This study aimed to determine the level of utilization of routine health data and its associated factors among healthcare workers in health centers in the Bukedi region. The findings from Bukedi, where only 30.99% of participants utilized data to develop Annual Health Work Plans, reveal that data utilization falls short of the ministry of health planning tool HMIS001 and PHC sector guidelines, which recommend data utilization rates of 80% and above. This reflects a gap in the use of data for health planning and interventions. These results are consistent with broader trends observed in Uganda and other low- and middle-income countries, as highlighted in a study by Ayele and others, which reported data utilization rates ranging from 38.4% in Northern Ethiopia to 48.1% in Kenya [14].

The low data utilization rate (30.99%) for AHWPs development in Bukedi, Uganda, can be attributed to several factors, including ineffective leadership by managers and challenges related to poor-quality data in health centers, which may discourage healthcare workers from using data for health planning. Additionally, healthcare workers may lack the necessary skills to effectively utilize data for health planning, as observed in this study. However, the data utilization rate in Bukedi is lower than that observed in studies from the North Gondar Zone in the Amhara region (78.5%) and the Hadiya Zone (69.3%) [5, 15]. This discrepancy could be due to differences in the type of decisions the data was used for, the type of facility, the participants involved, and the criteria used to measure data utilization.

Importantly, it has been suggested that the Health Information System Strengthening Model (HISSM) strengthens health information systems by integrating diverse health data sources, including facility, community, electronic health records, population-based data, human resources, financial, supply chain, and surveillance data, to support informed decision-making. This highlights the lack of a similar approach in health centers in the Bukedi region. Perhaps, adopting such models in Bukedi could improve data utilization for AHWPs development.

Meetings influenced data utilization for AHWPs development; however, an unexpected result was observed: health facilities that conducted regular meetings had significantly lower odds of utilizing data for their annual health work plans compared to facilities that did not hold regular meetings. This suggests that simply having regular meetings does not automatically lead to effective data use in health planning. This could be due to the fact that the content of the facility meetings may not focus on data analysis or health planning, or the meetings may be predominantly administrative, addressing issues unrelated to data utilization. Such meetings may not contribute to the improved use of data. Furthermore, frequent meetings without clear objectives, agendas, or actionable outcomes may be less effective than fewer, well-organized meetings that prioritize data-driven decision-making. Notably, some regular meetings might create complacency among staff, who may feel that merely attending meetings fulfills their responsibilities, even if data is not being effectively analyzed or applied.

Contrary to the findings above, several studies have found that regular meetings positively influence the utilization of data for decision-making and health planning. Regular meetings provide a platform for staff to discuss data findings, share insights, and collaborate on interpreting results, as noted in Mphatswe et al.’s study, which observed that facilities conducting routine data review meetings saw improvements in data quality and utilization as staff collaboratively identified issues and developed solutions [16]. Similarly, regular meetings can serve as training opportunities where staff enhance their skills in data analysis and interpretation, as seen in Gimbel et al.’s study in Mozambique, which found that incorporating data discussions into regular meetings fostered a data-driven culture and improved staff competencies [17]. Regular data discussions in meetings promote accountability among health workers, leading to increased data utilization through feedback mechanisms and cultivating an organizational culture that values evidence-based practices, which are essential for sustained data use in health systems [18]. Although the study’s initial analysis suggests that regular meetings do not necessarily enhance data utilization for annual health work plan development, evidence from other studies indicates that meetings can positively influence data use when they are well-structured and focused on data-related activities. Therefore, healthcare staff in the Bukedi region should adopt this model for their meetings.

The study findings also revealed that, in health centers in the Bukedi region, facilities that experienced poor data quality—such as incomplete data or lack of access to reliable data—had significantly lower odds of utilizing data for annual health work plans compared to those without data quality issues. This result concurs with a study conducted in Eastern Ethiopia, which found that data quality, among other factors, significantly influenced the utilization of health data [19]. This stark difference highlights the profound impact that data quality has on the confidence and ability of health workers to use data effectively. This can likely be attributed to various factors, such as inaccurate and unreliable data, which can lead to misleading or faulty information. This makes it challenging for health workers to make informed decisions, as incomplete and inconsistent data can create confusion and mistrust, reducing the willingness and ability to use it to identify trends, patterns, and poorly performing indicators needed for data-driven decision-making required in developing annual health work plans.

This is supported by Ndabarora and others in their study in LMICs, who noted that errors and inconsistencies in the data can cause health workers to distrust the information, leading them to avoid using it in decision-making processes [20]. Similarly, untimely and outdated data can result in delayed decision-making, reducing its relevance and making it difficult for health workers to use data for planning and decision-making embedded in the annual health facility work plans. This finding supports the literature, which highlights the importance of data quality in promoting data utilization, as demonstrated by Nutley and Reynolds, and Asiimwe in Uganda [18, 21]. The statistical significance of poor data quality suggests that this factor is a critical predictor of data utilization, and addressing it could positively impact health facility planning and decision-making for the allocation of resources and inputs, ultimately enhancing service delivery.

Contrary to the findings above, some studies suggest that poor data quality does not always lead to low data utilization. Scott et al. observed that, in some contexts, health professionals continue to use available data for decision-making despite quality issues, often supplementing it with local knowledge and experience [22]. Additionally, Lippeveld argued that the relationship between data quality and utilization is not always linear and requires complementary efforts to promote data demand and create an information-driven culture to realize the benefits of improved data quality [23]. This could possibly be the missing link among health workers in the Bukedi region, who may rely on experience and knowledge, alongside other measures, to enhance data utilization for AHWPs development in their health centers.

The multivariate analysis further indicates that participants from health facilities that received feedback from the Ministry of Health about data utilization had significantly higher odds of using data for annual health work plans compared to those that did not receive such feedback. This finding aligns with Mekonnen’s and Gebeyehu’s systematic review and meta-analysis, which identified regular top-level feedback as a key factor for enhanced utilization of routine health information [24]. This could be attributed to the positive impact of feedback from top-level technical officers, which fosters accountability, improves skills, enhances communication, identifies data quality issues, and motivates health workers. In the end, this leads to more effective data-driven decision-making among health workers. This is supported by various studies, including one by Hotchkiss et al., which demonstrated that feedback from the Ministry of Health creates a sense of accountability among health workers, encouraging them to use data more effectively. The knowledge that their data contributions are reviewed and valued at higher levels motivates staff to prioritize data use [25]. Similarly, top-level feedback often includes recommendations and guidance on data management, usually presented in debrief meetings and documented in support supervision books. This can improve health workers’ skills and competencies in data analysis and interpretation. Furthermore, Lippeveld observed that feedback can help facilities identify errors and inconsistencies in their data, allowing them to address these issues promptly and improve overall data quality. Over time, this boosts morale and motivates health workers to continue using data for developing AHWPs [18, 23].

However, despite the general trend, some studies have found that feedback from higher authorities does not always lead to increased data utilization. It has been reported in some studies that top level authority feedback may be ineffective due to infrequency, lack of actionability and failing to address local needs, but also neglect local facilities situations, limiting impact of feedback [26]. Furthermore, excessive or overly critical feedback can demotivate health workers, while negative feedback without guidance is worthless [27]. These contrasting findings suggest that the effectiveness of feedback from higher authorities depends on its quality, relevance, and the way it is communicated. Feedback that is timely, specific, and supportive is more likely to enhance data utilization than feedback that is delayed or punitive.

The findings highlight a central role of MOH feedback in enhancing the utilization of health data for decision-making and planning. Feedback can boost data utilization, but effectiveness requires regular, specific, and constructive feedback. Health centres should develop tailored feedback processes to foster evidence-based decision-making.

Study findings further revealed a significant association between the presence of a leadership culture that emphasizes data utilization and the effective use of data in annual health work plans. In particular, participants who work in health facilities that fostered a leadership culture of data use had substantially higher odds of utilizing data compared to those that did not cultivate such a culture. This finding is consistent with existing literature by Hoxha and others in their study conducted on understanding the challenges associated with the use of health data in low- and middle-income countries which found which found that organizational culture, leadership, and resources played a role in influencing data utilization [28]. This could be probably because health facilities led by leaders who prioritize data use are able to create a supportive environment by prioritizing data use, allocating resources, providing training, promoting open communication, setting goals, leading by example, and fostering teamwork which in the end influences data utilization for developing AHWPs. In Gimbel and others, effective leaders allocate necessary resources, including training, technology, and infrastructure, to support data collection, management, and analysis [17]. Additionally, effective leadership cultivates a culture of accountability and motivation, driving health workers to prioritize data use. By setting clear expectations and recognizing data-driven achievements, leaders encourage staff to value data quality and utilization [23]. Moreover, leaders who champion data use foster a supportive environment, empowering staff to access, analyze, and apply data, promoting collaboration and evidence-based decision-making [25].

These trends supported by various studies as in Gimbel and others in Mozambique who reported that health facilities with proactive leadership were more likely to utilize data effectively for planning and resource allocation further observing that leadership support was instrumental in overcoming barriers to data use, such as limited technical skills and inadequate infrastructure [17]. Similarly, Hotchkiss and others demonstrated that leadership engagement is vital for the continuous improvement of health information systems where leaders who actively participate in data review meetings and decision-making processes set a precedent for their teams to follow suit [25].

Contrarily, while the majority of studies support the positive influence of leadership culture on data utilization, some research presents contrasting findings. It has been reported that, effective health facility leadership alone is insufficient to promote data utilization in health facilities, as entrenched organizational structures, resistance to change, superficial commitment, and top-down approaches can undermine efforts, highlighting the need for collaborative and sustained leadership engagement [27, 26, 29]. These contrasting findings indicate that leadership culture significantly promotes data utilization, but its success depends on authentic, consistent, and comprehensive leadership efforts, supported by systemic changes, genuine engagement, and a holistic approach to addressing barriers.

Consistent with existing literature, strong and committed leadership fosters an environment that prioritizes data use, allocates necessary resources, builds capacity, and holds staff accountable for data-driven outcomes. However, the effectiveness of leadership in promoting data utilization for developing AHWPs is not guaranteed and depends on the depth of commitment, the presence of supportive systems, and the ability to address some of the health system challenges. Health systems aiming to improve data utilization should focus on promoting a leadership culture that genuinely values and integrates data use into all aspects of health service delivery including development of AHWPs.

A significant association between staff training and data utilization for AHWPs development was also observed. Participants who reported having adequately trained staff in their facilities had substantially higher odds of utilizing data for annual health work plans compared to those without trained staff. This finding concurs with the findings reported by Asemahagn and Alene in Ethiopia which observed that availability of resources was significantly associated with the utilization of health data [19]. This could be probably because adequately trained staff in health facilities are more likely to utilize data effectively for AHWPs development due to teamwork, shared data utilization responsibility and accurate planning.

The findings are further supported by various studies that observed staff training significantly enhances data utilization by equipping health workers with essential skills and competencies for accurate data collection, management, analysis, and interpretation [27]. Additionally, training not only ensures high-quality data for effective health planning and resource allocation but also boosts staff morale and motivation, fosters a culture of evidence-based decision-making, and builds capacity for proactive problem-solving through data analysis [30, 31, 25].

While staff training is positively correlated with data utilization, research shows that training alone may not be sufficient to increase health data use. Studies have identified various barriers that hinder the effective application of training, including systemic and organizational issues. For example, high workloads, lack of management support, inadequate infrastructure, insufficient incentives, and entrenched decision-making processes can limit data utilization despite training [32, 33]. These findings suggest the need for comprehensive approaches that address underlying organizational barriers and support sustained changes in data-driven practices.

Interestingly, the study findings also revealed that participants from health facilities with internet access had significantly lower odds of utilizing data for annual health work plans compared to those without internet access. This could be due to several factors, including the possibility that some health workers lacked computer skills, had no training on data systems, or resisted new technology. Additionally, poor internet connectivity and inadequate equipment may have further complicated data use for AHWPs development. Healthcare data utilization is hindered by several key obstacles, such as limited technical skills among health workers, insufficient training on digital health systems, resistance to change due to familiarity or technological fears, and infrastructure challenges like unreliable electricity and slow internet. These interconnected barriers must be addressed to enhance healthcare data utilization and improve patient outcomes [22, 31, 26, 34].

This unexpected finding is supported by previous studies, which indicate that merely having internet access does not ensure efficient data use. Kimaro and Sahay found that in Tanzania, the implementation of internet-enabled health information systems did not significantly improve data use due to organizational and socio-cultural barriers [26]. Despite having internet access, health workers continued to rely on traditional paper-based methods. Similarly, Chib et al. observed that internet connectivity did not enhance data utilization in rural health facilities in Asia because health workers lacked the necessary training and support to use digital tools effectively.

Conversely, studies in Mozambique, Ethiopia, and elsewhere indicate that internet availability can enhance data utilization, but its effectiveness depends on complementary factors such as training, system design, and organizational culture, highlighting variability in outcomes [17, 35, 31].

In conclusion, internet access alone is not sufficient for effective healthcare data utilization for AHWPs development in health centers in the Bukedi region. A holistic approach is needed, including comprehensive training, user-friendly systems, reliable infrastructure, change management, leadership support, and cultural shifts. These elements are essential to leveraging internet availability and enhancing data-driven decision-making for improved health planning.