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SSuN in Jefferson County, AL

SSuN in Jefferson County, AL. Let the SSuN shine in What SSuN has done for us so far… Methodology for selecting and interviewing patients. Bringing SSuN into the STD clinic. A. Create fields in existing Electronic Medical Record for SSuN data elements: - Improve sex partner data

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SSuN in Jefferson County, AL

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  1. SSuN in Jefferson County, AL • Let the SSuN shine in • What SSuN has done for us so far… • Methodology for selecting and interviewing patients

  2. Bringing SSuN into the STD clinic A. Create fields in existing Electronic Medical Record for SSuN data elements: - Improve sex partner data - Add detailed HIV history elements - Add HPV vaccination history - Expand risk factors - Expand demographic/residency data collection - Add detailed field for visit type

  3. Example of EMR before and after SSuN Before SSuN Sex partner for 12 months After SSuN Sex partner for 3 months NEW!

  4. Challenge — Creating Fields for New Elements Our EMR is limited to a fixed number of field descriptions from which to chose Can only use a given descriptor once Predetermined field descriptors are automatically imported into clinician notes (No free text allowed for basic field descriptors) Often none of the existing descriptors fit the SSuN data requirements Shuying “created” applicable fields by identifying existing choices and tacking on verbage to clarify For example, she adapted the Reported Medical History descriptor for the question “Have you met sex partners through the internet in the past 12 mos?” Clinician note reads “Reported Medical History of meeting sex partners through the internet in the past 12 mos.”

  5. B. Developing Data Dictionary Since SSuN fields in EMR are not what they were originally intended to be, a dictionary was developed for SAS coding Data Dictionary

  6. C. Educating Providers B. Self-Reported Risk A. Demographics

  7. What SSuN has done for us so far… • Obtain line lists of GC County morbidity records from State: • - Identified imperfection in state system: • Problem: 1) ~ 5 % of records duplicated • Solution: Unduplicated state-reported cases in SAS before randomization and notified state of discrepancy • - Process now in place allows us to obtain line-listed data for other STDs, as well • Developing electronic interview database: Microsoft SQL Server • - Will be integrated into new DIS data management system

  8. Methodology for selecting and interviewing patients for the Population component Extract data from STD*MIS monthly SAS • Randomize every case: Rand=ranuni(0)*100 • Select all cases with Random<=40 • Save both selected cases and unselected cases into permanent files • Merge with unselected cases • Clean up and coding as CDC requirement CDC Selected cases after interview Selected cases before interview Interview database DIS conducting phone interview

  9. Thank You !!

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