PHIConnect CDC Center of Excellence in Public Health Informatics
E S PE S P
Michael Klompas MD, MPH, FRCPCCDC Center of Excellence in Public Health Informatics (NCPHI
PH000238D)Harvard Medical School, Boston, MA
Electronic medical recordSupport forPublic health
Integrated Surveillance Seminar SeriesNational Center for Public Health Informatics
December 12, 2007
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
“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 in
Public Health Reports, 1913-1951
PHIConnect CDC Center of Excellence in Public Health Informatics
PHIConnect CDC Center of Excellence in Public Health Informatics
The evolution of notifiable disease reporting
Traditional paper based reporting Clinically detailed Slow, often incomplete, labour intensive,
dependent on clinician initiativeWeb based notifiable disease reporting
Great improvement in speed and accessibility of data (received in electronic form)
But still requires clinician initiative to reportElectronic laboratory based reporting
Fast, accurate, often digital, no need for clinician initiative
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
Practice EMR’s
ESP Server
D P H
Health Department
HL7 electronic
case reports of notifiableconditions
ESP: Automated detection and reporting of notifiable conditions
diagnoseslab results
meds
demographics
vital signs
PHIConnect CDC Center of Excellence in Public Health Informatics
Decoupled architecture
ESP decoupled from host electronic medical record (EMR)
Implications
Makes the system agnostic to the source EMR
Universal
Less onerous to add / change disease definitions
Flexible
Can still remain within host practice’s firewall
Secure
Offloads computing burden from clinical systems and invisible to clinicians
Unobtrusive
EMR
ESP
PHIConnect CDC Center of Excellence in Public Health Informatics
All incoming data mapped to universal nomenclatures
Category Format
Diagnostic codes ICD9
Lab test orders & results
CPT codes mapped to LOINC
Prescriptions NDC codes and generic names
Diagnoses and organisms
SNOMED
PHIConnect CDC Center of Excellence in Public Health Informatics
Case Management Interface
All potential cases available for review by infection control personnel prior to transmission to the health department (optional functionality)
PHIConnect CDC Center of Excellence in Public Health Informatics
PHIConnect CDC Center of Excellence in Public Health Informatics
PHIConnect CDC Center of Excellence in Public Health Informatics
Report to Health Department
Patient demographicsResponsible clinician, site, contact infoBasis for condition being detectedTreatment givenSymptoms (ICD9 code & temperature)Pregnancy status (if pertinent)
PHIConnect CDC Center of Excellence in Public Health Informatics
Atrius Health27 multispecialty practices in MA
EPIC EMR ~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
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Boston, MA
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
Case Identification Logic: Chlamydia
Positive test for any of the following:Test Name CPT
Component LOINC
CHLAMYDIA PCR, URINE (MALES 86631 83521613-
5
CHLAMYDIA TRACHOMATIS CULTURE 87110 3474 6349-5
CHLAMYDIA GENPROBE DNA 87491 131220993-
2
PEDIATRIC URINE CHLAMYDIA 87491 248721613-
5
CHLAMYDIA TRACHOMATIS DNA, SDA 87491 280121613-
5
CHLAMYDIA TR DNA 87491 287821613-
5
CHLAMYDIA TR DNA URN 87491 287921613-
5
CHLAMYDIA TR. DNA 87491 290621613-
5
CHLAMYDIA TRACHOMATIS, DNA PROBE, FEMALE 87491 4312
20993-2
CHLAMYDIA TRACHOMATIS, DNA, SDA 87491 432021613-
5
CHLAMYDIA TRACHOMATIS 87491 480321613-
5
PEDIATRIC URINE CHLAMYDIA 87591 248716601-
7
URINE GC AND CHLAMYDIA, PEDIATRIC BY APT 87591 268636902-
5
CHLAMYDIA & GC WITH REFLEX TO IDENTIFICATION 87800 4310
36902-5
PHIConnect CDC Center of Excellence in Public Health Informatics
Case Identification Logic:Acute Hepatitis B
Both of the following: ICD9 for jaundice OR liver function tests > 5x normal IgM to core antigen
OR 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
OR Transition from negative to positive Heb B surface
antigen
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
Manual versus electronic reportingAtrius Health, June 2006 - July 2007
ManualReports
*
ESPChang
e
Chlamydia 545 758 39%
Gonorrhea 62 95 53%
Pelvic Inflammatory Disease
0 20
Acute Hepatitis A 1 4
total 608 877 44%*generated by dedicated infection control reporting staff
PHIConnect CDC Center of Excellence in Public Health Informatics
Manual versus electronic reportingAtrius Health, June 2006 - July 2007
ManualReports
ESPChang
e
Pregnancy status reported
22/445 (5%)
649/649(100%)
20x
Number of pregnancies identified
5/445(1%)
86/649(13%)
12x
PHIConnect CDC Center of Excellence in Public Health Informatics
Manual versus electronic reportingAtrius Health, June 2006 - July 2007
ManualReports
ESPChang
e
Treatment details reported
524/607(86%)
873/873(100%)
16%
Transcription errors (patient names)*
34/607(6%)
NA
* Including transposition of first and last name, incorrect first name, and spelling errors
* EMR spelling presumed as gold standard
PHIConnect CDC Center of Excellence in Public Health Informatics
Accuracy
Condition Total Cases
FalsePositiv
es
PositivePredictive
Value
Chlamydia 1143 0 100%
Gonorrhea 151 0 100%
Pelvic inflammatory disease
25 1 96%
Acute hepatitis A 6 1 83%
Acute hepatitis B 5 0 100%
Tuberculosis 11 2 82%
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
Sorting through positive Hep B Results - ESP versus ELR
138 distinct patients
5 acute
133chronic cases
600 positive test results for hepatitis BE L
R
E L
R
E S
P
E S
P
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
Next Stepsadd more conditions
Additional diseases to be added to ESP In progress:
Lyme disease Measles Mumps Rubella and others…
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
Next StepsNew applications to broaden utility of the ESP
platformVaccine 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
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
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
Boston
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PHIConnect CDC Center of Excellence in Public Health Informatics
Next StepsDisseminating ESP beyond Massachusetts
ESP software is freely available under a lesser general public license
But…
Installation and maintenance of new ESP systems will require significant IT, epidemiologic, and administrative expertise and resources
Is this a role for CDC?
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
Heterogenous coding practices
Problem: Different EMR systems use different coding
systems Coding often arbitrary and idiosyncratic
Solution: Map proprietary codes to universal
nomenclatures LOINC, SNOMED, ICD9, NDC
Only need to map codes pertinent to notifiable disease detection
thus far about 30 code maps in ESP
PHIConnect CDC Center of Excellence in Public Health Informatics
Local codes mapped to universal codesCPT to LOINC mapping (Atrius Health)
Test Name CPT COMPONENT LOINC
CHLAMYDIA PCR, URINE (MALES) 86631 835 16601-7
CHLAMYDIA GENPROBE DNA LA0219 1312 16600-9
CHLAMYDIA GENPROBE DNA 87178 16600-9
CHLAMYDIA GENPROBE DNA 87491 1312 16600-9
CHLAMYDIA LCR, URINE 87492 2026 16601-7
CHLAMYDIA GENPROBE DNA 87800 1312 16600-9
CHLAMYDIA GENPROBE DNA 87800 2178 16600-9
PEDIATRIC URINE CHLAMYDIA 87591 2487 16601-7
PHIConnect CDC Center of Excellence in Public Health Informatics
CPT to LOINC Map - Challenges
Test Name CPT COMPONENT LOINC
CHLAMYDIA PCR, URINE (MALES) 86631 835 16601-7
CHLAMYDIA GENPROBE DNA LA0219 1312 16600-9
CHLAMYDIA GENPROBE DNA 87178 16600-9
CHLAMYDIA GENPROBE DNA 87491 1312 16600-9
CHLAMYDIA LCR, URINE 87492 2026 16601-7
CHLAMYDIA GENPROBE DNA 87800 1312 16600-9
CHLAMYDIA GENPROBE DNA 87800 2178 16600-9
PEDIATRIC URINE CHLAMYDIA 87591 2487 16601-7
Proprietary code
Multiple codes for same test
Incorrect code Obsolete code
PHIConnect CDC Center of Excellence in Public Health Informatics
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 AMTo: 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')]
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
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
PHIConnect CDC Center of Excellence in Public Health Informatics
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]