Tom Oluoch1, Nicolette de Keizer2, Patrick Langat3, Irene Alaska3, Kenneth Ochieng3, Nicky Okeyo3, Daniel Kwaro3, Ronald Cornet4. 1. U.S. Centers for Disease Control and Prevention, Division of Global HIV/AIDS, Nairobi, Kenya. Electronic address: toluoch@cdc.gov. 2. Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands. 3. Kenya Medical Research Institute - CDC Collaborative Program, Kisumu, Kenya. 4. Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands; Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
Abstract
INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. Published by Elsevier Inc.
INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. Published by Elsevier Inc.
Authors: S Trent Rosenbloom; Randolph A Miller; Kevin B Johnson; Peter L Elkin; Steven H Brown Journal: J Am Med Inform Assoc Date: 2006-02-24 Impact factor: 4.497
Authors: Edwin Libamba; Simon Makombe; Eustice Mhango; Olga de Ascurra Teck; Eddie Limbambala; Erik J Schouten; Anthony D Harries Journal: Bull World Health Organ Date: 2006-04-13 Impact factor: 9.408
Authors: B Ledergerber; M Egger; V Erard; R Weber; B Hirschel; H Furrer; M Battegay; P Vernazza; E Bernasconi; M Opravil; D Kaufmann; P Sudre; P Francioli; A Telenti Journal: JAMA Date: 1999-12-15 Impact factor: 56.272
Authors: Agnes N Kiragga; Barbara Castelnuovo; Petra Schaefer; Timothy Muwonge; Philippa J Easterbrook Journal: J Int AIDS Soc Date: 2011-01-20 Impact factor: 5.396