Health Professionals' Experience On District Health Information Management System And Its Utilization At Local Levels In The Tamale Central Municipality

TABLE OF CONTENT

DECLARATION

ACKNOWLEDGEMENT

DEDICATION

LIST OF TABLES

LIST OF FIGURES

ABBREVIATIONS

ABSTRACT

CHAPTER ONE INTRODUCTION
1.1 Background to the study
1.2 Problem Statement
1.3. Objectives of the Study
1.3.1. General Objective
1.3.2. Specific Objectives
1.4. Research Questions
1.5. Significance of the study
1.6. Organization of Chapters

CHAPTER TWO LITERATURE REVIEW
2.1. Introduction
2.2. Knowledge Level of Health Professionals on DHIMS 2
2.2.1. Awareness of DHIMS
2.2.2. Familiarity with Most Important Features of DHIMS
2.3 Level of Utilization and Perceived Usefulness of DHIMS2 in Routine Health Information Management and Reporting
2.3.1. Frequency of DHIMS 2 use
2.3.2 Perceived usefulness and benefits of DHIMS 2
2.4. Level of Training and Technical Support Health Professionals Receive for Using DHIMS 2 at the Local Levels
2.4.1 DHIMS2 Training Programs
2.4.2. Resources and technical assistance
2.5. Conclusion

CHAPTER THREE METHODOLOGY
3.1 Background of the Study Area
3.2 Study Design and Type
3.3 Study Population
3.4 Sample Size and Sampling Technique
3.4.1 Sample Size
3.4.2 Sampling Technique
3.5 Study Variables
3.6 Data Collection Tool and Technique
3.7 Data Processing and Analysis
3.8 Limitation of the study
3.9 Ethical Considerations

CHAPTER FOUR RESULTS
4.1 Introduction
4.2 Demographic Characteristics
4.3 Knowledge level of health professionals on DHIMS2
4.4 Level of utilization of DHIMS2 in routine health Information Management and reporting
4.4 Level of training and technical support health professionals receive for using DHIMS

CHAPTER FIVE DISCUSSION OF FINDINGS
5.0 Introduction
5.1 Knowledge level of health professionals on DHIMS
5.2 Level of utilization of DHIMS2 in routine health Information Management and reporting
5.3 Level of training and technical support health professionals receive for using DHIMS2

CHAPTER SIX SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS
6.0 Introduction
6.1 Summary of Findings
6.2 Conclusion
6.3 Recommendations

APPENDIX I

DATA COLLECTION TOOL

QUESTIONNAIRE

REFERENCES

LIST OF TABLES

Table 1: Selected Sub-municipals and health facilities 21

Table 2: Study Variables 22

Table 3: Demographic Characteristics 26

Table 4: Knowledge level of health professionals on DHIMS 2 33

Table 5: Level of utilization of DHIMS2 in routine health Information Management and reporting 35

Table 6: Level of training and technical support health professionals receive for using DHIMS2 43

Table 7 Regression analysis showing association between background characteristics of respondents’ and DHIMS2 usage level 47

LIST OF FIGURES

Figure 1: Map of Tamale Central Municipal

Figure 2: Subdistrict

Figure 3: Gender

Figure 4: Category of staff

Figure 5: Years of service

Figure 6: Frequency of DHIMS2

Figure 7: Educational status

Figure 8: Age of respondents

Figure 9: Type of Health Facility

Figure 10: Ownership of facility

Figure 11: overall knowledge on the part of respondents

Figure 12: Frequency of DHIMS2 usage

Figure 13: Task(s) DHIMS2 is used for

Figure 14: Time spent using DHIMS2 weekly

Figure 15: Type of data regularly entered into DHIMS2

Figure 16: Challenges faced during DHIMS2 usage

Figure 17: Specific challenges faced during DHIMS2 usage

Figure 18: Receipt of formal training on DHIMS2

Figure 19: Desire to receive formal training on DHIMS2

Figure 20: Adequacy of DHIMS2 training received

Figure 21: Rate of availability of technical support receipt when faced with challenges

Figure 22: Time taken to resolve technical issues when reported

ABBREVIATIONS

CHP Community Health Planning

DHIMS2 District Health Information Management System

DHIS2 District Health Information System

EHRs Electronic Health Records

HIS Health Information Systems

MOH Ministry of Health

ABSTRACT

This study assessed knowledge, application, and technical support acquired by health professionals (practitioners) of District Health Information Management System 2 (DHIMS2) in Tamale Central Municipal, Ghana, locally. Despite the fact that DHIMS2 was deployed throughout the nation since 2012 to enhance the health information management, its application at the district level is not high due to infrastructural, training, and access challenges.

Information was collected from 210 randomly selected health workers using a cross-sectional study with a structured questionnaire and analyzed using STATA version 17.0. Findings revealed serious gaps: only 11.9% of the respondents had adequate knowledge about DHIMS2 functions, while 80% faced problems of poor connectivity and accessibility. Furthermore, whereas 63.3% of the respondents never received formal training, 100% of the respondents desired to receive it—indicating massive demand for formal capacity building.

The study concludes that the DHIMS2's potential to improve health outcomes through evidence-based decision support is facilitated by knowledge, training, and infrastructure gaps. It suggests mandatory training interventions, infrastructural investment, and continuous feedback-informed program monitoring. How training interventions affect data quality and delivery of services must be examined in future research.

CHAPTER ONE INTRODUCTION

1.1 Background to the study

The effective application of health information in decision-making, policy, and performance improvement in healthcare delivery globally is critical. Health information management systems such as the District Health Information Management System (DHIMS2) play a critical role in enhancing health systems by creating a chance for the collection, analysis, and use of health information. DHIMS2, a web-based, open-source application developed on the DHIS2 platform, is a tool for ease in the management of health information and monitoring of healthcare services at all echelons (Bhatt et al., 2024a).

Health information systems have revolutionized the collection and use of health data worldwide, thereby enhancing surveillance, distribution of resources, and eventually service delivery. For instance, both Norway and India have implemented the use of DHIS2 for tracking maternal health, immunizations, and disease outbreaks in real-time, among other uses (Mensah Abrampah et al., 2024). The national health systems within Norway have integrated with DHIS2 to advance real-time reporting, even in scattered rural areas of the country for increased access to care. The system has enhanced the government’s ability to monitor an increased number of health indicators and direct resources with real-time information. In India, similarly, DHIS2 has been used in streamlining big-basis management of big programs such as programs for maternal and child health, with increased targeting and improved rates of coverage (Odei-Lartey et al., 2020).

In addition to these, DHIS2 has supported global efforts in countering new emerging public health crises, including the COVID-19 pandemic. In the pandemic, DHIS2 was soon repurposed for tracking COVID-19 testing, vaccination, and case reporting in over 40 countries, including Sri Lanka, the Philippines, and Brazil (Byrne & Sæbø, 2022a)This demonstrated its expansibility and adaptability in countering medical crises. In addition, DHIS2 has performed in poor-resource settings, and it has empowered low- and middle-income countries to overcome infrastructure barriers and improve the reporting of health statistics (Poppe, 2017a).

The wide usage of DHIS2 around the world is surely an indication of its future potential in bridging information gaps, empowering decision-makers with actionable information, and improving accountability in health care delivery (Odei-Lartey et al., 2020b). The fact that its use is widespread in many health systems indicates that information plays a critical role in the attainment of Universal Health Coverage and Sustainable Development Goals.

In Africa, whose health systems have unique challenges such as scarcity in resources and restrictions in infrastructure, DHIS2 has become widespread in use in strengthening data-based decision-making. Kenya, Uganda, and Rwanda have all exhibited significant success in utilizing DHIS2 to strengthen reporting accuracy and support interventions in health (Kanfe et al., 2021a). Despite this, lack of training, poor infrastructure, and variable information present significant barriers in using the system in full in most of the continent.

In Ghana, its launch in 2012 in the form of DHIMS2 marked a significant move towards having a uniform system of health information (Byrne & Sæbø, 2022a). It is being used in collecting, storing, and analyzing routine information in health facilities in the country. DHIMS2 is backing key healthcare programs, including disease surveillance, mother and child care programs, and for immunization programs. There have been reports about its use, competency of the professionals, and its level of integration at the decision level, despite its potential, and for that reason, additional studies on its use and efficiency are in orders.

1.2 Problem Statement

Poor user training, technical issues, and problems relating to accessibility are just a few of the major obstacles standing in the way of the workers in this great field globally; analyzing and utilizing these data in health decision-making by health workers remains key (Henderson, 2017a). These limit health information systems in their full realization of their intentions of being impactful in health delivery through informed, evidence-based decisions. In Africa, though the adoption of DHIS2 has expanded, poor infrastructure, irregular connectivity, and lacking capacity-building processes still prevail across most countries. It fosters continuity in low data quality, and delayed reporting, and limits utilization for health programming at the district and community levels (Byrne & Sæbø, 2022b).

In Ghana, for example, DHIMS2 has increased health data collection and reporting at the national level significantly since it was implemented in 2012. The system combines data from over 5,000 health facilities into a central database for tracking national health indicators by the Ghana Health Service (Ghana Health Service, 2016). However, its use remains very poor at local levels due to various intransigent challenges that persist.

The inability of the health professionals to be trained; for instance, only 42% of district-level workers have been formally trained on DHIMS2. Most of them depend on informal methods like guidance from their peers or self-learning, which in most cases is associated with errors and inefficiencies (Henderson, 2017a). This problem is further compounded by the absence of continuous capacity-building activities; most users have been struggling to adapt to changes and new functionalities of the system.

DHIMS2, especially in rural districts where only 36% of health facilities have reliable access to the internet compared to 78% in the urban areas (MOH, 2022). Financial challenges aggravate the situation, with the district health teams left without resources for infrastructure and staff incentives. The poor mechanism for feedback and irregular supervision weakens the motivation of the health workers further, making efficient reporting of correct data and prompt responses to diseases slow, like the recent measles outbreaks in Ghana (Ministry of Health Ghana, 2016). Strategic investments in continuous training increased technical support, and infrastructure will be needed for the optimization of DHIMS2. Incentivizing, increasing supervision, and responding to the challenges faced by the most deprived areas will narrow disparities and ensure equal access. This will be important in enhanced evidence-based decision-making for better health outcomes, moving Ghana forward in the light of Universal Health Coverage and the Sustainable Development Goals.

1.3. Objectives of the Study

1.3.1. General Objective

To assess health professionals’ experience with District Health Information Management System and its utilization at local levels in the Tamale Central Municipal.

1.3.2. Specific Objectives

1. To assess the knowledge level of health professionals on DHIMS2 2. To determine the level of utilization and perceived usefulness of DHIMS2 in routine

health information management and reporting

3. ⁠To explore the level of training and technical support health professionals receive for

using DHIMS 2 at the local levels.

1.4. Research Questions

1. What is the knowledge level of health professionals on DHIMS 2?
2. What is the level of utilization and perceived usefulness of DHIMS 2 in routine health Information Management and reporting?
3. ⁠What is the level of training and technical support health professionals receiving

1.5. Significance of the study

This will add significant value, as it will outline the experience of the health professionals who interact with DHIMS2 and the challenges related to its utilization. The study should help in mapping out ways of improving its utilization in making informed decisions at sub-national levels based on evidence. Equally important, this would eventually help in increasing efficiency in health delivery and proper resource allocation at the local level. The findings will be useful in capacity building through the identification of gaps in training and technical support. In this regard, health professionals can efficiently be equipped with the skills and competencies needed to navigate DHIMS2, hence managing and reporting health data at increased levels of quality. The research will strengthen the quality and timeliness of reporting by dealing with problems affecting data, including inaccuracy, delays, and incomplete entry into the system. Quality information is important for the monitoring and evaluation of health programs at district and national levels.

The evidence-based recommendations provided from this study will therefore add to priorities by policymakers and key stakeholders Ghana Health Service-digital infrastructure, technical support, and internet connectivity remain very crucial in breaking certain infrastructural barriers that hinder effective usage of the DHIMS2 in the Tamale Central Municipal district.

With a focus on studying utilization at the local level, the research contributes also in equity issues regarding health information systems when trying to cover the existing gap between accomplishments reached nationally and the real challenge faced at locality levels: an attempt to ensure every region could receive benefit brought on the systemic to contribute an equable renovation within the delivery services in the health sector. Finally, this study aligns with broader efforts by Ghana toward attaining Universal Health Coverage and the Sustainable Development Goals. This will surely help in ensuring that an optimized DHIMS2 at the local level will perform better in informing health interventions towards better health status among communities within Tamale Central Municipal.

1.6. Organization of Chapters

The research is therefore organized into five chapters to help in the in-depth study of the research topic. Chapter one introduces the study and therefore provides the background, states the problem, and gives the aims of the research. This is followed by chapter two, a critical review of the related literature and theoretical framework of the study. The gaps identified are what the research aims to address. Chapter three explains the methodology used for the research in the form of approach, method of data collection, and techniques used in the analysis of the research data that has been performed to reach answers to the questions. Chapter four presents the findings through the interpretation discussion of findings relating to the literature, implications of findings, and statement of limitations. Finally, chapter five, Conclusion summarizes the key findings, reiterates the contribution of the thesis to the field as well as recommendations for future research. The research is concluded by an overall reference list of citations in the document with possible appendices for support material like a data collection instrument.

CHAPTER TWO LITERATURE REVIEW

2.1. Introduction

The Digital Health Information Management System (DHIMS2) has been implemented across health institutions to improve the management, analysis, and reporting of health data. Its successful adoption and implementation are vital to enhancing the quality of healthcare services. This study focuses on evaluating various aspects of DHIMS2 in the Tamale Central Municipal, assessing the knowledge level of health professionals, their utilization and perception of the system’s usefulness, as well as the training and technical support they receive. These objectives are essential for understanding how DHIMS2 functions within this region and identifying potential areas for improvement.

2.2. Knowledge Level of Health Professionals on DHIMS 2

2.2.1. Awareness of DHIMS2

Globally, health information systems (HIS) have become important for enhancing care systems' performance, efficiency, and accountability (Byrne & Sæbø, 2022). In part, such a worldwide transition towards computerized care management is DHIMS2, a strong platform for collecting, storing, and processing care information (B. Mensah et al., 2023). Familiarity with HIS and its processes forms a critical success criterion for using and installing such care software programs. In developed nations, awareness and integration of HIS in general tend to be high, in consideration of the long-term use of electronic care records (ECRs) and care management programs (Nabunnya et al., 2020).

The same is not, however, happening in any homogenous manner in Africa. South African, Kenyan, and Rwandan governments have taken bold steps in developing care information structures, but most African nations have not effectively assured widespread awareness and use of such software programs (Nii Akai Nettey et al., 2024). Factors have ranged from a lack of infrastructure to poor training and cultural resistance towards computerized structures. Implementation of DHIMS2 in Ghana can, therefore, be seen in terms of a general transition towards care information management improvements in Africa. Nevertheless, several studies have confirmed that most care professionals in general in Africa, including in Ghana, lack awareness and little information about care information structures (Nsiah, Anum-Doku, Nyarko, et al., 2022).The integration of DHIMS2 into Ghana’s health system was intended to address such challenges, ensuring accurate data collection and reporting at the district level.

In Ghana, DHIMS2 is designed to facilitate the timely reporting of health data, improve decision-making, and monitor health indicators effectively at the district level (Ministry of Health Ghana, 2016). However, despite its value, studies have determined that awareness regarding the system for health professionals isn't uniform in regions of the world. Inadequate access to proper training programs, expertise, and familiarity with the system underlie awareness and use at a lesser level (Edum-Fotwe et al., 2019). Health professionals' awareness about system parts, such as reporting capabilities, data-entry, and integration of the system in a broader information environment for health, is an issue in a grave lack of improvement in an attempt to maximize its efficiency and effectiveness.

2.2.2. Familiarity with Most Important Features of DHIMS2

The effectiveness of any health information system (HIS) anywhere in any part of the world is a function of its users' expertise in utilizing key parts of the system well-implemented HIS, which includes efficient data entry, accurate reporting, and easy data retrieval, can support decision-making, improve the quality of care, and foster accountability in health management (Rashida, 2022). In Western countries and America, professionals in the healthcare sector are well-trained in utilizing EHRs and similar computer programs. EHRs and such computer programs become a part of care in terms of delivering care and supporting decision-making for policies (B. Mensah et al., 2023). Having an awareness of working with full use of such care programs such as DHIMS2 is a key consideration towards enhancing care in terms of care worldwide. The picture in Africa, however, is different. As Rwanda, Kenya, and South Africa make improvements in computerizing care programs, most African countries, such as Ghana, have yet not gone past obstacles in having full access to such a system. Challenges include limited training, poor technical support, and maintenance funding (Odei-Lartey et al., 2020). For a case such as DHIMS2 in Ghana, unawareness amongst professionals in terms of entering, reporting, and collecting information can generate incomplete and false information, and such a scenario owes value to such a system. Furthermore, inadequate knowledge of the system’s features can affect the system’s role in promoting informed decision-making for health managers and policymakers (Nsiah, Anum-Doku, Nyarko, et al., 2022).

For example, DHIMS2’s data entry features allow health professionals to input crucial health information such as disease surveillance data, vaccination records, and clinic reports. However, the system’s effectiveness is dependent on the accuracy and consistency of data entered. Studies have shown that health professionals who are not adequately trained in data entry processes often make errors, which compromises data quality (Skouby et al., 2019). Additionally, DHIMS2’s reporting capabilities are integral for generating monthly, quarterly, and annual health reports, which provide insight into public health trends and guide decision-making. If health professionals do not fully understand the reporting functionality, the value of these reports for planning and policy is diminished (Yao Azumah et al., 2021).

Moreover, the data retrieval feature of DHIMS2 is vital for accessing historical health data to track trends, compare health indicators over time, and monitor the progress of health initiatives. Health professionals must be proficient in retrieving relevant data efficiently and accurately, as this is essential for making evidence-based decisions that can improve public health outcomes (B. Mensah et al., 2023). Without a deep understanding of how to access and interpret the data, health professionals may miss key insights needed for timely interventions.

In Ghana, despite the promise of DHIMS2 to improve health information management at the district level, there is evidence that health professionals may not fully grasp the system’s features and their potential to contribute to effective decision-making. Inadequate training and technical support contribute to this knowledge gap, which ultimately affects the functionality of the system and limits its impact on health management (Odei-Lartey et al., 2020b).

2.3 Level of Utilization and Perceived Usefulness of DHIMS2 in Routine Health Information Management and Reporting

2.3.1. Frequency of DHIMS 2 use

These are critical factors that drive the frequency of use of HIS, such as DHIMS2, and mostly depend on ease of use, integration into healthcare workflows, and the level of training provided to users. Systems that are integrated into the existing health services and user-friendly are more frequently used by health professionals because they do not disrupt daily activities but instead enhance the efficiency of the routine tasks performed (Bukht and Heeks, 2018). Equally important, training is another very important factor that would promote regular use of the system. Research has documented that the frequent use of the digital health system results in more accurate data entry, monitoring of health indicators, and management of health programs in general. For instance, digital systems allow real-time data collection, hence timely decision-making and resource allocation are facilitated easily (Byrne & Sæbø, 2022a). With the regular use of the system, health professionals will be able to follow trends and outbreaks, and then respond to the health crisis with greater effectiveness. In addition, it may enhance quality care and public health outcomes (Poppe, 2017).

However, usage is not uniformly distributed in all regions, particularly in resource-constrained settings, like many countries in Africa. In Ghana, for instance, the extent to which DHIMS2 is used in health facilities can vary significantly depending on local health system structures, availability of resources, and the level of infrastructure (Ghana-Health-Service, 2017). While some areas enjoy good access to the needed hardware and reliable Internet access, personnel are responsible for maintenance; others face multiple challenges to using them on a routine basis, whether in terms of frequent downtime, lack of adequate training, or technical support. Besides, it is relevant to DHIMS 2's alignment with the way local health managers used to work. There would be a high likelihood of lesser utilization by health professionals for some time and low data quality or gaps in reporting if the system is cumbersome, requires many changes to existing workflows, or lacks options in local languages. Conversely, those systems that are well-integrated into existing workflows, and those that align with the local context, such as user-friendly interfaces or customizable features, tend to realize higher rates of usage (Odei-Lartey et al., 2020).

Moreover, the political and institutional support status justifies the reason for the variation in the frequency of use of digital health systems. In those countries that have expressed high political will for implementing digital health, supported by relevant institutions, there has always been high investment in training, technical support, and infrastructure, leading to higher utilization of the system (Kanfe et al., 2021). While DHIMS2 is rolled out across The Ghanaian utilization often depends on several commitments from regional health authorities themselves, training of staff, resolving all technical issues, and an equitable distribution of all resources (Mensah Abrampah et al., 2024).

To conclude on this, the nature of use in frequency in the use of DHIMS2 in Ghana and most other African countries is a multifaceted one, thus calling for holistic action to better access to resources, better training, and, above all, solid technical support at local levels. The greater the frequency of use, the more the contribution toward improved management of health data, thereby bringing an improvement in decision-making and, subsequently, ultimate health improvement.

2.3.2 Perceived usefulness and benefits of DHIMS 2

The general usability of DHIMS 2 and similar systems within developing countries usually pertains to the contribution such systems make to ease collection, increasing the accuracy of reporting, and improving monitoring by health professionals on health programs (Bhatt et al., 2024). This could lessen the administrative burden because of having to enter and analyze data manually, since, in most cases, reports are generated automatically, and access is given to real-time data. This will make more time for health professionals for the care and clinical decisions of the patients, resulting in better functioning of health systems (Edum-Fotwe et al., 2019). The system will also lead to improved monitoring of health status, identification of gaps in service provision, and on-time intervention. Therefore, this will be highly essential for monitoring public health management.

In Africa, where most health systems are challenged for resources, the perceived usefulness of DHIMS2 is one of the key determinants in its adoption and acceptance. Health professionals in such settings may initially be skeptical about the value of technology in addressing systemic challenges, particularly if they lack prior exposure to digital health solutions (Rashida, 2022). Such skepticism can only be overcome by the ability of the system to provide data in good time, with accuracy, and of sufficient quality to be acted upon. In Ghana, DHIMS2 is vital in facilitating the timely collection, management, and reporting of health data, which is still crucial to informing decisions, allocating resources, and implementing health programs at the district and national levels (Ghana-Health-Service, 2017).

However, in Ghana, the perceived usefulness of DHIMS2 is usually weakened by persistent challenges (Skouby et al., 2019a). For instance, system downtimes disrupt the availability of data in real-time and may be the cause of delays in reporting. Very limited training opportunities, along with a lack of regular refresher courses, leave health professionals ill-equipped to maximize the functionalities of the system (Bukht & Heeks, 2018). Moreover, the technical support at the sub-national levels is very poor; this adds to frustrations when users encounter any difficulties, further affecting the perception by users of its reliability and usefulness. As stated by Nsiah et al. in 2022, addressing these through investment in infrastructure, continuous capacity-building programs, and proper technical support structures is key to increasing perceptions about the usefulness of DHIMS2, which becomes very important to ensure its adoption and utilization for the long term. In addition, considering that with the contributions of its own users, in order to further improve the system, its usefulness for health professionals will never stop growing; it will be an intensely trusted and relied-on digital solution in health matters.

2.4. Level of Training and Technical Support Health Professionals Receive for Using DHIMS 2 at the Local Levels

2.4.1 DHIMS2 Training Programs

Health information system training is one of the foundational elements in successful implementation across the globe. Most countries with established healthcare systems have large training programs in place to enable health professionals to use digital systems efficiently (Byrne & Sæbø, 2022). On-the-job and continuous training have been shown to enhance the use of health information systems and boost the confidence of health workers (Poppe, 2017). Training in the use of DHIMS2 in Africa, and particularly in Ghana, has been more problematic. Whereas DHIMS2 was intended to ease health data management, many health professionals in Ghana are still facing challenges in using it effectively and efficiently due to inadequate or infrequent training (Odei-Lartey et al., 2020). The success of DHIMS2 in Ghana may depend on good training at both central and local levels. Geographically, the quality of training also varies because health workers in remote or interior areas receive less update through this type of training, sometimes even refresher courses (Kanfe et al., 2021).

2.4.2. Resources and technical assistance

These, through continuous monitoring, assessment, and decision-making globally, will, in turn, contribute to the improvement of health care delivery processes toward better treatment and more support (Mensah Abrampah et al., 2024).

It also, however, entails strong technical backstopping so that the HIS systems are truly long-lasting but usable. For instance, authors such as Bhatt et al., (2024) realized that poor technical support may mean that users' frustrations reduce the system usage and attainment of full benefits from HIS. This will therefore call for the creation of help desks, systems administrators, and troubleshooting mechanisms as will enable the user to resolve some of the challenges experienced while using the system. These elements are the key to user confidence, data accuracy, and system adoption (Edum-Fotwe et al., 2019).

The majority of African countries, therefore, have very limited technical support for HIS because of resource constraints, inadequate infrastructure, and a lack of personnel with adequate training. It is expected, therefore, that these challenges are further magnified in such low-resource settings where investments in information technology and workforce development are usually not up to the mark (Rashida, 2022). Lack of technical support has led to a delay in addressing system issues that increases the likelihood of errors and slows down health facilities in generating reports in a timely and accurate manner. Such constraints weaken the ability of HIS to address the health needs and weaken the efforts to address the improvement of public health (Odei-Lartey et al., 2020).

The DHIMS 2 is the principal system for the management of health information in Ghana. Although the system has a centralized technical support arrangement, access to technical support at the local levels remains very variable. Some of the barriers to this usually include receiving timely technical support, shortage of skilled personnel, and lack of facilities for troubleshooting (Nsiah, Anum-Doku, Takramah, et al., 2022), which often afflict health professionals more in rural settings like Tamale Central Municipal. The gaps make them prone to mistakes related to entry, delayed reporting, and loss of faith in the system; hence, more challenges add to managing the health data at the local government level.

It hence intends to ascertain how far the technical support DHIMS2 offers is available to, and effectively used by, the lowest levels of local organization in a district setup that has fewer means. The timeliness of support services being provided will be looked into, as resources concerning accessibility, such as manuals, online support, physical troubleshooting of problems, and perceptions among health professionals of quality in technical support. It is by understanding these dynamics that this study attempts to identify gaps and proffer solutions to improve the efficiency and sustainability of DHIMS2. This will be very important in ensuring the continuity of the system in meeting the increasing data management requirements of Ghana's healthcare system for improved health outcomes across the country.

2.5. Conclusion

Chapter Two reviewed the literature on the current level of knowledge of health professionals on DHIMS2, use of the system, and perceived usefulness, and training and technical support to health workers at local levels. The review has also highlighted that DHIMS2 can be used effectively by only a few health professionals, while the majority of the health workforce, particularly in rural areas, faces huge knowledge gaps due to limited refresher training, inadequate user resources, and low levels of digital literacy. Although DHIMS2 is recognized as a fundamental tool that makes collecting data, reporting, and decision-making easier, technical challenges, weak infrastructure, and poor internet connectivity impede usage in some cases, especially at the most peripheral levels of health care. The review also identified that while there is initial training at the time of system rollout, the absence of ongoing training and localized technical support has resulted in data entry errors, delays in reporting, and low confidence in the system. The findings call for sustained capacity-building efforts, enhancement of the technical support infrastructure, and investment in the identified challenges to ensure that DHIMS2 works optimally in routine health information management and reporting.

CHAPTER THREE METHODOLOGY

3.1 Background of the Study Area

The study was conducted withing the Tamale Central Municipal. The Tamale Metropolitan Assembly was established by legislative instrument (LI 2068) which elevated the then Municipal Assembly into a Metropolis in 2004. At present, it is one of the six Metropolitan Assemblies in the country and the only Metropolis in the three Northern regions namely: Upper East, Upper West and Northern regions. It has Tamale as the Metropolitan capital city and at the same time the regional capital of the Northern Region.

The Tamale Metropolis is one of the 26 districts in the Northern Region. It is located in the central part of the Region and shares boundaries with the Sagnarigu District to the west and north, Mion District to the east, East Gonja to the south and Central Gonja to the south-west. The Metropolis has a total estimated land size of 646.90180sqkm (GSS2010). Geographically, the Metropolis lies between latitude 9º16 and 9º 34 North and longitudes 0º 36 and 0º 57 West. Tamale is strategically located in the Northern Region and by this strategic location, the Metropolis has a market potential for local goods from the agricultural and commerce sectors from the other districts in the region. Besides the comparative location of the Metropolis within the region, the area stands to gain from markets within the West African region from countries such as Burkina Faso, Niger, Mali and the northern part of Togo and also en-route through the area to the southern part of Ghana. There are 115 communities in the Metropolis. Most of the rural communities have a large expanse of land for agricultural activities and serve as the food basket for the Metropolis. However, these communities still lack basic social and economic infrastructure such as good road networks, school blocks, hospitals, markets and recreational centers, thereby hindering socio-economic development, poverty reduction and reducing the general phenomenon of rural-urban migration.

Illustrations are not included in the reading sample

Figure 1: Map of Tamale Central Municipal

3.2 Study Design and Type

The research adopted a descriptive cross-sectional design in which health professionals assess the experiences with DHIMS2 and its use at the local level within the Tamale Central Municipal. The choice of this research design was because it is able to attain data collection at one point in time snapshot that best describes the prevailing state of knowledge on the use of DHIMS2 among health professionals. This design is particularly advantageous for identifying associations and trends within a defined population without requiring a long-term commitment of time and resources (Schantz & Lindeman, 2016)

Such studies are also suitable for capturing response variations among diverse respondents, in this case, health professionals working at the various levels of the healthcare system. The application of this approach enables the assessment of a range of variables, from demographic characteristics to usage patterns and barriers, within a relatively short period (Adebiyi & Abayomi, 2016). The study applied a quantitative research design with a structured questionnaire administered as the principal means of gathering data. It ensures that responses are gathered consistently and allows for quantitative analysis into the degree to which DHIMS2 has been used and some factors that were considered for its eventual adoption. As such, using this method tends to allow standardized data on a larger scale, which enhances reliability in the results generalized from it, as seen from (Ishtiaq, 2019).

The current study combined the strengths of a descriptive cross-sectional design with a quantitative methodology to have useful insights into the experiences of health professionals on DHIMS2 for the best strategies at optimal implementation at the local level.

3.3 Study Population

The target population involved health professionals working in health facilities within the Tamale Central Municipal and included any health professional directly or indirectly engaged in the different components of health service delivery at clinical care, community health, and health information management who use DHIMS2 to routinely capture and report health information.

3.4 Sample Size and Sampling Technique

3.4.1 Sample Size

The sample size was determined using Cochran's formula for calculating sample sizes in cross-sectional studies as follows (Bartlett et al., 2017):

n = z2xp(1-p) e2

Where

n = sample size required;

z = standard normal deviate, which is 1.96 for 95% confidence level;

p = estimated population of health professionals in the Tamale Central Municipal who use DHIMS2 for routine health information management and repots, assumed at or 0.07 based on previous studies conducted in similar contexts (Odei-Lartey et al., 2020).

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n = 209.6

Therefore, based on approximation, n = 210.

3.4.2 Sampling Technique

A simple random sampling technique was used for selecting a sample size of 210 respondents from selected health facilities. The steps of doing it are as follows:

Step 1: Specification of the Sub-Municipals and Facilities

Two sub-municipals Bilpeila and Tamale Central were purposively chosen for the study. From each sub-municipal, five facilities were selected, adding up to a total of 10 facilities. These health facilities included a mix of health centers, clinics, and CHPS compounds to make it representative of health professionals engaging in DHIMS2 in the different facility types. These 10 facilities in the two sub-municipals were selected in order to get a manageable and representative sample, as required within the limitation of this study.

Selected Sub-Municipals and Facilities:

Table 1: Selected Sub-municipals and health facilities

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Step 2: Proportional Allocation of the Sample Size

The study used a sample size of 210. In sharing this number between the selected facilities: the sample size (210) was divided by the number of facilities (10) which equals 21. Each facility in was assigned 21 participants.

Step 3: Random Selection of Participants

Once the sample size for each facility has been determined, the next step is the random selection of health professionals from each health facility. This was done by preparing the list of health professionals working within each selected facility. It was done through consultations with managers of the health facilities for the assurance of updating.

Random Selection: A random selection for each facility was done with the list of health professionals through an online random number generator (https://pinetools.com/random-number-generator). This is to ensure that the selection was not biased and all are given equal opportunity.

3.5 Study Variables

Table 2: Study Variables

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3.6 Data Collection Tool and Technique

Data was collected electronically, of using the ODK-collect app through a structured questionnaire covering information on participants' demographic characteristics, knowledge of DHIMS2, use in terms of pattern and trend, perceived usefulness, and the level of training and technical support. Prior to actual use, the questionnaire was pre-tested in the Tamale Teaching Hospital for its validity and reliability.

Selected participants were invited to take part in this study. Participation in the study data collection will entail a structured questionnaire focusing on their experience with the use of DHIMS2 and various challenges that have emerged. These were distributed per health facility concerning the random inclusion of participants therein and completed therein.

3.7 Data Processing and Analysis

Data was cleaned, coded, and entered into a statistical software, STATA version 17.0, for analysis. Data on background characteristics, the knowledge level of health professionals on DHIMS2, the level of utilization and perceived usefulness of DHIMS2 in routine health Information Management and reporting and the level of training and technical support health professionals receive on DHIMS 2 were analyzed by descriptive statistics, including frequencies, percentages and were presented in tables and charts. The overall knowledge level on DHIMS2 among respondents is categorized as follows:

1. Low knowledge: When less than 20% of respondents provide (Yes) response to a knowledge item.
2. Moderate knowledge: When 20% to 50% of respondents provide (Yes) response to a knowledge item.
3. High knowledge: When more than 50% of respondents provide (Yes) response to a knowledge item.

3.8 Limitation of the study

Some of the limitations that encountered are response bias, whereby respondents gave socially desirable rather than actual experiences. The cross-sectional design limited the establishment of causality among variables. Due to time and financial constraints, the depth of data collection and analysis were also be restricted, which affected the comprehensiveness of the findings.

3.9 Ethical Considerations

Ethical approval for this study was sought from the Ethical Review Committee of Catholic University as well as the Tamale Central Municipal Health Directorate before embarking on this research. After the explanation of the purpose of the study, procedures to be followed, potential risks, and benefits, written informed consent will be requested from all participants. Participation in this study was on a voluntary basis; participants were, therefore, given every right to withdraw at any time without penalty. Strict confidentiality and anonymity was maintained throughout the study, observing participants' privacy.

CHAPTER FOUR RESULTS

4.1 Introduction

This chapter on results presents a descriptive analysis of the information collected from health workers on DHIMS2. It provides details on demographic factors, levels of knowledge, patterns of system usage, and training and support experience. The results provide an overview of the level of DHIMS2 awareness, application, and technical assistance in local settings, which serves as the foundation for identifying areas of need to ensure improvement in support of health information management.

4.2 Demographic Characteristics

The demographic data indicate that the majority of the participants are from Tamale Central sub-district and account for 80.0% (168) and Bilpeila accounts for 20.0% (42). Females make up 30.5% (64) and males make up 69.5% (146). The participant's activities include being a community health officer and health information officer as most common, accounting for 18.1% (38) and 20.5% (43) respectively. There are also medical officers, midwives, nurses, and the rest, of which nurses constitute the highest with 22.9% (48).

On the basis of experience, 38.1% (80) have 1–3 years of experience in the current job, followed by 4–6 years at 32.9% (69), and 7 years or more at 23.3% (49). A fewer percentage, 5.7% (12), has less than 1 year of experience. Frequency of use of the DHIMS2 system shows that there are daily users at 23.3% (49), monthly users at 36.2% (76), occasional users at 9.5% (20), and weekly users at 30.9% (65).

With regards to educational background, most participants have a bachelor's degree at 36.7% (77), with diploma holders at 31.9% (67) being the next. Certificate holders are at 19.5% (41), and master's degree and above are 11.9% (25). In terms of age distribution, the largest population is 25–34 years at 69.1% (141), followed by 3.9% (8) of 18–24 years, 24.0% (49) of 35–44 years, and only 2.9% (6) of 45 years and older.

Respondents are primarily working in health facilities such as hospitals (50.0%, 102), health centers (30.4%, 62), and CHPS compounds (11.3%, 23) with fewer working in clinics (3.43%, 7) and others. The facilities are owned mostly by government at 71.9% (151), followed by 26.7% (56) private facilities, and a very minor 1.4% (3) in NGO-owned facilities.

Table 3: Demographic Characteristics

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Source: (Field data, 2025)

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Figure 2: Subdistrict

Figure 2 shows sub-districts of respondents with Bilpeila at 20.0% (yellow) and Tamale central at 80.0% (green).

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Figure 3: Gender

Figure 3 shows the split of two gender groups: Male and Female. The Male slice, as blue, is 32% of the whole, and the larger orange Female slice is 68%.

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Figure 4: Category of staff

Figure 4 shows the percentage of various health occupations and their corresponding percentages. The Nurse occupation is highest at 22.90%, denoted as green, followed very closely by Health information officer at 20.50, denoted as light green. The Community health officer is at 18%, denoted as yellow, and other occupations are at 18.60%, denoted as yellow-orange. The Medical officer and Midwife careers have lower percentages, at 10.90% and 9.10% respectively, as indicated by orange and red.

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Figure 5: Years of service

Figure 5 shows the distribution of levels of experience in current role. The biggest segment, which has 1-3 years of experience, is blue and constitutes 38% of the population. Then there is the category of 4-6 years, which is orange and occupies 33%. The category for 7 or more years, which is gray, constitutes 23%, while the smallest section, which shows Less than 1 year of experience, is yellow and represents 6%.

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Figure 6: Frequency of DHIMS2

Figure 6 shows the frequency of using DHIMS2 among the respondents. The widest segment, which represents Weekly usage, is orange and contributes 36%. Next in line is occasionally shown in gray and comprises 10%. The Monthly category is yellow and contributes 31%, and the thinnest segment, Daily activity, is blue and contributes 23%.

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Figure 7: Educational status

Figure 7 illustrates the distribution of respondents based on their educational qualification. The most dominant percentage is under the category of Bachelor's degree and occupying 36.70%, colored in green. Placing second, the Diploma category occupies 32.00%, also in light green. For Certificate, it records a significantly lower percentage of 19.50, shown in yellow. The Master's degree occupies the lowest share with a percentage of 11.90%, as shown in red.Abbildung in dieser Leseprobe nicht enthalten

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Figure 8: Age of respondents

Figure 8 illustrates the distribution of the respondents by age group. The largest share is that of the respondents aged 25-34 years, whose share is 69% and which is represented in orange. Secondly, the respondents aged 18-24 years, represented in blue, are 4%. Thirdly, the respondents aged 35-44 years, represented in gray, are 24%. Lastly, the lowest share, that of individuals aged 45 years and above, represented in yellow, is 3%.

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Figure 9: Type of Health Facility

Figure 9 displays the distribution of the types of health facilities used by respondents. The largest proportion is Hospitals at 48.50%, and this is blue. Second in rank is the health center at 29.50%, also blue. Lower is the CHPS compound at 10.90% and then the health directorate at 7.50%. The Clinic proportion is the lowest at 3.30%.

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Figure 10: Ownership of facility

Figure 10 shows the breakdown of types of organizations used by the respondents. Government organizations have the highest percentage with 72% in blue. Second, the NGO only has a negligible percentage of 1% in orange. The Private sector is at 27% in gray.

4.3 Knowledge level of health professionals on DHIMS2

Table 4: Knowledge level of health professionals on DHIMS 2

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Source: (Field data, 2025)

Table 4 presents levels of health worker knowledge across various regions of the District Health Information Management System 2 (DHIMS2). The findings are such that merely 11.9% of the respondent’s demonstrated knowledge of the primary components and modules of DHIMS2, with the rest (88.1%) not having such knowledge. Similarly, merely 11.9% identified the function of DHIMS2 in the collection, analysis, and reporting of health information, which reflects overall ignorance about the functional significance of the system.

On the topic of what are the key health indicators monitored using DHIMS2, 11.4% of the respondents were able to name them correctly, while 88.6% were not. Confidence in applying DHIMS2 to generate health reports was comparatively high at 21.9% of the participants expressing confidence, but the majority (78.1%) were not self-confident. The facilitation of retrieval and reading of DHIMS2 data was also demonstrated by 27.6% of the participants as compared to the other issues studied. Lastly, knowledge of what DHIMS2 is was quite low, with only 10.9% of the respondents correctly knowing its meaning and 89.1% not knowing DHIMS2's full form and purpose.

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Figure 11: overall knowledge on the part of respondents

Figure 11 shows that 12% of respondents answered "Yes" to the knowledge item, the overall knowledge level among the respondents is considered low, as it is less than 20%.

4.4 Level of utilization of DHIMS2 in routine health Information Management and reporting

Table 5: Level of utilization of DHIMS2 in routine health Information Management and reporting

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Source: (Field data, 2025)

Table 5 gives an overview of the usage level of DHIMS2 in everyday health information management and reporting. It shows how frequently the users use the system, their primary activities, the number of hours they use the system per week, the type of data they enter, and the type of issues they encounter when using DHIMS2.

DHIMS2 usage levels differs among users, with 35.7% applying it weekly, 30.4% monthly, 18.5% on a daily basis, and 15.7% occasionally. Data entry and analysis are employed most frequently under the chief functions, with 60.5% of users applying data entry and analysis to the same extent, and 19.5% only applying data analysis.12.4% apply DHIMS2 in performing all the activities as a whole, data entry, analysis, and monitoring health indicators. The weekly average time spent on DHIMS2 varies where 59.1% utilize it for 1–3 hours, 18.4% for less than 1 hour, 14.8% for 4–6 hours, and 7.6% for over 6 hours.

In terms of the inputs of data, the most frequent data inputs are disease surveillance data and immunization data, with 25.7% and 20% of users respectively. The other data inputs are service delivery data, maternal and child health data, and immunization and disease surveillance data, although they are not submitted as often. Only 0.48% submitted nutrition reports.

Challenges faced in the application of DHIMS2 include access issues in the system, network issues, report entry issues, and analysis issues. Specifically, 40% of the users said that they could not access the system, and 20% of both had network, report entry issues, or analysis issues. Despite such issues, the majority of the users, 80%, have not encountered any major issues with DHIMS2.

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Figure 12: Frequency of DHIMS2 usage

Figure 12 shows how often the users utilize the DHIMS2 system. Most of the users, 35.7%, utilize DHIMS2 on a weekly basis, which is the highest usage frequency period. The second is 30% of the users who utilize the system on a monthly basis. Less users, 18.5%, indicated that they utilize DHIMS2 on a daily basis, while 15.7% indicated that they utilize it occasionally.

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Figure 13: Task(s) DHIMS2 is used for

Figure 13 shows that most of DHISM2 users (62%) mainly use DHIMS2 for entering data. Data analysis is the next most common activity, and 21% of the users perform this action. Report generation accounts for 12%, indicating that fewer percentage of the users use report generation. 6% of the users also perform data entry, data analysis, and monitoring health indicators. The smallest fraction, 1%, includes activities that involve report generation and data activity

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Figure 14: Time spent using DHIMS2 weekly

Figure 14 indicates clearly the weekly time used on utilizing DHIMS2 by its users. A vast majority, who comprise 59%, utilize between 1 to 3 hours for this activity. This indicates that most of the users are content with this time to meet their needs within the system. On the other hand, 15% of users utilize DHIMS2 from 4 to 6 hours weekly, suggesting that there may be a lesser segment with more time-consuming or intricate tasks to complete. Secondly, 18% of users use less than 1 hour a week, which may either reflect minimal system usage or successful task completion within a short span of time. Lastly, just 8% of users spend over 6 hours on DHIMS2 weekly.

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Figure 15: Type of data regularly entered into DHIMS2

Figure 15 shows the types of data entered on a regular basis in DHIMS2 by end-users. Disease surveillance data is input by 20%, i.e., it is the most common type of data. Immunization data is input by the highest percentage of 25.7%, i.e., it is input the most. Both immunization and disease surveillance data are input concurrently by 12.4%. 17.6% of users enter maternal and child health data, and only a small percentage, 0.48%, enter maternal and child health data and disease surveillance data. Another 0.48% of users also enter nutritional reports under "Other." Service delivery data is entered most frequently, with 20% of users regularly entering this type of data into DHIMS2.

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Figure 16: Challenges faced during DHIMS2 usage

Figure 16 illustrates the responses to difficulty in using DHIMS2 in stark two-way contrast of Yes and No. The majority of 80% of the users responded that they do encounter difficulty in using the system, represented by the orange segment of the pie. The very high rate shows that most of the users do find it difficult, and it could affect their overall experience and productivity in using DHIMS2. On the other hand, 20% indicated no problems, as shown by the extremely small blue portion.

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Figure 17: Specific challenges faced during DHIMS2 usage

Figure 17 specifies certain challenges for DHIMS2 users in percentages. The most significant challenge is being unable to access the system, which was experienced by 40% of users. This is a critical issue that prevents users from utilizing the system efficiently, leading to potential disruptions in their activities. Followed by network issues, difficulty entering reports, and cannot perform analysis by account for 20% of the answers.

4.4 Level of training and technical support health professionals receive for using DHIMS2

Table 6: Level of training and technical support health professionals receive for using DHIMS2

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Source: (Field data, 2025)

Table 6 provides an overview of training and technical support received by the health professionals in DHIMS2 use. On formal training, only 36.7% of the respondents indicated that they have received it, while a staggering 63.3% indicated that they have not, highlighting a severe gap in initial training opportunities. Lack of formal training may inhibit users from realizing the full use of the system. However, it is noteworthy that 100% of the respondents expressed a need for official training on DHIMS2, pointing to a strong interest in formal learning opportunities to increase user proficiency.

In determining whether training received was adequate, 67.5% of the respondents deemed training as adequate, while just 2.5% considered it inadequate. A further 7.7% responded neutrally, and 22.1% found their training as very adequate.

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Figure 18: Receipt of formal training on DHIMS2

Figure 18 shows that majority of respondents (63%) indicated they have not received any formal training on DHIMS2 while 37% stated they have received training.

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Figure 19: Desire to receive formal training on DHIMS2

Figure 19 shows that all (100%) of respondents who indicated they have not received any formal training on DHIMS2 desire to receive such training.

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Figure 20: Adequacy of DHIMS2 training received

Figure 20 shows that, 67.5% stated DHIMS2 training they have received is adequate, 22.1% said is very adequate, 7.7% stated the adequacy level of the training is neutral and 2.5% stated the training is inadequate.

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Figure 21: Rate of availability of technical support receipt when faced with challenges

The pie chart shows how respondents rate the technical support they receive when they face challenges, with 47% classifying it as good and 29% as excellent. Nonetheless, 15% are neutral, and some percentage is dissatisfied, with 5% classifying it as poor and 4% as very poor. Overall, the results support overall satisfaction but leave potential for improvement to sort out the concerns of the neutral and dissatisfied users.

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Figure 22: Time taken to resolve technical issues when reported

Most issues are resolved within a day (47%), indicating speedy handling. Instant resolutions stand at 19%, and those that take a week account for 30%, indicating ongoing responsiveness. Few, at 3%, remain unresolved, indicating areas for improvement. Only 1% take more than a week, indicating that most issues are resolved quite fast.

Table 7 Regression analysis showing association between background characteristics of respondents’ and DHIMS2 usage level

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Source: (Field data, 2025)

Regression analysis Table 4 examines the relationship between respondents' background variables and utilization of DHIMS2. The tested variables were age, gender, education level, and years of experience.

Secondly, the variable of age carries a negative coefficient of -0.35832, which suggests that for each additional year of age, intensity or probability of utilization of DHIMS2 declines by about 0.36 units. This is a statistically significant relationship according to the p-value of 0.01, which suggests that age is a predictor of intense DHIMS2 utilization among the respondents.

The gender variable has a positive coefficient of 0.245418, implying that membership in any one gender (coded maybe as 1 for male or female depending on the coding convention) increases the level of usage of DHIMS2 by around 0.25 units. The p-value of 0.13 implies, however, that this association is not significant at the 5% level, and gender maybe does not affect the level of usage within this data set.

Educational status has a very small positive coefficient (0.034967), suggesting that use rises modestly as education rises. However, the trend is not statistically significant since the p-value is 0.42, so there is very little evidence that educational status affects the use of DHIMS2.

Similarly, years of experience carries a coefficient of 0.041098 with a low positive relationship with use but marginal statistical significance with a p-value of 0.04. The confidence intervals of all variables include zero once more, showing that only age shows a strong and significant relationship with DHIMS2 use in this analysis. The intercept (_cons) is 2.862, the baseline level of use when all predictors are zero, and is strongly significant.

CHAPTER FIVE DISCUSSION OF FINDINGS

5.0 Introduction

This chapter summarizes the main results on DHIMS2 knowledge, utilization, and training of health professionals. The main gaps in awareness, system usage patterns, data management, and user problems are identified. It underlines the necessity for better training and assistance to maximize system effectiveness and health data management.

5.1 Knowledge level of health professionals on DHIMS2

The findings of awareness and knowledge of the District Health Information Management System 2 (DHIMS2) among respondents are extremely alarming with a critical deficiency of knowledge among health staff. Astonishingly, only 11.9% of respondents showed knowledge on the most important features and functionalities of DHIMS2 and the same percentage described its function in the collection, analysis, and reporting of health information. These findings echo that of Bhatt et al., (2024), whose study found that knowledge gaps in health information management systems were present in most areas, limiting their proper utilization and undermining public health initiatives (Kanfe et al., 2021).

When analyzing the knowledge of key health indicators monitored in DHIMS2 for the respondents, the results are just as disturbing with 11.4% of them having it right. This unfamiliarity is consistent with the findings of Rashida, (2022), since they showed that poor training and unfamiliarity with health information systems were factors behind underuse and inefficiency in healthcare delivery in Sub-Saharan Africa (Odei-Lartey et al., 2020). Their research pointed out that health workers tend to fail in utilizing accessible health information because they lack proper knowledge, reflecting the same scenario evident in this present study.

Self-efficacy in generating health reports using DHIMS2 was slightly higher at 21.9%. This result varies from the result of Edum-Fotwe et al., (2019), who found a considerably higher level of confidence (around 50%) among the specifically trained health workers on health information systems (Byrne & Sæbø, 2022). Their results suggest that formal training sessions could significantly improve confidence, as well as competence, in using health management systems.

Also, the 27.6% of respondents who could successfully retrieve and read information on DHIMS2 perfectly mirrors a critical skills gap, supporting research by Mensah Abrampah et al., (2024), arguing that practical experience in the application of health information systems is the key to workforce competence in health data management (Poppe, 2017). Improving the competence of health workers through continuing professional development might close some of the gaps observed in the current study.

Finally, the remarkably low level of awareness of what DHIMS2 is, where only 10.9% of the respondents knew its complete acronym, speaks to an acute need for health worker awareness campaigns and education interventions. Oldie et al. (2019) noted that awareness and knowledge of health information systems among healthcare providers is a requirement to improve the quality and use of data (Woldie, M., Mesfin, N., & Asres, Y. 2019). Through increased awareness of such health systems as DHIMS2, stakeholders can improve data-driven decision-making and hence improve health service delivery.

5.2 Level of utilization of DHIMS2 in routine health Information Management and reporting

The patterns of use and issues encountered with the use of DHIMS2 mirror how effective and usable this health information system is. As can be seen from the study, the most common use of DHIMS2 is weekly since 35.7% of the users accessed the system at this frequency. This result shows that even if there are users who are interactive with the system daily, there is still a high percentage (30%) who use it onNsiah, Anum-Doku, Nyarko, et al., (2022) a monthly basis, followed by daily (18.5%) and occasional (15.7%) usage. The studies by Kusi et al. (2021) agree that frequent use of health information systems is what forms familiarity and proficiency, leading to better health care outcomes (Bukht & Heeks, 2018).

In terms of work accomplished via the utilization of DHIMS2, data entry was the most dominant function, as 49% of the respondents indicated this as their primary activity. The finding demonstrates a crucial aspect of health information management in that data must be appropriately entered to ensure the promotion of the integrity of health information. The percentage for data analysis (20%) indicates that although users perform analysis, it is much less emphasized than data entry. The findings are consistent with the findings of Nalliah et al. (2022), who emphasized training healthcare professionals to enhance their competencies in data entry and analysis to facilitate improved decision-making (Yao Azumah et al., 2021).

Looking at time spent on DHIMS2 weekly, 59% of users spent 1 to 3 hours per week on the system, which is a modest time allocation that can facilitate effective data handling and also reflect user satisfaction with the level of their involvement. Conversely, 15% of users spent 4 to 6 hours a week on the system, which may reflect more complicated or demanding work. Additional strength in these results comes from the fact that, according to Nsiah, Anum-Doku, Takramah, et al., (2022), time management plays an important role in allowing health workers to undertake needed functions without overloading timetables (Skouby et al., 2019).

With respect to the type of data entry, disease surveillance has the highest share with a high percentage (20%) in reflecting its prominence in activities of public health surveillance. Immunization and maternal and child health data also have high entry points (12.9% and 12.4%, respectively), consistent with international priority for these issues in the last few years (Nabunnya et al., 2020). The mention of numerous quantities of data speaks to the complexity and amount of health data that ought to be managed under DHIMS2, bearing witness to the need for robust data management strategies.

However, the issues of users in terms of using DHIMS2 are a significant deterrent to effective implementation. An astounding 80% of the users reported having encountered issues, and this suggests that there is a need for improved user experience and training, according to Henderson, (2017), which confirmed that issues such as navigation and ease of access of the system significantly influence user effectiveness and satisfaction in using health information systems (Nii Akai Nettey et al., 2024).

The most reported challenge was inaccessibility of the system, reported by 40% of the users, which means that having a stable access to DHIMS2 is crucial to its performance. The other mentioned challenges included network connectivity and data entry and report generation issues (20% each), pointing to the need for infrastructure development and end-user training for seamless operations. Kanfe et al., (2021) stress in their work that the removal of these barriers is required for enhancing the system's usability and overall effectiveness in the public health environment (B. Mensah et al., 2023).

5.3 Level of training and technical support health professionals receive for using DHIMS2

The study reveals findings regarding formal training and technical support that have been offered to health workers regarding the utilization of the District Health Information Management System 2 (DHIMS2). One of the significant findings is that only 36.7% of the respondents indicated that they had undergone formal training on DHIMS2, a highly significant proportion of 63.3% receiving no formal training. This reveals a wide training gap that may effectively discourage users from taking advantage of the capabilities of the system. These findings underscore the importance of formal training courses in improving the effective use of health information systems, as testified by Bhatt et al., (2024) who indicated that proper training enhances information quality and the effectiveness of health personnel in using information systems (Rashida, 2022).

Surprisingly, even though most of the health workers were not formally trained, 100% of the participants desired to have formal training in DHIMS2. This is a clear indication by the users of their ignorance and potential for improvement through formal training. The research agrees with Odei-Lartey et al., (2020), which made sure that training must be conducted to enable health workers and further develop them to effectively handle health information systems (Edum-Fotwe et al., 2019). This shared passion in training not only indicates a commitment to professional development among health workers but also directs health authorities to the imperative of investing in holistic training programmes as soon as possible.

Furthermore, in determining the adequacy of training received, 67.5% of the respondents rated their training as sufficient. Moreover, 22.1% of the users rated their training as very sufficient, while only 2.5% rated it as insufficient. The general impression of training adequacy presents a comforting picture; however, it still underscores the comparative nature of training effectiveness. This fear is supported by findings identified by Byrne & Sæbø, (2022), whereby perceived suitability in training is not always equivalent to actual competency and proficiency in the utilization of health information systems. Hence, it gives considerable importance to regular evaluation of training schemes to ensure that they are not only sufficient in theory but also effective in practice.

CHAPTER SIX SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATIONS

6.0 Introduction

This chapter presents a summary of the most relevant findings of the research study on the knowledge, usage, and training on DHIMS2 among the health workers. It highlights important and unimportant findings of the research and concludes with recommendations for improving system awareness, infrastructure, and capacity building.

6.1 Summary of Findings

Demographic characteristics reveal that most of the respondents are from Tamale Central and Bilpeila and are mostly females (69.5%) between 25-34 years (69.1%). They are mostly health workers such as community health officers, nurses, and midwives with 1–3 years of working experience and practicing in various health facilities which are mostly government-owned.

Knowledge-wise, it was only 12% of the respondents who were adequate on DHIMS2's function, key elements, and key health indicators. Their self-assessment to use the system was low as well, with only around 22% feeling confident in report generation and 11.9% knowing what DHIMS2 is. The general level of knowledge among the respondents was low, and less than 20% were highly knowledgeable.

Usage patterns differ with 35.7% of them using DHIMS2 weekly and 30.4% monthly. The most common use is data entry, particularly disease surveillance data and immunization data. The average weekly time used is largely 1-3 hours. Inaccessibility of the system, network failure, and report generation pose problems, with 40% not being able to access the system at times. Training data reveal a large gap: 36.7% of health workers who were trained formally only, though all are keen to receive it. The majority who received training found the training adequate, but overall inadequate training may hinder proper utilization of DHIMS2.

6.2 Conclusion

The study reveals significant knowledge gaps in the knowledge, use, and training of health workers in relation to the District Health Information Management System 2 (DHIMS2). Alarmingly, 11.9% of the respondents demonstrated only a minimum level of knowledge of DHIMS2's essential functionalities and features. The large knowledge gap not only inhibits the optimal utilization of this important health information system, but also compromises the potential for improved public health outcomes. The findings reflect previous research that calls out inadequate knowledge and training for health workers, which then hampers the efficiency of delivering healthcare.

Secondly, how much DHIMS2 is accessed by health workers reflects an urgent trend. Whereas a huge number of users function within the system on a weekly basis, quite a number of them only access it on a monthly or periodic basis. Most importantly, data entry remains the primary function, with data analysis, essential to fact-based decision-making, secondary. The high proportion of users—80%—who report difficulty in using DHIMS2, more so in terms of accessibility and connectivity, indicates the urgent need for enhanced infrastructure and training assistance. As the study has indicated, these hurdles have to be surmounted in order to improve user experience and foster a culture of efficient data management.

Findings on training produce a paradox where, while the majority of the health practitioners are not trained in DHIMS2, demand for training is highly apparent with 100% of respondents seeking formal training packages. This level of simplicity of demand provides a window for opportunities for health authorities to invest in extensive training efforts for the purpose of enriching healthcare practitioners. Despite a majority that was favourable reporting adequate training, there is always a likelihood of gap between the adequacy perceived at training and actual competence. A constant checkup and upgrade in training plans would become a hallmark for guaranteeing that training strategies keep up with evolving health worker needs and boost their capabilities for effective use of health information systems.

In conclusion, the study emphasizes the need for greater targeted awareness drives, effective training programs, and infrastructure interventions to support DHIMS2 use among health professionals. Strengthening these priorities would improve health data management quality so that it supports better public health decisions and service delivery outcomes for the healthcare sector.

6.3 Recommendations

Based on the conclusion of the study, the following recommendations were made:

Policy makers

1. The Tamale Central Municipal Health Directorate should prepare and make mandatory formal training programs for all the health workers about DHIMS2 to grasp correctly and utilize the system correctly. The programs should include practice exercise as well as confidence test along with competency.

Practice

1. The Ministry of Health/ Ghana Health Service should invest in infrastructure development so that there is round-the-clock and assured access to DHIMS2. It includes solutions for the problem of network connectivity as well as installs necessary technical support to stakeholders for streamlined process systemization.
2. The Ghana Health Service should provide awareness camps for health workers on sensitization on awareness on DHIMS2, operations, and implications of effective management of data. This is via seminars, workshops, and documentation materials.
3. The user needs and program training should be checked regularly to determine gaps and weak points. Comment should be obtained from the health professionals as part of an attempt to realign training and support services in accordance with their evolving needs.

Future research

1. Future studies should identify the impact of DHIMS2 training interventions on data quality and outcomes of health service delivery, such as whether new knowledge can lead to improved data accuracy, timeliness, and use in decision-making.

APPENDIX I

RESPONDENT CONSENT SECTION

Please read each of the following statements carefully before responding. By selecting Yes or No, you consent to participate in this survey.

- The purposes behind this questionnaire were explained to me.
- I affirm that my participation is entirely voluntary and that I reserve the right to withdraw at any time without facing any repercussions.
- I know that the information I provide will be kept confidential and used only for research purposes.
- Data obtained from me would be anonymous, and no information that can identify me would ever be shared.

Do you agree to participate in this survey?

☐ Yes

☐ No

We will appreciate your consideration.

APPENDIX I

DATA COLLECTION TOOL

QUESTIONNAIRE

TOPIC: HEALTH PROFESSIONALS’ EXPERIENCE ON DISTRICT HEALTH INFORMATION MANAGEMENT SYSTEM (DHIMS2) AND ITS UTILIZATION AT LOCAL LEVELS IN THE TAMALE CENTRAL MUNICIPAL.

{INSTRUCTIONS: Please, tick the appropriate response(s)}

SECTION A: Background Data on Respondents

1. Sub-Municipal
a) Bilpeila [ ]
b) Tamale Central [ ]
2. Gender:
a) Male [ ]
b) Female [ ]
3. Role/Position
a) Medical Officer [ ]
b) Nurse [ ]
c) Midwife [ ]
d) Health Information Officer [ ]
e) Community Health Officer [ ]
f) Other (Please specify): ______________
4. Years of Experience in Current Role:
a) Less than 1 year [ ]
b) 1–3 years [ ]
c) 4–6 years [ ]
d) 7 years or more [ ]
5. Frequency of DHIMS2 Use:
a) Daily [ ]
b) Weekly [ ]
c) Monthly [ ]
d) Occasionally [ ]
6. Educational Background:
a) Certificate [ ]
b) Diploma [ ]
c) Bachelor’s Degree [ ]
d) Master’s Degree or higher [ ]
e) Other (Please specify): ______________
7. Age Group:
a) 18–24 years [ ]
b) 25–34 years [ ]
c) 35–44 years [ ]
d) 45 years or older [ ]
8. Type of Health Facility:
a) CHPS Compound [ ]
b) Clinic [ ]
c) Health Centre [ ]
d) Hospital [ ]
e) Maternity Home [ ]
f) Other (Please specify): ______________
9. Ownership of facility
a) Government [ ]
b) Private [ ]
c) NGO [ ]

SECTION B: Knowledge level of health professionals on DHIMS2

Illustrations are not included in the reading sample

SECTION C: Level of utilization and perceived usefulness of DHIMS2 in routine health Information Management and reporting

17. How frequently do you use DHIMS2 for health information management?
a) Daily [ ]
b) Weekly [ ]
c) Monthly [ ]
d) Occasionally [ ]
18. Which of the following tasks do you primarily use DHIMS2 for? (Select all that apply):
a) Data entry [ ]
b) Data analysis [ ]
c) Generating reports [ ]
d) Monitoring health indicators [ ]
e) Decision-making support [ ]
f) Other (Please specify): _____________
19. On average, how much time do you spend using DHIMS2 weekly?
a) Less than 1 hour [ ]
b) 1–3 hours [ ]
c) 4–6 hours [ ]
d) More than 6 hours [ ]
20. What types of data do you regularly input into DHIMS2? (Select all that apply):
a) Maternal and child health data [ ]
b) Immunization data [ ]
c) Disease surveillance data [ ]
d) Service delivery data [ ]
e) Other (Please specify): _____________
21. Have you faced any challenges while using DHIMS2 for routine tasks?
a) Yes [ ]
b) No [ ]
c) If yes, please describe: _______________
22. DHIMS2 has simplified the process of collecting and managing health data.
a) Strongly Agree [ ]
b) Agree [ ]
c) Neutral [ ]
d) Disagree [ ]
e) Strongly Disagree [ ]
23. DHIMS2 has improved the timeliness and accuracy of health reports in your facility.
a) Strongly Agree [ ]
b) Agree [ ]
c) Neutral [ ]
d) Disagree [ ]
e) Strongly Disagree [ ]
24. Using DHIMS2 has enhanced your ability to monitor and evaluate health indicators.
a) Strongly Agree [ ]
b) Agree [ ]
c) Neutral [ ]
d) Disagree [ ]
e) Strongly Disagree [ ]
25. DHIMS2 provides actionable insights that help you make informed decisions in health service delivery.
a) Strongly Agree [ ]
b) Agree [ ]
c) Neutral [ ]
d) Disagree [ ]
e) Strongly Disagree [ ]
26. The availability of real-time data on DHIMS2 has reduced delays in reporting and decision-making.
a) Strongly Agree [ ]
b) Agree [ ]
c) Neutral [ ]
d) Disagree [ ]
e) Strongly Disagree [ ]
SECTION D: Level of training and technical support health professionals receive for using DHIMS2
27. Have you received any formal training on how to use DHIMS2?
a) Yes [ ]
b) No [ ]
28. If yes, how would you rate the adequacy of the training?
a) Very Adequate [ ]
b) Adequate [ ]
c) Neutral [ ]
d) Inadequate [ ]
e) Very Inadequate [ ]
29. How frequently do you receive refresher training on DHIMS2?
a) Regularly (e.g., annually or biannually) [ ]
b) Occasionally [ ]
c) Rarely [ ]
d) Never [ ]
30. How would you rate the technical support available to assist with challenges in using DHIMS2?
a) Excellent [ ]
b) Good [ ]
c) Neutral [ ]
d) Poor [ ]
e) Very Poor [ ]
31. When you encounter technical issues with DHIMS2, how quickly are they typically resolved?
a) Immediately [ ]
b) Within a day [ ]
c) Within a week [ ]
d) Longer than a week [ ]
e) Not resolved [ ]

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