phiconnect cdc center of excellence in public health informatics e s p michael klompas md, mph,...

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


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 in

Public Health Reports, 1913-1951


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


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

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

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


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


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


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


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


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

Patient demographicsResponsible clinician, site, contact infoBasis for condition being detectedTreatment givenSymptoms (ICD9 code & temperature)Pregnancy status (if pertinent)


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

© Google Maps

Boston, MA


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

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

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


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


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 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



ManualReports

ESPChang

e

Pregnancy status reported

22/445 (5%)

649/649(100%)

20x

Number of pregnancies identified

5/445(1%)

86/649(13%)

12x



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


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%


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

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


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 Stepsadd more conditions

Additional diseases to be added to ESP In progress:

Lyme disease Measles Mumps Rubella and others…


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 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


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 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

© Google Maps


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?


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

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

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


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



PEDIATRIC URINE CHLAMYDIA 87591 2487 16601-7


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 LCR, URINE 87492 2026 16601-7



PEDIATRIC URINE CHLAMYDIA 87591 2487 16601-7

Proprietary code

Multiple codes for same test

Incorrect code Obsolete code


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')]


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

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 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]

phiconnect cdc center of excellence in public health informatics e s p michael klompas md, mph,...

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