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Lessons Learned: Implications of a Web-Based Disease Morbidity Reporting System

Lessons Learned: Implications of a Web-Based Disease Morbidity Reporting System. May 9, 2006 National STD Prevention Conference Kathryn E. Macomber, MPH Michigan Department of Community Health. STD Reporting History. Former system was dbase (MI developed)

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Lessons Learned: Implications of a Web-Based Disease Morbidity Reporting System

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  1. Lessons Learned: Implications of a Web-Based Disease Morbidity Reporting System May 9, 2006National STD Prevention Conference Kathryn E. Macomber,MPH Michigan Department of Community Health

  2. STD Reporting History • Former system was dbase (MI developed) • Paper case reports sent through local health departments and entered at state • PHIN-compliant, web-based disease reporting system implemented July 2004 • all communicable diseases • STDs integrated January 10, 2005

  3. New Case Entry • HTML screens have common variables such as sex, race, date of birth • Interacts with pdf disease-specific forms • NETSS variables exported from html and pdf

  4. Disease Specific Form: Gonorrhea Corresponding Variables from HTML Screens Interface With Fields on Disease Specific Form Referral Info, Lab Info, Specimen Info, Treatment Info, Concurrent Co-Infection, and local fields on second page

  5. Advantages over Paper-Based System • Standard Reports • Real-time data • Geocoding and Mapping • Deduplication Process • Both person and disease • Improved data quality • Additional variables • Former system captured only NETSS required variables

  6. MDSS Standard Reports

  7. Map

  8. Easy Access to Coordinate DataGonorrhea, Chlamydia by School District

  9. Patient De-Duplication

  10. Case De-Duplication MDSS also checks for existing cases for this patient across all LHJs as there may be multiple referrals (e.g., lab and provider) CREATE - Creates a new case with case information and merged patient information. MATCHES EXISTING - Does not create new case for patient. PLACE IN QUEUE – Defers to Administrator

  11. Changes in Data Quality

  12. Completeness-CT and GC

  13. Completeness-Race

  14. Changes in Timeliness • Pre-MDSS we did not collect date of specimen • # of days to NETSS transmission could not be calculated (no CSPS indicators) • MDSS does collect this variable • Complete for 92% of GC/CT cases in 2005 • 45% of GC/CT transmitted within 30 days • 65% of GC/CT transmitted within 60 days

  15. Information Source • Old System: “STD Clinic” or “Private Physician/HMO” • New System: Drop down menu of complete list

  16. Information Source-GC and CT

  17. Obstacles over Paper-Based System • Speed • Impact of Lab Reporting • Jurisdictional Issues • Syphilis Labs and Follow-up

  18. Speed • MI dbase system had only 11 variables • 1 case every 20 seconds • MDSS has approximately 50 variables • I case every 60 seconds • Data entry speed is dependant on number of users on the system • Slow processing between case entry • Due to deduplication, geocoding, pdf

  19. Impact of Lab Reporting • 5 MI laboratories are entering results, either manually or electronically • Quest soon to come online (HL7 transfer) • Labs do not have patient demographics or treatment information • Doctors no longer receive disease reporting form from laboratory • Missing link in the reporting system

  20. Jurisdictional Issues • 30% of the cases at the Oakland County STD clinic are residents of Detroit • Oakland County can enter Detroit morbidity but can not modify or update information once case has been submitted • Incomplete information, no follow-up • Issue across state • Detroit has jurisdiction over several Wayne County cities

  21. What to do with Syphilis? • Syphilis electronic labs are transferred into the system • But syphilis lab does not mean morbidity • Once a lab is staged and determined to be morbidity, cases are re-entered into the system • Can’t upload or download labs • Still being hand entered into historical record search database

  22. Lessons Learned • MDSS was designed for communicable diseases, not for the high morbidity of STDs • Local STD programs are currently limited on staffing • Electronic lab reports are frequently incomplete • Interactive PDF forms complicate entry

  23. Lessons Learned • However, we have gained higher quality information • And maintained completeness of essential variables • Better epidemiological capacity, especially at the local level

  24. Next Steps? • How to support local data entry (Detroit, 2005) • Need to plan for how to integrate syphilis • How do we integrate program module or a CDC released STD PAM?

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