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 -> Documents & Presentations

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

Estimated annual mortality
Air travel deaths
AIDS
Breast cancer
Highway fatalities
Preventable medical errors
( 1 jet crash/day )
300
16,500
43,000
43,500
44,000 -
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.

• 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 2003;348:2635-2645.
• 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 1992;268(2):240-8.

• 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 1993;269(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
1999;6(4):313-21.

• 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 1998;280(15):1339-46.

• 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

• 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”

• 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

True State of Patient
Diagnostic study
Clinician
Paper chart Dictation
Data entry clerk Transcription
CPR/Chart Representation
Hogan, Wagner. JAMIA 1997;4:342-55

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

• 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

Laboratory
Web/VS
CMA
Medical
Logic
Billing &
Financial
Pharmacy
Database
Interface
Radiology
CDR
CHARLIE
(CTS)
Data
Warehouse

• 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)

• 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

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

• 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

• 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

• 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

• 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

• 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

• 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

• 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 2005;12(5):505-16.

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

• 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

• 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)

• RMRS: McDonald (IU), 1974
• TMR: Stead & Hammond (Duke), 1975
•
STOR: Whiting-O’Keefe (UCSF), 1985

• No advantage if not used!
• Varying prevalence in USA
– 20-25% (CHCF, “Use and Adoption of Computerbased 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, costsharing

• 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

• 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

• 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

CCR HEADER
CCR
BODY
CCR FOOTER

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

• Doctor’s 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

• 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

• 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

• 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)

• 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

• 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”

• 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 2000;:764-768.

• 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

• 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 Women’s Hospital, Harvard Medical School,
2002. Decision Systems Group Technical Report DSG-TR-2002-001.

• 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 2002;:355-359.

• 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 objectoriented medical data models for guideline modeling. Proc AMIA Symp
2001;:523-527.

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

• 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 2001;271-275.

• 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

• 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).

• Order Set: Document containing a group of orders for specific care episodes (disease states or presentations)
–
Examples: Admission for chest pain; communityacquired 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

• 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: USN&WR, Hospitals & Health Networks
• Education
– GME: 300+ residents/fellows
– Health professions education: UCLA, USC, CSU
•
Burns & Allen Research Institute: $80M/year

• 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

Laboratory
Web/VS
CMA
Medical
Logic
Billing &
Financial
Pharmacy
Database
Interface
Radiology
CDR
CHARLIE
(CTS)
Data
Warehouse

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

•
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,
1997;117-30.
• 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
2003;10:1-10.

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

jenders@ucla.edu
http://jenders.bol.ucla.edu -> Documents & Presentations