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Re-purposing The Electronic Medical Record For Public Health Sylvain DeLisle MD, MBA VA Maryland Health Care System and University of Maryland.

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Presentation on theme: "Re-purposing The Electronic Medical Record For Public Health Sylvain DeLisle MD, MBA VA Maryland Health Care System and University of Maryland."— Presentation transcript:

1 Re-purposing The Electronic Medical Record For Public Health Sylvain DeLisle MD, MBA VA Maryland Health Care System and University of Maryland

2 Parade to Promote Sale of War Bonds, Philadelphia, September 28, 1918

3 Overall Objective To find out if a comprehensive EMR can contribute to the early detection of an infectious disease epidemic

4 Stop Y N OutbreakValid? OutbreakSign.? Escalate PHS Case Detector Outbreak Detector Y CaseValid? N Y N EMR

5 Stop Y N OutbreakValid? OutbreakSign.? Escalate PHS Case Detector Outbreak Detector Y CaseValid? N Y N CPRS

6 CPRS: Provider Interface

7 CPRS: Free-text data entry

8 CPRS: Structured data entry

9

10

11 CPRS is really VISTA

12 Data Extraction: MDE

13 SQL: Data Transformation Sequences

14 SQL: Primary Warehouse

15 Stop Y N OutbreakValid? OutbreakSign.? Escalate PHS Case Detector Outbreak Detector Y CaseValid? N Y N CPRS

16 Stop Y N OutbreakValid? OutbreakSign.? Escalate PHS Case Detector Outbreak Detector Y CaseValid? N Y N Data Extractor SQL Database VISTA/CPRS Outbreak Detector

17 Stop Y N OutbreakValid? OutbreakSign.? Escalate PHS Case Detector Outbreak Detector Y CaseValid? N Y N Data Extractor SQL Database VISTA/CPRS Outbreak Detector

18 Focus on ILI We focused on influenza-like illness (ILI) as a syndrome that may indicate an event of public health significance –Anthrax –Plague –SARS –Influenza

19 ILI Case Definition Positive influenza culture or antigen OR Any two of the following (<= 7 days duration) –Cough –Fever or chills or night sweats –Pleuritic chest pain –Myalgia –Sore throat –Headache AND Illness not attributable to a non-infectious etiology

20 Gold Standard Detector: Manual Case Review VA Maryland Health Care System (VAMHCS) and the Salt Lake City VA (SLCVAMC) Study period: 10/01/03 to 3/31/04 15,377 (of 253,818) random sample, ER and selected outpatient clinics All ILI cases and a 10% subsample of the records were re- reviewed by a MD, discordant pairs were adjucated by a panel of three MDs Found 280 cases

21 ICD-9-based ILI Detectors Compared respiratory ICD-9 groupings from –BioSense (CDC) –Essence (DoD) –Optimized (VA)

22 Structured Parameters-based Case Detectors Vitals: Temp >38, RR > 22, HR > 100 Orders/dispense for Rx: expectorants, antibiotics, antitussives, decongestants, anti-emetics, antidiarrheals Order/results for tests: CBC, Diff, Strep. screen, Sputum cultures, Gram stain, Respiratory serologies, Influenza cultures/antigens, Chest/sinus XRays or CT scans

23 ILI Case Detector Retained parameters Cold remedies Fever >= 38ºC

24 Cold Remedies CN101 opioid analgesics like codeine" OR CN900 CNS medications, other acetaminophen/diphenhidramine, OR MS102 non-salicylate NSAIS (does not include antirheumatics), OR NT100 decongestants, nasal, OR NT200 anti-inflammatories, nasal, OR NT400 antihistamine, nasal, OR NT900 nasal and throat, topical, use other throat lozenge" only, OR RE200 decongestants, systemic, OR RE301 opioid-containing antitussives/expectorants, OR RE302 non-opioid-containing antitussives/expectorant, OR RE501 antihist/decongest, OR RE502 antihist/decongest/antitussive, OR RE503 antihist/decongest/expectorant, OR RE507 antihist/antitussive, OR RE508 antihist/antitussive/expectorant, OR RE513 decongest/antitussive/expectorant, OR RE516 decongest/expectorant, OR RE599 cold remedies, OR AH102 antihistamines, ethanolamine, OR AH104 antihistamines, alkylamine

25 TEXT-based ILI Case Detectors Examine the text of all clinical encounter notes on the day of an index visit Used modified NegEx algorithm (Wendy Chapman)

26 Mumbo jumbo fever nonsense trivia blabla etc TXT ILI Case Detectors NegEx

27 Mumbo jumbo fever nonsense trivia blabla etc TXT ILI Case Detectors NegEx

28 Negation? Mumbo jumbo fever nonsense trivia blabla etc

29 TXT ILI Case Detectors NegEx Negation? Mumbo jumbo fever nonsense trivia blabla etc

30 TXT ILI Case Detectors NegEx Negation? Mumbo jumbo fever nonsense trivia blabla etc

31 TXT ILI Case Detectors NegEx Negation? Mumbo jumbo fever nonsense trivia blabla etc

32

33 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 Sensitivity (%) ARIILI …OR Text)…OR Text NegEx MedLEE ICD9…(ICD9… Positive Predictive Value (%) …AND Text)…AND TextICD9… (ICD9… 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100 AND Temp

34 Which One Should We Use?

35 Stop Y N OutbreakValid? OutbreakSign.? Escalate PHS Case Detector Outbreak Detector Y CaseValid? N Y N Data Extractor SQL Database VISTA/CPRS Outbreak Detector

36 1999 2000 2001 2002 2003 2004 0 10 20 30 40 50 60 70 80 Number of ILI Cases Time Series: VAMHCS, Jan 1999 – Dec 2004

37 ILI Cases VAMHCS 2002-2003 JulAugSepOctNovDecJanFebMarAprMayJun 0 10 20 30 40 50 60 70 Number of ILI Cases

38 Stop Y N OutbreakValid? OutbreakSign.? Escalate PHS Case Detector Outbreak Detector Y CaseValid? N Y N Data Extractor SQL Database VISTA/CPRS Outbreak Detector Outbreak Generator

39 Outbreak Generator for Baltimore 1.Age-structured deterministic epidemic model generates the number of new infections in each age group, each day, for each zip code 2.Stochastic metapopulation spatial model determines how the outbreak will extend in space-time 3.Stochastic clinical features algorithm determines which of these infections will be recognized cases at the VA, and the severity, clinical profile and outcome for each recognized case

40 ILI Cases VAMHCS 2002-2003 JulAugSepOctNovDecJanFebMarAprMayJun 0 10 20 30 40 50 60 70 Number of ILI Cases

41 ILI Cases VAMHCS 2002-2003 JulAugSepOctNovDecJanFebMarAprMayJun 0 10 20 30 40 50 60 70 Number of ILI Cases

42 ILI Cases VAMHCS 2002-2003 JulAugSepOctNovDecJanFebMarAprMayJun 0 10 20 30 40 50 60 70 Number of ILI Cases

43 Stop Y N OutbreakValid? OutbreakSign.? Escalate PHS Case Detector Outbreak Detector Y CaseValid? N Y N Data Extractor SQL Database VISTA/CPRS Outbreak Detector Outbreak Generator

44 ILI Cases VAMHCS 2002-2003 JulAugSepOctNovDecJanFebMarAprMayJun 0 10 20 30 40 50 60 70 Number of ILI Cases

45 BIOSENSE (Current)

46 Fixed Threshold (p-value)

47

48

49

50

51 Optimized Threshold

52 Improved ICD-9 Codesets

53 Adding Structured Parameters

54 Adding Text Analysis

55 Optimizing for Positive Predictive Value

56 Surveillance for Febrile_ILI

57 EMR data can significantly enhance automated Case detection of ILI compared to the use of ICD- 9 codes alone Whole-system simulation and is required to evaluate and calibrate the performance of alternative single-case detectors Conclusions (1)

58 For influenza surveillance, case-detection algorithms should aim for high positive predictive value, and target ILI cases who are febrile Conclusions (2)

59 Stop Y N OutbreakValid? OutbreakSign.? Escalate PHS Case Detector Outbreak Detector Y CaseValid? N Y N Outbreak Generator Data Extractor SQL Database VISTA/CPRS Bob Sawyer Shawn Loftus Brett South Ericka Kalp Sylvain DeLisle Trish Perl Steve Altman, Raju Vatsaval Jill Anthony Shobha Phansalkar Brett South Matt Samore Gary Smith Holly Gaff Hongzhang Zheng Zhilian Ma Fang Tian Paul Sun Vibrio

60

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