The perceptions and acceptance of health management information system by health data managers in Rwanda: A Cross Sectional Study

doi:10.21203/rs.3.rs-3550593/v1

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The perceptions and acceptance of health management information system by health data managers in Rwanda: A Cross Sectional Study

https://doi.org/10.21203/rs.3.rs-3550593/v1

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Background

Health Management Information System (HMIS) has the unlimited potential to enhance the quality of healthcare. However, technology can be implemented and still face resistance from users. Since the introduction of Rwanda's HMIS, studies are still scarce to assess its adoption and level of acceptance from the end-users' perspective. This study aimed to assess the perceived usefulness and acceptance of HMIS by Health Data Managers (HDMs) in Rwanda. A cross-sectional study was conducted using the Technology Acceptance Model (TAM) to assess HMIS usage. A mixed-method approach was used to collect data from 280 HDMs. Data analysis utilized descriptive statistics for distribution and ANOVA to test the correlations between perceptions, institutional factors, technology, and the acceptability of HMIS.

Results

Perceived usefulness (PU), perceived ease of use (PEU), and technology factors were significantly inter-correlated with each other and with the acceptance of the HMIS system. The degree of HMIS acceptance by HDMs is at 64.4%. HDMs' perceived usefulness (PU) of HMIS highlights its core functionalities.

Conclusion

The significance of HMIS usefulness and acceptance among HDMs in Rwanda is evident but requires further improvement such as continuous training on the use of Health Management Information System, making the system more user-friendly, increasing user support when needed, and ensuring timely communication of updates. HDMs acknowledge the positive influence of HMIS on their achievements.

Health Management Information System

Health data managers

electronic health record

health information technology

HMIS acceptance

System acceptability

TAM model

Health information technology (HIT) presents a breakthrough for enhancing healthcare quality and safety, leading to cost reductions and facilitating innovations in services [1]. When accompanied by behavior change of individual users, technological advances offer a great chance of dealing with modern health challenges [2]. Health information system (HIS) has been recognized by the World health organization (WHO) and classified among the six building blocks of the health system [3]. The acceptance of health information technology in the United State of America has been good since the introduction of HIT for economic and clinical health in 2011[4]. The developing nations are quite lagging behind, yet they can learn from the experiences of developed countries to expedite IT adoption, saving time and resources [5, 6].

A health management information system (HMIS) is a way of recording, storing and processing health information for planning, policy making, implementation, monitoring and evaluation of health programs [7, 8]. HMIS is viewed as a success in Low- and middle-income countries (LMICs) but its impact on the decision making in Africa is still limited [6, 8, 9]. The factors such as Technology, institution, individual, and logistical abilities are perceived to affect realization of HMIS full potential. Other reports highlight lack of training, poor adoption, and under-reporting using HMIS as an issue in sub–Saharan Africa region [8].

The Rwanda HMIS was established in 1998 till 2012 as a strategic intervention to enhance access and quality of healthcare data collected, patient care and support evidence-based decision making [7, 10]. Since its introduction in Rwanda, no previous study has been done on adoption of HMIS by health data managers using TAM to inform the ministry of health on the actual situation to ensure best practices in utilizing HMIS, data driven decision making, resource allocation, and quality improvement in the healthcare sector.

The technology acceptance model (TAM) is often used in Health information system adoption research with strategic explanation [4]. TAM was first proposed by Davis in 1989, with the purpose of understanding the reasons behind individuals' decisions to accept or reject a new technology [11, 12]. TAM postulates technology perceptions result in its acceptance, then acceptance ends up in actual technology usage [11]. The key factors in TAM that impact attitude, are perceived usefulness (the user's belief that using the system will enhance their performance) and ease of use (the user's perception of how effortless and easy it is to use the system) [13, 14]. The adoption of healthcare information technology still faces resistance, particularly in developing nations [15]. When designing and developing health systems, the focus is often on the technical aspects, which may result in less effectiveness in meeting the needs of organizations and individual users, and could even pose risks to patients. Therefore, TAM is utilized to assess and explain the acceptance and behaviors of technology users associated with the introduction of HIT [4]. Behavioral and human factors play a significant role in the success or failure of a system, as factors such as changes in human behavior and system usage can predict whether users will accept and use the system or not [16]. The modified TAM with Perceived usefulness, perceived ease of use, technology factors, institution factors, and actual HMIS system use constructs was used during this study with more details in Fig. 1 below.

(Insert Fig. 1)

The aim of this study was to assess the usefulness and acceptance of HMIS among Health Data Managers in Rwanda, practicing in health centers and hospitals.

Study design

Considering that this study is explanatory in nature, a cross-sectional study was conducted from November 2022 till July 2023. The study used a convenient sampling procedure to enroll HDMs and ensured both urban and rural areas were represented. A convenient sampling is defined as a non-probability sampling approach where the sample of participants is selected and included in the study based on specific criteria such as easy availability to the researcher and their level of suitability to the research [17]. For our study, the geographical proximity, availability at a given time of the data collection and the expressed willingness to participate in this research have been taken into consideration during a convenient sampling of the participants in this research.

Study setting

The study enrolled heath data managers from the four provinces of Rwanda and Kigali city. The following hospitals and health centers within their catchment areas were selected: Nyarugenge Hospital, Muhima Hospital, kibagabaga Hospital, kacyiru Hospital, and masaka Hospital located in Kigali. Nyamata Hospital, rwamagana Hospital, Rwinkwavu Hospital, kirehe Hospital, and nyagatare Hospital located in Eastern province. Gicumbi Hospital, gatonde Hospital, rutongo Hospital, kinihira Hospital located in Northern Province. Remera rukoma Hospital, ruhango Hospital, kabgayi Hospital, kabutare Hospital, gitwe Hospital located in Southern province. And Kabaya Hospital, gisenyi Hospital, kibogora Hospital, shyira Hospital located in Western province. Both rural and urban areas were considered.

Study participants

Health data managers who utilize the HMIS system for reporting healthcare data for at least six months or more, working in hospitals and health centers across Rwanda were eligible. The country presently has a decentralized healthcare system consist of 1700 health posts (under health centers catchment areas), 500 health centers (under district hospitals catchment areas), 42 district hospitals, and five national referral hospitals. This study used Slovin's formula to ensure a high level of representativeness of the sample from the population. Using the known population size (N = 542 hospitals and health centers in Rwanda) with an acceptable error margin (e = 0.05), the formula resulted in an appropriate sample size (n). Therefore, n = N/ (1 + Ne2) = 230 HDMs (23 from Hospitals and 207 from health centers).

Data collection

The study used mixed methods to collect the data. Mixed method is a research strategy that involve both quantitative and qualitative approaches in order to achieve a comprehensive understanding of the study question [18]. A questionnaire with two parts was used during data collection: one part of the questionnaire captured quantitative data using structured questions and the other captured qualitative data using semi-structured guides. The tool was initially developed in English and translated in Kinyarwanda as a local language. The tool was pre-tested to assess validity and reliability before being adopted for the study. Quantitative data was collected on HDMs’ acceptance levels, their perceived ease of use, and the system's impact on their performance and efficiency during health data reporting while qualitative information was collected as complementary to explore health data managers' perspectives on HMIS as everyday users.

Validation of tool

The construct validity and reliability of the research tool were performed using IBM SPSS Statistics 20 with details in Table 1 below.

Table 1

Constructs validity and reliability (Insert Table 1)
Tested constructs	Number of items	Factor-Loadings (Validity)	Cronbach’s-Alpha (Reliability)
Perceived Usefulness (PU)	6	.631	.839
Perceived Ease of Use (PEU)	6	.773	.981
Institutional Factors (INST)	8	.926	.964
Technology Factors (TECH)	4	.543	.889
HMIS acceptance	6	.817	.800

Data management and analysis

Microsoft Excel was used to organize and process quantitative data and then analyzed using SPSS. Data analysis used descriptive statistics for distribution and ANOVA to test the relationships between the perceptions, institutional factors, technology and acceptability of HIMS. Qualitative data were processed, organized and analyzed using MAXQDA to find cohesion and quotations.

As detailed in Table 2 above, this study involved 280 health data managers (HDMs) from hospitals and health centers in Rwanda. Male respondents were greater than females and accounted (53%). 78.3% of the respondents have held bachelors’ degree and above. 97% of the respondents had a working experience of more than one year. 97.8% of the respondents were between twenty-five and forty-five years old during the study period.

Table 3 Level of HMIS acceptance and Factors that positively influence its acceptance

(Insert Table 3)

Tested constructs	HMIS acceptance
	ANOVA and Regression analysis		Spearman’s Correlation
	unstandardized coefficient	standardized coefficient	Spearman’s Correlation
Perceived usefulness (PU)	.250*	.320*	.628^*
Perceived ease of use (PEU)	.036*	.092*	.534^*
Institutional factor (INST)	.009	.041	.081
Technology factor (TECH)	.433*	.397*	.621^*
*P-value <0.001. R square: .644			*P-value <0.001

The Perception of Health Data Managers Regarding the Usefulness of HMIS since Its Implementation

The responses from health data managers were collected to assess their views on the usefulness of the Health Management Information System (HMIS) since its implementation. Some health data managers described how HMIS significantly save their time and hence improved productivity.

"I use the HMIS system on a daily basis. It is highly beneficial and saves me a significant amount of time. For instance, when I require aggregated data, I can swiftly obtain the necessary reports, which would otherwise be time-consuming without HMIS."

In addition, HDMs stated how using HMIS helps them to produce a real time, complete and quality reports.

"I appreciate the contribution of HMIS to my work. It simplifies my job by generating comprehensive reports with high-quality health data."

HDMs are among high tasked staffs, and they defined HMIS as a useful tool that helps them to deal with high workload.

"Data managers are often overwhelmed by excessive data and reporting demands. Thus, the use of HMIS in health data reporting enhances my job performance."

Participants also described HMIS as reliable tool when reporting and managing health data.

"HMIS is a reliable system to use. It makes our job easier with production of accurate, real-time, and high-quality reports."

Other participants expressed appreciation of HMIS and how it contribute to their overall productivity.

"HMIS is an incredibly useful system, making my work easier, quicker, and boosting my overall productivity."

Then again, participants revealed their gratitude on how HMIS is accessible using various devices.

"HMIS is highly practical in my workplace and can be accessed through any connected device, such as a telephone."

Suggestions from Health Data Managers to Ensure Future HMIS Utilization

The responses from health data managers were collected to capture their recommendations and what they propose to ensure future HMIS utilization and sustainability. Some health data managers recognize the role of continuous trainings as an important element for the future success of HMIS.

"The HMIS system is beneficial and resolves many issues. To ensure the quality of reports and wider adoption of HMIS, it is crucial to provide quality training for data managers."

In addition, HDMs highlighted that developing a more easy to use and user-friendly interface would increase future utilization of HMIS.

"The HMIS system is useful and easy to use, but its technology needs to be simplified and made more user-friendly."

Participants also recommended for interoperable HMIS in the future. Since HDMs dealt with more other systems. It would make their works easier.

"In the healthcare system, various systems are in use, and it would be immensely helpful if HMIS could seamlessly communicate with these existing systems.”

Then again, participants suggested for timely supports and communication from HMIS support and management team.

"We greatly appreciate the technical support provided by HMIS when needed. It is essential for this support to be sustainable. And timely communication and training on every update related to data management are necessary."

This study explored the impact of perceived usefulness, perceived ease of use, technological and institutional factors on the acceptance of the Health Management Information System (HMIS) by health data managers (HDMs). Our findings are consistent with several previous studies. A study conducted by Mohammedjud et al. in Ethiopia found that factors like effort expectancy, performance expectancy, facilitating conditions, and social influence positively influenced the use of electronic medical records by healthcare professionals [19]. Using the Technology Acceptance Model (TAM), Doan Hoa Do et al. found that perceived usefulness and ease of use, among other factors, influenced the use of iPads among undergraduate medical students in the United State of America [20]. All the constructs employed in this study were found to be both valid and reliable. In Table 1, all the modified TAM constructs used scored above .543 and .800 for loading factors (measuring validity) and Cronbach’s Alpha analysis (measuring reliability), respectively, demonstrating the effective validation of the model. The accepted validity threshold is 50% (0.5), and the reliability threshold is .70 (70%). Therefore, all the constructs employed in this study were found to be valid and reliable.

This study determined the level of acceptance and usage of HMIS by health data managers. According to the B coefficient statistics, health data managers’ perceived usefulness, ease of use, and technological factors positively influence their acceptance of HMIS usage. However, there was no significant relationship between institutional factors and the acceptance of HMIS usage by HDMs. As a result, the degree of acceptance is at 64.4% (R² = .644), which represents the variability explained by perceived usefulness, ease of use, institutional factors, and technological factors as detailed in Table 3.

Perception of HMIS Usefulness by HDMs since Implementation

HDMs’ responses regarding the perceived usefulness of HMIS emphasize its core functionalities, which include speed, increased productivity, effectiveness, simplification of tasks, and overall utility. Scholarly investigations on HMIS have demonstrated that effectively implemented HMIS can result in enhanced precision and timeliness in data collection, analysis, and reporting. As a consequence, this can play a pivotal role in optimizing resource allocation, bolstering disease surveillance, and shaping effective policy formulation [21].

Factors Positively Influencing the Acceptance and Use of HMIS by HDMs

As described in Table 3, the results from Spearman’s correlation analysis confirm that health data managers’ perceived usefulness, ease of use, and technological factors of HMIS are critical for the adoption of HMIS systems by health data managers. Although the TAM model used in this study does not suggest a direct relationship between institutional factors and system usage, a study conducted by Byungura on SciPro system adoption at the University of Rwanda shows a significant correlation between institutional factors, such as facilitating conditions, and system adoption [22]. Hosein Barzekar and colleagues found that management and administrative support encouraged nurses at Khorramabad Training Hospital to use the HIS leading to an increased level of perceived ease of use and usefulness and greater acceptance [23]. They also highlighted training and the presence of technical support as important factors for user satisfaction and system adoption. Training was similarly emphasized as a key factor in the adoption of any HIS by end-users in a study conducted by Hamed Nadri and his colleagues [24]. Factors such as institutional, technological, individual, and logistical capacities have been shown to have a significant influence on HMIS implementation and sustainability in Africa [25]. To enhance system usage and acceptance, the usefulness and ease of use of HMIS, as well as technological factors like user-friendliness, reliability, accessibility, and interoperability, need to be ensured.

Recommendations by HDMs to Ensure Future HMIS Use

HDMs recommend continuous training on the Health Management Information System, making the system more user-friendly, increasing user support when needed, and ensuring timely communication of updates. Nshimyiryo A. et al, assessing the accuracy of HMIS data for maternal and newborn health by comparing the consistency of HMIS reports with original facility documents, also propose ongoing investigations into data quality and the provision of training to bridge existing gaps [26]. HDMs express their willingness to use a system that is both useful and easy to navigate. Therefore, during the design and development of the system, special attention should be given to maximizing system functionalities and technology. Addressing these recommendations can contribute to the long-term success and effectiveness of the HMIS.

Study Limitations

This study focused only on health data managers (HDMs) who had been using the HMIS for at least six months or longer. Due to the heavy workload that HDMs typically face, it proved challenging to locate and engage them for survey questionnaire responses. In order to attain the desired objectives, the study employed a convenience sampling strategy, and e-questionnaires were considered as a viable alternative. Future studies could delve further into this topic and include HDMs with less than six months of work experience.

In conclusion, the importance of HMIS usefulness and acceptance among HDMs in Rwanda is evident, yet there is room for further improvement. HDMs recognize the significant role that HMIS plays in their achievements. Factors such as Perceived Usefulness, Ease of Use, and technology are pivotal for the adoption of HMIS. Additionally, HDMs have recommended ongoing training, a more user-friendly system, enhanced technical support, improved interoperability, and timely communication of updates as key steps for the future enhancement of HMIS utilization. This study contributed to the scientific body of research in digital health transformation with insights on how HMIS tools are adopted in the context of low-resource setting such as the Rwandan healthcare sector. Further studies in this area could extend the context of a similar research by involving the leadership and decision makers at country level and also private healthcare providers.

HMIS: Health management and information system; TAM: Technology acceptance model; HDM: Health data manager; UR: university of Rwanda; WHO: World health organization; HIT: Health information technology; HIS: Health information system; PU: perceived usefulness; PEU: perceived ease of use; INST: institution factors; TECH: technology factors; LMICs: Low and middle income countries.

Acknowledgements

Authors acknowledge the financial support from the Center of Excellence in Biomedical engineering and E_Health (CEBE). Our thanks also goes to the health data managers who provided us with useful information for this project work.

Authors’ contributions

All authors have participated in the research work and have read and approved the manuscript. HF developed the proposal, data collection, analyzed data and prepared the research report. NA was main supervisor of the project. BJC were co-supervisor of the project and contributed in the development of tools. TD was advisor of this project with paramount contribution on manuscript writing.

Funding

CEBE provided transport and communication support during data collection. The source of funds for this study was the authors.

Availability of data and materials.

The data set analyzed during this research is available from the corresponding author on reasonable request.

Ethical consideration

The study was approved by University of Rwanda, college of medicine and health sciences institutional review board (IRB) with ref: CMHS/IRB/487/2022. Before engaging participants in the research, participants were informed about the purpose of the study and were ensured confidentiality and voluntary participation in the study. The written informed consent form was signed by each participating participant prior to their participation. Responses are kept confidential and no name specification in the reports. All methods in this research were carried out in accordance with appropriate guidelines and regulations in the ethical declarations.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

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The perceptions and acceptance of health management information system by health data managers in Rwanda: A Cross Sectional Study

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Version 1

Abstract

Background

Results

Conclusion

Figures

INTRODUCTION

METHOD

Study design

Study setting

Study participants

Data collection

Validation of tool

Data management and analysis

RESULTS

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

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