Electronic medical record (EMR) system, which is an integral part of the larger health management information system (HMIS), is a software that records health-related information of patients on computers. It is a program that collects, manages, and generates data that are used by authorized workers within health facilities. EMR systems provide substantial benefits to health facilities and organizations in patient management and are quickly replacing paper-based systems. They solve many of the limitations of paper-based systems and have proved to be cost-effective in improving the quality of health services (1). They improve the accessibility of health records as well as the quality and accuracy of patient information. Given these benefits, institutions have widely adopted EMR systems in healthcare globally (2).

Previously, all tasks related to the administration and management of patients, such as human resources, procurement, and clinical management, were managed using paper-based record-keeping systems, which proved to be inefficient in terms of information retrieval, security, and data quality and did not allow concurrent data access. However, paper-based systems are still used by health facilities largely because many facilities have not installed EMR systems and the health workers are more familiar with paper-based records due to the long-term use and the fact that they do not require a high level of technical knowledge and skills compared with the electronic systems (1).

An EMR system is a powerful tool for improving clinical and administrative/managerial decision-making by providing access to accurate and relevant information (3). It provides a comprehensive way to manage patients and streamline administrative tasks, and is one of the best healthcare analytics solutions as it collects and analyzes patient data to identify trends and patterns and develop predictive models (4). The increasing integration of highly diversified technologies in the healthcare field has resulted in the need for such an organized gathering of accurate data for informed decision-making in the health sector (5).

Globally there is an increasing volume of health-related data being generated, accelerating the trend towarddigitalization in health. However, there are persistent data gaps and fragmented approaches to the governance of health data in different contexts, which has contributed to inadequate data use (6). EMR systems have begun to be widely used in healthcare facilities worldwide as a data collection and aggregation tool (7). A systematic review of publications from 15 Sub-Saharan African countries revealed that about 91% reported the use of open-source healthcare software. However, the use of EMR systems in low-income countries remains in its initial stages (8, 9).

In developed countries like the USA, 94% of the hospitals use electronic health records (EHR) data in decision-making, such as for quality improvement (82%), monitoring patient safety (81%), and measuring organizational performance (77%). EHR data were least commonly used to develop approaches to query for patient data (51%), assess adherence to clinical practice guidelines (59%), and identify care gaps for specific patient populations (60%). While EHRs are used by multiple care providers and health organizations, hospital characteristics (public, private, rural, and urban) significantly impact the use of EMR data. However, there was substantial variation in the use of EHR data (10).

In Sub-Saharan Africa, a study conducted in a Malawian hospital revealed that utilization of EMR functionalities varied among departments as well as among users. Health facility workers used half of the system functions, and the most commonly used were capturing demographics (82.9%) and capturing and assessing clinical data (68.8%). These functions were frequently used because they applied to almost all the patients who visited the hospital. Gender, age, and previous computer use did not influence EMR systems usage. However, education and employment levels were predictors of EMR systems use (1).

In Tanzania, public health facilities use different EMR systems; some of the EMR systems used include the government of Tanzania hospital management information system (GoTHOMIS), Jeeva, Afya Care, and CTC2 database (11, 12). An EMR system is intended to improve and strengthen electronic data capture at the point of care to improve patient clinical care and facility management. The use of electronic data systems has led to improvements in the quality of health services delivery, including improvement in revenue collection, human resource management, supply chain management, health information management, and improved planning and decision-making at different levels of the health system (13, 14).

A study conducted in Tanzania on paper-based HMIS data use in decision-making revealed that about 56.9% of the facilities had functional HMIS, 18% of the facilities had used their data for planning and services improvement, 26.3% had disseminated data, and about 9.1% of the facilities had proper medical records. The level of the facility was associated with the use of data, with hospitals and health centers showing higher use (15). Another study (16) on paper-based data use in decision-making revealed that 60% of the respondents reported using HMIS data in decision-making, of which data was most used to compare service coverage (53%).

The government of Tanzania, through its fifth Health Sector Strategic Plan (HSSP V), has set the priority to improve the application of digital health technologies. The aim is to facilitate the attainment of high-quality health services to all citizens. Despite this, very few health facilities have installed and made use of EMR systems as data gathering and aggregation tools. Expansion of the system to all government-owned facilities is a priority (14). Owing to the limited resources, most electronic systems are still being used side by side with paper documentation, which is creating a burden on the healthcare workers (2, 17).

HSSP V prioritizes the application of digital health technologies, including the use of EMR systems in public health facilities (14). The government, through support from implementing partners, started the development and implementation of comprehensive electronic medical record systems in 2015 (18). As of now, the AfyA Care and GoT-HoMIS have been approved for scale-up country wide (19). Currently, the GoT-HoMIS system has been deployed in 1,424 healthcare facilities (20). Despite EMR systems supporting data synthesis and visualizations, the use of EMR systems in decision-making in Tanzania is still inadequate (13, 21).

The increasing adoption of the EMR system in Tanzania as a data collection and aggregation tool should be implemented together with improvements in the capacity of health managers to harness its potential. The weakness in general managerial capacity of the health system has been cited as one of the contributory factors in failing to scale up effective health interventions. Hence, understanding how health facilities are currently using data generated by EMR systems in decision-making is important as one of the policy initiatives (14). A study (16) on paper-based systems revealed that 60% of health facility workers and 38.5% of district officials use routine health information for decision-making, with the majority using it in comparing service coverage and monitoring disease trends over time and health promotion activities.

In 2023, the government introduced a data use toolkit for the health sector that insists that the availability of timely, retrievable, and accessible quality data is a cornerstone of all health systems. The health facilities are advised to conduct regular data review meetings (weekly or monthly) to understand their data, identify challenges, and set action items to improve health services. The health facility management team (HMT) has a role in conducting data use review meetings, monitoring the use of data, ensuring evidence-based planning and documentation of data use activities, and increasing the capacity of health workers in data use skills. One of the data sources insisted on in this guideline is EMR systems such as GoT-HoMIS (22).

Despite large investments to support the adoption of EMR systems in Tanzania, so far limited studies have been conducted to give an insight into the acceptance and use of data generated by EMR systems in decision-making in the study area. Therefore, this study aims to fill the knowledge gap related to EMR data utilization in decision-making and its associated factors among health facility managers in the public primary health facilities in Dodoma Region, Central Tanzania.

This study set out to assess the EMR system data use in decision-making and its associated factors in public primary health facilities. We assessed variables related to (1) sociodemographic, (2) organizational, (3) technical, and (4) behavioral factors.

The findings of this study revealed that approximately half (56.5%) of health managers were current users of the EMR system, of whom 73.6% used the system on a daily basis. Among them, approximately 125 (40.6%) used EMR data in decision-making. This rate was higher than that in a study conducted in Ethiopia, which revealed that EMR system data were used by 26.6% of the healthcare workers (2). Most of the facilities were partially electronic and partially paper-based. This finding is the same as those revealed in studies conducted in Ethiopia and Malawi (1, 2, 28).

The relatively high use of EMR system data may be attributed to the fact that most of the users preferred EMRs over paper-based systems (93.5%). In addition, respondents also had good computer and data use skills ranging from 50.93% to 93.83%, which might have contributed to this level of implementation. This is supported by a study conducted in Dire Dawa (2) and Eastern Ethiopia (28).

Health professionals with excellent knowledge of computers and data use skills were more likely to use EMR systems than others. This finding is similar to that from a study conducted in Tanzania, which shows that the knowledge gap and skills among healthcare workers significantly influence EMR system use and the data management process (16, 29). In a study conducted in Ethiopia, it was revealed that staff with good data analysis skills were more likely to use health information system data than others (30). Moreover, a study conducted at Muhimbili National Hospital revealed that one of the challenges in the implementation of EMRs was inadequate skills of healthcare workers in the use of computers, which was because most of the old staff were aging and had inadequate basic skills in the use of computers, and the new staff turnover at the facility was high. All this resulted in poor quality of clinical note documentation. Refresher trainings and interfacility forums to share real practical experience were recommended (31).

In this study, the age of the health managers did not significantly impact their likelihood of using EMR data. This was different from a study conducted in Ethiopia that revealed that health professionals in the age group of 26–30 years were 1.61 times more likely to use the EMR system than those whose age was more than 40 years (32). However, another study in Ethiopia revealed that health professionals with a higher age, above 35 years, were more likely to use EMR systems than others (28). The different outcomes might be influenced by contextual factors such as the availability of training programs, the complexity of the EMR system, and the overall technological environment in the healthcare setting.

In this study, it was further revealed that motivation (managerial support) for the use of EMR system data was less likely to be associated with improved use of EMR data. This is different from a study conducted in Bahir Dar City, Ethiopia, which revealed that healthcare workers with managerial support were more willing to use EMR systems than others (33). However, another study (34) revealed that good governance (leadership, participatory monitoring, regular review of data) was associated with improved data use practices. In addition, performance-based financing was also found to have a role in promoting data use. In another study too, conducted at Mount Meru Hospital, EMR use was limited by low knowledge and skills of healthcare workers on information and communication technology (ICT) and inadequate management support (35). Therefore, the differences in findings could be due to variations in the healthcare settings, organizational cultures, levels of managerial support, and overall readiness of healthcare workers to adopt and utilize EMRs.

Those who were working in rural areas were less likely to use EMR data compared with those working in urban areas. This is congruent with a study conducted in the USA (10) in which hospital characteristics influenced use of EHR data, with the small, rural, state/local government run, and non-teaching hospitals having the lowest rates of EHR data use. This might be because EMR adoption rates in rural areas are significantly lower than in urban areas. This could be attributed to factors such as technology access, limited resources, and lower EMR adoption rates in rural settings.

Furthermore, respondents with good perceived EMR information quality were three times more likely to use EMR system data than those with poor perceived information quality. However, in this study, perceived system quality and service quality did not have a significant influence on EMR use. This is different from a study conducted in Ethiopia in which EMR system service quality and system quality were independent predictors of EMR use (2). Another study conducted in Gabon revealed that perceived EMR system quality and information quality were 1.7 times more likely to impact use of the system (36). This may be because data use is influenced by how users perceive the quality of the data produced.

The degree of EMR implementation was also a predictor of EMR use. Those who had EMR systems in all departments/units were more likely to use EMR systems than others. This is congruent with a study conducted in Ayder Hospital in Ethiopia in which 95% of the units were using the EMR system; hence, EMR use was higher (37). Another study conducted in Kenya revealed that EMR use was not adequate due to infrastructure challenges (38). In addition, another study revealed that the availability of health information system resources, such as EMR systems, organizational structure, and training, potentially influences data use (34). This may be because the availability of the EMR system in all departments increases the chance that in hospital discussions, all staff will be discussing data generated from the EMR system rather than a mixture of sources of data.

Training on EMR use had no influence on EMR system data use in this study. This was in line with a study conducted in Dire Dawa, Ethiopia (2). However, this finding is in contrast with findings from other studies conducted in different regions of Ethiopia (28), Malawi (1), and Kenya (38). It is well known that training has great potential to improve EMR use, as it can improve the attitude, knowledge, and skills of health managers. While training is a crucial component, its effectiveness in promoting EMR data use is intertwined with other factors, such as managerial support, cultural considerations, and resource availability. Hence, a comprehensive approach that addresses these factors is essential for maximizing the benefits of EMRs implementation.

A higher education level was also associated with EMR system data use, in which those with a diploma and a degree or above were 3 and 5 times, respectively, more likely to use EMR system data than those with a certificate. This is congruent with a study conducted in Ethiopia in which health professionals with an educational level of first degree and above were 1.92 times more likely to use the EMR system than diploma holders (32). This finding suggests that providing higher education and training opportunities to healthcare professionals could enhance their ability to engage with EMR systems and leverage the data they provide. This could lead to improved decision-making processes and patient care.

Overall, the study identified several factors that influenced the use of EMR system data among health managers. These factors included education level, professional role, managerial experience, access to computers, discussions about EMR, system implementation status, perceived system quality, and computer and data use skills.

The electronic medical records system has a proven advantage of enabling easy retrieval of data, including data analysis and use. This can potentially lead to more informed decision-making in the health sector. However, in this study, almost half of the respondents were current users of the EMR system, and 40.6% of the health managers were using EMR data in decision-making. This indicates that although many health managers are using EMR systems, a notable portion of decision makers have not fully integrated EMR data into their decision-making practices. Thus, for improvement in EMR system data use, future investments in issues such as training, installation of the system in all units/departments, education, and integration of EMR data into established decision-making workflows should be advocated. Since it was found that training in itself is insufficient to improve use of EMR, other organizational aspects of work routine that may serve as a challenge must be considered. Many health projects focus on training and technical aspects rather than improving organizational culture. Therefore, a comprehensive approach that addresses these factors is essential for maximizing EMR use in decision-making.