Understanding the Relationship between Staff, Processes, ICT Infrastructure and Data Quality

Experiences from the AIDS Healthcare Foundation Uganda Cares

  • Paul Muliika Uganda Management Institute
  • Dr. Mary B. Muhenda Uganda Management Institute
  • Julius Kimuli Uganda Management Institute
Keywords: Data, Data Quality, Electronic Records System, ICT Infrastructure

Abstract

Millions of health-related records are generated every day from various sources. However, the trustworthiness of the data held within the data management systems has been called to question. Addressing this gap, a study on electronic medical records system and data quality was undertaken at Aids Healthcare Foundation Uganda Cares organization to examine the relationship between the two variables and come up with best practices that organizations could adopt to improve the quality of their electronic databases. A correlational study was conducted on 95 randomly selected employees involved in data management for HIV services. Seven (7) Programme Managers were purposively selected and interviewed to provide in-depth information about the study. Quantitative and qualitative data collection methods were used to obtain data from a total of 102 staff. Additionally, secondary data related to the study was gathered from journals, textbooks and web pages. Descriptive statistics were derived, and correlation and regression data analysis was done. In the study, Pearson’s correlation coefficient to establish bi-variate relationships in terms of significance and direction of relationships between electronic medical records system and data quality was used. Regression statistics were done to determine the predictive strength of electronic medical records system on data quality and to describe the distribution of responses in a meaningful way, while descriptive statistics in form of frequencies, percentages, means and standard deviations were also used to summarize and present the study results. The qualitative data gathered through interviews was analyzed and interpreted using content analysis. Patton’s six generic steps were used in this process, namely: organization and preparation of data; reading through the data to get a general sense of the meaning; coding; generation of themes; representation of themes and interpretation. The results showed that processes had a higher impact on data quality as compared to staff and ICT infrastructure. The conclusion is that data quality can be improved if processes are strengthened and that ICT infrastructure is not significant in relation to data quality.

Published
2019-09-10