Acute Hepatitis C Surveillance using Electronic Health Record Data
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1 Acute Hepatitis C Surveillance using Electronic Health Record Data Hepatitis C National Summit Centers for Disease Control and Prevention June 18, 2014 Michael Klompas MD, MPH, FRCPC, FIDSA CDC Center of Excellence in Harvard Medical School and Harvard Pilgrim Health Care Institute Boston, MA
2 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,
3
4 Electronic Laboratory versus Paper Reporting Total Number of Reports Time from Diagnosis to Report 4.4 Fold Increase in Total Number of Reports 7.9 Day Decrease in Mean Time from Diagnosis to Report Paper reports Electronic lab reports Paper reports Electronic lab reports Am J Public Health 2008;98:344
5 Limitations of Electronic Laboratory Reporting Missing detailed contact info for patient and/or clinician Cannot report purely clinical diagnoses No data on symptoms, pregnancy status, or treatment Does not integrate multiple tests to yield a diagnosis Generates multiple reports for same episode
6 Our goal Clinician initiated manual reporting Electronic laboratory reporting Automated disease detection and reporting from electronic medical records Combine the best of traditional clinician-initiated reporting and electronic laboratory reporting systems: Fast, accurate, clinically detailed, digital reports Generalizable model
7 Electronic Support for Public Health (ESPnet) Software and architecture to extract, analyze, and transmit electronic health information from providers to public health. Surveys codified electronic health record data for patients with conditions of public health interest Generates secure electronic reports for the state health department Designed to be compatible with any EHR system JAMIA 2009;16:18-24 MMWR 2008;57: Am J Pub Health 2012;102:S325 S332
8 esphealth.org Source code and documentation available free of charge from esphealth.org
9 ESPnet: Automated disease detection and reporting for public health Practice EMR s ESPnet Server Health Department diagnoses D P H lab results meds vital signs demographics HL7 electronic case reports or aggregate summaries JAMIA 2009;16:18-24 Am J Pub Health 2012;102:S325 S332
10 Current ESPnet installations Cambridge Health Alliance 20 sites 400,000 patients Mass League of Community Health Centers 18 sites 300,000 patients Atrius Health 27 Sites 700,000 pts MetroHealth Cleveland, OH 250,000 patients Google Maps
11 Case Reports to Health Department Patient demographics Responsible clinician, site, contact info Basis for condition being detected Treatments prescribed Symptoms (ICD9 code & temperature) Pregnancy status (when pertinent) Vaccine history (when pertinent)
12 ESPnet Case Counts Atrius, CHA, MetroHealth, Condition Total Cases Chlamydia 22,001 Gonorrhea 4,554 Pelvic inflammatory disease 311 Acute hepatitis A 34 Acute hepatitis B 112 Acute hepatitis C 227 Tuberculosis 437 Syphilis 1478
13 ICD9 s ESP acute hepatitis A acute hepatitis B acute hepatitis C
14 Limitations of diagnosis by ICD9 s Condition Sensitivity Positive Predictive Value Chlamydia 75% 80% Tuberculosis 100% 17% Syphilis 93% 47% Acute hepatitis C 37% 20%
15 Solution Integrate multiple streams of data from the EMR to increase sensitivity and specificity Lab orders Lab results (present and past) ICD9 diagnoses (present and past) Medication prescriptions
16 ACUTE HEPATITIS C
17 ICD9 ALONE 100 patients with ICD charts selected at random for review Case Identification Logic: Acute Hepatitis C Positive Predictive Value 20% (95% confidence interval, 8-39%)
18 Case Identification Logic: Acute Hepatitis C Clinical algorithm modeled on CDC/CSTE criteria acute hepatitis, positive test(s) for hep C, and negative tests for hep A and hep B OR ELISA positive and history of negative ELISA within 1 year OR RNA positive and history of negative ELISA within 1 year
19 Clinical Algorithm ICD9 for jaundice OR ALT >400 AND HCV ELISA+ (signal 3.8 if reported) or RNA+ RIBA+ (if done) HCV RNA+ (if done and if case based on +ELISA) AND HAV IgM or HAV total negative HBcIgM or HBc total negative If no hep B core results then HBSAg neg AND Case Identification Logic: Acute Hepatitis C No prior +ELISA or HCV RNA or RIBA or ICD9 for hepatitis C
20 Acute Hepatitis C Atrius Health, Count Probable or Confirmed False Positive Clinical criteria ELISA neg to pos ELISA neg, RNA pos OVERALL Positive Predictive Value 88% (95% confidence interval, 76-96%)
21 Acute Hepatitis C Comparison to conventional reporting, Proportion of true cases known to MA DPH: 35/38 (92%) Proportion of true cases known as acute: 14/38 (37%) 150% increase in acute case detection
22 14 cases detected 13/14 confirmed (PPV 93%) Revalidation Atrius Health false positive: 25 yo woman, IVDU, new to practice, HCV diagnosed 14 months prior to presentation Epidemiology 8 females, 6 males Average age of 14 age of 14 with substance abuse
23 Cases per Year PHIConnect CDC Center of Excellence in Cases per 100,000 patients Acute Hepatitis C Atrius Health,
24 Summary EHRs contain a wealth of data that can inform public health surveillance Algorithms that integrate current and prior labs, orders, meds, and ICD9 codes can enhance accuracy over ICD9 codes alone Automated surveillance and reporting using EHR data can improve the completeness, timeliness, and clinical detail of surveillance EHRs can facilitate active surveillance of large populations at low marginal cost per person
25 Harvard Dept of Population Medicine Richard Platt Jessica Malenfant Michael Murphy Julie Lankiewicz Emma Eggleston Massachusetts Dept of Public Health Al DeMaria Gillian Haney Kathy Hsu Sita Smith Josh Vogel Paul Oppedisano Ohio Department of Health Lilith Tatham MetroHealth, OH David Kaelber Guptha Baskaran Atrius Health Ben Kruskal Mike Lee Cambridge Health Alliance Michelle Weiss Brian Herrick Vivian Li Thank You! Commonwealth Informatics Catherine Rocchio Bob Zambarano David Fram Carolina Chacin Rich Schaaf Contact:
26 ACUTE HEPATITIS B
27 Strategy 1: ICD9 Acute hepatitis B Atrius Health, Random selection of 50 patients with ICD within the past two years Charts reviewed Positive Predictive Value 0% (95% confidence interval, 0-6%)
28 Strategy 2: current lab tests ALT or AST > 5x normal AND Positive hepatitis B surface antigen Acute hepatitis B Atrius Health, Positive Predictive Value 47% (95% confidence interval, 41-53%)
29 Acute hepatitis B Atrius Health, 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%)
30 Acute hepatitis B Atrius Health, 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, %) Sensitivity 99% Specificity 94%
31 Sorting through positive Hep B Results - ESP versus ELR 8 acute 593 chronic cases 601 distinct patients 2648 positive test results for hepatitis B
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