Leveraging cloud computing for improved health service delivery: Findings from public health facilities in Kisumu County, Western Kenya-2019.

INTRODUCTION: Healthcare delivery systems across the world have been shown to fall short of the ideals of being cost-effective and meeting pre-established standards of quality but the problem is more pronounced in Africa. Cloud computing emerges as a platform healthcare institutions could leverage to address these shortfalls. The aim of this study was to establish the extent of cloud computing adoption and its influence on health service delivery by public health facilities in Kisumu County. METHODS: The study employed a cross-sectional study design in one-time data collection among facility in-charges and health records officers from 57 public health facilities. The target population was 114 healthcare personnel and the sample size (n = 88) was computed using Yamane formula and drawn using stratified random sampling. Poisson regression was used to determine the influence of cloud computing adoption on the number of realized benefits to health service delivery. RESULTS: Among 80 respondents, Cloud computing had been adopted by 42 (53%) while Software-as-a-Service, Platform-as-a-Service and Infrastructure-as-a-Service implementations were at 100%, 0% and 5% among adopters, respectively. Overall, those who had adopted cloud computing realized a significantly higher number of benefits to health service delivery compared to those who had not (Incident-rate ratio (IRR) =1.93, 95% confidence interval (95% CI) [1.36-2.72]). A significantly higher number of benefits was realized by those who had implemented Infrastructure-as-a-Service alongside Software-as-a-Service (IRR = 2.22, 95% CI [1.15-4.29]) and those who had implemented Software-as-a-Service only (IRR = 1.89, 95% CI [1.33-2.70]) compared to non-adopters. We observed similar results in the stratified analysis looking at economic, operational, and functional benefits to health service delivery. CONCLUSION: Cloud computing resulted in improved health service delivery with these benefits still being realized irrespective of the service implementation model deployed. The findings buttress the need for healthcare institutions to adopt cloud computing and integrate it in their operations in order to improve health service delivery.

Evaluation of existing district health management information systems a case study of the district health systems in Kenya.

INTRODUCTION: This paper discusses some of the issues and challenges of implementing appropriate and coordinated District Health Management Information System (DHMIS) in environments dependent on external support especially when insufficient attention has been given to the sustainability of systems. It also discusses fundamental issues which affect the usability of DHMIS to support District Health System (DHS), including meeting user needs and user education in the use of information for management; and the need for integration of data from all health-providing and related organizations in the district. METHODS: This descriptive cross-sectional study was carried out in three DHSs in Kenya. Data was collected through use of questionnaires, focus group discussions and review of relevant literature, reports and operational manuals of the studied DHMISs. RESULTS: Key personnel at the DHS level were not involved in the development and implementation of the established systems. The DHMISs were fragmented to the extent that their information products were bypassing the very levels they were created to serve. None of the DHMISs was computerized. Key resources for DHMIS operation were inadequate. The adequacy of personnel was 47%, working space 40%, storage space 34%, stationery 20%, 73% of DHMIS staff were not trained, management support was 13%. Information produced was 30% accurate, 19% complete, 26% timely, 72% relevant; the level of confidentiality and use of information at the point of collection stood at 32% and 22% respectively and information security at 48%. Basic DHMIS equipment for information processing was not available. This inhibited effective and efficient provision of information services. CONCLUSIONS: An effective DHMIS is essential for DHS planning, implementation, monitoring and evaluation activities. Without accurate, timely, relevant and complete information the existing information systems are not capable of facilitating the DHS managers in their day-today operational management. The existing DHMISs were found not supportive of the DHS managers' strategic and operational management functions. Consequently DHMISs were found to be plagued by numerous designs, operational, resources and managerial problems. There is an urgent need to explore the possibilities of computerizing the existing manual systems to take advantage of the potential uses of microcomputers for DHMIS operations within the DHS. Information system designers must also address issues of cooperative partnership in information activities, systems compatibility and sustainability.

Evaluation criteria for district health management information systems: lessons from the Ministry of Health, Kenya.

INTRODUCTION: There has been no comprehensive evaluation of the district health management information systems (DHMISs) since the establishment of these systems by the Ministry of Health (MoH), in Kenya. This is partly due to lack of defined criteria for evaluating the systems. The objective of this study is to design evaluation criteria for assessing the viability, sustainability and ultimate contribution of DHMIS in the management of the district health system (DHS) in Kenya. METHODS: This descriptive cross-sectional study was undertaken in three DHSs in Kenya. Empirical evidence was collected through interviews, complemented by a comprehensive review of relevant literature, reports and operational manuals of various health information systems in Kenya. RESULTS: A set of evaluation criteria for DHMISs in Kenya was designed for each of the three phases of implementation: phase one-pre-implementation evaluation criteria (categorized as policy and objectives, technical feasibility, financial viability, political viability and administrative operability) to be applied at the design stage; phase two-concurrent (operational) implementation evaluation criteria to be applied during implementation of the new system; phase three-post-implementation evaluation criteria (classified as internal-quality of information; external-resources and managerial support; ultimate-systems impact) to be applied after operating the implemented system for at least 3 years. CONCLUSIONS: In designing a DHMIS model there is need to have built-in these three sets of evaluation criteria which should be used in a phased manner. Pre-implementation evaluation criteria should be used to evaluate the system's viability before more resources are committed to its implementation; concurrent (operational) implementation evaluation criteria should be used to ascertain the status of the on-going implementation with the view to either fine-tune or abandon it altogether before more resources are used on it; and post-implementation evaluation criteria should be used to assess its overall effectiveness (if it has achieved its hypothesized benefits) towards the management of DHS.

Evaluation criteria for the district health management information systems: lessons from the Ministry of Health, Kenya.

BACKGROUND: The District Health Management Information Systems (DHMISs) were established by the Ministry of Health (MoH) in Kenya more than two decades ago. Since then, no comprehensive evaluation has been undertaken. This can partly be attributed to lack of defined criteria for evaluating them. OBJECTIVE: To propose evaluation criteria for assessing the design, implementation and impact of DHMIS in the management of the District Health System (DHS) in Kenya. METHODS: A descriptive cross-sectional study conducted in three DHSs in Kenya: Bungoma, Murang'a and Uasin Gishu districts. Data was collected through focus group discussions, key informant interviews, and documents' review. The respondents, purposely selected from the Ministry of Health headquarters and the three DHS districts, included designers, managers and end-users of the systems. RESULTS: A set of evaluation criteria for DHMISs was identified for each of the three phases of implementation: pre-implementation evaluation criteria (categorised as policy and objectives, technical feasibility, financial viability, political viability and administrative operability) to be applied at the design stage; concurrent implementation evaluation criteria to be applied during implementation of the new system; and post-implementation evaluation criteria (classified as internal - quality of information; external - resources and managerial support; ultimate - systems impact) to be applied after implementation of the system for at least three years. CONCLUSIONS: In designing a DHMIS model there is need to have built-in these three sets of evaluation criteria which should be used in a phased manner. Pre-implementation evaluation criteria should be used to evaluate the system's viability before more resources are committed to it; concurrent (operational) - implementation evaluation criteria should be used to monitor the process; and post-implementation evaluation criteria should be applied to assess the system's effectiveness.

Capacity indicators for disaster preparedness in hospitals within Nairobi County, Kenya.

The goal of this study was to assess hospital capacity for disaster preparedness within Nairobi County. This information would be valuable to institutional strategists to resolve weaknesses and reinforce strengths in hospital capacity hence ensure efficient and effective service delivery during disasters. Analytical cross-sectional research design was used. Indicator variables for capacity were hospital equipment, hospital infrastructure, surrounding hospital environment, training, drills, staff knowledge and staff capabilities. Thirty two hospitals were studied of which nine of them were public hospitals. Data analysis was done using SPSS and presented in the form of frequency tables at p < 0.05. Study results indicated that hospital capacity to disaster preparedness in Nairobi County existed in 22 (68.88%) hospitals, in 6 (64.95%) public hospitals and 16 (69.64%) private hospitals. The difference in capacity between public and private hospitals within the County was less than 5%. This showed that both public and private hospitals were relatively at par, with regard to the capacity to handle disaster cases. Study findings also revealed that the surrounding hospital environment was the most highly rated indicator while inter hospital training and drills were the least rated. Although existent in hospitals within Nairobi County, for maximum hospital capacity and disaster preparedness within Nairobi County to be achieved, the existent gap in inter hospital training and inter hospital drills, both of which fall under the finance health systems pillar, required addressing.