Skip this Video
Download Presentation

Loading in 2 Seconds...

play fullscreen
1 / 44

E S P - PowerPoint PPT Presentation

  • Uploaded on

E S P. E lectronic medical record S upport for P ublic health. Integrated Surveillance Seminar Series National Center for Public Health Informatics December 12, 2007. Michael Klompas MD, MPH, FRCPC CDC Center of Excellence in Public Health Informatics (NCPHI PH000238D)

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'E S P' - aden

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript


Electronic medical recordSupport forPublic health

Integrated Surveillance Seminar SeriesNational Center for Public Health Informatics

December 12, 2007

Michael Klompas MD, MPH, FRCPC

CDC Center of Excellence in Public Health Informatics (NCPHI PH000238D)

Harvard Medical School, Boston, MA

CDC Center of Excellence in Public Health Informatics (Boston)funded by the National Center for Public Health Informatics
  • Harvard Medical School / Harvard Pilgrim Health Care Department of Ambulatory Care and Prevention
  • Children’s Hospital Informatics Program
  • Massachusetts Department of Public Health
  • Harvard Vanguard Medical Associates (for Atrius Health)
  • Brigham and Women’s Hospital Channing Laboratory

“No health department, State or local, can effectively prevent or control disease without knowledge of when, where, and under what conditions cases are occurring”

Introductory statement printed each week inPublic Health Reports, 1913-1951

the evolution of notifiable disease reporting
The evolution of notifiable disease reporting
  • Traditional paper based reporting
    • Clinically detailed
    • Slow, often incomplete, labour intensive, dependent on clinician initiative
  • Web based notifiable disease reporting
    • Great improvement in speed and accessibility of data (received in electronic form)
    • But still requires clinician initiative to report
  • Electronic laboratory based reporting
    • Fast, accurate, often digital, no need for clinician initiative
limitations of electronic laboratory reporting
Limitations of Electronic Laboratory Reporting
  • Often missing detailed demographic information on patient and clinician contact details
  • No information on patient symptoms, pregnancy status, or prescribed treatment
  • Typically does not integrate multiple tests to yield a diagnosis
    • e.g. negative HIV ELISA and high viral load = acute HIV
  • No clues that lab test might be false positive
    • e.g. positive Hep A IgM but no order for liver function tests
  • Cannot report purely clinical diagnoses
    • e.g. Pelvic inflammatory disease, Lyme erythema migrans
  • Typically generates multiple reports on the same patient for the same condition
    • e.g. chronic hepatitis B
our goal
Our goal
  • Combine the best of traditional clinician-initiated reporting and electronic laboratory reporting systems:
    • Fast, accurate, clinically detailed, digital reports

Clinician initiated manual reporting

Electronic laboratory reporting

Automated disease detection and reporting from electronic medical records

allied goals
Allied goals
  • Create a generalizable architecture for disease detection and reporting that is agnostic to the source EMR system
  • Digitize notifiable disease reporting at the provider level to potentially feed NEDSS reporting from states to CDC
e lectronic s upport for p ublic health esp
Electronic Support for Public health (ESP)
  • Software and architecture to automate detection and reporting of notifiable diseases
    • Surveys codified electronic medical record data for patients with notifiable conditions
    • Generates and sends secure case HL7 reports to the health department
esp automated detection and reporting of notifiable conditions

Health Department


lab results

HL7 electronic case reports of notifiableconditions


vital signs



ESP: Automated detection and reporting of notifiable conditions

Practice EMR’s

ESP Server

decoupled architecture


Decoupled architecture

ESP decoupled from host electronic medical record (EMR)


case management interface
Case Management Interface
  • All potential cases available for review by infection control personnel prior to transmission to the health department (optional functionality)
report to health department
Report to Health Department
  • Patient demographics
  • Responsible clinician, site, contact info
  • Basis for condition being detected
  • Treatment given
  • Symptoms (ICD9 code & temperature)
  • Pregnancy status (if pertinent)
current status operational in atrius health january 2007 to present
Atrius Health

27 multispecialty practices in MA


~600,000 patients

>500 clinicians

ESP server resides in the central data processing center

Analyzes data from all 27 sites

Current Status: Operational in Atrius HealthJanuary 2007 to present

Boston, MA

© Google Maps

current status
Current Status
  • Currently reporting chlamydia, gonorrhea, pelvic inflammatory disease, and acute hepatitis A. To date:
      • 1143 cases of chlamydia
      • 151 cases of gonorrhea
      • 25 cases of pelvic inflammatory disease
      • 6 cases of acute hepatitis A
  • Definitions under validation for:
      • Acute and chronic hepatitis B
      • Acute hepatitis C
      • Tuberculosis
case identification
Case Identification
  • Logical combinations of laboratory test results, diagnostic codes, vital signs, and / or medication prescriptions
  • Case definitions tested and refined against up to 18 years of historical EMR data
    • Charts reviewed on all patients identified by algorithms
    • Comparison with Massachusetts DPH disease lists to identify patients missed by the algorithms
    • Repeatedly refine algorithm to maximize accuracy
case identification logic chlamydia
Case Identification Logic:Chlamydia

Positive test for any of the following:

case identification logic acute hepatitis b
Case Identification Logic:Acute Hepatitis B
  • Both of the following:
    • ICD9 for jaundice OR liver function tests > 5x normal
    • IgM to core antigen


  • All five of the following:
    • ICD9 for jaundice OR liver function tests > 5x normal
    • Bilirubin ≥1.5
    • Hep B surface antigen or ‘e’ antigen present
    • No prior positive Hep B specific lab tests
    • Absence of ICD9 code for chronic hepatitis B


  • Transition from negative to positive Heb B surface antigen
case identification logic active tuberculosis
Case Identification LogicActive Tuberculosis
  • Any of the following:
    • Prescription for pyrazinamide OR
    • Order for AFB smear or culture followed by ICD9 code for TB within 60 days OR
    • Order for 2 or more anti-tuberculous medications followed by an ICD9 code for TB within 60 days
manual versus electronic reporting atrius health june 2006 july 2007
Manual versus electronic reportingAtrius Health, June 2006 - July 2007

*generated by dedicated infection control reporting staff

manual versus electronic reporting atrius health june 2006 july 200725
Manual versus electronic reportingAtrius Health, June 2006 - July 2007

* Including transposition of first and last name, incorrect first name, and spelling errors

* EMR spelling presumed as gold standard

clinical details on false positive cases
Clinical details on false positive cases
  • Pelvic inflammatory disease
    • Pelvic pain, positive cultures for Herpes simplex and Chlamydia
  • Acute Hepatitis A
    • Young woman with 10 days pharyngitis and fatigue, monospot negative, HAV IgM+ and EBV VCA IgM+
  • Tuberculosis
    • Patient exposed to MDR TB but no active disease
    • Patient with prior history of TB presenting with hemoptysis and nodules on chest radiograph
sorting through positive hep b results esp versus elr

5 acute

133chronic cases

138 distinct patients

600 positive test results for hepatitis B

Sorting through positive Hep B Results - ESP versus ELR



missed cases
Missed Cases
  • 5 cases known to DPH missed by ESP (versus 266 cases known to ESP but missed by DPH)
    • 0.6% of all known cases
    • All missed cases were tests that were edited after placement into EMR – updated results were not forwarded to ESP
  • 11 cases missed during upgrade of source EMR due to transient interruption of data flow to ESP
    • Subsequently discovered and retrieved
next steps add more conditions
Next Stepsadd more conditions
  • Additional diseases to be added to ESP
  • In progress:
    • Lyme disease
    • Measles
    • Mumps
    • Rubella
    • and others…
protocol for vaccine preventable diseases
Protocol for vaccine preventable diseases
  • Measles / mumps / rubella
    • Report any patient with ICD9 code or lab order for IgM to measles / mumps / rubella
      • ICD9 code and lab orders are proxies for clinician suspicion
      • Immediate reporting to jump start public health investigation
    • Include patient’s immunization history in the report
    • Include clinician contact number to facilitate investigation
    • Simultaneously send ordering clinician a brief electronic questionnaire on patient exposures, symptoms, etc. that ESP will immediately forward to public health
next steps new applications to broaden utility of the esp platform
Next StepsNew applications to broaden utility of the ESP platform
  • Vaccine adverse event surveillance and reporting
    • Prospective surveillance of patients given a vaccine for 30 days
    • Seek novel diagnoses, suggestive biochemical changes, and new vaccine allergies suggestive of possible vaccine adverse effect
    • Elicit clinician comment on purported adverse reaction
    • Immediate electronic reporting to VAERS if clinician agrees
next steps new applications to consider
Next StepsNew applications to consider
  • The ESP model could also be a suitable platform for other public health priorities
    • Patient safety initiatives
      • e.g. follow-up on critical test results, drug interactions, renal dose adjustments, medication adverse effects, missing health maintenance activities, vaccine registries…
    • Syndromic surveillance
    • Asthma surveillance and cluster detection
  • Add insurance claims to increase the robustness and completeness of disease identification
next steps implement esp in a new site
Next stepsimplement ESP in a new site
  • Planning underway to implement ESP in the health information exchange of North Adams, MA (serving 14 local practices)
  • Different EMR, different user culture

North Adams


© Google Maps

next steps disseminating esp beyond massachusetts
Next StepsDisseminating ESP beyond Massachusetts
  • ESP software is freely available under a lesser general public license


  • Installation and maintenance of new ESP systems will require significant IT, epidemiologic, and administrative expertise and resources
  • Is this a role for CDC?
barriers to broader implementation of esp
Barriers to broader implementation of ESP
  • Only about 35% of multi-physician practices have EMR’s
  • Limited breadth of information capture by many EMR’s
  • Different coding nomenclature & cultures in different EMR’s
  • Constant influx of new lab, diagnosis, and med codes
  • Absence of standardized disease definitions tailored to electronic data
  • Absence of standardized reporting elements for most diseases
  • Paucity of resources to support implementation and support of ESP-like systems
  • Public wariness of electronic surveillance and health reporting
heterogenous emr systems
Heterogenous EMR systems
  • Problem:
    • Vast array of different EMR systems on the market with different capabilities and operating protocols
  • Solution:
    • ESP decoupled from the host EMR to permit compatibility with multiple different EMR systems
    • Host EMR need only be capable of exporting plain text files with recent encounter data
heterogenous coding practices
Heterogenous coding practices
  • Problem:
    • Different EMR systems use different coding systems
    • Coding often arbitrary and idiosyncratic
  • Solution:
    • Map proprietary codes to universal nomenclatures
    • Only need to map codes pertinent to notifiable disease detection
      • thus far about 30 code maps in ESP
cpt to loinc map challenges
CPT to LOINC Map - Challenges

Proprietary code

Obsolete code

Multiple codes for same test

Incorrect code

new lab and drug codes
New lab and drug codes
  • Problem:
    • New lab and drug codes constantly being added to EMR’s
  • Solution:
    • ESP constantly scans all incoming data to identify new candidate codes

-----Original Message-----

From: [email protected]

Sent: September 27, 2007 8:18 AM

To: Klompas, Michael,M.D.

Subject: ESP management on 2007-09-27 12:17:39.187975

New (CPT,COMPT,ComponentName):

[(\'87591\', \'4323\', \'NEISSERIA GONORRHOEAE, DNA, SDA, OTV\')]

standardization and maintenance of disease definitions
Standardization and Maintenanceof Disease Definitions
  • Problem:
    • Currently no standardized definitions for identification of notifiable diseases from EMR data
      • Standardization essential for data comparability across sites
      • Validation of definitions requires large populations to assure algorithm accuracy for rare diseases
  • Possible solutions:
    • A role for CDC? CSTE? Health departments? Academics?
    • CDC and CSTE already collaborating to define electronic reporting elements for notifiable diseases
dissemination of esp like systems
Dissemination of ESP-like systems
  • Problem:
    • Where should disease detection and reporting be integrated into the health care system?
  • Possible solutions:
    • Integrate ESP logic into EMR software
      • Make notifiable disease reporting a HITSP standard for EMR certification
    • Install ESP-like systems in regional health information exchanges
      • Can CDC lead and support this effort?
    • Use ESP case identification definitions on Biosense data
esp team
ESP Team
  • Harvard Medical School / Harvard Pilgrim Health Care Department of Ambulatory Care and Prevention
    • Richard Platt MD, MSc  Ross Lazarus MBBS, MPH, MMed
    • Julie Dunn MPH  Michael Calderwood MD
    • Ken Kleinman ScD Yury Vilk PhD
    • Kimberly Lane MPH
  • Harvard Vanguard Medical Associates
    • Francis X. Campion MD
    • Benjamin Kruskal MD, PhD
  • Massachusetts Department of Public Health
    • Alfred DeMaria MD  Bill Dumas RN
    • Gillian Haney MPH  Daniel Church MPH
    • James Daniel MPH  Dawn Heisey MPH
  • Channing Laboratory of Brigham and Women’s Hospital
    • Xuanlin Hou MSc

Collaborators Wanted!

Contact: [email protected]