...
tblFamilies: FamilyAddress
tblInsuree: CHFID, LastName, OtherNames, passport, Phone, CurrentAddress, GeoLocation
tblClaimAdmin: LastName, OtherNames, DOB, Phone, EmailId
tblOfficer: LastName, OtherNames, DOB, Phone, EmailId, permanentaddress, VEOLastName, VEOOtherNames, VEODOB, VEOPhone
tblUsers: LastName, OtherNames, DOB, Phone, EmailId
tblPayer: PayerName, PayerAddress, Phone, eMail, Fax
tblPhotos: CHFID, PhotoFileName
Extracted data
To In order to develop the an AI algorithm for claim categorization, we need to have access to a database of labeled claims (after static validation or manual evaluation). This data, represented in FHIR JSON format, correspond to resource related to ClaimResponse, Patient, Location, HeathcareService, Condition, ActivityDefinition and Medication. Not all the fields from openIMIS database tables are mapped to FHIR. However, we are considering that all the necessary fields for the AI model were selected. This will be further validated in collaboration with Nepal Medical Officers.
The available openIMIS database received from Nepal openIMIS implementation contains 531900 claim submitted from May 2016 to June 2018.
To migrate the openIMIS data to FHIR. For this, we use the openIMIS to FHIR migration tool. For large data sets, ClaimResponse and Patient, we developed SQL scripts (GetClaimResponseJSON1Line.sql, GetPatientJSON1Line.sql) to generate the FHIR data directly from database.
...