The technological, organizational and environmental determinants of adoption of mobile health applications (m-health) by hospitals in Kenya.

INTRODUCTION: Sub-Saharan Africa lags in adoption of mobile health (m-health) applications and in leveraging m-health for sustainable development goals. There is a need for a comprehensive investigation of determinants of hospitals' adoption of m-health in Sub-Saharan Africa to inform policies, practices and investments. METHODS: This investigation used a logit regression model to analyze the determinants of m-health adoption in Kenyan hospitals applying the Technological, Organizational and Environmental (TOE) framework and the Diffusion of Innovation (DOI) theory. A representative sample of 211 executives of Level 4-6 hospitals in 24 counties provided primary data on Patient-Centered (PC) and Facility-Centered (FC) m-health applications. RESULTS: Both PC and FC m-health adoption were predicted by competition for patients (PC p = 0.041, FC p = 0.021), IT human resource capacity (PC p = 0.048, FC p = 0.037), and hospital pursuit of market growth through technological leadership (PC p = 0.010, FC p = 0.020). Further determinants of PC m-health adoption included hospital access to slack financial resources (p = 0.006), acquisition strategy (p = 0.011), compatibility with the hospital systems (p = 0.015), trialability (p = 0.019), medical insurance company support (p = 0.025),patient pressure (p = 0.036), and perceived effect of global medical tourism (p = 0.039). FC m-health adoption was predicted by hospital size (p = 0.008), ICT infrastructure capacity (p = 0.041), and government support (p = 0.013). CONCLUSION: A differentiated approach is required to scale up m-health adoption. PC m-health requires emphasis on establishing national and regional compatibility and interoperability, developing trialability processes and validation mechanisms, incentivizing patient competition and mobility, establishing innovative and cost-effective acquisition strategies, and ensuring integration of digital services within national insurance schemes and policies. These policies require support from patients and communities to drive demand and spur investment in adequate IT human resources to maintain reliability. Pilot PC m-health projects should prioritize hospitals with slack financial resources, while FC m-health should target large facility size. FC m-health applications are more complex and costly than PC, requiring government incentives to trigger hospital investments and national investment in ICT infrastructure. Investors and hospital managers should integrate m-health into market growth strategies for sustainable m-health scale-up in Kenya and beyond.

The Moderating Role Of Top Executives' Sex, Level Of Education And Knowledge On Adoption Of Mobile Health Applications By Hospitals In Kenya.

BACKGROUND: Kenya lags behind other countries in adoption of mobile health (m-health) applications. Understanding factors affecting adoption of m-health by hospitals is required to inform strategic scale up and leverage m-health for sustainable development goals. This study investigated the moderating effects of Top Executives' (TEs) traits, namely sex, level of education and knowledge of m-health, on adoption of Patient Centered (PC) and Facility-Centered (FC) m-health applications. METHODS: This study applied the Technological, Organizational and Environmental (TOE) framework and the Diffusion of Innovation (DOI) theory to test hypotheses that TEs' traits individually or combined had no statistically significant moderating effect on adoption of PC and FC m-health applications. Primary data were collected through a self-administered questionnaire from a representative sample size of 211 TEs from level 4 to 6 hospitals. The Logit Regression Model was used to determine the significance of each predictor. RESULTS: Most TEs of hospitals are predominantly male (75.3%). Most TEs (65%) rated their knowledge of m-health at medium level. Most TEs reported having completed undergraduate (46%) or post-graduate (38.4%) degrees. At 5% level of significance, the study found that being a male TE (p=0.041) and having higher level of knowledge of m-health (p=0.009) were statistically significant moderators of adoption of PC m-health applications by hospitals in Kenya. However, all TEs' traits combined or individually were not statistically significant moderators of FC m-health applications. The moderating effect of TEs' traits is thus affected by the focus, level of complexity of the technology, and by the required organizational change management. For PC scale-up, there is an urgent need to integrate digital health training in the medical education curricula and in the professional development programs and to develop policy incentives that remove any gender-related barriers to adoption of m-health. However, scale up of FC m-health may require other strategies such as pre-existence of systems and infrastructure and a cohesive change management strategy. CONCLUSION: This study recommends a differentiated approach to introduction, scale up, and investigation of PC and FC m-health applications.

Ensuring quality of services in HIV prevention research settings: findings from a multi-center quality improvement pilot in East Africa.

Quality improvement (QI) has been widely implemented in health services but has not been widely applied in HIV prevention research. Most prevention research centers have commonly employed traditional approaches (e.g., checklists) to quality control that document what has been done but not the quality of what has been done. Unlike other health settings, prevention research settings have unique characteristics and ethical requirements that require the development or adaptation of specific quality indicators. A QI model for health services was adapted for use in prevention research settings and was piloted between August 2006 and July 2007 at three research centers in East Africa. Four hundred and twenty-six volunteers exit interviews were administered in two cycles. Quantitative and qualitative data were analyzed using Excel worksheets. QI meeting reports and QI plans were used to complement data from exit interviews. On average, 52% of total enrolled volunteers participated in the exit interview. The designed QI plans successfully helped reduce volunteers' reported waiting time to see counselors (p<0.001) and pharmacists (p<0.001). It also increased the percentage of interviewed volunteers who reported being counseled on family planning at clinical trials (from 66 to 93%; p=0.02) at follow-up visits, and who were refreshed on informed consent at follow-up visits (from 90 to 96%; p=0.009). The percentage of interviewed volunteers that expressed satisfaction with services received from counselors increased (from 87 to 94%; p=0.009) while the percentage of volunteer satisfied with services from trial physicians remained constant (93%). The majority of volunteers interviewed reported satisfaction with other major components of research such as confidentiality, understanding of trial objectives, benefits and risks of participation, and risk reduction counseling. However, satisfaction with services from community outreach workers and other staff at research centers dropped over the course of the study (from 88% in Cycle 1 to 74% in Cycle 3; p= < 0.001). Increased commitment to QI is crucial in ensuring quality of services and ethical conduct of HIV prevention research centers.