Advanced Disease Identification Electronic Medical Record Data with Electronic Medical Record Data Public Health Information Network Conference August 28, 2008 Michael Klompas MD, MPH, FRCPC CDC Center of Excellence in Public Health Informatics Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA

CDC Center of Excellence in Public Health Informatics (Boston) 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 2

Overview Overview of Electronic medical record Support for Public Health (ESP) Available data elements in ESP Sample disease identification algorithms Algorithm development example: acute hepatitis B

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 electronic case reports to the state health department MMWR 2008;57:372-375 Advances Disease Surveillance 2007;3:3 4

ESP: Automated detection and reporting of notifiable conditions Practice EMR’s ESP Server D P H Health Department diagnoses lab results meds demographics vital signs HL7 electronic case reports of notifiable conditions I added “vital signs”

ESP Operational in Atrius Health January 2007 to present ~27 multispecialty ambulatory practices in MA EpicCare EMR ~600,000 patients 750 clinicians An ESP server resides in the central data processing center Analyzes data from all 27 sites Boston, MA I added “vital signs” © Google Maps 6

Available Data Elements in ESP Category Format Demographic Data Carat-delimited text Vital signs Encounter types / dates Diagnostic codes ICD9 Lab test orders CPT codes mapped to LOINC Lab test results Prescriptions NDC codes and generic names Organism names SNOMED

Electronic Support for Public Health (ESP) Currently reports: Chlamydia Gonorrhea Pelvic inflammatory disease Acute hepatitis A Acute hepatitis B Acute hepatitis C Active tuberculosis Under validation: Syphilis Measles Mumps Chronic hepatitis B

Algorithm Accuracy Atrius Health, June 2006-present Condition Total Cases False Positives Positive Predictive Value Chlamydia 1790 100% Gonorrhea 224 Pelvic inflammatory disease 39 1 97% Acute hepatitis A 6 83% Acute hepatitis B 15 Acute hepatitis C 14 Tuberculosis 5 67%

ESP vital signs orders ICD9’s lab results meds chlamydia active tuberculosis acute hepatitis B

Case Identification Logic: Chlamydia Positive test for any of the following: Test Name CPT Component LOINC CHLAMYDIA PCR, URINE (MALES 86631 835 21613-5 CHLAMYDIA TRACHOMATIS CULTURE 87110 3474 6349-5 CHLAMYDIA GENPROBE DNA 87491 1312 20993-2 PEDIATRIC URINE CHLAMYDIA 2487 CHLAMYDIA TRACHOMATIS DNA, SDA 2801 CHLAMYDIA TR DNA 2878 CHLAMYDIA TR DNA URN 2879 CHLAMYDIA TR. DNA 2906 CHLAMYDIA TRACHOMATIS, DNA PROBE, FEMALE 4312 CHLAMYDIA TRACHOMATIS, DNA, SDA 4320 CHLAMYDIA TRACHOMATIS 4803 87591 16601-7 URINE GC AND CHLAMYDIA, PEDIATRIC BY APT 2686 36902-5 CHLAMYDIA & GC WITH REFLEX TO IDENTIFICATION 87800 4310

What about diseases that cannot be diagnosed from a lab result alone? examples: culture-negative tuberculosis acute hepatitis B acute hepatitis C pelvic inflammatory disease early lyme disease early syphilis

Lab diagnosis (positive culture or PCR) Limitations of diagnosis by labs or ICD9s alone the example of tuberculosis Lab diagnosis (positive culture or PCR) Excellent positive predictive value But misses culture negative cases (~20% of all TB cases) ICD9 based surveillance Similar sensitivity to lab-based surveillance Also misses ~20% of cases Poor positive predictive value (~15%) False positives: remote history of tuberculosis or +PPD

Solution Integrate multiple streams of data from the EMR to increase sensitivity and specificity Lab orders Lab results (present and past) Medication prescriptions ICD9 diagnoses

Case Identification Logic Active Tuberculosis Any of the following: Prescription for pyrazinamide OR Order for (AFB smear or culture) and an ICD9 code for TB within 60 days Order for 2 or more anti-tuberculous medications and an ICD9 code for TB within 60 days

Tuberculosis algorithm validation Algorithm applied to Atrius Health, June 2006 to present Identified 15 cases 10/15 ultimately confirmed to have active disease 9 of the 10 previously reported to health department 1 case previously unknown to health department No cases known to health department missed by ESP False positives - suspected cases of active TB that don’t pan out Woman with 3 months cough after travel to India, started on empiric TB therapy, ultimately diagnosed with Mycobacteria gordonae

Developing Case Identification Algorithms The example of Acute Hepatitis B

Is this acute or chronic hepatitis B? The Problem How does one electronically distinguish acute hepatitis B from flares of chronic hepatitis B? Scenario: 33 year old gent from Vietnam presenting with fatigue Liver enzymes elevated to the mid-600’s Viral hepatitis panel positive for hep B surface antigen Is this acute or chronic hepatitis B?

CDC Definition for Acute Hepatitis B An acute illness with discrete onset of symptoms and Jaundice or elevated serum aminotransferase levels and IgM antibody to hepatitis B core antigen positive OR hepatitis B surface antigen (HBsAg) positive

Acute hepatitis B Development of a detection algorithm Derivation: Begin with CDC case definition Translate into electronic terms Apply to historic data, Harvard Vanguard, 1990-2006 Review charts of all identified patients Refine algorithm based on chart reviews Validation: Apply to prospective data, Atrius Health, 2006-2008 Compare findings with health department records Total number of cases identified Missed cases repeat

Acute hepatitis B Development of a detection algorithm Strategy 1: ICD9 Random selection of 50 patients with ICD9 070.30 within the past two years Charts reviewed Positive Predictive Value 0% (95% confidence interval, 0-6%)

Acute hepatitis B Development of a detection algorithm Strategy 2: current lab tests ALT or AST > 5x normal AND Positive hepatitis B surface antigen Positive Predictive Value 47% (95% confidence interval, 41-53%)

Acute hepatitis B Development of a detection algorithm Strategy 3: current & past lab tests & ICD9’s ALT or AST > 5x normal AND Positive hepatitis B surface antigen AND No prior positive hepatitis B surface AND No ICD9 code for chronic hepatitis B ever Positive Predictive Value 68% (95% confidence interval, 61-75%)

Acute hepatitis B Development of a detection algorithm Strategy 4: current & past lab tests & ICD9’s ALT or AST > 5x normal AND Positive hepatitis B surface antigen AND No prior positive hepatitis B surface AND No ICD9 code for chronic hepatitis B ever AND Total bilirubin >1.5 Positive Predictive Value 97% (95% confidence interval, 94-100%) Sensitivity 99% Specificity 94%

Acute Hepatitis B Algorithm Validation Final algorithm applied to ESP’s prospective dataset June 2006 to April 2008 8 cases identified Chart review: 100% positive predictive value Comparison to MA Dept of Health Records Only 1 acute case known to health department 3 of the known cases misclassified by DPH as chronic 4 previously unreported cases found by ESP No missed cases PLoS ONE 2008;3:e2626

Sorting through positive Hep B Results - ESP versus ELR 601 distinct patients 8 acute 593 chronic cases 2648 positive test results for hepatitis B E S P E L R

EMR algorithms for disease detection Surveillance using lab data alone misses cases and misclassifies others Algorithms combining multiple current and prior lab tests, ICD9 codes, and med orders can identify complex conditions with high accuracy Validation labour intensive but essential Standardization of electronic case definitions could make surveillance more efficient, complete, and comparable across states Community of practice opportunity?

Contact: mklompas@partners.org 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: mklompas@partners.org