A mobile phone based tool to identify symptoms of common childhood diseases in Ghana: Development and evaluation of the integrated clinical algorithm in a cross-sectional study

Konstantin H. Franke, Ralf Krumkamp, Aliyu Mohammed, Nimako Sarpong, Ellis Owusu-Dabo, Johanna Brinkel, Julius N. Fobil, Axel Bonacic Marinovic, Philip Asihene, Mark Boots, Jürgen May, Benno Kreuels

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

Background: The aim of this study was the development and evaluation of an algorithm-based diagnosis-tool, applicable on mobile phones, to support guardians in providing appropriate care to sick children. Methods: The algorithm was developed on the basis of the Integrated Management of Childhood Illness (IMCI) guidelines and evaluated at a hospital in Ghana. Two hundred and thirty-seven guardians applied the tool to assess their child's symptoms. Data recorded by the tool and health records completed by a physician were compared in terms of symptom detection, disease assessment and treatment recommendation. To compare both assessments, Kappa statistics and predictive values were calculated. Results: The tool detected the symptoms of cough, fever, diarrhoea and vomiting with good agreement to the physicians' findings (kappa = 0.64; 0.59; 0.57 and 0.42 respectively). The disease assessment barely coincided with the physicians' findings. The tool's treatment recommendation correlated with the physicians' assessments in 93 out of 237 cases (39.2% agreement, kappa = 0.11), but underestimated a child's condition in only seven cases (3.0%). Conclusions: The algorithm-based tool achieved reliable symptom detection and treatment recommendations were administered conformably to the physicians' assessment. Testing in domestic environment is envisaged.

Original languageEnglish
Article number23
JournalBMC Medical Informatics and Decision Making
Volume18
Issue number1
DOIs
Publication statusPublished - 27 Mar 2018

Keywords

  • Africa
  • Algorithm
  • Children
  • Decision making, computer assisted
  • Interactive voice response
  • mHealth
  • Symptom assessment

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