Data quality is one of many challenges experienced in electronic healthcare (e-health) services in South Africa. The collection of data with substandard data quality leads to inappropriate information for health and management purposes. Evidence of challenges with regard to data quality in e-health systems led to the purpose of this study, namely to prioritise data quality challenges experienced by data users of e-health systems in South Africa. The study followed a sequential QUAL-quan mixed method research design to realise the research purpose. After carrying out a literature review on the background of e-health and the current status of research on data quality challenges, a qualitative study was conducted to verify and extend the identified possible e-health data quality challenges. A quantitative study to prioritise data quality challenges experienced by data users of e-health systems followed. Data users of e-health systems in South Africa served as the unit of analysis in the study. The data collection process included interviews with four data quality experts to verify and extend the possible e-health data quality challenges identified from literature. This was followed by a survey targeting 100 data users of e-health systems in South Africa for which 82 responses were received.
A prioritised list of e-health data quality challenges has been compiled from the research results. This list can assist data users of e-health systems in South Africa to improve the quality of data in those systems. The most important e-health data quality challenge is a lack of training for e-health systems data users. The prioritised list of e-health data quality challenges allowed for evidence-based recommendations which can assist health institutions in South Africa to ensure future data quality in e-health systems. / School of Computing / M. Sc. (Computing)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:umkn-dsp01.int.unisa.ac.za:10500/20100 |
| Date | 10 1900 |
| Creators | Botha, Marna |
| Contributors | Both, A., Herselman, M. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource ( xiv, 207 leaves) : illustrations (some color) |
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