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NASTAD EMR Request

NASTAD EMR Request. Data reporting for the National Alliance of State & Territorial AIDS Directors By Claire Tucker. Initial request for data:. What questions should the report attempt to answer?. …continued. Initial request for data:. General summary of needs:

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NASTAD EMR Request

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  1. NASTAD EMR Request Data reporting for the National Alliance of State & Territorial AIDS Directors By Claire Tucker

  2. Initial request for data: What questions should the report attempt to answer?

  3. …continued

  4. Initial request for data: • General summary of needs: • Data from southern regions of Haiti only (Nippes, Grande-anse, Sud, Sud-est). • Ability to track individual patients or groups of patients over time. • Ability to track prevalence of particular symptoms, behaviors or other characteristics across the dataset. • Ability to compile aggregate data based on a given variable or combination of variables.

  5. First steps : • Map each requested data element to corresponding location/field on forms. • Map each field to its location in the database. • Send initial documentation to Genevive Meredith (Maine Center for Disease Control and Prevention), Chris Delcher and Carrie Dolan (Virginia Department of Health).

  6. Example of mapping data to database location:

  7. Challenges at first stages of processing: • Organizing and consolidating data request: • Many elements in data request were identical in terms of adult vs. pediatric or intake vs. follow-up. • Some elements were more difficult to map to their corresponding forms. For example, OI (opportunistic infection) diagnoses are not specified on any forms.

  8. Challenges at first stages of processing: • Determining needs of users: • All symptoms/stages, or only WHO-specified? • HIV symptoms by stage or by specific symptom?

  9. Next step: documenting possible values for each data element…

  10. Writing database procedure: basic structure… create proc sp_nastad as if exists (select * from dbo.sysobjects where id = object_id(N'[dbo].[nastadTable]') and OBJECTPROPERTY(id, N'IsTable') = 1) drop table [dbo].[nastadTable] create table dbo.nastadTable (nid bigint identity, sitecode char(10), patientid varchar(11), visitdate smalldatetime null, dataElement int, value varchar(200) null); /* Facility Name */ insert into nastadTable (sitecode,patientid,visitdate, dataelement, value) select distinct s.sitecode, patientid, visitDate, 6, s.Site from v_patients p, siteName s where s.sitecode = p.sitecode and encounterType = 10 /* patient address */ insert into nastadTable (sitecode,patientid,visitdate, dataelement, value) select distinct sitecode, patientid, visitDate, 7, addrDistrict + ' ' + addrSection + addrTown from v_patients p where encounterType = 10 /* Mother's first name */ insert into nastadTable (sitecode,patientid,visitdate, dataelement, value) select distinct sitecode, patientid, visitDate, 11, fnameMother from v_patients where encounterType = 10 /* nationalid */ insert into nastadTable (sitecode,patientid,visitdate, dataelement, value) select distinct sitecode, patientid, visitDate, 12, nationalid from v_patients where encounterType = 10

  11. Many queries were written with case statements to facilitate analysis: /* HIV symptoms by stages - adult */ insert into nastadTable select distinct sitecode, patientid, visitdate, 21, case when asymptomaticWHO = 1 then 'asymptomaticWHO' when weightLossLessTenPercMo = 1 then 'weightLossLessTenPercMo' when weightLossPlusTenPercMo = 1 then 'weightLossPlusTenPercMo' when diarrheaLessMo = 1 then 'diarrheaLessMo' when diarrheaPlusMo = 1 then 'diarrheaPlusMo' when chronicWeakness = 1 then 'chronicWeakness' when feverLessMo = 1 then 'feverLessMo' when feverPlusMo = 1 then 'feverPlusMo' when wtLossTenPercWithDiarrMo = 1 then 'wtLossTenPercWithDiarrMo' when abpain = 1 then 'abpain' when anorexia = 1 then 'anorexia' when cough = 1 then 'cough‘…

  12. Next step: creating NASTAD table and NASTAD lookup table… Sample data from NastadTable:

  13. Next step: creating NASTAD table and NASTAD lookup table… Sample data from NastadLookup table:

  14. Using the tables together: • Example query: “select top 5 sitecode, patientid, visitdate, n.dataelement, fieldname, descriptionEnglish, descriptionFrench, value from nastadTable n, nastadLookup l where n.dataelement = l.dataelement and l.dataelement = 7” • Results:

  15. Next steps: transmitting the data to NASTAD people… • Tried sending an Excel file, which we generated using the Enterprise Manager, by creating a view based upon nastadTable and nastadLookup ... However, the resulting dataset was too large for an Excel table so we created a text file containing the data. Sample of the resulting text file: "sitecode","nationalID","dataElement","fieldname","value","Visitdate" "21100 ","",1,"nationalID"," ",2001-01-01 00:00:00 "21100 "," VL0460D",1,"nationalID"," VL0460D",2001-01-01 00:00:00 "21100 ","AB0781Y",1,"nationalID","AB0781Y",2001-01-01 00:00:00 "21100 ","AB0781Y",2,"address","Rue du Port ( Zone ti Mouillage ) Jacmel Cayes Jacmel", 2007-05-30 00:00:00 "21100 ","AB0781Y",3,"fnameMother","Yline",2007-05-30 00:00:00 "21100 ","AB0781Y",4,"birthPlace","Marigot ",2007-05-30 00:00:00 "21100 ","AB0781Y",5,"site","Hopital St Michel ",2007-05-30 00:00:00

  16. Transmitting the data to NASTAD people (continued) … • It was necessary to run the dbproc and create the tables and view in the consolidated database on sapote (itechUWfailover) since many of the southern department clinics are now running in-country. • Text file was placed on NASTAD sftp location for download.

  17. Sample data from view v_nastad:

  18. Wrapping things up… • Fielding questions from the requestors and performing necessary modifications to the result set. • Missing data: there were some concerns about the completeness of the dataset. This was due to many fields being left blank on patient forms. • Minor errors in dataset; for example, we misspelled “Grande-anse” so data for that region was left out initially. The error was noted by NASTAD researchers and we provided modified files for them. • Providing supplementary material (Access file containing information on localities in each region) to assist in analysis.

  19. Suggestions for simplifying execution of future data requests: • Create a lookup table for all data-elements, mapping them to their corresponding forms, labels, and database locations. • Also include French labels/descriptions. • Improving data quality will improve results of data requests and reduce incidence of null result sets.

  20. In summary: a framework for future data requests… • Map form elements to their database locations. • Normalize the data; ensure all data is represented in a uniform format. Use of views is helpful. • Generate a result dataset. Queries for individual data variables may be written against a master table.

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