Electronic Health Records

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Electronic Health Records

• Robert A. Jenders, MD, MS, FACP
• Associate Professor, Department of Medicine
• Cedars-Sinai Medical Center
• University of California, Los Angeles
• Co-Chair, Clinical Decision Support Technical
  Committee, HL7
• 6 October 2005

http//jenders.bol.ucla.edu -gt Documents
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Overview EMRs

• Using the EMR Why we need it
• History aspects of the EMR
• Adoption
• Barriers
• Improving adoption standards, interoperability
• Case study CSMC
• Demonstration Centricity

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Need for EHR CDSS Medical Errors

• Estimated annual mortality
• Air travel deaths 300
• AIDS 16,500
• Breast cancer 43,000
• Highway fatalities 43,500
• Preventable medical errors 44,000 -
• (1 jet crash/day) 98,000
• Costs of Preventable Medical Errors
• 29 billion/year overall
• Kohn LT, Corrigan JM, Donaldson MS eds.
  Institute of Medicine. To Err is Human
  Building a Safer Health System. Washington, DC
  NAP, 1999.

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Need for EHR/CDSSEvidence of Poor Performance

• USA Only 54.9 of adults receive recommended
  care for typical conditions
• community-acquired pneumonia 39
• asthma 53.5
• hypertension 64.9
• McGlynn EA, Asch SM, Adams J et al. The quality
  of health care delivered to adults in the United
  States. N Engl J Med 20033482635-2645.
• Delay in adoption 10 years for adoption of
  thrombolytic therapy
• Antman EM, Lau J, Kupelnick B et al. A
  comparison of results of meta-analyses of
  randomized control trials and recommendations of
  clinical experts. Treatments for myocardial
  infarction. JAMA 1992268(2)240-8.

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Examples of EHR/CDSS Effectiveness

• Reminders of Redundant Test Ordering
• intervention reminder of recent lab result
• result reduction in hospital charges (13)
• Tierney WM, Miller ME, Overhage JM et al.
  Physician inpatient order writing on
  microcomputer workstations. Effects on resource
  utilization.JAMA 1993269(3)379-83.
• CPOE Implementation
• Population hospitalized patients over 4 years
• Non-missed-dose medication error rate fell 81
• Potentially injurious errors fell 86
• Bates DW, Teich JM, Lee J. The impact of
  computerized physician order entry on medication
  error prevention. J Am Med Inform Assoc
  19996(4)313-21.

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Examples (continued)

• Systematic review
• 68 studies
• 66 of 65 studies showed benefit on physician
  performance
• 9/15 drug dosing
• 1/5 diagnostic aids
• 14/19 preventive care
• 19/26 other
• 6/14 studies showed benefit on patient outcome
• Hunt DL, Haynes RB, Hanna SE et al. Effects of
  computer-based clinical decision support systems
  on physician performance and patient outcomes a
  systematic review. JAMA 1998280(15)1339-46.

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Summary Need for EHR (CDSS)

• Medical errors are costly
• Charges/Costs
• Morbidity/Mortality
• CDSS technology can help reduce
• errors
• costs
• EHR
• Collection and organization of data
• Vehicle for decision support

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Definitions

• Computer-based Patient Record (CPR) Electronic
  documentation of care, integrating data from
  multiple sources (clinical, demographic info)
• EMR Single computer application for recording
  and viewing data related to patient care,
  typically ambulatory
• EHR Suite of applications for recording,
  organizing and viewing clinical data
• Ancillary systems, clinical data repository,
  results review, CIS, HIS

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Uses of the Medical Record

• Main purpose Facilitate patient care
• Historical record What happened, what was done
• Communication among providers ( patients)
• Preventive care (immunizations, etc)
• Quality assurance
• Legal record
• Financial coding, billing
• Research prospective, retrospective

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Characterizing the RecordRepresenting the
Patients True State
True State of Patient
Diagnostic study
Clinician
Paper chart
Dictation
Transcription
Data entry clerk
CPR/Chart Representation
Hogan, Wagner. JAMIA 19974342-55
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Characterizing the RecordRepresenting the
Patients True State

• Completeness Proportion of observations
  actually recorded
• 67 - 100
• Correctness Proportion of recorded observations
  that are correct
• 67 - 100

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Functional Components

• Integration of data
• Standards Messaging (HL7), terminology (LOINC,
  SNOMED, ICD9, etc), data model (HL7 RIM)
• Interface engine
• Clinical decision support
• Order entry
• Knowledge sources
• Communication support Multidisciplinary,
  consultation

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Laboratory
Pharmacy
Radiology
Data Warehouse
CDR
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History of the Medical Record

• 1910 Flexner Report--Advocated maintaining
  patient records
• 1940s Hospitals need records for accreditation
• 1960s Electronic HIS--communication (routing
  orders) charge capture
• 1969 Weed--POMR
• 1980s IOM report, academic systems
• 1990s - present Increasing commercial systems,
  increasing prevalence, emphasis on
  interoperability standards (ONCHIT, etc)

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Trend Toward Outpatient Records

• Inpatient record structured first
• Regulatory requirement
• Many contributors (vs solo family practitioner)
• Reimbursement More money than outpatient visits
• Now, more attention to outpatient records
• Multidisciplinary/team care
• Managed care

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Who Enters Data

• Clerk
• Physician Primary, consultant, extender
• Nurse
• Therapist
• Lab reports/ancillary systems
• Machines Monitors, POC testing

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Fundamental Issue Data Entry

• Data capture External sources
• Laboratory information systems, monitors, etc
• Challenges Interfaces, standards
• Data input Direct entry by clinicians staff
• Challenge Time-consuming and expensive
• Free text vs structured entry

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Data Input

• Transcription of dictation Very expensive,
  error-prone
• Encounter form Various types
• Free-text entry
• Scannable forms
• Turnaround document Both presents captures
  data
• Direct electronic entry
• Free-text typing
• Structured entry Pick lists, etc
• Voice recognition

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Weakness of Paper Record

• Find the record Lost, being used elsewhere
• Find data within the record Poorly organized,
  missing, fragmented
• Read data Legibility
• Update data Where to record if chart is missing
  (e.g., shadow chart)
• Only one view
• Redundancy Re-entry of data in multiple forms
• Research Difficult to search across patients
• Passive No decision support

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Advantages of Computer Records

• Access Speed, remote location, simultaneous use
  (even if just an electronic typewriter)
• Legibility
• Reduced data entry Reuse data, reduce redundant
  tests
• Better organization Structure
• Multiple views Aggregation
• Example Summary report, structured flow sheet
  (contrast different data types)
• Alter display based on context

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Advantages of Computer Records (continued)

• Automated checks on data entry
• Data prompts Completeness
• Range check (reference range)
• Pattern check ( digits in MRN)
• Computed check (CBC differential adds to 100)
• Consistency check (pregnant man!)
• Delta check
• Spelling check

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Advantages of Computer Records (continued)

• Automated decision support
• Reminders, alerts, calculations, ordering advice
• Limited by scope/accuracy of electronic data
• Tradeoff Data specificity/depth of advice vs
  time/cost of completeness
• Cross-patient analysis
• Research
• Stratify patient prognosis, treatment by risks
• Data review Avoid overlooking uncommon but
  important events

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Advantages of Computer Records (continued)

• Saves time?
• 1974 study find data 4x faster in flow sheet vs
  traditional record (10 of subjects could not
  even find some data
• 2005 systematic review
• RN POC systems decreased documentation time 24
• MD increased documentation time 17
• CPOE More than doubled time

Poissant L, Pereira J, Tamblyn R, Kawasumi Y.
The impact of electronic health records on the
time efficiency of physicians and nurses a
systematic review. J Am Med Inform Assoc
200512(5)505-16.
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Key Ingredients for CPR Success

• Wide scope of data
• Sufficient duration of data
• Understandable representation of data
• Sufficient access
• Structured data More than just a giant word
  processor

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Disadvantages of Computer Records

• Access Security concerns
• Still, logging helps track access
• Initial cost
• Attempted solutions Reimbursement, Office VistA
• Delay between investment benefit
• System failure
• Challenge of data entry
• Coordination of disparate groups
• Data diversity Different data elements, media
  (images, tracings), format, units, terminology,
  etc

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Examples Classical EMRs

• COSTAR
• Originally in 1960s, disseminated in late 1970s
• Encounter form input
• Modular design security, registration,
  scheduling, billing, database, reporting
• MQL ad hoc data queries
• Display by encounter or problem (multiple views)

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Classical EMRs (continued)

• RMRS McDonald (IU), 1974
• TMR Stead Hammond (Duke), 1975
• STOR Whiting-OKeefe (UCSF), 1985

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Adoption

• No advantage if not used!
• Varying prevalence in USA
• 20-25 (CHCF, Use and Adoption of Computer-based
  Patient Records, October, 2003)
• 20 (MGMA, January, 2005)
• 17 (CDC ambulatory medical care survey 2001-3,
  published March, 2005)
• Higher prevalence elsewhere
• Netherlands 90, Australia 65
• Reasons Single-payer system, certification,
  cost-sharing

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Barriers to EHR Adoption

• Financial Up-front costs, training, uncertain
  ROI (misalignment of benefits costs), finding
  the right system
• Cultural Attitude toward IT
• Technological Interoperability, support, data
  exchange
• Organizational Integrate with workflow,
  migration from paper

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Improving Adoption

• Interoperability Increase chance that EHRs can
  be used with each other other systems
• Systemic Interoperability Commission
• Compensation
• CPT code CMS trial
• P4P Reporting measures decision support to
  improve performance
• Standards
• Certification CCR, EHR Functional Model
  Specification
• HIPAA/NCVHS CHI

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Improving Adoption CCR

• ASTM E31 WK4363 (2004). Coalition AAP, AAFP,
  HIMSS, ACP, AMA, etc
• Defines the core data elements content of the
  patient record in XML
• Read/write standard data elements Snapshot of
  the record
• Therefore increases interoperability
• Uses Record sharing, eRx (allergies,
  medications), certification
• Components standard content elements
  spreadsheet implementation guide XML schema

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CCR HEADER
CCR BODY
CCR FOOTER
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Improving AdoptionEHR Functional Model
Specification

• HL7 2004 Funded by US Government
• Identifies key functions of the EHR
• Purpose
• Guide development by vendors
• Facilitate certification
• Facilitate interoperability
• Certification governance CCHIT

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Improving Adoption DOQ-IT

• Doctors Office Quality - Information Technology
• Outgrowth of CMS-funded QIOs
• ACP, Lumetra, etc
• Goal Overcome barriers to EHR adoption
• Interventions
• Expert advice Needs assessment, vendor
  selection, case management, workflow integration
• Peer-to-peer dialog Share best practices
• Does not provide funding, day-to-day assistance

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Improving Adoption Office VistA

• VistA Veterans Information System Technology
  Architecture
• M-based comprehensive VA EHR
• Includes CPRS Computer-based Patient Record
  System
• Office VistA
• Outpatient version
• Due for release Q4 2005 (available under FOIA)
• Challenge Free up front, but need to implement
  and maintain

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Improving Adoption RHIOs

• Facilitates interoperability Mechanism for
  exchanging data between organizations
• Important elements
• Standards Messaging, data model, terminology
• Mechanism Clearinghouses
• Part of a federated NHIN
• Important driver Public health
• Integrate data from many HCOs
• Syndromic surveillance (e.g., RODS, etc)
• Examples Santa Barbara Indiana CalRHIO

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Improving Adoption through StandardsArchitectura
l Elements to Support EHRs

• Components to support decision support
• Central data repository Data models
• Controlled, structured vocabulary
• Data messaging (HL7 v2.x, v3)
• Decision Support
• Knowledge acquisition
• Knowledge representation (KR)

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HL7 EHR/CDSS Standards Efforts

• Components
• Data model RIM
• (Standard vocabularies)
• CDA documents
• Access CCOW
• Knowledge representation
• Common Expression Language (GELLO)
• Arden Syntax
• Clinical guidelines GEM vs GLIF3 vs ?
• InfoButton
• Order Set
• EHR Functional Model and Standard

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Standard Data Models

• Candidates
• RIM HL7 Reference Information Model
• vMR Virtual Medical Record
• Purpose Promote knowledge transfer
• Standardize references to patient data in rules
• Goal Avoid manual rewriting of data references
  when sharing rules

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Standard Data Models HL7 RIM

• High-level, abstract model of all exchangeable
  data
• Concepts are objects Act (e.g., observations),
  Living Subject, etc
• Object attributes
• Relationship among objects
• Common reference for all HL7 v3 standards
• Facilitates interoperability Common model for
  messaging, queries
• Schadow G, Russler DC, Mead CN, McDonald
  CJ. Integrating medical information and
  knowledge in the HL7 RIM. Proc AMIA Symp
  2000764-768.

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Standard Vocabularies

• CHI NCVHS efforts Patient Medical Record
  Information (PMRI) terminology standards
• Examples SNOMED-CT, ICD-9, LOINC, CPT, etc
• Facilitation Free licensing of SNOMED in USA as
  part of UMLS
• Use HL7 Common Terminology Services (CTS)
  standard

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Common Expression Language (GELLO)

• Purpose Share queries and logical expressions
• Query data (READ)
• Logically manipulate data (IF-THEN, etc)
• Current work GELLO (BWH) Guideline Expression
  Language
• Current status ANSI standard release 1, May,
  2005
• Ogunyemi O, Zeng Q, Boxwala A. Object-oriented
  guideline expression language (GELLO)
  specification Brigham and Womens Hospital,
  Harvard Medical School, 2002. Decision Systems
  Group Technical Report DSG-TR-2002-001.

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Arden Syntax

• ASTM v1 1992, HL7 v2 1999, v2.1 (ANSI) 2002, v2.5
  2005 http//cslxinfmtcs.csmc.edu/hl7/arden/
• Formalism for procedural medical knowledge
• Unit of representation Medical Logic Module
  (MLM)
• Enough logic data to make a single decision
• Generate alerts/reminders
• Adopted by several major vendors
• Jenders RA, Dasgupta B.  Challenges in
  implementing a knowledge editor for the Arden
  Syntax knowledge base maintenance and
  standardization of database linkages. Proc AMIA
  Symp 2002355-359.

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Guideline Model GLIF

• Guideline Interchange Format
• Origin Study collaboration in medical
  informatics
• Now GLIF3
• Very limited implementation
• Guideline Flowchart of temporally ordered steps
• Decision action steps
• Concurrency Branch synchronization steps
• Peleg M, Ogunyemi O, Tu S et al. Using features
  of Arden Syntax with object-oriented medical data
  models for guideline modeling. Proc AMIA Symp
  2001523-527.

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GLIF (continued) Levels of Abstraction

• Conceptual Flowchart
• Computable Patient data, algorithm flow,
  clinical actions specified
• Implementable Executable instructions with
  mappings to local data

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Guideline Model GEM

• Guideline Elements Model Current ASTM standard
• Mark up of a narrative guideline into structured
  format using XML
• Not procedural programming
• Tool GEM Cutter
• Resulting structure might be used to translate to
  executable version
• Shiffman RN, Agrawal A, Deshpande AM, Gershkovich
  P. An approach to guideline implementation with
  GEM. Proc Medinfo 2001271-275.

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GEM (continued)

• Model 100 discrete elements in 9 major
  branches
• identity and developer, purpose, intended
  audience, development method, target population,
  testing, revision plan and knowledge components
• Iterative refinement Adds elements not present
  verbatim but needed for execution
• Customization Adding meta-knowledge
• controlled vocabulary terms, input controls,
  prompts for data capture

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Infobutton Standard

• Infobutton software that mediates between an
  information system (EHR) and a knowledge source
  (electronic textbook, drug reference, etc)
• Goals
• Standard interface to maximize access to
  knowledge sources
• Tailored access to relevant bits
• Status Under development (HL7).

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Order Set Standard

• Order Set Document containing a group of orders
  for specific care episodes (disease states or
  presentations)
• Examples Admission for chest pain
  community-acquired pneumonia
• Features
• Checklist Remind clinicians what to do
• Advice Provide therapeutic options, dosing, etc
• Goals Allow selection of parts or all of order
  set within a CPOE system. Facilitate sharing.
• Current status (HL7) Under development

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Case Study / DemonstrationCedars-Sinai Medical
Center
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CSMC Characteristics

• Academic medical center 800-bed campus in West
  LA founded 1902
• 8500 employees 1800 physicians (200 UCLA
  faculty)
• 45,000 discharges 28,000 clinic visits 77,000
  ED visits
• Rankings USNWR, Hospitals Health Networks
• Education
• GME 300 residents/fellows
• Health professions education UCLA, USC, CSU
• Burns Allen Research Institute 80M/year

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EHR at CSMC

• Components
• CDR
• HL7 communication interfaces (lab, imaging, etc)
• Vocabulary server (CHARLIE using CTS)
• Accessing data Electronic health records
• Web/VS
• Logician
• Knowledge sources
• Electronic textbooks
• Bibliographic access
• InfoButtons
• Order Sets

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Laboratory
Pharmacy
Radiology
Data Warehouse
CDR
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Centricity Demonstration
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Summary

• EHR needed
• Many advantages, some disadvantages
• Key integration of data
• Aspects of the EHR Functions, advantages,
  disadvantages
• Improving adoption
• Standards, interoperability

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Additional Resources

• Shortliffe Chapter 9 (new edition due 2006)
• Degoulet P, Fieschi M. Managing patient records.
  Chapter 9 in Introduction to Clinical
  Informaitcs. New York Springer-Verlag,
  1997117-30.
• van Bemmel JH, Musen MA. The patient record
  (chapter 7) Structuring the computer-based
  patient record (chapter 29) in Handbook of
  Medical Informatics. Houten, Netherlands
  Springer, 1997.
• Bates DW, Ebell M, Gottlieb E et al. A proposal
  for electronic medical records U.S. primary care.
  J Am Med Inform Assoc 2003101-10.

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Additional Resources Web

• www.astm.org
• www.hl7.org
• www.calrhio.org
• www.cchit.org

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Thanks!

• jenders_at_ucla.edu
• http//jenders.bol.ucla.edu -gt Documents
  Presentations