A structured approach to recording AIDS-defining illnesses in Kenya: A SNOMED CT based solution
Published Date:Jul 13 2015
Source:J Biomed Inform. 56:387-394.
Keywords:Acquired Immunodeficiency Syndrome
AIDS-related Opportunistic Infections
Information Storage And Retrieval
Medical Records, Problem-Oriented
Medical Records Systems, Computerized
Quality Of Healthcare
Quality Of Health Care
Reproducibility Of Results
Systematized Nomenclature Of Medicine
Tomography, X-Ray Computed
Pubmed Central ID:PMC4987091
Funding:GH000048-04/GH/CGH CDC HHS/United States
U01 GH000048/GH/CGH CDC HHS/United States
Several studies conducted in sub-Saharan Africa (SSA) have shown that routine clinical data in HIV clinics often have errors. Lack of structured and coded documentation of diagnosis of AIDS defining illnesses (ADIs) can compromise data quality and decisions made on clinical care.
We used a structured framework to derive a reference set of concepts and terms used to describe ADIs. The four sources used were: (i) CDC/Accenture list of opportunistic infections, (ii) SNOMED Clinical Terms (SNOMED CT), (iii) Focus Group Discussion (FGD) among clinicians and nurses attending to patients at a referral provincial hospital in western Kenya, and (iv) chart abstraction from the Maternal Child Health (MCH) and HIV clinics at the same hospital. Using the January 2014 release of SNOMED CT, concepts were retrieved that matched terms abstracted from approach iii & iv, and the content coverage assessed. Post-coordination matching was applied when needed.
The final reference set had 1054 unique ADI concepts which were described by 1860 unique terms. Content coverage of SNOMED CT was high (99.9% with pre-coordinated concepts; 100% with post-coordination). The resulting reference set for ADIs was implemented as the interface terminology on OpenMRS data entry forms.
Different sources demonstrate complementarity in the collection of concepts and terms for an interface terminology. SNOMED CT provides a high coverage in the domain of ADIs. Further work is needed to evaluate the effect of the interface terminology on data quality and quality of care.
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