PROF. DANIEL ORWA OCHIENG

The use of mobile technology in healthcare, known as mHealth is being explored across the developing countries as part of the effort to tackle growing disease burden through, prevention and appropriate and prompt intervention strategies. Although the
outcomes of some of the implemented mHealth projects have been successful with very promising results, a significant number of the projects have failed after a short period of use. Studies carried out on the failed projects pointed to lack of sustainability. Review of
existing technology evaluation model against the cited challenges reveals significant deficiencies in the models and thus not suitable to evaluate sustainability of mHealth system in developing countries. It is clear that there exist a knowledge gap and hence the need to
develop and validate a suitable mHealth system sustainability evaluation model.
Keywords: Evaluation, mHealth, Model, Sustainability

Background: In 2010, Kenya initiated the process of adoption and implementation of a web-based system (DHIS2) as the national HIS to facilitate management of routine health information for evidence-based decision making. To reap maximum benefit from this implementation, DHIS2 needed to gain acceptance from all categories of targeted users. This study, conducted between June and August 2014, sought to develop a new technology acceptance model that can better explain the key determinants of acceptance and use of DHIS2 in Kenya.
Methods: The model was adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT). An exploratory study was conducted primarily through the use of quantitative methods, but qualitative Key Informant Interview (KII) data was also collected in a pre-study to provide the background and contextual information used in refining the model. In the main phase of the study, a questionnaire was administered to health workers through cross-sectional survey both at national and regional levels.
Results: The total number of valid questionnaires returned was 269 against the 300 that were issued. This number represents slightly more than 20% of the approximately 1,100 health workers who have been trained on DHIS2 in Kenya, and these were drawn from at least 10 of Kenya’s 47 counties. Analysis of the survey data was done in two parts: descriptive analysis was performed using SPSS statistical analysis tool for the purpose of obtaining frequencies, means, standard deviation, skewness and kurtosis. Subsequently Structural Equation Modeling (SEM) and specifically Partial Least Square path modeling (PLS), was used to analyze the conceptual model and test the proposed hypotheses.
Conclusion: The resulting model revealed that social influence was the most pertinent predictor of behavioral intention in the study setting, while facilitating condition and computer anxiety play a significant role in predicting actual use of DHIS2. Findings from this case study can be extended to explain acceptance and use of health IT in other similar settings. Future research can test more variables and moderators to increase the overall predictive levels of the model.