Facility Staff and VHTs are positively receptive of an optimal health data management and utilization process and underscore the value therein
We discovered that, healthcare workers perceive an optimal data collection routine and process positively just as in studies carried out in Ethiopia, Tanzania and Kenya [22, 31, 40]. Their attitudes dwindle upon prevailing discrepancies associated with the public health service and current HMIS which they yearn for a solution. There is a possibility that the data collection outset in practice discourages determined healthcare workers.
This further asserts that that optimal HMIS/EMRS data management and utilization is mostly negated by systemic factors instead of personal factors [1, 3, 5, 23].
The facility needs an enabling environment in terms of space, equipment, staffing and renumeration
The findings reaffirmed that most rural lower HCs in LICs like Mirambi HC III lack the basic structural and operational capacity, more so in the aspect of data and records management [36, 4]. Critical understaffing leaves healthcare staff overwhelmed. They therefore render less focus to authentic data collection and handling. Lack of Computerized data handling system has also predisposed the collected data to avoidable losses to rain, parasites, omission, and disintegration. The supporting VHTs are not renumerated at all and find themselves less motivated to collect community data [29]. Final datasets may have highly misleading data that may not facilitate reliable data-based decision making [10]. This also misrepresents the health picture of the community the facility serves. Introduction of a computerized system and recruitment of critical staff will improve efficiency, safety and quality of patient care [14, 33].
There is a need to develop and implement a robust, elaborate and standard training module in the core aspects of data management (collection, handling and utilization) for the data focal personnel and other healthcare staff
Healthcare personnel and data staff explicitly agitated for routine, elaborate training in the data flow chain starting from data collection, but most importantly in analysis and utilization. Limited history of ICT training, and subsequent non-exposure to Information Technology (IT) tools has interfaced with rapidly evolving healthcare ICT trends [37]. Healthcare staff admitted that the training they received was insufficient just as in Malawi [3], but were more worrisome of the 2 weeks certification for previously naïve data handlers.
Health data becomes useless if key facility staff can’t translate it into functional units. Healthcare workers need capacity to ensure authenticity of data so they can plan according to the documented consumption and output of their respective departments [9, 20]. The health data focal personnel should have analytical skills to help deliver this key information Training health workers has improved data management endpoints in studies [30].
Develop quality control, and M&E protocols but tailor them to the facility needs and output
We discovered a conspicuous omission of data quality control, and standardized, practical Monitoring and Evaluation (M&E) just as Asiimwe, Mboera and Mazengia [5, 22, 23] did. This is not only a standalone problem, but also a compounded effect from other challenges at the facility. The facility didn’t have a standing in-charge, and therefore lacked a supervisory figure.
There is a need to generate or adopt, execute and update standing health data management Standard Operating Procedures (SOPs) at facility level. These ought to be embedded into routine clinical service execution for one or more of the following reasons:
First, being a repetitive process, the data chain ought to have checks and balances at every critical stage to ensure uniformity.
Secondly, given the rapid evolution of technologies in healthcare [15], and presence of multiple support and implementing partners, there is an urgent need for uniform, clear data quality standards of optimally applicable value [45].
Thirdly, certain quantitative measurements could be misleading if not well elaborated. This involves most of the routine service provision targets like, positive Rapid Diagnostic Tests (RDTs) per day/week. Data can be fabricated to please funders or supervisors hence the need to review standing M&E protocols.
The facility would benefit from procurement and supply policies that promote and utilize the aspects of optimal routine data management and utilization
We discovered that the facility feeds from the push supply system. However, this has left a string of bad luck. First, the healthcare workers are discouraged from using the data they collect, and so they lose the purpose of data collection. Secondly, this system doesn’t build capacity to translate data into workable plans. Thirdly, it has created shortages in the data chain its self, from registers to field gear used by VHTs, further impacting data management negatively. More severely, it has created shortage of critical medical supplies, undercutting healthcare efforts.
Healthcare delivery is evolving towards personalization. This caters for both the needs of individual community members, individual communities, and thus individual HCs. For this to be achieved, the individual, community and facility needs ought to be quantified from available data [35]. This means procurement should be based on the dynamic quantified needs of the facility rather that rationed proportions [39, 41]. Given the current delays in the data chain, supplies may serve lapsed demand [19].
Bottom-top approach to planning, backed by authentic, well managed data would not only optimize the demand–supply chain, but would also build capacity in facility staff, and create a sense of responsibility over supplies thus reducing shortages and wastage.
There is a need for improved coordination between the facility and its implementation partners to create harmony in health data management
Our findings reveal a need for harmonized recruitment and training of staff, supervision, M&E and quality control in health data across the service provision spectrum. Different trainers from different implementing partners may only cause confusion and mayhem. It was discovered that different implementing partners operate different lines of care at different intensities respectively. However, there is a significant degree of overlap across the different clinical services. Multiple poorly coordinated subsystems may ambiguate data reporting demands, overwhelm and overburden facility staff, create omissions and duplicity and disconnect related facility departments [45, 18, 34]. Subsequently, this injures the quality, integrity and reliability of data.
The HC III also oversees the entire subcounty, and is mandated to aggregate data from the implementing partners, and private practitioners into the district datasets. If private facilities data is missed, then the healthcare picture of the community is incomplete [5].
Reduction in information relay time, and improved community engagement will optimize the impact of health data utilization
We discovered that analysis is earmarked for the DHIS. But data analyzed locally would best suit facility needs. It also brings an advantage of easy, timely and convenient accessibility thus timely utilization. Timely availability of analyzed data would create a timely basis of action during routine clinical care and public health surveillance [14, 33, 45].
Coupled with enhanced community engagement, improved health information and services uptake would be realized hence an amplified data utilization impact. VHTs advanced the idea of involving facility staff in community engagement to improve the reception of health information in the community and countering political and economic biases associated with VHTs.
