Post on 28-Dec-2015
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.
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• Policy Context• EHR Features Affecting Research
– functionality and adoption– naming data– getting data out
• Personal Health Records• What Now for PHRs?• Summary
Outline
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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?
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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)
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• Policy Context• EHR Features Affecting Research
– functionality and adoption– naming data – getting data out
• Personal Health Records• What Now for EHRs?• Summary
Outline
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• 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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• 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
Health System Minnesota: 50 paper, 50 computer
200,000 lives, 460 physicians
Types of Data Needed
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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?
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• Policy context• EHR Features Affecting Research
– functionality and adoption– naming data– getting data out
• Personal Health Records• What Now for EHRs?• Summary
Outline
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
How Structured is the Data?
• Structured data does not equal coded data
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• 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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
Notable Clinical Vocabularies
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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)
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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%
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
AMA, 2010 http://www.ama-assn.org/ama1/pub/upload/mm/399/icd10-icd9-differences-fact-sheet.pdf
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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)
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• Policy context• EHR Features Affecting Research
– functionality and adoption– naming data– getting data out
• Personal Health Records• What Now for EHRs?• Summary
Outline
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• 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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• 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
Health System Minnesota: 50 paper, 50 computer
200,000 lives, 460 physicians
Types of Data Needed
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 14, 2012: I. Sim OverviewMedical Informatics
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?
February 14, 2012: I. Sim OverviewMedical Informatics
EHR/Data Repository Comparison
• Enterprise viewpoint more appropriate for QI and research
• Data repository cleans and aggregates data from multiple sources
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• Policy Context• EHR Features Affecting Research
– functionality and adoption– naming data– getting data out
• Personal Health Records• What Now for EHRs?• Summary
Outline
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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?
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
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
Pat
ient
Tra
nsac
tions
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• Policy Context• EHR Features Affecting Research
– functionality and adoption– naming data– getting data out
• Personal Health Records• What Now for EHRs? • Summary
Outline
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
Breaking Out of X-HR
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
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…)?
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• Policy Context• EHR Features Affecting Research
– functionality and adoption– naming data– getting data out
• Personal Health Records• What Now for EHRs?• Summary
Outline
February 21, 2012: I. Sim EHRs and ResearchEpi 206 — Medical Informatics
• 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
February 21, 2012: I. Sim OverviewMedical Informatics
Next Class