– getting data out

• Personal Health Records• What Now with Health IT• Summary

Outline


Recovery Act and Health IT

• $19 billion of “stimulus” $ to “HITECH” Act– administered by Office of the National Coordinator

(ONC) for Health IT• Goals

– test and set health IT standards– support development of health IT workforce– address privacy, regional data sharing, and

governance policies– support advanced informatics research

• Gives $17 billion(!!) to promoting use of EHRs


Promotion of EHR Adoption

• $17.2 billion through Medicare/Medicaid payment 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 I (2011)

• Core (ie required) objectives– 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

• “Menu set” -- choose 2 to implement to qualify for MU– formulary checks, lab results as “structured data”, disease

registries, med reconciliation, public health reports, advanced directives, preventive reminders to patients, etc.


Meaningful Use Stages II and III

• Stage II (2013) and Stage III (2015) – ramp up degree of implementation of all Stage I

criteria– increased use of clinical decision support– increased data exchange– increased patient-facing services (e.g., patient

reminders, education, PHR, online secure messaging)

• Currently open for public comments– http://healthit.hhs.gov/portal/server.pt?open=512&

objID=2996&mode=2

http://healthit.hhs.gov/portal/server.pt?open=512&objID=2996&mode=2

http://healthit.hhs.gov/portal/server.pt?open=512&objID=2996&mode=2


“Certified” EHRs

• In Aug. 2010, ONC certified two groups to certify EHRs– www.cchit.org and www.drummondgroup.org

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

• 269 products, 192 products meeting all Core criteria• (Epic products from 2008,2009,2010 listed separately)

– inpatient • 101 products, 40 products meeting all Core criteria• GE Centricity (aka UCare) certified, but we dropped

them due to problems with CPOE

http://www.cchit.org/

http://www.drummondgroup.org/

http://onc-chpl.force.com/ehrcert


Are EHRs Ready for $19b Push?

• “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)

http://www.nap.edu/catalog.php?record_id=12572

Meaningful Use, Driver of HIT

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

CRMSs

Meaningful Use

. .

Patient





Outline


Low Penetrance of EHRs

• National survey of acute care hospitals [Jha, 2009] – 1.5% of hospitals use comprehensive EHR– 7.6% have basic systems– 17% have computerized physician order entry (CPOE)

• In CA: 13% of CA hospitals “use EHR” [CHCF, June 2008]

– 37% individual MDs use EHR vs 28% nationally– 25% MDs write prescriptions and order refills electronically

• Higher penetrance in medical groups w/ >20 docs – 42% use e-prescribing [Robinson, et al, Med Care 2009]


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

– now hospitals spend avg 20% of capital investment on HIT [CHCF, Jun 2008]

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

• $19 billion from Recovery Act is huge


Costs (Outpatient)

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

• Initial costs >$20-30K/MD for full-function EHRs– 3-person practice total costs $124K to $225K1

– ASP (ie web services) versions cheaper, as low as $99.95/month

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

• Ongoing costs $7-9K annually per MD

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

1http://www.pwc.com/us/en/healthcare/publications/rock-and-a-hard-place.jhtml2R Miller, I Sim, Health Aff 2004; 23(12):116-126


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

• Intangible (“accountable care organizations” rule making from health reform law is still pending)– 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


• Recovery Act and Health IT • Use, Costs, and Benefits of EHRs• EHR Features Affecting Research

– functionality and adoption– naming data – getting data out

• Personal Health Records• What Now with Recovery Act• 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?

• Prefer certified products that meet Meaningful Use criteria– more likely to stick around– more likely to develop their systems to meet Stage II and III

criteria– http://onc-chpl.force.com/ehrcert/EHRProductSearch

• For research purposes, need an EHR that provides needed functionality for study protocol– patient demographics– problem list– medication list – clinical documents and notes

• The more structured and coded the data, the better

http://onc-chpl.force.com/ehrcert/EHRProductSearch


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


• Use, Costs, and Benefits of EHRs• EHR Features Affecting Research



Outline


How Structured is the Data?

• Structured data does not mean coded data• Are structured templates used?



How Coded is the Coded Data?

• Availability of coding does not mean coding is used!

• Are discrete data elements in a note coded?– e.g., height, chief complaint, Glasgow coma scale

• e.g., Problem List criteria– “more than 80% of patients have at least one entry

in structured data” – is coding to ICD-9, SNOMED used?– who does the coding? “gamed”?


• 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 VocabulariesVocabulary Name Domain Use

SNOMED-CT Standardized Nomenclatureof Medicine

ClinicalMedicine

EHRDocumentation

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

READ Read Clinical Classification ClinicalMedicine

EHRs in the UK


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

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


EHR for Research Summary

• Low adoption of EHRs limiting impact on 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 (ie more defined fields), the better– if just coded sporadically in ICD-9

• problem with gamed codes, poor semantic coverage – 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)




• Personal Health Records• What Now with Reoovery Act• 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.





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 UCare” to pull out data for outcomes

research• Epic has very new user interface for querying

across patients


MICU

FinanceResearch

QA

Clinical / ResearchData Repository

Internet

ADT Chem EHR XRay PBM Claims

• Integrated historical data common to entire enterprise

Repository Solution


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 9 class on clinical research informatics




• Personal Health Records• What Now with Recovery Act• Summary

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


Types of PHRs

• Independent websites for patients to manually enter information

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

• 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


HealthVault

• Free, requires MSN pwd– patients type data, or fax or upload PDF documents

• no scanning, optical character recognition, coding

– may include connections to Eclipsys, UC StayWell, Beth Israel

Deac patient portal, Allscripts ePrescribe, etc. – can upload BP, HR, glucometer, etc. data from participating

devices (e.g., Lifescan glucometer)– can access access Health Vault applications (e.g., fitness tools),

search health websites, etc. not tied to chart data• Future plans

– upload demographic data to hospital chart, authorize clinics/ERs

to view/download data, streamline discharge instructions


Google Health

• Free, requires Google password• 2008 pilot with Cleveland Clinic

– CC keeps 120,000 records on MyChart (Epic)– 1500 to 10,000 patients to volunteer to transfer

data to Google for “forever” access• prescriptions, allergies, histories

• Now partnered with IBM to use Continua alliance standards to share data with mobile devices


Current PHRs moving towards...

• Accepting data electronically from multiple sources (labs, clinics, hospitals)

• Integrating with disease management and knowledge sources

• Enabling “social computing” (e.g., patient communities)

• Enabling research participation• Tie-in to mobile health (mHealth) -- will discuss in

mHealth class Mar 9


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?


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)





Outline


Reactions to HITECH Act

• Lots of activity, churn, money spent to meet Meaningful Use as program details are barely ahead of the deadlines

• Will be very difficult for many providers to install systems to meet meaningful use

• Level of data exchange being mandated is unlikely to improve care quality, decrease cost

• Regional Extension Centers and workforce grants helpful to support EHR adoption but may not be sustainable

• HITECH being implemented with very little tie-in to Affordable Care Act (health reform)


PCAST Health IT Report

"In analyzing the path forward, we conclude that achievement of the President’s goals requires significantly accelerated progress toward the

robust exchange of health information." (President's Council of Advisors on Science and Technology, Report on

Health IT, 2010)


Rationale

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

• Non-started 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


Why Hasn’t This Happened Yet?

• Health care is siloed, many proprietary interests• “Most healthcare organizations that utilize electronic

health records (EHRs) view them as purely internal resources, and have little incentive for investment in secondary or external uses, such as making them accessible in appropriate form to patients, to a patient’s healthcare providers at other organizations, and in de-identified or aggregated form to public health agencies and researchers.”

• Privacy and security concerns• “Health IT has historically been oriented toward

administrative functions, not better care” in part due to perverse incentive structure


PCAST and Meaningful Use

• “Though the [meaningful use] rule expresses an intent to require more robust exchange of health information among providers at later stages of meaningful use, its initial requirements that EHR systems communicate with each other are very modest. This creates a danger that EHR adoption during early stages of meaningful use may exacerbate the problem of incompatible legacy systems."


PCAST Recommendations

• Develop a “univeral exchange language” – based on tagged data elements– call-out to semantic web technologies, ontologies,

non-traditional data management strategies, all “in the cloud”

• Federal leadership needed to coordinate infrastructure development as level playing field as a public good– “market forces are unlikely to generate appropriate

incentives for the necessary coordination to occur spontaneously”


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

– e.g., should national policy be to move patients to

PHRs rather than hospitals/clinics to EHRs?• How to improve design of commercial systems? • What kinds of health IT implementation now are

appropriate given – current poorly designed and limited systems– perverse incentive system that does not reward

improved care quality• How to ensure that privacy concerns don’t erect

insurmountable barriers to research?





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 IT and informatics– “meaningful use” is driving EHR products and adoption– mid-course correction with PCAST report?– is mHealth a disruptive innovation that will “change

everything”?

Take-Home Points

Documents

February 22, 2011: I. Sim EHRs and Research Epi 206 — Medical Informatics Ida Sim, MD, PhD February 22, 2011 Division of General Internal Medicine, and