1. 6th Annual PHIN Conference August 25-28, 2008
Direct Import of Electronic Laboratory Reports into Ohio’s Disease Reporting System
Lynn K. Giljahn, MPH
Infectious Disease Surveillance
Ohio Department of Health
6th Annual PHIN Conference
August 25-28, 2008

2. Contributors Lilith Tatham Jenny Seiler Jim Gallant Greg Buskirk
Bob Campbell
Others on Ohio Disease Reporting System Team at Ohio Department of Health

3. Objectives Describe: Outline: benefits, challenges, and next steps
receiving, processing, and directly importing electronic laboratory reports
automated and manual processing of reports
Outline:
benefits, challenges, and next steps

4. Definitions ELR = Electronic Laboratory Reporting
HL7 = Health Level 7 standard for electronic data exchange in healthcare environments
ELR standard for order and structure of data in an electronic message
LOINC = Logical Observation Identifiers Names and Codes
ELR standard for test names
SNOMED = Systematized Nomenclature of Medicine clinical terms
ELR standard for non-numeric results

5. Lab data stored in database
ELR Import Process
Lab 1 - HL
Lab 2 - HL7 2.3.z
Lab 3 -ASCII
HL7 Gateway
Parsing
Normalization
ODRS
HL7 2.5
Lab data stored in database
Identification
STD*MIS
ASCII

6. Step 1 – Lab Sends a HL7 File
assembles list of test names/codes and result names/codes for tests performed in lab and submits to ODH
sets up a secure mechanism for file transfer
composes a complete and properly formatted HL7 message
works with ODH to test messages and fix problems

7. Test Names/Codes LOINCs are preferred
Labs map local test codes to LOINCs
Labs reporting to ODH via ELR have identified over 400 LOINCs they may be sending
Over 2400 additional LOINCs identified by ODH and mapped to Ohio’s reportable diseases

8. Result Names/Codes SNOMED preferred
Laboratories map results to SNOMED codes
ODH has mapped 165 SNOMED codes to Ohio’s reportable diseases
Mappings are used when LOINC code does not identify the reportable disease

9. Step 2 - File is Processed in HL7 Gateway
Data parsed and normalized
Test name is identified
Test name determines reportable condition
Reportable condition determines information system to receive the report
Report is routed to the appropriate disease surveillance system for import

10. Step 2 – HL7 Gateway Manual Processes
Messages that fail in parsing
Messages that fail in normalization
Messages with tests not yet mapped to a reportable condition

11. Step 3 – Ohio’s Disease Reporting System (ODRS)
Web-based, functionalities similar to NEDSS base system, person-based
Accommodates traditional paper-based reporting
Disease reports entered by state/local public health, more recently health care providers
Imports ELR messages with minimal human intervention

12. Step 3: Report is Imported into ODRS
Search for person match
Search for disease match
Search for event match

13. Step 3 – Automated Import
Person Match
Various matching algorithms based on combinations of
last name, first name, sex, date of birth, zip code, county, medical record number, facility name, specimen ID, lab name, reportable disease
One person match set to ‘exact’ – last name, first name, sex, and date of birth
All other matches are set to ‘possible’

14. Step 3 – Automated Import
Disease Match
Exact match to a reportable condition or a group of reportable conditions
Examples of “grouped” reportable conditions
hepatitis B acute and chronic
E. coli O157:H7 and E. coli unknown serotype
If ‘exact’ reportable condition, system checks for event match

15. Step 3 – Automated Import
Event Match
Determines whether report is new episode of disease based on event date range
Examples of event date range
7 days for Chlamydia and gonorrhea
30 days for Salmonella
infinite for hepatitis B
Within event date range set to ‘exact’

16. Step 3 – Automated Import
Person Match
If ‘exact’ person match, demographic fields on existing person record are updated with any additional information in ELR record
If neither ‘exact’ nor ‘possible’ person match, a new person and disease report are created

17. Step 3 – Automated Person Record

18. Step 3 – Automated Import
Disease/Event Match
If ‘exact’ disease and event match, laboratory fields on existing disease report are updated with any additional information in ELR record.
If ‘exact’ disease but not event, a new disease report is created for the person.
If neither ‘exact’ nor ‘group’ disease match, a new disease report is created for the person.

19. Step 3 – Automated Lab Record

20. Step 3 – Automated Lab Record

21. Step 3 – Other Automated Processes
Data edited to meet ODRS formats
Person addresses are geocoded and jurisdiction determined
Providers and facilities are associated with existing information
Anything processed automatically through ELR import is noted

22. Step 3 - Automated ELR Notes

23. Step 3 – Manual Import Manual ‘Possible’ person match
‘Exact’ person match with “grouped” disease match
‘Possible’ person and either ‘exact’ or “grouped” disease match

24. Step 3 – Manual Import ODRS Home Page

25. Step 3 – Manual Import ELR Queue

26. Manual ELR Import ‘Grouped’ Disease Match

27. ELR Import ‘Grouped’ Disease Match

28. ELR Import ‘Grouped’ Disease Match

29. ELR Import ‘Grouped’ Disease Match

30. Step 3 – Other Manual Processes
Determine reportable disease
‘generic’ test name (“ODRS unknown”) with unmapped result or locally coded result or result in note
result only reportable for certain ages
result only reportable for certain specimen types
Map test name, test result, or text result
Discard test

31. Test Name Mapping

32. Test Result Mapping

33. Text Result Mapping

34. Step 3 – End Result New person and disease report created OR
New disease report created for an existing person OR
Existing disease report updated OR
Report discarded

35. Results January-June 2008 Type of import Type of processing
25.2% new person and new disease report
1.5% new disease report for an existing person
73.3% update of existing disease report
Type of processing
84.3 % automatically
15.7% manually

36. Timeliness January-June 2008
Reportable Disease
ELR Created
NOT ELR Created
# Reports
Mean Lag (days)
Hepatitis B Chronic
254
4.7
968
17.4
Hepatitis C Chronic
1424
3.0
3833
18.4
Aseptic Meningitis 16
5.7
239
8.5
Pertussis 27
12.1
300
14.3
Varicella 17
3.6
1590
10.9

37. Benefits to ODH More timely reporting More complete reporting
Reduction in paper and manual processing
Receipt of information in a standardized format

38. Benefits for Laboratories
Reduction in paper and manual processing
Elimination of the need to determine jurisdiction for case reporting
Ability to send all disease reporting information to ODH in a standardized format
Ability to send all disease reporting information to a single location at ODH

39. Challenges Limitations of lab IT systems:
don’t always capture patient demographics
can’t send a HL7 file
file can’t be formatted to PHIN standard
Non-reportable results
Time investment to set up – labs and ODH
Other IT systems at ODH not ready to receive HL7 files

40. Next Steps
ELR from more labs – state lab, other national labs, other hospital labs
Develop and implement use of standard ASCII ELR format
Implement use of PHIN-MS
Accept non-infectious disease ELR reports

41. Contacts Lily Tatham – Program Jenny Seiler – IT
Jenny Seiler – IT