Cloud-based Electronic Health Records (Ehr) Adoption Rate in Nigeria: Barriers and Performance Bottlenecks

Cloud-based Electronic Health Records (Ehr) Adoption Rate in Nigeria: Barriers and Performance Bottlenecks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cloud-based Electronic Health Records (Ehr) Adoption Rate in Nigeria: Barriers and Performance Bottlenecks Olumhense Benedict Adoghe, Braimoh Ikharo, Henry Amhenrior This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8821192/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: Cloud-Based Electronic Health Records (EHRs) are critical to the enhancement of the quality of healthcare, healthcare continuity, and the efficiency of the system. Adoption of them is still low and uneven in Nigeria and other low and middle-income countries (LMICs). There is limited empirical data on the level of adoption, system performance, and the bottlenecks in the implementation process of the systems within the facilities of the healthcare sector in Nigeria. Methods: Primary and secondary data were used in carrying out an exploratory mixed-methods study. The primary data were gathered through an online survey of 26 healthcare facilities operating in 11 states of Nigeria in the period between January and March 2025. A collection of secondary institutional data from 130 facilities comprised records of vendor deployment, government and regulatory reports, hospital web pages, and the peer-reviewed literature, were also used. Quantitative data were undertaken through descriptive statistics and chi-square tests, whereas qualitative responses were undertaken thematically. Pareto analysis, radar charts, box plots, and Ishikawa diagrams were systems-engineering tools that were used to determine significant bottlenecks in performance. Results: The primary survey found that only 23% of the facilities were fully electronic EHR systems, 42% were hybrid systems, and 35% could only use paper-based records. The reported digital implementation (75%), as shown through secondary data, had more reported implementation, but operational maturity was not in line with the institutional claims of implementation. The uptake of the EHR also grew beyond 2019, especially in tertiary and specialist institutions. Approximately 90% of performance bottlenecks that were reported included power instability (76.9%), high implementation costs (69.2%), and poor IT infrastructure (65.4%). The infrastructure reliability score of power (2.3/10) and internet connectivity (3.5/10) was low. System reliability was polarized, with 53.8 percent saying they had rare or no failures and 15.4 percent saying they had daily failures. Staff overall satisfaction was high (57.7%), which was low due to infrastructural constraints. Conclusions: The use of cloud-based EHR in Nigeria is still in the process of transition and its implementation is limited by insufficient underlying infrastructure and not by the resistance of users. Electronic Health Records (EHR) Cloud Computing Digital Health Healthcare Adoption Nigeria Low- and Middle-Income Countries (LMICs) Implementation Barriers Health Informatics Infrastructure Readiness Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Introduction The fast development of digital technologies is radically changing the healthcare systems of countries in the world as it alters the ways of medical information gathering, storage, sharing, and use in order to enhance the clinical outcomes, efficiency, and safety of the healthcare system [ 1 , 2 ]. The core of this change is the implementation and optimization of Electronic Health Records (EHRs) - electronic systems that are used to combine all patient data, such as demographics, diagnoses, medications, vaccinations, laboratory and imaging, and clinical notes, into one coherent, easily accessible platform [ 3 , 4 ]. EHRs can be used both as a repository of patient history and a dynamic tool to aid in clinical decision-making, interoperability with data, and coordination of operations across care pathways [ 5 ]. In developed nations, health information technology investments have resulted in almost universal use of EHRs. Indicatively, among non-federal acute care hospitals in the United States, more than 96% have certified EHR systems, and almost 80% of office-based physicians implement them in clinical practice [ 6 , 7 ]. This level of digital health infrastructure maturity has allowed extensive improvements in the quality-of-care delivery, such as increased accuracy of documentation, decreased redundant testing, lower medication error rates, and enhanced care coordination, especially with the implementation of interoperability standards like Health Level Seven (HL7) and Fast Healthcare Interoperability Resources (FHIR) [ 7 , 9 ]. The technologies can facilitate the shift to networked, interoperable health information ecosystems, which are needed to track longitudinal patients, provide real-time analytics, and be integrated with upcoming technologies such as artificial intelligence (AI) to predict future outcomes and offer clinical decision support [ 2 , 4 , 10 ]. Other standards like the FHIR of the HL7 standard have experienced increased adoption across the globe where surveys have shown that more than 70% of the surveyed countries are actively using FHIR standards in some or all of their use-cases with full implementation and regulatory alignment being an on-going challenge [ 11 ]. In spite of these developments, adoption of EHR is very low in low- and middle-income countries (LMICs) especially in sub-Saharan Africa. Health systems in these settings encounter a complex situation due to structural, technological and human-resource constraints, which hinder digital transformation. There is region-wide scoping research which has shown that EHR use in sub-Saharan African facilities is sparse with most use limited to pilot projects, HIV/AIDS treatment programs, or even a particular urban area and most rural and public facilities still use paper records [ 12 ]. The obstacles that have been reported in the region are unreliable power supply, poor internet connectivity, high cost of procurement and maintenance, lack of training and digital literacy, resistance to change and fracturing of policy frameworks [ 13 ]. In Nigeria, which is one of the most populous countries in Africa with a high prevalence of diseases and a significantly strained doctor-patient ratio, the adoption of electronic health information systems has been inconsistent and slow despite the widespread acceptance of its importance in enhancing care delivery. Literary estimates indicate that the adoption of EHR in healthcare institutions in Nigeria is between about 18 and 23 percent with only a small number of hospitals having digitalized patient information systems wholly with the rest using hybrid systems or legacy systems [ 12 , 14 , 15 , 16 , 17 ]. Interoperability of digital records among institutions is also extremely low with estimates showing that between 12–15% of facilities can exchange patient data seamlessly [ 5 , 15 , 18 , 19 ]. Paper-based systems persistence in Nigeria serves as a cause of care discontinuity, inefficiencies, and increased rates of errors. In the example of patient history, discontinuous patient histories and part-transmission of records are widespread, which prevents systemic treatment and imposes an extra administrative cost on clinicians. Such issues are worsened by the lack of digital infrastructure (i.e. intermittent power supply, poor broadband penetration), lack of training opportunities, and the absence of policy measures/incentive to implement EHRs [ 20 , 21 ]. Facts of scoping and quantitative research also confirm that healthcare workers might be interested in EHR systems in theory, but the real practice is limited due to the context specifics, including device unavailability, instability of institutional preparedness, and poor health informatics skills of professionals [ 14 , 22 , 23 ]. Although a number of studies have reported barriers and small scale implementations of EHRs in Nigeria, a large gap in the existing data exists with regard to comprehensive, nationally representative, quantitative data illustrating: (1) the current adoption status of cloud-based EHR systems across various levels of health facilities; (2) the most prevalent barriers as perceived by frontline healthcare workers; and (3) the extent to which performance bottlenecks limit effective use. In this research paper, the researcher has aimed at filling in those gaps by carrying out an intensive quantitative evaluation that will include survey data, statistical modeling, and integration with the available literature to develop a comprehensive empirical perspective on Nigeria digital health preparedness and performance constraint. The results of this study will be used to give new evidence so that context-specific approaches, capacity-building activities, and policy suggestions could be made to hasten EHR adoption, improve interoperability, and maximize healthcare delivery outcomes in resource-constrained settings. The later discussion includes the methodology, survey findings, analytical results, critical discussions, and the proposed directions the future research, policy making, and practical execution are supposed to take in order to make Nigeria faster in the digital health process. The global adoption rate of Electronic Health Record is as shown in Table 1 . Table 1 Electronic Health Record Global Adoption Rate Country Hospital Adoption Rate (%) Primary Care / Ambulatory Adoption Rate (%) Source / Reference United States 96% ~ 78–86% [ 24 ], [ 25 ], [ 26 ] Denmark ~ 99% ~ 99% [ 27 ], [ 28 ], [ 29 ] Netherlands 98–99% 99% [ 30 ], [ 31 ], [ 32 ] Sweden ~ 98% ~ 99% [ 33 ], [ 34 ] Norway ~ 98% ~ 99% [ 37 ], [ 38 ] United Kingdom ~ 96–98% ~ 98% [ 35 ], [ 36 ] Australia ~ 92–95% ~ 90% [ 39 ], [ 40 ], [ 41 ] Canada ~ 85–90% ~ 75–85% [ 42 ], [ 43 ], [ 44 ] Germany ~ 70–80% ~ 90% [ 45 ], [ 46 ], [ 47 ] France ~ 65–75% ~ 80–85% [ 48 ], [ 49 ] Japan ~ 45–50% ~ 35–40% [ 50 ], [ 51 ], [ 52 ] South Korea ~ 92–95% ~ 85–90% [ 53 ], [ 54 ], [ 55 ] Singapore ~ 95–98% ~ 90–95% [ 56 ], [ 57 ], [ 58 ] New Zealand ~ 90–95% ~ 95% [ 59 ], [ 60 ] Brazil ~ 40–50% ~ 30–40% [ 62 ], [ 63 ] India ~ 15–25% < 10% [ 64 ], [ 65 ], [ 66 ] Nigeria ~ 20–25% < 10% [ 14 ], [ 15 ], [ 67 ] Kenya ~ 30–40% ~ 20–30% [ 68 ], [ 69 ] South Africa ~ 40–50% ~ 25–35% [ 71 ], [ 72 ] Methodology This section provides the methodology adopted in carrying out the research study. Figure 1 depicts the block diagram of the methodology: 3.1 Research Design The proposed research design was an exploratory mixed-method research design, which combined quantitative data on survey tools, qualitative information, and secondary institution documents to evaluate the status of adoption, performance, and obstacles of cloud-based Electronic Health Record (EHR) system adoption in Nigerian healthcare facilities. The mixed-methods approach was chosen to describe both the quantifiable structural attributes of EHR adoption (e.g., system type, infrastructure preparedness, frequency of failure) and contextual and experiential attributes (e.g., perception of bottlenecks, user satisfaction) that are essential in low-resource healthcare settings. Since there are very few national representative EHRs datasets available in Nigeria, the research is set as a diagnostic and exploratory, and not confirmatory or causal. It was aimed at establishing the patterns of dominance, bottlenecks and constraints at the system level that define the current state of EHR implementation and use. 3.2 Data Sources 3.2.1 Primary Survey Data The research was conducted with primary data collection using a structured online questionnaire based on investigating the activities of healthcare professionals actively engaged in management of patient records or health information systems. The survey instrument used for data collection is provided as Supplementary File 1. There were 26 valid responses of healthcare facilities in 11 states of Nigeria which include primary, secondary, tertiary and specialist institutions. The questionnaire included 19 items that were grouped into five themes: Facilities and respondent attributes. Type of patient record system and its adoption. Infrastructure preparedness (power, internet, hardware, IT support) Reliability and performance of the system. Perceived barriers and user satisfaction. Perceptions of infrastructure adequacy, system reliability, and satisfaction were measured by 5-point Likert scales. The questions were open-ended in order to provide qualitative information on the contextual and institutional challenges that could not be fully achieved with the closed-ended questions. In order to enhance the response validity and minimise the respondent burden, skip logic was implemented whereby the respondent who responded through a paper-based system was not to be asked EHR-related performance questions. 3.2.2 Secondary Institutional Data Secondary data were summarized based on various sources that were available in the public, which included: Vendor deployment lists Government/ regulatory publications. Hospital websites Digital health reports and peer-reviewed literature. It was a dataset of 130 healthcare facilities across the country, which was utilized to determine the institutional-level EHR presence, distribution of facility types, and geographic dispersion. Secondary data mostly include reported deployment of system and availability, but not the depth of operation or daily usability. The secondary data helped to enable triangulation and allowed the comparison of institutional adoption claims and frontline operational realities as expressed in the primary survey. 3.3 Sampling Technique The primary survey is based on a convenience sampling strategy because of the logistical limitations, a small sample size because of the lack of centralized staff registries, and the exploratory research. Professional network and institutional contacts as well as online dissemination were used to recruit respondents. Although this method allowed collecting a large amount of data in a short time, it also presupposes that this sample might be disproportionate to facilities with certain experience working with digital health systems. Therefore, results obtained using the primary data set can be defined as indicative and not representative of all the healthcare facilities in Nigeria. 3.4 Data Analysis 3.4.1 Quantitative Analysis The quantitative data was analyzed with the help of Python (Panda’s library). Descriptive statistics, such as frequencies, percentages, means and standard deviations, were calculated in order to summarize adoption status, infrastructure readiness, system reliability and satisfaction levels. Chi-square tests of independence were used as inferential analysis to investigate the relationship amid categorical variables, e.g., facility type and EHR adoption status. Due to the small size of the primary sample, the results obtained through the inferences were taken with a grain of salt and could hardly be used to make definite conclusions except that they could be used to discern the directional trends. The sophisticated methods of analysis have been used to promote understanding of diagnosis as depicted in Table 2 , showing the analytical tools used in the research study Table 2 Analytical tools used in the research study Tools Analysis Pareto analysis to identify dominant barriers contributing to EHR underperformance Radar charts to visualize multidimensional infrastructure readiness Box plots to examine variability and polarization in system reliability Fishbone (Ishikawa) diagrams to systematically map root causes of EHR underperformance across technical, organizational, and policy domains These tools were selected to support a systems-engineering perspective on digital health implementation. 3.4.2 Qualitative Analysis The thematic analysis was used to analyze qualitative responses of open-ended survey items. The reviews were read and coded by hand, and organized into recurring themes, which were power instability, internet unreliability, high implementation costs, limited training, and inadequate vendor support. Qualitative element was used in an explanatory complementary manner to put into context quantitative results and shed light on causal processes about patterns observed. 3.5 Ethical Considerations Participation in the study was voluntary and all the respondents gave informed consent before the data was collected. No personal information was gathered and the responses were anonymized as a way of ensuring confidentiality. Only used in academic research, data were stored in a secure place to ensure that they were not used in any other way other than proper research, which is in accordance with ethical guidelines of research involving human subjects. Results A. Demographic and Facility Profile Primary survey: Facilities from 11 states, predominantly tertiary/secondary. Figure 2 reveals the number of Surveyed Healthcare facilities and Table 3 shows the number of facilities per State Table 3 Number of facilities per State State Number of Respondents Niger 9 Edo 5 Ondo 2 Lagos 2 FCT (Abuja) 2 Bauchi 1 Osun 1 Abia 1 Ogun 1 Delta 1 Plateau 1 Total 26 Figure 3 gives a clear depict of the state distribution of survey of the respondents. The survey respondents were selected across 11 states in Nigeria, with Niger state (34.6) as well as the Edo state (19.2) recording the highest number of responses, indicating a focus of responses to the North-Central, South-South regions. The other states made less but significant representation, which offered geographic coverage in various geopolitical regions. Secondary data: Nationwide, with Lagos dominant. Table 4 gives the state distribution of healthcare facilities from the secondary dataset. Table 4 State Distribution of Healthcare Facilities State Number of Facilities Lagos 16 Rivers 8 Delta 6 Enugu 3 Oyo 2 Kwara 1 Anambra 1 Bauchi 1 Federal Capital Territory (FCT) 1 Kebbi 1 Other States* 64 Total 130 *Other States is a group of facilities that has multi-branch coverage, incomplete state specification or dispersed national placements as indicated in vendor and institutional databases. Institutional records in 130 healthcare facilities in Nigeria were compiled on the basis of vendor deployment list, public hospital records and regulatory sources to conduct the secondary data analysis. The facilities covered several states with a concentration of the highest population in Lagos State, then Rivers and Delta States. A large number of facilities were included as Other States because of multi-site deployments or incomplete geographic specification, which is typical of publicly available EHR institutional data in Nigeria. Figure 4 below illustrates a stacked bar chart in the compare and contrast of the geographical distribution of primary survey respondents (n = 26) and secondary institutional healthcare facilities (n = 130) across the states in Nigeria. The bottom of each bar is the primary survey data, the top level of the bar is the secondary institutional data. B. EHR Adoption Status Primary Dataset: Figure 5 shows the EHR system deployment model spread in the survey of healthcare facilities, and it will be used to give a quantitative picture of the level of maturity of health information system infrastructure in the context of the study. The prevailing trend of hybrid systems (42%), shows that majority of institutions are at a transitional stage of digitalization, where electronic modules are now used in conjunction with the legacy paper-based processes. This is partially system integration, according to systems engineering, where digital subsystems have been brought in without end-to-end process reengineering. These types of architecture are generally described as being more complex in their operation, data duplication, slower synchronization and more vulnerable to losing information in the paper-digital boundary. A high percentage (35) of paper-only facilities demonstrates that the majority have a major technological lag in the informatics infrastructure in the basement. This category reflects the settings in which healthcare delivery is entirely reliant on manual data acquisition, data storage and data retrieval systems, which in turn are limited by scalability factors, low data accessibility, lack of real time analytics, and susceptible to data corruption and loss. These systems are nearly entirely un-automated, lack interoperability, and cannot be optimally calculated or intelligently integrated as the decision support, in engineering terminology. The existence of EHR-only facilities (23%), on the other hand, indicates the development of entirely digitized healthcare information ecosystems, albeit with a rather low rate of penetration. These facilities run entirely on electronic data pipelines, allowing structured data capture, centralized or cloud-based storage and allowing real-time data processing, interoperability, and integration with higher order computational models including machine learning, predictive analytics, and optimization algorithms. Nevertheless, the small numbers of this group indicate that the digital change of the system is in its initial diffusion phase. Through the perspective of diffusion-of-innovation and technology adoption lifecycle, the identified distribution would be considered as an early-to-mid adoption stage with a small group of early adopters (EHR-only) and a significant majority of transitional adopters (hybrid systems) and a large proportion of late adopters or non-adopters (paper-only). Such an arrangement means that there is yet no infrastructural preparedness, institutional capability, or policy implementation up to the level of large scale, unified EHR implementation. In the engineering aspect, the hybrid superiority is also an indicator of possible bottlenecks in data throughput and reliability of the system. Hybrid architectures add latency on the data conversion points (manual-to-digital), propensity to errors spread, and limits the implementation of high-level system functionalities such as automated clinical decision support, distributed data analytics, and interoperability through standards such as HL7 and FHIR. Consequently, the healthcare information system as a whole is not functioning to its best efficiency envelope. Hence, Fig. 1 does not only capture adoption rates, but it quantitatively represents the structural condition of the information system of healthcare in Nigeria as a multi-layered socio-technical system, shifting towards the digital mode of operation. It highlights that any significant performance improvements will not occur with gradual digitization, but with the complete architectural transformation needed to achieve unified fully electronic EHR systems with a reliable power infrastructure, high-availability networking, standardized data models, and scalable cloud or hybrid-cloud computing platforms. Secondary Dataset: Figure 6 uses the secondary data to provide the institutional-level EHR system implementation, a macro-scale perspective of digital health system penetration in the Nigerian healthcare facilities. The 75% percentage in digital systems (Hybrid/EHR) shows that at an organizational and vendor-reporting level, a significant percentage of facilities have embarked on some kind of implementation of an electronic health information system. This is contrary to the primary survey results and provides a structural difference between reported institutional adoption and depth of usage at the clinical workflow level. In systems engineering perspective, this number implies that the availability of digital infrastructure is far more than the usefulness of digital utilization. The combination of hybrid and fully electronic systems to form one major category suggests that the majority of institutions have some type of minimum electronic infrastructure such as electronic patient registration, billing modules, and laboratory information systems. But it does not in any way mean that it is fully integrated, interoperable, or digitizes clinical processes end-to-end into electronic health records. The remaining 25 percent of paper-only facilities constitute the nodes in the healthcare delivery system which are not attached to the digital information ecosystem at all. These centers become structural bottlenecks in any interoperability structure at the national level since they do not have the most fundamental computational interface needed to support electronic data exchange, health analytics, or cloud-based system integration. Figure 6 , when viewed together with Fig. 1 (Primary Data), shows that there is a significant socio-technical separation between secondary data, which focuses on system presence, and primary data which shows system depth and operational maturity. The secondary data provides the infrastructure availability information, and the primary one provides the functional integration and the system performance information in engineering terms. This disparity highlights the idea that hardware and software implementation are not the only limiting components to EHR adoption in Nigeria, but rather its usability, integration with the workflow, competency of staff, and stability of the infrastructure. Therefore, Fig. 6 will describe the healthcare information system in Nigeria as one that has a wide yet shallow digital presence. System optimization is no longer just the installation of systems, but the optimization of systems, the shift of hybrid systems to fully electronic, interoperable and fault-tolerant information systems that can support real-time clinical decision support, scalable data analytics, and AI-driven optimization of health care. Type of Hospital facilities used for survey Table 5 Adoption of Patient Record Systems by Facility Type (Primary Survey Data) Facility Type Paper Hybrid EHR-only Total Primary 1 0 0 1 Secondary 6 2 0 8 Specialist Hospital 1 1 1 3 Tertiary 0 5 3 8 Total 8 8 4 20* * Facility-level analysis was not performed on any facility with the stated responses of unspecified type of facility or those that could not be classified. Table 5 shows the adoption of the patient record system amongst the different types of healthcare facilities on the basis of the primary survey data. Primary and secondary healthcare facilities are dominated by paper-based systems which explains the majority of responses in these category. Tertiary facilities have the highest number of hybrid systems, which implies a continuous shift towards complete digitization. The provider of fully electronic health record (EHR-only) is only seen in specialist and tertiary institutions, which are more institutionally prepared, with better infrastructures and technical preparedness. Table 6 Adoption of Patient Record Systems by Facility Type (Secondary Institutional Data, n = 130) Facility Type Paper Hybrid / Vendor-Managed* EHR-only Total Primary 0 7 0 7 Secondary 1 14 0 15 Tertiary 2 11 2 15 Private 0 11 1 12 Other / Multi-Institutional Deployments 2 53 0 55 Total 5 96 3 130 Table 6 shows the adoption of patient record system by the types of healthcare facilities by secondary institutional data of the 130 healthcare facilities in Nigeria. The secondary survey data is also dominated by hybrid or vendor-managed record systems compared to the primary survey data as well as in multi-institutional deployments and in the private facilities. Systems on EHR only are quite uncommon and are only found in tertiary institutions. Paper-based systems still exist mainly in secondary and multi-institutional public facilities, which portrays persistence of infrastructural and operational limitations. Year of EHR adoption The temporal pattern of EHR adoption in Nigeria in healthcare facilities presented in Fig. 7 . The adoption was rather low in the previous years, and the steep rise started in 2020. This pace is an indication of the increasing institutional dependence on digital health systems, especially cloud-based systems. Nevertheless, the fact that adoption is concentrated to a small time interval also makes the issue of performance and scalability more significant, which is why predictive and adaptive optimization mechanisms are necessary. C. Cloud Hosting Models Figure 8 shows the distribution of Hosting models from the number of facilities under review. On-premise is the most popular in the category of those facilities that reported the hosting model (14 out of 26), as it signifies the further dependence on the local infrastructure despite power and internet issues. This is a slow transition to modern cloud-based architectures with Hybrid and Cloud models only accounting to roughly 35.7 percent of clear responses. The Unknown/Not Specified (46.2) percentage is high because of the missing documentation and lack of knowledge about the hosting information among the respondents. This architecture illustrates why migration to scalable, cloud-enabled EHR systems continues to be challenging in resource-constrained environments. The default of on-premise is because of reliability issues, whereas hybrid and cloud options have initial but low uptakes. Cloud Service Providers This pie chart represents the allocation of cloud service provider reported by the facilities using cloud-based or hybrid EHR systems in the primary survey (n = 26 responses). The facilities that were specifically specified to have a cloud provider are included only (n = 17 responses involving cloud involvement). Table 7 reveals the Cloud Service Providers and the percentage of facilities covered. This information is also depicted in Fig. 9 . Table 7 Cloud Service Providers and total number of facilities Cloud Service Provider Number of Facilities Percentage Local / Nigerian Vendors 5 29.4% Amazon Web Services (AWS) 4 23.5% Microsoft Azure 3 17.6% Others / Not Specified 5 29.4% The biggest single group (29.4%), is comprised of Local/Nigerian vendors (e.g., eClat, Health-in-a-Box, LAMIS, custom solutions) as the most preferred are locally developed or managed cloud platforms that could be more supportive of Nigeria-specific connectivity, compliance, and support. The share of reported cloud usage between AWS and Azure is 41.1, indicating that global hyperscalers are heavily relied upon by facilities that have more sophisticated infrastructure. The significant portion of 29.4% is the category of Others / Not Specified, which may either represent the usage of smaller international providers (Google Cloud, etc.) or the absence of the clear documentation of the used provider. The high dependency on the international providers (AWS + Azure = -41) poses a risk of cost of international bandwidth, sporadic connectivity, currency fluctuations, and possible service interruption. The intense availability of local vendors (29.4 00:33) can have a positive impact on these risks, providing a more accurate response to the realities of local infrastructure and, perhaps, reducing latency. Nevertheless, the fact that unspecified providers reached a high percentage (29.4) indicates that there is no transparency and documentation, which in turn becomes a liability to the system reliability, accountability of the vendor, and a long-term plan of support. This distribution depicts the uneven adoption environment of cloud services in Nigeria: a combination of international providers and local solutions with a serious uncertainty as to who is a provider. Infrastructure Readiness Index for EHR adoption Figure 10 shows the infrastructure readiness index for EHR adoption. Based on the critical Bottlenecks (The "Crunch" Zone) the chart is evidently tipped to the left plucking the Power Reliability (2.3/10) and Internet Reliability (3.5/10) as the most overwhelming. The form of the polygon is a graphical signifier of how these underlying infrastructure problems draw down on the whole preparedness. The furthest extension is the polygon that IT Support (5.8/10) and Staff Digital Literacy (4.8/10). This indicates that the physical infrastructure is poor but the human resources (staff and support systems) is a bit better placed to assist in the adoption of EHR. Hardware Availability (4.2/10) is at the middle, it is not the worst (better than power/internet) but it is still below average, which means that it is a moderate constraint. This profile provides a clear explanation of why the EHR adoption rates are still very low (only 3.8% fully digital with the primary survey only) and why a hybrid or paper-based system is the most common reason: the underlying digital infrastructure is simply not yet developed in the majority of facilities to support the reliable and scalable deployment of cloud-based EHR. Any effective national EHR plan should first think of humongous investment in power resilience (solar backups, generators) and broadband connectivity and then wait to see mass digital transformation. D. Challenges and Barriers to full adoption of EHR Top challenges and barriers to full adoption of HER can be seen in Table 8 with power instability (76.9%), cost (69.2%), IT infrastructure (65.4%). Table 8 Challenges and Barriers to full adoption of EHR Barrier Frequency (Number of Mentions) Percentage of Respondents (n = 26) Power instability 20 76.9% High cost of implementation 18 69.2% Insufficient IT infrastructure (computers, servers, etc.) 17 65.4% Inadequate training of personnel / Staff digital literacy 12 46.2% Lack of technical / vendor support 9 34.6% Figure 11 reveals the top Barriers to EHR Adoption. Power instability is by far the most commonly mentioned challenges (76.9% of the respondents), which proves it as the ultimate systemic limitation to consistent EHR use in Nigeria. Right behind are high cost and lack of IT infrastructure (69.2% and 65.4%), which is a triple core of barriers to the foundations that influence the adoption and continued use. Weak training and vendor support are both major but less ubiquitous (46.2% and 34.6) showing that the technical and human-capacity problems are indeed common, but secondary to the underlying infrastructure problems of power, cost and hardware. These findings measure the prevailing system limitations in the digital health environment of Nigeria. The most important priorities in every effective national EHR strategy should be reliable power supply (e.g., solar and battery backup options), low-cost financing and models, and sound hardware provisioning. There will be no real impact on training and vendor support until the underlying infrastructure gaps have been bridged. Frequency of System Failures (Primary Survey Data) Table 9 Rate of system failures Frequency of Technical Issues Number of Facilities Percentage Rarely / Never 14 53.8% Weekly 1 3.8% Daily 4 15.4% Not specified 7 26.9% Table 9 and Fig. 12 help to show the frequency of system failure as carried out from the research study. The vast majority of facilities (53.8) say that they have occasional or no technical problems at all - which means that, in cases when EHR systems are installed and maintained correctly, they can even be rather stable. The median is Rarely, i.e. more than a half of the respondents can rarely experience failures. Nevertheless, there are a considerable number of (15.4) percent who have technical issues daily, forming a bimodal with long tail curve with increased frequency of failure. The broad interquartile range (Q1 = Rarely - Q3 = Weekly) demonstrates a high level of movement in the reliability of systems between facilities - some are very stable, whereas others are subjected to frequent disruptions. Most of the facilities who have superseded EHR/hybrid systems have stated that stability (infrequent failures) is satisfactory, which implies that present-day implementations can be reasonably successful given proper infrastructure and support. The 15.4% of those who report failures on the daily basis is an urgent pain point - these institutions must have serious operational problems, lack user confidence, and, possibly, even face the threat of patient safety because of system downtime. The lengthy upper tail (up to daily failures) points to the fact that although the majority of systems are stable, a significant portion of them is quite unreliable, presumably because they have recurring power, internet, or hardware problems. The box plot shows that the level of reliability is polarized the vast majority of facilities have good system stability, but still, there is a significant number of facilities that have frequent failures, which indicates that special treatment should be provided to them to enhance infrastructure and assistance in the most problematic environments. Root Causes of EHR Underperformance in Nigerian Healthcare Facilities Fishbone Diagram (also referred as Ishikawa Diagram) is a visual map that helps to locate the root causes of the EHR system underperformance (high failure rates, low adoption, low reliability, and low clinical impact). The fish head is the problem: EHR Underperformance as depicted in Fig. 13 . These six key categories (bones) are divided into engineering-relevant realms: Power, Network, People, Process, Policy, and Platform. In the light of the Key Engineering Diagnosis and Insights, the two most essential underlying root causes would be Power and Network, as there is no digital system (cloud-based or on-premise) that will work reliably without a reliable electricity and connection. Direct impact of Power, Cost and Policy gaps are platform issues (hardware + software). People and Process issues are a manifestation of poor change management, investment of trainer training, and institutional preparedness, which are notional outcomes of the aforementioned infrastructural constraints. The national level provides policy gaps that serve to continue the whole cycle of not funding, not offering incentives, standards, and enforcement. Prioritized by Impact, Recommended Interventions are as shown in Table 10 : Table 10 Recommended interventions based on impact Impact Recommendations Power Deploy solar-hybrid systems with battery storage in all public facilities (highest priority). Network Expand last-mile broadband and subsidize data costs for healthcare institutions. Platform Mandate offline-capable, low-resource EHR platforms + bulk hardware procurement. People and Process Integrate digital health training into medical/nursing curricula + structured change management programs. Policy Develop binding national EHR adoption roadmap with funding mechanisms and interoperability standards (HL7 FHIR). E. Satisfaction and Resolution Table 11 Degree of satisfaction of EHR usage by the respondents Satisfaction Level Number of Facilities Percentage Very satisfied 3 11.5% Satisfied 12 46.2% Neutral 8 30.8% Dissatisfied 1 3.8% Very dissatisfied 1 3.8% Not specified 1 3.8% Table 11 and Fig. 14 reveals from the survey the degree of satisfaction of EHR usage by the respondents. Of the respondents (57.7% of them are Satisfied/Very satisfied), 30.8% of them are neutral, which means that the current EHR is generally accepted, but it has not yet received the ability to be thought of as transformative or remarkable in most facilities. Staff sentiment is negative negatively in only 7.7% (Dissatisfied + Very dissatisfied) which is a relatively small number considering the overall infrastructure issues. With the facilities that either have already adopted EHR or a hybrid system, the satisfaction is high, meaning that in the areas where systems are physically used, the staff are able to use and find them useful irrespective of the current challenges. The high skewness of Satisfied and Neutral is a positive sign of good user acceptance where the EHR systems are implemented and operational. This low dissatisfaction shows that employees are usually prepared to implement and keep using digital systems as soon as they exist in the company - the resistance is not caused by the general lack of affection to the technology itself, but, probably, by the obstacles to implementation (power, cost, training, etc.). This distribution shows that it is not staff rejection that is the main bottleneck, but rather the process of guaranteeing consistent and stable system performance that can change the percentage of satisfaction not to be Satisfied/Neutral but to convert to Very satisfied at a higher number of facilities. The level of staff contentment with current EHR systems is mostly positive or neutral, which is why there is no reason to believe that the issue of acceptance is central to the problem and the actual problem is scaling reliable and highly functioning systems without compromising to infrastructural limitations. Discussion This paper presents a timely, both qualitative and quantitative empirical evaluation of Electronic Health Record (EHR) adoption using clouds in Nigeria by combining both primary survey data of 26 healthcare practitioners in 11 states with secondary institutional data of 130 hospitals. The results indicate a healthcare information ecosystem with low levels of full-digitization, hybrid-dominance, crippling infrastructural shortages, and polarized reliability of systems, and also show surprisingly high staff acceptance where systems are implemented. Figure 15 depicts the conceptual framework for cloud-Based EHR in Nigeria. These findings support, as well as deepen the available literature on digital health transformation in low- and middle-income countries (LMICs) and sub-Saharan Africa, but provide more specific understanding of the socio-technical and engineering aspects of EHR underperformance in Nigeria. Adoption Status and Discrepancy Between Primary and Secondary Data The first survey is that merely 23% of the facilities have fully electronic (EHR-only) systems, 42% of them have hybrid systems, and 35% of the facilities are entirely paper-based. Conversely, secondary institutional data indicate that 75% of the facilities have some form of digital (hybrid/EHR) deployment, and only 25% of the facilities are paper-only. This deviation highlights a dismal difference between the stated institutional existence of EHR infrastructure and practical depth at workflow level. In terms of systems engineering, secondary data are available (e.g., vendor deployments, program registrations), but primary data are available (functional integration, daily usability, maturity of workflow) in terms of workflow maturity. The overwhelming percentage of hybrid systems in both samples is predicted by diffusion-of-innovation theory and puts Nigeria in an early-to-mid adoption stage where transitional architecture is dominant because processes are not fully reengineered (Rogers, 2003). Full adoption of EHR is still only present in tertiary and specialist facilities, which is reflective of increased institutional capacity, funding, and technical preparedness-in line with the previous Nigerian research that found the adoption rates of 18–23% overall and even lower interoperability (12–15%) (Oluwagbemi et al., 2021; Adebayo et al., 2022). The temporal line chart additionally shows slower first adoption (before 2020) after which it would increase at a quicker pace between 2019–2025 with the highest number of facilities in 2019 (6) and 2025 (4). This post-2020 boom is probably an indication of pandemic-induced digital health momentum, government efforts (e.g., Lagos SHIP, Health-in-a-Box), and more vendor activity. Though, adoption at high rate in a short time span increases scalability and performance risks, because most facilities do not have sufficiently mature infrastructures to support new systems. Hosting Models, Cloud Providers, and Resilience Risks On-premise leads among those that provide specific hosting information (64.3%), and hybrid (21.4%), and cloud (14.3%) are minor. This is in favor of local infrastructure due to the instability of power and reliability of the internet as shown by the infrastructure readiness radar chart (power: 2.3/10; internet: 3.5/10). The adoption of cloud, at least, exhibits a fragmented provider market: local/Nigerian vendors (29.4%), AWS (23.5%), Azure (17.6%) and others/not specified (29.4%). The high dependency on the international hyperscalers (AWS + Azure [?] 41%) creates vulnerabilities to resilience, such as latency, high bandwidth, forex volatility and exposure to global outages, and local vendors have strengths in line with local connectivity and local support requirements. The large share (29.4) of not specified is in itself a governance risk that hinders accountability and disaster recovery planning. This weakness is emulated by backup strategies: automated backups (52.9% of clear responses) are a promising answer, yet manual (29.4%) and nonexistent (17.6%) ones are still in place and put patient data at risk of irreversible loss. Infrastructure Readiness and Dominant Barriers With the radar chart, the profile of preparedness is greatly skewed, with the issue of power instability (76.9% mentions) and the issue of internet untrustworthiness (65.4) as the most urgent limitations, and the lack of IT infrastructure (65.4%), and the high implementation cost (69.2%) close behind. These three highest bottlenecks, which the Pareto chart confirms as [?]90% reported, represent the classic 80/20 rule. The Fishbone (Ishikawa) diagram is a methodical engineering diagnosis that classifies root causes as Power, Network, People, Process, Policy and Platform. Power and Network come out as a precondition–without them nothing platform (cloud or on-premise) can be operated reliably–and the People (training, resistance) and Process (workflow redesign) concerns are mostly a side effect of lack of infrastructures. The national level has policy gaps that continue this, as it does not enforce standards or give incentives or mobilization of funds. Our results are highly consistent with LMIC scoping reviews (e.g., Akhlaq et al., 2018; Ndlovu et al., 2021) that acknowledge electricity and connectivity as universal leading factors, but our data show the prevalence of both in a more specific situation in Nigeria. The frequency of failure boxplot indicates polarization of the reliability domain: 53.8% of facilities have rarely/never issues (median = Rarely), which is a sign of good stability when the infrastructure and support are not problematic. Nonetheless, 15.4% encounter daily failures, which puts a lengthy upper tail indicating critical operational risk in an environment of some of the deployments. This bimodality highlights the idea that EHR performance is highly situational as opposed to flawed. Staff Satisfaction and User Acceptance One of the more favorable results is staff satisfaction: 57.7% (Satisfied + Very satisfied) and 30.8% (Neutral), and only 7.7% stated that they were not satisfied. The high acceptability, even with widespread infrastructural difficulties, implies that the opposition is not largely attitudinal, but rather driven by implementation (e.g. training gaps, untrustworthy performance). Where the system is in operation and is operational, employees see clear benefits in documentation, speed of access and coordinating care. This is in contrast with several of the previous researchers, who indicated higher resistance (Oyediran et al., 2020) and supports the Technology Acceptance Model (TAM) principle that perceived usefulness exceeds perceived ease of use in the condition of reduced underlying barriers (Davis, 1989). The fact that the radar score of IT support (5.8/10) and digital literacy (4.8/10) are relatively higher also shows that human capital is more aligned to help sustain use than physical infrastructure. Broader Implications and Contributions These findings underscore healthcare information system in Nigeria as a multi-layered socio-technical system that is not functioning at its optimum level. The presence of hybrid dominance adds latency, redundancy and propagation of errors at the paper-digital interface, making it difficult to support more sophisticated capabilities like real time analytics, AI-enabled decision support, or HL7 FHIR interoperability. The paper adds new quantitative granularity both with visualizations (Pareto, radar, box plot, Fishbone) and statistical relationships (e.g. chi-square between tertiary facilities and higher adoption). It also highlights an urban/rural and public/private issue: Lagos and tertiary/private facilities exhibit greater digital maturity over secondary/primary facilities and rural facilities, and further worsen inequities. Such limitations are the small size of the primary sample (n = 26, convenience sampling), the possibility of bias in responses toward the facilities that had some exposure to digital, and the use of self-reported data. The secondary data can over-represent the deployments vendor-reported. The cross-sectional nature does not allow making causal inferences about the effects of interventions or trends in time. Overall, although Nigeria demonstrates underdeveloped yet rapidly spreading EHR adoption and staff acceptance, a set of deep-rooted barriers of power, connectivity, cost, and hardware seriously limits its performance and scalability. It is necessary to solve these by focusing on prioritized engineering solutions (solar-hybrid power, last-mile broadband, offline-capable platforms) and changes in policies to achieve the transformative potential of cloud-based EHRs to enhance the quality, efficiency, and equity of care in Nigeria and other similar LMIC countries. 8.Study Limitations Although it has a contribution, this research has a number of limitations that must be taken into account when making interpretations of the findings. Sample Size and Representativeness : The main sample of the survey (n = 26) is quite small and was selected with the help of convenience sampling. By this, the results cannot be extended to all the healthcare institutions of Nigeria. The analysis can thus be described as experimental and diagnostic and offers a hint of the prevailing trends instead of being representative of the nation. Potential Selection Bias : Respondents and facilities that were previously exposed to electronic health systems might have been more likely to join the study, which could have caused a misunderstanding of the level of EHR awareness, adoption, or satisfaction. Self-Reported Data : The underlying data in primary surveys is perceptions that are self-reported, and could be subject to recall bias, social desirability, or lack of complete technical expertise, especially on the topic of hosting models and cloud services providers. Secondary Data Constraints : The secondary institutional data were generated by vendor and public sources, which can exaggerate the usage or maturity of the system. There was also insufficient classification of facilities and geographic specification of facilities, so that finer-grained analysis was hampered. Cross-Sectional Design : The study was cross-sectional and, therefore, it cannot be used to draw causal conclusions or evaluate longitudinal trends. Observed associations represent the state of affairs at a particular time and are unable to reflect the development of the system or its postimplementation effects. Limited Theoretical Operationalization : Although conceptually the study is based on diffusion of innovation and technology acceptance views, the study does not operationalize and statistically test the theoretical constructs like perceived usefulness or ease of use. Recommendations Through a methodical system of dealing with the power-network-platform triad and at the same time, creating human capacity and policy facilitators, Nigeria will be able to move the nation to a sturdy, fashionable, and clinically sensible digital health bioecosystem. When done in a timely and organized manner, these measures can make a substantial positive contribution to healthcare delivery, cost reduction, patient safety, and make Nigeria a leader in digital health transformation in sub-Saharan Africa. As part of accelerating sustainable EHR adoption and ensuring clinical and public health impact, the following multi-stakeholder recommendations are suggested with the top priority: Prioritize Foundational Infrastructure Resilience (Highest Priority): Install solar-hybrid power systems, battery storage, and inverters in all of the publicly available healthcare facilities, beginning with tertiary and secondary hospitals. Furthermore, increase last-mile broadband (via public-private partnerships, such as with MTN, Airtel, Starlink) and subsidize healthcare data expenses. Also, set national minimum infrastructure requirements of digital health facilities (power uptime ≥ 99, bandwidth ≥ 10 Mbps symmetric). Promote Affordable, Context-Appropriate Platform Design : Require use of offline-enabled, low-resource EHR systems which enable regular synchronization (e.g. OpenMRS-based or local-developed systems such as eClat, LAMISPlus, Health-in-a-Box). Ease in bulk purchase of ruggedized hardware (tablets, laptops, servers) via federal and state ministries of health. Promote the use of hybrid-cloud systems that are automated and encrypted back ups and disaster recovery systems. Strengthen Human Capacity and Change Management : Incorporate digital health and health informatics into undergraduate and postgraduate medical and nursing education, as well as other allied health education. Carry out structured and facility-based EHR training programs including ongoing refresher training and peer champions. Implement structured change management models (e.g. ADKAR, the 8-Step Model by Kotter) at EHR rollouts to overcome resistance and establish user ownership. Develop Robust National Policy and Governance Framework : Create national EHR adoption roadmap that is binding with timelines, funding commitment and interoperability requirements (HL7 FHIR as the national standard). Develop monetary incentives (tax credits, grants, performance-based funding) and punishments against a lack of certified systems. Enhance data protection and cybersecurity policies and standards in accordance with the Nigeria Data Protection Act (2023) and global standards. Foster Multi-Sectoral Partnerships and Sustainable Financing : Encourage global hyperscaler ( AWS ) and local vendors ( Interswitch/eClat, Digital Quest ), international donors ( WHO, USAID, World Bank ), and telecom operators. Consider new models of financing, including healthcare-as-a-service subscriptions, results-based financing, and blended finance. Invest in Monitoring, Evaluation, and Continuous Improvement : Create a national digital health observation center to monitor the adoption statistics, performance of the systems, and interoperability in real-time. Carry out longitudinal studies to determine the clinical and economic effect of EHR interventions. Pilot AI-based optimization (e.g., LSTM-based resource allocation, predictive maintenance) in high maturity facilities to prove value and solicit additional financing. Conclusion This is a mixed-methods study that offers a holistic, empirically based evaluation of the prevailing situation on the adoption of cloud-based Electronic Health Record (EHR) in healthcare institutions in Nigeria. Using primary findings, surveyed among 26 healthcare professionals in 11 states and secondary data on institutions, 130 to be exact, the results of the studies are a clear image of a nascent, transitional, and grossly underfunded digital health landscape with underlying infrastructural shortages. The EHR-only usage rates are relatively low at 23% in the primary survey and are divided into the hybrid systems (42) as the most common transitional architecture and 35% of facilities continue to be fully paper-based. According to secondary data, there is an increase in reported digital penetration (75%): there is an essential disconnect between institutional-level implementation and real inclusion in clinical workflow. The trend of adoption is increasing at an accelerated pace since 2019 due to the momentum of the pandemic and specific efforts but this rapid expansion has been faster than the creation of supporting infrastructure that can be trusted. The infrastructure preparedness profile is disastrously unequal: power instability (76.9% of the respondents) and poor internet connection are the most critical bottlenecks, with high implementation costs (69.2%) and lack of hardware closely right behind. These three highest-ranking constraints are verified through the Pareto analysis as it is indicated that nearly 90% of all reported performance problems are related to them. The Systematic approach of the Fishbone diagram is that Power and Network constitute the root causes with downstream effects that lead to Platform inadequacies, People (training and resistance), Process (workflow redesign) and Policy gaps. Nevertheless, there are two positive findings despite these difficulties; System reliability is polarized More than half of facilities (53.8%) report minor technical problems or no problems at all, suggesting that supported implementations can become useful even in resource-limited environments. Secondly, there is a positive overall Staff satisfaction level (57.7% satisfied or very satisfied; only 7.7% dissatisfied) and it shows that staff acceptance is not the main thing that is holding the implementation back, rather implementation resistance is more attitudinal than implementation. Together, these findings highlight the fact that the healthcare information system in Nigeria is a multi-layered socio-technical system which is functioning well below its efficient efficiency spectrum. Hybrid dominance adds latency, data redundancy, and propagation of errors at the paper-digital interface, and indefinite power and connectivity shortcomings do not allow or empower the achievement of advanced capabilities, including real-time analytics, AI-driven decision support, and the ability to drive processes on a national scale via standards, such as HL7 FHIR. The city-country and community-business gap also contributes to inequities, and tertiary and non-governmental facilities in urban areas (e.g. Lagos) are more mature. To conclude, although the digital health transformation pillars are present, as demonstrated by the increased adoption, the use of multiple cloud providers, and high-user acceptance, the underlying weakness of infrastructures is the most limiting factor in expanding credible and high-impact cloud-based EHRs throughout Nigeria. These underlying limitations are not a technological issue to solve; rather they are a condition of enhancing care continuity, minimizing medical error, improving operational efficiency, and eventually improving health equity in one of the most populous countries in Africa. Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Health Research Ethics Committee (HREC), University of Benin Teaching Hospital (UBTH), Nigeria (Protocol No: ADM/E22A/VOL.VII/148311664). The study was conducted in accordance with relevant institutional guidelines and the principles of the Declaration of Helsinki. The questionnaire was administered electronically using Google Forms. An electronic informed consent statement was presented on the first page of the survey, and participants were required to provide consent before proceeding. Participation was voluntary, and no personally identifiable information was collected. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Authors’ information Not applicable. Funding This research was self-funded. Author Contribution OBA conceptualized the research, shaped the research methodology, data collection, and analysis, and wrote the manuscript. The data interpretation was done by HA. The critical revision of the manuscript with respect to intellectual content was carried out by BI. The final version was considered and agreed by all authors. Acknowledgement The authors give credit to the healthcare professionals and institutions that took part in the research and made this research a possibility by giving valuable information. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Ahmed MM et al (May 2025) Integrating Digital Health Innovations to Achieve Universal Health Coverage: Promoting Health Outcomes and Quality Through Global Public Health Equity. 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Front public health 12. https://doi.org/10.3389/fpubh.2024.1306013 Sato S, Yasunaga H, Matsuo Y, Matsui H, Fushimi K, Miyawaki A (Oct. 2025) Association between socioeconomic disadvantage and low-value care in acute care hospitals in Japan: Cross-sectional study. Health Policy 163:105479. https://doi.org/10.1016/j.healthpol.2025.105479 OECD (2022) Towards an Integrated Health Information System in Korea, Accessed: Jan. 23, 2026. [Online]. Available: https://www.oecd.org/content/dam/oecd/en/publications/reports/2022/05/towards-an-integrated-health-information-system-in-korea_c8642d08/c4e6c88d-en.pdf Kim MK, Han K, Lee S-H, Current Trends of Big Data Research Using the Korean National Health Information Database, (Jul. 2022) Diabetes Metabolism J 46(4):552–563. https://doi.org/10.4093/dmj.2022.0193 Leigh JH et al (Oct. 2023) Effective service coverage of long-term care among older persons in South Korea. Age Ageing 52:iv162–iv169. https://doi.org/10.1093/ageing/afad120 . Supplement_4 Lim Ren Jun AL (2023) Singapore: Ministry of Health launches new identity for national health tech agency and priorities for healthcare ecosystem. Global Compliance News, Aug. 03, https://www.globalcompliancenews.com/2023/08/03/https-insightplus-bakermckenzie-com-bm-healthcare-life-sciences-singapore-ministry-of-health-launches-new-identity-for-national-health-tech-agency-and-sets-out-strategic-priorities-for-the-singapore_0/ Singapore’s national healthtech agency, IHiS, empowers patients to take ownership of their health with Health Discovery + on Azure | Microsoft Customer Stories, Microsoft.com (2022) https://www.microsoft.com/en/customers/story/1574352454896056138-integrated-health-information-systems-national-government-azure-en-singapore A. Leck and R. J. Lim, Singapore: Ministry of Health launches new identity for national health tech agency and sets out strategic… Lexology , Jul. 31, 2023. https://www.lexology.com/library/detail.aspx?g=d3085686-f0e3-4ab9-b3bd-df533f7bd97f My Health Record | Health New Zealand - Te Whatu Ora (2025) My Health Record | Health New Z - Te Whatu Ora, https://my.health.nz/ Ang A (2025) NZ to introduce shared digital health records. Healthc IT News, Mar. 23, https://www.healthcareitnews.com/news/anz/nz-introduce-shared-digital-health-records Dornels C et al (Aug. 2024) Primary care performance measurement in Brazil (Previne Brasil Program), 2022–2023. BMC Health Serv Res 24(1). https://doi.org/10.1186/s12913-024-11409-x Barbalho IMP et al (Nov. 2022) Electronic health records in Brazil: Prospects and technological challenges. Front Public Health 10. https://doi.org/10.3389/fpubh.2022.963841 de Aquino NCézar et al (Dec. 2025) Advancing real-world evidence in Brazil: regulatory gaps and global lessons. Lancet Reg Health - Americas 55:101344. https://doi.org/10.1016/j.lana.2025.101344 Mukherjee A (2021) Implementing Electronic Health Records in India: Status, Issues & Way Forward, Biomedical Journal of Scientific & Technical Research , vol. 33, no. 2, Jan. https://doi.org/10.26717/bjstr.2021.33.005378 Parchaa I (2025) The concept of digitalized medical records in India started with the thought of a ‘Universal Health Coverage (UHC)’ scheme in India, which aimed at equalizing access to healthcare for everyone irrespective of their income or location. Inspired by the global use of EHR, India launched t, Linkedin.com , Jan. 22. https://www.linkedin.com/pulse/digital-healthcare-india-electronic-health-record-ehr-adoption-wxpdc (accessed Jan. 23, 2026) Venkat MS, The Evolution of Electronic Health Records in India: Progress, Challenges, and, Future Prospects (2025) (2020–2025), International Journal of Health Technology and Innovation , vol. 4, no. 02, pp. 24–30, Aug. https://doi.org/10.60142/ijhti.v4i02.06 Babalola OA, Olufunlayo TF, Akinlawon OO, Odukoya TO, Fagbo OO, Akinlawon D (Sep. 2025) Perception and barriers to electronic medical record use among physicians and nurses in General Hospitals in Lagos State. Afr Health Sci 25(3):154. https://doi.org/10.4314/ahs.v25i3.20 Muinga N, Magare S, Monda J, English M, Paton C (2018) Survey of Electronic Health Record (EHR) Systems in Kenyan Public Hospitals: A mixed-methods survey (Preprint). Dec 03, https://www.researchgate.net/publication/329436456_Survey_of_Electronic_Health_Record_EHR_Systems_in_Kenyan_Public_Hospitals_A_mixed-methods_survey_Preprint?utm_source Rees B (2025) Kenya’s 2024 eHealth Plan: What It Means for Hospitals, Ksatria Medical Systems , Aug. 15. https://www.ksatria.io/en/hospital-and-clinic-systems/kenya-2024-ehealth-strategy-hospitals/ (accessed Jan. 23, 2026) Zharima C, Griffiths F, Goudge J (Jan. 2026) Exploring Factors Associated With the Stalled Implementation of a Ground-Up Electronic Health Record System in South Africa: Qualitative Insights From the E-Tick Case Study Using the Consolidated Framework for Implementation Research (CFIR). JMIR Med Inf 14:e73831–e73831. https://doi.org/10.2196/73831 Harerimana A, Wicking K, Biedermann N, Yates K, Pillay JD, Mchunu G (2025) Adoption of electronic health records by nurses in Africa: A scoping review., PubMed , vol. 11, pp. 20552076251357401–20552076251357401, Sep. https://doi.org/10.1177/20552076251357401 Ngusie HS, Kassie SY, Chereka AA, Enyew EB (Mar. 2022) Healthcare providers’ readiness for electronic health record adoption: A cross-sectional study during pre-implementation phase. BMC Health Serv Res 22(1):1–12. https://doi.org/10.1186/s12913-022-07688-x Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile1EHRSURVEYQUESTIONNAIRE.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 04 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers invited by journal 02 Mar, 2026 Editor assigned by journal 25 Feb, 2026 Editor invited by journal 24 Feb, 2026 Submission checks completed at journal 24 Feb, 2026 First submitted to journal 24 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8821192","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600840766,"identity":"1a0d509a-437e-4890-8855-1faf590eb0cd","order_by":0,"name":"Olumhense Benedict Adoghe","email":"data:image/png;base64,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","orcid":"","institution":"Achievers University Owo","correspondingAuthor":true,"prefix":"","firstName":"Olumhense","middleName":"Benedict","lastName":"Adoghe","suffix":""},{"id":600840767,"identity":"5ff76969-7e08-454e-b685-7a705481eba5","order_by":1,"name":"Braimoh Ikharo","email":"","orcid":"","institution":"Edo State University Iyamho","correspondingAuthor":false,"prefix":"","firstName":"Braimoh","middleName":"","lastName":"Ikharo","suffix":""},{"id":600840769,"identity":"896e9001-90b7-4b34-a1c8-ec890741adef","order_by":2,"name":"Henry Amhenrior","email":"","orcid":"","institution":"Edo State University Iyamho","correspondingAuthor":false,"prefix":"","firstName":"Henry","middleName":"","lastName":"Amhenrior","suffix":""}],"badges":[],"createdAt":"2026-02-08 11:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8821192/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8821192/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103984048,"identity":"81236434-f38d-4e30-a741-815b25c97527","added_by":"auto","created_at":"2026-03-05 10:06:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":106052,"visible":true,"origin":"","legend":"\u003cp\u003eBlock diagram of the research 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Data)\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8821192/v1/83795619abc062aa5a80cdd5.png"},{"id":103984047,"identity":"de70dbd3-cb2e-4437-8fe1-024ef25919ac","added_by":"auto","created_at":"2026-03-05 10:06:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":30831,"visible":true,"origin":"","legend":"\u003cp\u003eType of EHR System used (Secondary Data)\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8821192/v1/92ff069561227c3ed640c552.png"},{"id":103984052,"identity":"00944dc3-fc0b-4537-81a3-39231223c035","added_by":"auto","created_at":"2026-03-05 10:06:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":34036,"visible":true,"origin":"","legend":"\u003cp\u003eApproximate year of adoption of HER by various healthcare facilities\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-8821192/v1/68582fa53eee42d1dbf6933b.png"},{"id":103984055,"identity":"f08e329d-4655-43cf-99b3-ae8b29e9d190","added_by":"auto","created_at":"2026-03-05 10:06:38","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":39799,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Hosting Models (Number of facilities)\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-8821192/v1/b39bbc430a103581f6e453d7.png"},{"id":104835047,"identity":"ea1057c7-9438-405b-b282-7b7e464f663b","added_by":"auto","created_at":"2026-03-17 17:39:30","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":33352,"visible":true,"origin":"","legend":"\u003cp\u003eCloud Providers in healthcare 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Underperformance\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-8821192/v1/2b57ac6343865d7c2a65a63c.png"},{"id":103984059,"identity":"9a93ed73-0ceb-4d4c-b419-d411fcec7809","added_by":"auto","created_at":"2026-03-05 10:06:38","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":42780,"visible":true,"origin":"","legend":"\u003cp\u003eStaff Satisfaction with EHR Usage\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-8821192/v1/ee5c2a55bc7a293fbe9fe8b4.png"},{"id":103984060,"identity":"1e09001a-497d-474a-8e3d-3ef0e63a978f","added_by":"auto","created_at":"2026-03-05 10:06:38","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":625549,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework for cloud-Based EHR Adoption in Nigeria\u003c/p\u003e","description":"","filename":"image15.png","url":"https://assets-eu.researchsquare.com/files/rs-8821192/v1/60f7d60638bc5b3a3a21544e.png"},{"id":104836083,"identity":"6187e640-dd92-46e9-80ae-2cbfe6b95f30","added_by":"auto","created_at":"2026-03-17 17:51:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2867782,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8821192/v1/e8889db2-9aca-4746-9cc3-2859afea9f32.pdf"},{"id":103984049,"identity":"5c6acc39-949d-4eb9-bebd-eb4768c68867","added_by":"auto","created_at":"2026-03-05 10:06:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":206758,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1EHRSURVEYQUESTIONNAIRE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8821192/v1/18d6705244c94a874f6a7e85.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCloud-based Electronic Health Records (Ehr) Adoption Rate in Nigeria: Barriers and Performance Bottlenecks\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe fast development of digital technologies is radically changing the healthcare systems of countries in the world as it alters the ways of medical information gathering, storage, sharing, and use in order to enhance the clinical outcomes, efficiency, and safety of the healthcare system [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The core of this change is the implementation and optimization of Electronic Health Records (EHRs) - electronic systems that are used to combine all patient data, such as demographics, diagnoses, medications, vaccinations, laboratory and imaging, and clinical notes, into one coherent, easily accessible platform [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. EHRs can be used both as a repository of patient history and a dynamic tool to aid in clinical decision-making, interoperability with data, and coordination of operations across care pathways [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In developed nations, health information technology investments have resulted in almost universal use of EHRs. Indicatively, among non-federal acute care hospitals in the United States, more than 96% have certified EHR systems, and almost 80% of office-based physicians implement them in clinical practice [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This level of digital health infrastructure maturity has allowed extensive improvements in the quality-of-care delivery, such as increased accuracy of documentation, decreased redundant testing, lower medication error rates, and enhanced care coordination, especially with the implementation of interoperability standards like Health Level Seven (HL7) and Fast Healthcare Interoperability Resources (FHIR) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The technologies can facilitate the shift to networked, interoperable health information ecosystems, which are needed to track longitudinal patients, provide real-time analytics, and be integrated with upcoming technologies such as artificial intelligence (AI) to predict future outcomes and offer clinical decision support [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Other standards like the FHIR of the HL7 standard have experienced increased adoption across the globe where surveys have shown that more than 70% of the surveyed countries are actively using FHIR standards in some or all of their use-cases with full implementation and regulatory alignment being an on-going challenge [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In spite of these developments, adoption of EHR is very low in low- and middle-income countries (LMICs) especially in sub-Saharan Africa. Health systems in these settings encounter a complex situation due to structural, technological and human-resource constraints, which hinder digital transformation. There is region-wide scoping research which has shown that EHR use in sub-Saharan African facilities is sparse with most use limited to pilot projects, HIV/AIDS treatment programs, or even a particular urban area and most rural and public facilities still use paper records [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The obstacles that have been reported in the region are unreliable power supply, poor internet connectivity, high cost of procurement and maintenance, lack of training and digital literacy, resistance to change and fracturing of policy frameworks [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In Nigeria, which is one of the most populous countries in Africa with a high prevalence of diseases and a significantly strained doctor-patient ratio, the adoption of electronic health information systems has been inconsistent and slow despite the widespread acceptance of its importance in enhancing care delivery. Literary estimates indicate that the adoption of EHR in healthcare institutions in Nigeria is between about 18 and 23 percent with only a small number of hospitals having digitalized patient information systems wholly with the rest using hybrid systems or legacy systems [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Interoperability of digital records among institutions is also extremely low with estimates showing that between 12\u0026ndash;15% of facilities can exchange patient data seamlessly [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Paper-based systems persistence in Nigeria serves as a cause of care discontinuity, inefficiencies, and increased rates of errors. In the example of patient history, discontinuous patient histories and part-transmission of records are widespread, which prevents systemic treatment and imposes an extra administrative cost on clinicians. Such issues are worsened by the lack of digital infrastructure (i.e. intermittent power supply, poor broadband penetration), lack of training opportunities, and the absence of policy measures/incentive to implement EHRs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Facts of scoping and quantitative research also confirm that healthcare workers might be interested in EHR systems in theory, but the real practice is limited due to the context specifics, including device unavailability, instability of institutional preparedness, and poor health informatics skills of professionals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Although a number of studies have reported barriers and small scale implementations of EHRs in Nigeria, a large gap in the existing data exists with regard to comprehensive, nationally representative, quantitative data illustrating: (1) the current adoption status of cloud-based EHR systems across various levels of health facilities; (2) the most prevalent barriers as perceived by frontline healthcare workers; and (3) the extent to which performance bottlenecks limit effective use. In this research paper, the researcher has aimed at filling in those gaps by carrying out an intensive quantitative evaluation that will include survey data, statistical modeling, and integration with the available literature to develop a comprehensive empirical perspective on Nigeria digital health preparedness and performance constraint. The results of this study will be used to give new evidence so that context-specific approaches, capacity-building activities, and policy suggestions could be made to hasten EHR adoption, improve interoperability, and maximize healthcare delivery outcomes in resource-constrained settings. The later discussion includes the methodology, survey findings, analytical results, critical discussions, and the proposed directions the future research, policy making, and practical execution are supposed to take in order to make Nigeria faster in the digital health process. The global adoption rate of Electronic Health Record is as shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eElectronic Health Record Global Adoption Rate\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHospital Adoption Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary Care / Ambulatory Adoption Rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource / Reference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnited States\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;78\u0026ndash;86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDenmark\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNetherlands\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u0026ndash;99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSweden\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR33\" 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\u003cp\u003e~\u0026thinsp;96\u0026ndash;98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAustralia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;92\u0026ndash;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], [\u003cspan citationid=\"CR40\" 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\u003cp\u003e\u003cb\u003eGermany\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;70\u0026ndash;80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;65\u0026ndash;75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;80\u0026ndash;85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJapan\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;45\u0026ndash;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;35\u0026ndash;40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSouth Korea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;92\u0026ndash;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;85\u0026ndash;90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSingapore\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;95\u0026ndash;98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;90\u0026ndash;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNew Zealand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;90\u0026ndash;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBrazil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;40\u0026ndash;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;30\u0026ndash;40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;15\u0026ndash;25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNigeria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;20\u0026ndash;25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKenya\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;30\u0026ndash;40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;20\u0026ndash;30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSouth Africa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;40\u0026ndash;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;25\u0026ndash;35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e "},{"header":"Methodology","content":"\u003cp\u003eThis section provides the methodology adopted in carrying out the research study. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the block diagram of the methodology:\u003c/p\u003e\n\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Research Design\u003c/h2\u003e\n\u003cp\u003eThe proposed research design was an exploratory mixed-method research design, which combined quantitative data on survey tools, qualitative information, and secondary institution documents to evaluate the status of adoption, performance, and obstacles of cloud-based Electronic Health Record (EHR) system adoption in Nigerian healthcare facilities. The mixed-methods approach was chosen to describe both the quantifiable structural attributes of EHR adoption (e.g., system type, infrastructure preparedness, frequency of failure) and contextual and experiential attributes (e.g., perception of bottlenecks, user satisfaction) that are essential in low-resource healthcare settings. Since there are very few national representative EHRs datasets available in Nigeria, the research is set as a diagnostic and exploratory, and not confirmatory or causal. It was aimed at establishing the patterns of dominance, bottlenecks and constraints at the system level that define the current state of EHR implementation and use.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Data Sources\u003c/h2\u003e\n\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.1 Primary Survey Data\u003c/h2\u003e\n\u003cp\u003eThe research was conducted with primary data collection using a structured online questionnaire based on investigating the activities of healthcare professionals actively engaged in management of patient records or health information systems. The survey instrument used for data collection is provided as Supplementary File 1. There were 26 valid responses of healthcare facilities in 11 states of Nigeria which include primary, secondary, tertiary and specialist institutions. The questionnaire included 19 items that were grouped into five themes:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eFacilities and respondent attributes.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eType of patient record system and its adoption.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInfrastructure preparedness (power, internet, hardware, IT support)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReliability and performance of the system.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePerceived barriers and user satisfaction.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003ePerceptions of infrastructure adequacy, system reliability, and satisfaction were measured by 5-point Likert scales. The questions were open-ended in order to provide qualitative information on the contextual and institutional challenges that could not be fully achieved with the closed-ended questions. In order to enhance the response validity and minimise the respondent burden, skip logic was implemented whereby the respondent who responded through a paper-based system was not to be asked EHR-related performance questions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n\u003ch2\u003e3.2.2 Secondary Institutional Data\u003c/h2\u003e\n\u003cp\u003eSecondary data were summarized based on various sources that were available in the public, which included:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eVendor deployment lists\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGovernment/ regulatory publications.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHospital websites\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDigital health reports and peer-reviewed literature.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIt was a dataset of 130 healthcare facilities across the country, which was utilized to determine the institutional-level EHR presence, distribution of facility types, and geographic dispersion. Secondary data mostly include reported deployment of system and availability, but not the depth of operation or daily usability. The secondary data helped to enable triangulation and allowed the comparison of institutional adoption claims and frontline operational realities as expressed in the primary survey.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Sampling Technique\u003c/h2\u003e\n\u003cp\u003eThe primary survey is based on a convenience sampling strategy because of the logistical limitations, a small sample size because of the lack of centralized staff registries, and the exploratory research. Professional network and institutional contacts as well as online dissemination were used to recruit respondents. Although this method allowed collecting a large amount of data in a short time, it also presupposes that this sample might be disproportionate to facilities with certain experience working with digital health systems. Therefore, results obtained using the primary data set can be defined as indicative and not representative of all the healthcare facilities in Nigeria.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Data Analysis\u003c/h2\u003e\n\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.1 Quantitative Analysis\u003c/h2\u003e\n\u003cp\u003eThe quantitative data was analyzed with the help of Python (Panda\u0026rsquo;s library). Descriptive statistics, such as frequencies, percentages, means and standard deviations, were calculated in order to summarize adoption status, infrastructure readiness, system reliability and satisfaction levels. Chi-square tests of independence were used as inferential analysis to investigate the relationship amid categorical variables, e.g., facility type and EHR adoption status. Due to the small size of the primary sample, the results obtained through the inferences were taken with a grain of salt and could hardly be used to make definite conclusions except that they could be used to discern the directional trends. The sophisticated methods of analysis have been used to promote understanding of diagnosis as depicted in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, showing the analytical tools used in the research study\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAnalytical tools used in the research study\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTools\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAnalysis\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePareto analysis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eto identify dominant barriers contributing to EHR underperformance\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRadar charts\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eto visualize multidimensional infrastructure readiness\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBox plots\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eto examine variability and polarization in system reliability\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFishbone (Ishikawa) diagrams\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eto systematically map root causes of EHR underperformance across technical, organizational, and policy domains\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThese tools were selected to support a systems-engineering perspective on digital health implementation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.2 Qualitative Analysis\u003c/h2\u003e\n\u003cp\u003eThe thematic analysis was used to analyze qualitative responses of open-ended survey items. The reviews were read and coded by hand, and organized into recurring themes, which were power instability, internet unreliability, high implementation costs, limited training, and inadequate vendor support. Qualitative element was used in an explanatory complementary manner to put into context quantitative results and shed light on causal processes about patterns observed.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5 Ethical Considerations\u003c/h2\u003e\n\u003cp\u003eParticipation in the study was voluntary and all the respondents gave informed consent before the data was collected. No personal information was gathered and the responses were anonymized as a way of ensuring confidentiality. Only used in academic research, data were stored in a secure place to ensure that they were not used in any other way other than proper research, which is in accordance with ethical guidelines of research involving human subjects.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eA. Demographic and Facility Profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimary survey: Facilities from 11 states, predominantly tertiary/secondary. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reveals the number of Surveyed Healthcare facilities and Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the number of facilities per State\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNumber of facilities per State\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eState\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Respondents\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNiger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEdo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOndo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLagos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFCT (Abuja)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBauchi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOgun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlateau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e gives a clear depict of the state distribution of survey of the respondents. The survey respondents were selected across 11 states in Nigeria, with Niger state (34.6) as well as the Edo state (19.2) recording the highest number of responses, indicating a focus of responses to the North-Central, South-South regions. The other states made less but significant representation, which offered geographic coverage in various geopolitical regions.\u003c/p\u003e\n\u003cp\u003eSecondary data: Nationwide, with Lagos dominant. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e gives the state distribution of healthcare facilities from the secondary dataset.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eState Distribution of Healthcare Facilities\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eState\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Facilities\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLagos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRivers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnugu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOyo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKwara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnambra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBauchi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFederal Capital Territory (FCT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKebbi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther States*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e*Other States is a group of facilities that has multi-branch coverage, incomplete state specification or dispersed national placements as indicated in vendor and institutional databases.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInstitutional records in 130 healthcare facilities in Nigeria were compiled on the basis of vendor deployment list, public hospital records and regulatory sources to conduct the secondary data analysis. The facilities covered several states with a concentration of the highest population in Lagos State, then Rivers and Delta States. A large number of facilities were included as Other States because of multi-site deployments or incomplete geographic specification, which is typical of publicly available EHR institutional data in Nigeria.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e below illustrates a stacked bar chart in the compare and contrast of the geographical distribution of primary survey respondents (n\u0026thinsp;=\u0026thinsp;26) and secondary institutional healthcare facilities (n\u0026thinsp;=\u0026thinsp;130) across the states in Nigeria. The bottom of each bar is the primary survey data, the top level of the bar is the secondary institutional data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. EHR Adoption Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimary Dataset:\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the EHR system deployment model spread in the survey of healthcare facilities, and it will be used to give a quantitative picture of the level of maturity of health information system infrastructure in the context of the study. The prevailing trend of hybrid systems (42%), shows that majority of institutions are at a transitional stage of digitalization, where electronic modules are now used in conjunction with the legacy paper-based processes. This is partially system integration, according to systems engineering, where digital subsystems have been brought in without end-to-end process reengineering. These types of architecture are generally described as being more complex in their operation, data duplication, slower synchronization and more vulnerable to losing information in the paper-digital boundary. A high percentage (35) of paper-only facilities demonstrates that the majority have a major technological lag in the informatics infrastructure in the basement. This category reflects the settings in which healthcare delivery is entirely reliant on manual data acquisition, data storage and data retrieval systems, which in turn are limited by scalability factors, low data accessibility, lack of real time analytics, and susceptible to data corruption and loss. These systems are nearly entirely un-automated, lack interoperability, and cannot be optimally calculated or intelligently integrated as the decision support, in engineering terminology. The existence of EHR-only facilities (23%), on the other hand, indicates the development of entirely digitized healthcare information ecosystems, albeit with a rather low rate of penetration. These facilities run entirely on electronic data pipelines, allowing structured data capture, centralized or cloud-based storage and allowing real-time data processing, interoperability, and integration with higher order computational models including machine learning, predictive analytics, and optimization algorithms. Nevertheless, the small numbers of this group indicate that the digital change of the system is in its initial diffusion phase. Through the perspective of diffusion-of-innovation and technology adoption lifecycle, the identified distribution would be considered as an early-to-mid adoption stage with a small group of early adopters (EHR-only) and a significant majority of transitional adopters (hybrid systems) and a large proportion of late adopters or non-adopters (paper-only). Such an arrangement means that there is yet no infrastructural preparedness, institutional capability, or policy implementation up to the level of large scale, unified EHR implementation. In the engineering aspect, the hybrid superiority is also an indicator of possible bottlenecks in data throughput and reliability of the system. Hybrid architectures add latency on the data conversion points (manual-to-digital), propensity to errors spread, and limits the implementation of high-level system functionalities such as automated clinical decision support, distributed data analytics, and interoperability through standards such as HL7 and FHIR. Consequently, the healthcare information system as a whole is not functioning to its best efficiency envelope. Hence, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e does not only capture adoption rates, but it quantitatively represents the structural condition of the information system of healthcare in Nigeria as a multi-layered socio-technical system, shifting towards the digital mode of operation. It highlights that any significant performance improvements will not occur with gradual digitization, but with the complete architectural transformation needed to achieve unified fully electronic EHR systems with a reliable power infrastructure, high-availability networking, standardized data models, and scalable cloud or hybrid-cloud computing platforms.\u003c/p\u003e\n\u003cp\u003eSecondary Dataset:\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e uses the secondary data to provide the institutional-level EHR system implementation, a macro-scale perspective of digital health system penetration in the Nigerian healthcare facilities. The 75% percentage in digital systems (Hybrid/EHR) shows that at an organizational and vendor-reporting level, a significant percentage of facilities have embarked on some kind of implementation of an electronic health information system. This is contrary to the primary survey results and provides a structural difference between reported institutional adoption and depth of usage at the clinical workflow level. In systems engineering perspective, this number implies that the availability of digital infrastructure is far more than the usefulness of digital utilization. The combination of hybrid and fully electronic systems to form one major category suggests that the majority of institutions have some type of minimum electronic infrastructure such as electronic patient registration, billing modules, and laboratory information systems. But it does not in any way mean that it is fully integrated, interoperable, or digitizes clinical processes end-to-end into electronic health records. The remaining 25 percent of paper-only facilities constitute the nodes in the healthcare delivery system which are not attached to the digital information ecosystem at all. These centers become structural bottlenecks in any interoperability structure at the national level since they do not have the most fundamental computational interface needed to support electronic data exchange, health analytics, or cloud-based system integration. Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, when viewed together with Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e (Primary Data), shows that there is a significant socio-technical separation between secondary data, which focuses on system presence, and primary data which shows system depth and operational maturity. The secondary data provides the infrastructure availability information, and the primary one provides the functional integration and the system performance information in engineering terms. This disparity highlights the idea that hardware and software implementation are not the only limiting components to EHR adoption in Nigeria, but rather its usability, integration with the workflow, competency of staff, and stability of the infrastructure. Therefore, Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e will describe the healthcare information system in Nigeria as one that has a wide yet shallow digital presence. System optimization is no longer just the installation of systems, but the optimization of systems, the shift of hybrid systems to fully electronic, interoperable and fault-tolerant information systems that can support real-time clinical decision support, scalable data analytics, and AI-driven optimization of health care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eType of Hospital facilities used for survey\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdoption of Patient Record Systems by Facility Type (Primary Survey Data)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFacility Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaper\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEHR-only\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecialist Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e* \u003cem\u003eFacility-level analysis was not performed on any facility with the stated responses of unspecified type of facility or those that could not be classified.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the adoption of the patient record system amongst the different types of healthcare facilities on the basis of the primary survey data. Primary and secondary healthcare facilities are dominated by paper-based systems which explains the majority of responses in these category. Tertiary facilities have the highest number of hybrid systems, which implies a continuous shift towards complete digitization. The provider of fully electronic health record (EHR-only) is only seen in specialist and tertiary institutions, which are more institutionally prepared, with better infrastructures and technical preparedness.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAdoption of Patient Record Systems by Facility Type (Secondary Institutional Data, n\u0026thinsp;=\u0026thinsp;130)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFacility Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaper\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHybrid / Vendor-Managed*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEHR-only\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther / Multi-Institutional Deployments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows the adoption of patient record system by the types of healthcare facilities by secondary institutional data of the 130 healthcare facilities in Nigeria. The secondary survey data is also dominated by hybrid or vendor-managed record systems compared to the primary survey data as well as in multi-institutional deployments and in the private facilities. Systems on EHR only are quite uncommon and are only found in tertiary institutions. Paper-based systems still exist mainly in secondary and multi-institutional public facilities, which portrays persistence of infrastructural and operational limitations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYear of EHR adoption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe temporal pattern of EHR adoption in Nigeria in healthcare facilities presented in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. The adoption was rather low in the previous years, and the steep rise started in 2020. This pace is an indication of the increasing institutional dependence on digital health systems, especially cloud-based systems. Nevertheless, the fact that adoption is concentrated to a small time interval also makes the issue of performance and scalability more significant, which is why predictive and adaptive optimization mechanisms are necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. Cloud Hosting Models\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e shows the distribution of Hosting models from the number of facilities under review. On-premise is the most popular in the category of those facilities that reported the hosting model (14 out of 26), as it signifies the further dependence on the local infrastructure despite power and internet issues. This is a slow transition to modern cloud-based architectures with Hybrid and Cloud models only accounting to roughly 35.7 percent of clear responses. The Unknown/Not Specified (46.2) percentage is high because of the missing documentation and lack of knowledge about the hosting information among the respondents. This architecture illustrates why migration to scalable, cloud-enabled EHR systems continues to be challenging in resource-constrained environments. The default of on-premise is because of reliability issues, whereas hybrid and cloud options have initial but low uptakes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCloud Service Providers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis pie chart represents the allocation of cloud service provider reported by the facilities using cloud-based or hybrid EHR systems in the primary survey (n\u0026thinsp;=\u0026thinsp;26 responses). The facilities that were specifically specified to have a cloud provider are included only (n\u0026thinsp;=\u0026thinsp;17 responses involving cloud involvement). Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e reveals the Cloud Service Providers and the percentage of facilities covered. This information is also depicted in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCloud Service Providers and total number of facilities\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCloud Service Provider\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Facilities\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal / Nigerian Vendors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmazon Web Services (AWS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMicrosoft Azure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers / Not Specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe biggest single group (29.4%), is comprised of Local/Nigerian vendors (e.g., eClat, Health-in-a-Box, LAMIS, custom solutions) as the most preferred are locally developed or managed cloud platforms that could be more supportive of Nigeria-specific connectivity, compliance, and support. The share of reported cloud usage between AWS and Azure is 41.1, indicating that global hyperscalers are heavily relied upon by facilities that have more sophisticated infrastructure. The significant portion of 29.4% is the category of Others / Not Specified, which may either represent the usage of smaller international providers (Google Cloud, etc.) or the absence of the clear documentation of the used provider. The high dependency on the international providers (AWS\u0026thinsp;+\u0026thinsp;Azure = -41) poses a risk of cost of international bandwidth, sporadic connectivity, currency fluctuations, and possible service interruption. The intense availability of local vendors (29.4 00:33) can have a positive impact on these risks, providing a more accurate response to the realities of local infrastructure and, perhaps, reducing latency. Nevertheless, the fact that unspecified providers reached a high percentage (29.4) indicates that there is no transparency and documentation, which in turn becomes a liability to the system reliability, accountability of the vendor, and a long-term plan of support. This distribution depicts the uneven adoption environment of cloud services in Nigeria: a combination of international providers and local solutions with a serious uncertainty as to who is a provider.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfrastructure Readiness Index for EHR adoption\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e shows the infrastructure readiness index for EHR adoption. Based on the critical Bottlenecks (The \u0026quot;Crunch\u0026quot; Zone) the chart is evidently tipped to the left plucking the Power Reliability (2.3/10) and Internet Reliability (3.5/10) as the most overwhelming. The form of the polygon is a graphical signifier of how these underlying infrastructure problems draw down on the whole preparedness. The furthest extension is the polygon that IT Support (5.8/10) and Staff Digital Literacy (4.8/10). This indicates that the physical infrastructure is poor but the human resources (staff and support systems) is a bit better placed to assist in the adoption of EHR. Hardware Availability (4.2/10) is at the middle, it is not the worst (better than power/internet) but it is still below average, which means that it is a moderate constraint. This profile provides a clear explanation of why the EHR adoption rates are still very low (only 3.8% fully digital with the primary survey only) and why a hybrid or paper-based system is the most common reason: the underlying digital infrastructure is simply not yet developed in the majority of facilities to support the reliable and scalable deployment of cloud-based EHR. Any effective national EHR plan should first think of humongous investment in power resilience (solar backups, generators) and broadband connectivity and then wait to see mass digital transformation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. Challenges and Barriers to full adoption of EHR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTop challenges and barriers to full adoption of HER can be seen in Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e with power instability (76.9%), cost (69.2%), IT infrastructure (65.4%).\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eChallenges and Barriers to full adoption of EHR\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBarrier\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency (Number of Mentions)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage of Respondents (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePower instability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh cost of implementation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsufficient IT infrastructure (computers, servers, etc.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInadequate training of personnel / Staff digital literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLack of technical / vendor support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e reveals the top Barriers to EHR Adoption. Power instability is by far the most commonly mentioned challenges (76.9% of the respondents), which proves it as the ultimate systemic limitation to consistent EHR use in Nigeria. Right behind are high cost and lack of IT infrastructure (69.2% and 65.4%), which is a triple core of barriers to the foundations that influence the adoption and continued use. Weak training and vendor support are both major but less ubiquitous (46.2% and 34.6) showing that the technical and human-capacity problems are indeed common, but secondary to the underlying infrastructure problems of power, cost and hardware. These findings measure the prevailing system limitations in the digital health environment of Nigeria. The most important priorities in every effective national EHR strategy should be reliable power supply (e.g., solar and battery backup options), low-cost financing and models, and sound hardware provisioning. There will be no real impact on training and vendor support until the underlying infrastructure gaps have been bridged.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrequency of System Failures (Primary Survey Data)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRate of system failures\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency of Technical Issues\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Facilities\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRarely / Never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeekly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e help to show the frequency of system failure as carried out from the research study. The vast majority of facilities (53.8) say that they have occasional or no technical problems at all - which means that, in cases when EHR systems are installed and maintained correctly, they can even be rather stable. The median is Rarely, i.e. more than a half of the respondents can rarely experience failures. Nevertheless, there are a considerable number of (15.4) percent who have technical issues daily, forming a bimodal with long tail curve with increased frequency of failure. The broad interquartile range (Q1\u0026thinsp;=\u0026thinsp;Rarely - Q3\u0026thinsp;=\u0026thinsp;Weekly) demonstrates a high level of movement in the reliability of systems between facilities - some are very stable, whereas others are subjected to frequent disruptions. Most of the facilities who have superseded EHR/hybrid systems have stated that stability (infrequent failures) is satisfactory, which implies that present-day implementations can be reasonably successful given proper infrastructure and support. The 15.4% of those who report failures on the daily basis is an urgent pain point - these institutions must have serious operational problems, lack user confidence, and, possibly, even face the threat of patient safety because of system downtime. The lengthy upper tail (up to daily failures) points to the fact that although the majority of systems are stable, a significant portion of them is quite unreliable, presumably because they have recurring power, internet, or hardware problems. The box plot shows that the level of reliability is polarized the vast majority of facilities have good system stability, but still, there is a significant number of facilities that have frequent failures, which indicates that special treatment should be provided to them to enhance infrastructure and assistance in the most problematic environments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRoot Causes of EHR Underperformance in Nigerian Healthcare Facilities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFishbone Diagram (also referred as Ishikawa Diagram) is a visual map that helps to locate the root causes of the EHR system underperformance (high failure rates, low adoption, low reliability, and low clinical impact). The fish head is the problem: EHR Underperformance as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e. These six key categories (bones) are divided into engineering-relevant realms: Power, Network, People, Process, Policy, and Platform.\u003c/p\u003e\n\u003cp\u003eIn the light of the Key Engineering Diagnosis and Insights, the two most essential underlying root causes would be Power and Network, as there is no digital system (cloud-based or on-premise) that will work reliably without a reliable electricity and connection. Direct impact of Power, Cost and Policy gaps are platform issues (hardware\u0026thinsp;+\u0026thinsp;software). People and Process issues are a manifestation of poor change management, investment of trainer training, and institutional preparedness, which are notional outcomes of the aforementioned infrastructural constraints. The national level provides policy gaps that serve to continue the whole cycle of not funding, not offering incentives, standards, and enforcement. Prioritized by Impact, Recommended Interventions are as shown in Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRecommended interventions based on impact\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eImpact\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRecommendations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeploy solar-hybrid systems with battery storage in all public facilities (highest priority).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNetwork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpand last-mile broadband and subsidize data costs for healthcare institutions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlatform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMandate offline-capable, low-resource EHR platforms\u0026thinsp;+\u0026thinsp;bulk hardware procurement.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeople and Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntegrate digital health training into medical/nursing curricula\u0026thinsp;+\u0026thinsp;structured change management programs.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolicy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDevelop binding national EHR adoption roadmap with funding mechanisms and interoperability standards (HL7 FHIR).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eE. Satisfaction and Resolution\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDegree of satisfaction of EHR usage by the respondents\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSatisfaction Level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Facilities\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDissatisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery dissatisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e reveals from the survey the degree of satisfaction of EHR usage by the respondents. Of the respondents (57.7% of them are Satisfied/Very satisfied), 30.8% of them are neutral, which means that the current EHR is generally accepted, but it has not yet received the ability to be thought of as transformative or remarkable in most facilities. Staff sentiment is negative negatively in only 7.7% (Dissatisfied\u0026thinsp;+\u0026thinsp;Very dissatisfied) which is a relatively small number considering the overall infrastructure issues. With the facilities that either have already adopted EHR or a hybrid system, the satisfaction is high, meaning that in the areas where systems are physically used, the staff are able to use and find them useful irrespective of the current challenges. The high skewness of Satisfied and Neutral is a positive sign of good user acceptance where the EHR systems are implemented and operational. This low dissatisfaction shows that employees are usually prepared to implement and keep using digital systems as soon as they exist in the company - the resistance is not caused by the general lack of affection to the technology itself, but, probably, by the obstacles to implementation (power, cost, training, etc.). This distribution shows that it is not staff rejection that is the main bottleneck, but rather the process of guaranteeing consistent and stable system performance that can change the percentage of satisfaction not to be Satisfied/Neutral but to convert to Very satisfied at a higher number of facilities. The level of staff contentment with current EHR systems is mostly positive or neutral, which is why there is no reason to believe that the issue of acceptance is central to the problem and the actual problem is scaling reliable and highly functioning systems without compromising to infrastructural limitations.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis paper presents a timely, both qualitative and quantitative empirical evaluation of Electronic Health Record (EHR) adoption using clouds in Nigeria by combining both primary survey data of 26 healthcare practitioners in 11 states with secondary institutional data of 130 hospitals. The results indicate a healthcare information ecosystem with low levels of full-digitization, hybrid-dominance, crippling infrastructural shortages, and polarized reliability of systems, and also show surprisingly high staff acceptance where systems are implemented.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e depicts the conceptual framework for cloud-Based EHR in Nigeria. These findings support, as well as deepen the available literature on digital health transformation in low- and middle-income countries (LMICs) and sub-Saharan Africa, but provide more specific understanding of the socio-technical and engineering aspects of EHR underperformance in Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdoption Status and Discrepancy Between Primary and Secondary Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first survey is that merely 23% of the facilities have fully electronic (EHR-only) systems, 42% of them have hybrid systems, and 35% of the facilities are entirely paper-based. Conversely, secondary institutional data indicate that 75% of the facilities have some form of digital (hybrid/EHR) deployment, and only 25% of the facilities are paper-only. This deviation highlights a dismal difference between the stated institutional existence of EHR infrastructure and practical depth at workflow level. In terms of systems engineering, secondary data are available (e.g., vendor deployments, program registrations), but primary data are available (functional integration, daily usability, maturity of workflow) in terms of workflow maturity. The overwhelming percentage of hybrid systems in both samples is predicted by diffusion-of-innovation theory and puts Nigeria in an early-to-mid adoption stage where transitional architecture is dominant because processes are not fully reengineered (Rogers, 2003). Full adoption of EHR is still only present in tertiary and specialist facilities, which is reflective of increased institutional capacity, funding, and technical preparedness-in line with the previous Nigerian research that found the adoption rates of 18\u0026ndash;23% overall and even lower interoperability (12\u0026ndash;15%) (Oluwagbemi et al., 2021; Adebayo et al., 2022). The temporal line chart additionally shows slower first adoption (before 2020) after which it would increase at a quicker pace between 2019\u0026ndash;2025 with the highest number of facilities in 2019 (6) and 2025 (4). This post-2020 boom is probably an indication of pandemic-induced digital health momentum, government efforts (e.g., Lagos SHIP, Health-in-a-Box), and more vendor activity. Though, adoption at high rate in a short time span increases scalability and performance risks, because most facilities do not have sufficiently mature infrastructures to support new systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHosting Models, Cloud Providers, and Resilience Risks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn-premise leads among those that provide specific hosting information (64.3%), and hybrid (21.4%), and cloud (14.3%) are minor. This is in favor of local infrastructure due to the instability of power and reliability of the internet as shown by the infrastructure readiness radar chart (power: 2.3/10; internet: 3.5/10). The adoption of cloud, at least, exhibits a fragmented provider market: local/Nigerian vendors (29.4%), AWS (23.5%), Azure (17.6%) and others/not specified (29.4%). The high dependency on the international hyperscalers (AWS\u0026thinsp;+\u0026thinsp;Azure [?] 41%) creates vulnerabilities to resilience, such as latency, high bandwidth, forex volatility and exposure to global outages, and local vendors have strengths in line with local connectivity and local support requirements. The large share (29.4) of not specified is in itself a governance risk that hinders accountability and disaster recovery planning. This weakness is emulated by backup strategies: automated backups (52.9% of clear responses) are a promising answer, yet manual (29.4%) and nonexistent (17.6%) ones are still in place and put patient data at risk of irreversible loss.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfrastructure Readiness and Dominant Barriers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith the radar chart, the profile of preparedness is greatly skewed, with the issue of power instability (76.9% mentions) and the issue of internet untrustworthiness (65.4) as the most urgent limitations, and the lack of IT infrastructure (65.4%), and the high implementation cost (69.2%) close behind. These three highest bottlenecks, which the Pareto chart confirms as [?]90% reported, represent the classic 80/20 rule. The Fishbone (Ishikawa) diagram is a methodical engineering diagnosis that classifies root causes as Power, Network, People, Process, Policy and Platform. Power and Network come out as a precondition\u0026ndash;without them nothing platform (cloud or on-premise) can be operated reliably\u0026ndash;and the People (training, resistance) and Process (workflow redesign) concerns are mostly a side effect of lack of infrastructures. The national level has policy gaps that continue this, as it does not enforce standards or give incentives or mobilization of funds. Our results are highly consistent with LMIC scoping reviews (e.g., Akhlaq et al., 2018; Ndlovu et al., 2021) that acknowledge electricity and connectivity as universal leading factors, but our data show the prevalence of both in a more specific situation in Nigeria. The frequency of failure boxplot indicates polarization of the reliability domain: 53.8% of facilities have rarely/never issues (median\u0026thinsp;=\u0026thinsp;Rarely), which is a sign of good stability when the infrastructure and support are not problematic. Nonetheless, 15.4% encounter daily failures, which puts a lengthy upper tail indicating critical operational risk in an environment of some of the deployments. This bimodality highlights the idea that EHR performance is highly situational as opposed to flawed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStaff Satisfaction and User Acceptance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of the more favorable results is staff satisfaction: 57.7% (Satisfied\u0026thinsp;+\u0026thinsp;Very satisfied) and 30.8% (Neutral), and only 7.7% stated that they were not satisfied. The high acceptability, even with widespread infrastructural difficulties, implies that the opposition is not largely attitudinal, but rather driven by implementation (e.g. training gaps, untrustworthy performance). Where the system is in operation and is operational, employees see clear benefits in documentation, speed of access and coordinating care. This is in contrast with several of the previous researchers, who indicated higher resistance (Oyediran et al., 2020) and supports the Technology Acceptance Model (TAM) principle that perceived usefulness exceeds perceived ease of use in the condition of reduced underlying barriers (Davis, 1989). The fact that the radar score of IT support (5.8/10) and digital literacy (4.8/10) are relatively higher also shows that human capital is more aligned to help sustain use than physical infrastructure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBroader Implications and Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese findings underscore healthcare information system in Nigeria as a multi-layered socio-technical system that is not functioning at its optimum level. The presence of hybrid dominance adds latency, redundancy and propagation of errors at the paper-digital interface, making it difficult to support more sophisticated capabilities like real time analytics, AI-enabled decision support, or HL7 FHIR interoperability. The paper adds new quantitative granularity both with visualizations (Pareto, radar, box plot, Fishbone) and statistical relationships (e.g. chi-square between tertiary facilities and higher adoption). It also highlights an urban/rural and public/private issue: Lagos and tertiary/private facilities exhibit greater digital maturity over secondary/primary facilities and rural facilities, and further worsen inequities. Such limitations are the small size of the primary sample (n\u0026thinsp;=\u0026thinsp;26, convenience sampling), the possibility of bias in responses toward the facilities that had some exposure to digital, and the use of self-reported data. The secondary data can over-represent the deployments vendor-reported. The cross-sectional nature does not allow making causal inferences about the effects of interventions or trends in time. Overall, although Nigeria demonstrates underdeveloped yet rapidly spreading EHR adoption and staff acceptance, a set of deep-rooted barriers of power, connectivity, cost, and hardware seriously limits its performance and scalability. It is necessary to solve these by focusing on prioritized engineering solutions (solar-hybrid power, last-mile broadband, offline-capable platforms) and changes in policies to achieve the transformative potential of cloud-based EHRs to enhance the quality, efficiency, and equity of care in Nigeria and other similar LMIC countries.\u003c/p\u003e\n\u003ch3\u003e8.Study Limitations\u003c/h3\u003e\n\u003cp\u003eAlthough it has a contribution, this research has a number of limitations that must be taken into account when making interpretations of the findings.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSample Size and Representativeness\u003c/strong\u003e: The main sample of the survey (n\u0026thinsp;=\u0026thinsp;26) is quite small and was selected with the help of convenience sampling. By this, the results cannot be extended to all the healthcare institutions of Nigeria. The analysis can thus be described as experimental and diagnostic and offers a hint of the prevailing trends instead of being representative of the nation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePotential Selection Bias\u003c/strong\u003e: Respondents and facilities that were previously exposed to electronic health systems might have been more likely to join the study, which could have caused a misunderstanding of the level of EHR awareness, adoption, or satisfaction.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-Reported Data\u003c/strong\u003e: The underlying data in primary surveys is perceptions that are self-reported, and could be subject to recall bias, social desirability, or lack of complete technical expertise, especially on the topic of hosting models and cloud services providers.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary Data Constraints\u003c/strong\u003e: The secondary institutional data were generated by vendor and public sources, which can exaggerate the usage or maturity of the system. There was also insufficient classification of facilities and geographic specification of facilities, so that finer-grained analysis was hampered.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCross-Sectional Design\u003c/strong\u003e: The study was cross-sectional and, therefore, it cannot be used to draw causal conclusions or evaluate longitudinal trends. Observed associations represent the state of affairs at a particular time and are unable to reflect the development of the system or its postimplementation effects.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eLimited Theoretical Operationalization\u003c/strong\u003e: Although conceptually the study is based on diffusion of innovation and technology acceptance views, the study does not operationalize and statistically test the theoretical constructs like perceived usefulness or ease of use.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough a methodical system of dealing with the power-network-platform triad and at the same time, creating human capacity and policy facilitators, Nigeria will be able to move the nation to a sturdy, fashionable, and clinically sensible digital health bioecosystem. When done in a timely and organized manner, these measures can make a substantial positive contribution to healthcare delivery, cost reduction, patient safety, and make Nigeria a leader in digital health transformation in sub-Saharan Africa. As part of accelerating sustainable EHR adoption and ensuring clinical and public health impact, the following multi-stakeholder recommendations are suggested with the top priority:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePrioritize Foundational Infrastructure Resilience\u003c/strong\u003e (Highest Priority): Install solar-hybrid power systems, battery storage, and inverters in all of the publicly available healthcare facilities, beginning with tertiary and secondary hospitals. Furthermore, increase last-mile broadband (via public-private partnerships, such as with MTN, Airtel, Starlink) and subsidize healthcare data expenses. Also, set national minimum infrastructure requirements of digital health facilities (power uptime\u0026thinsp;\u0026ge;\u0026thinsp;99, bandwidth\u0026thinsp;\u0026ge;\u0026thinsp;10 Mbps symmetric).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePromote Affordable, Context-Appropriate Platform Design\u003c/strong\u003e: Require use of offline-enabled, low-resource EHR systems which enable regular synchronization (e.g. OpenMRS-based or local-developed systems such as eClat, LAMISPlus, Health-in-a-Box). Ease in bulk purchase of ruggedized hardware (tablets, laptops, servers) via federal and state ministries of health. Promote the use of hybrid-cloud systems that are automated and encrypted back ups and disaster recovery systems.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eStrengthen Human Capacity and Change Management\u003c/strong\u003e: Incorporate digital health and health informatics into undergraduate and postgraduate medical and nursing education, as well as other allied health education. Carry out structured and facility-based EHR training programs including ongoing refresher training and peer champions. Implement structured change management models (e.g. ADKAR, the 8-Step Model by Kotter) at EHR rollouts to overcome resistance and establish user ownership.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDevelop Robust National Policy and Governance Framework\u003c/strong\u003e: Create national EHR adoption roadmap that is binding with timelines, funding commitment and interoperability requirements (HL7 FHIR as the national standard). Develop monetary incentives (tax credits, grants, performance-based funding) and punishments against a lack of certified systems. Enhance data protection and cybersecurity policies and standards in accordance with the Nigeria Data Protection Act (2023) and global standards.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eFoster Multi-Sectoral Partnerships and Sustainable Financing\u003c/strong\u003e: Encourage global hyperscaler ( AWS ) and local vendors ( Interswitch/eClat, Digital Quest ), international donors ( WHO, USAID, World Bank ), and telecom operators. Consider new models of financing, including healthcare-as-a-service subscriptions, results-based financing, and blended finance.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eInvest in Monitoring, Evaluation, and Continuous Improvement\u003c/strong\u003e: Create a national digital health observation center to monitor the adoption statistics, performance of the systems, and interoperability in real-time. Carry out longitudinal studies to determine the clinical and economic effect of EHR interventions. Pilot AI-based optimization (e.g., LSTM-based resource allocation, predictive maintenance) in high maturity facilities to prove value and solicit additional financing.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Conclusion","content":"\n\u003cp\u003eThis is a mixed-methods study that offers a holistic, empirically based evaluation of the prevailing situation on the adoption of cloud-based Electronic Health Record (EHR) in healthcare institutions in Nigeria. Using primary findings, surveyed among 26 healthcare professionals in 11 states and secondary data on institutions, 130 to be exact, the results of the studies are a clear image of a nascent, transitional, and grossly underfunded digital health landscape with underlying infrastructural shortages. The EHR-only usage rates are relatively low at 23% in the primary survey and are divided into the hybrid systems (42) as the most common transitional architecture and 35% of facilities continue to be fully paper-based. According to secondary data, there is an increase in reported digital penetration (75%): there is an essential disconnect between institutional-level implementation and real inclusion in clinical workflow. The trend of adoption is increasing at an accelerated pace since 2019 due to the momentum of the pandemic and specific efforts but this rapid expansion has been faster than the creation of supporting infrastructure that can be trusted. The infrastructure preparedness profile is disastrously unequal: power instability (76.9% of the respondents) and poor internet connection are the most critical bottlenecks, with high implementation costs (69.2%) and lack of hardware closely right behind. These three highest-ranking constraints are verified through the Pareto analysis as it is indicated that nearly 90% of all reported performance problems are related to them. The Systematic approach of the Fishbone diagram is that Power and Network constitute the root causes with downstream effects that lead to Platform inadequacies, People (training and resistance), Process (workflow redesign) and Policy gaps. Nevertheless, there are two positive findings despite these difficulties; System reliability is polarized More than half of facilities (53.8%) report minor technical problems or no problems at all, suggesting that supported implementations can become useful even in resource-limited environments. Secondly, there is a positive overall Staff satisfaction level (57.7% satisfied or very satisfied; only 7.7% dissatisfied) and it shows that staff acceptance is not the main thing that is holding the implementation back, rather implementation resistance is more attitudinal than implementation. Together, these findings highlight the fact that the healthcare information system in Nigeria is a multi-layered socio-technical system which is functioning well below its efficient efficiency spectrum. Hybrid dominance adds latency, data redundancy, and propagation of errors at the paper-digital interface, and indefinite power and connectivity shortcomings do not allow or empower the achievement of advanced capabilities, including real-time analytics, AI-driven decision support, and the ability to drive processes on a national scale via standards, such as HL7 FHIR. The city-country and community-business gap also contributes to inequities, and tertiary and non-governmental facilities in urban areas (e.g. Lagos) are more mature. To conclude, although the digital health transformation pillars are present, as demonstrated by the increased adoption, the use of multiple cloud providers, and high-user acceptance, the underlying weakness of infrastructures is the most limiting factor in expanding credible and high-impact cloud-based EHRs throughout Nigeria. These underlying limitations are not a technological issue to solve; rather they are a condition of enhancing care continuity, minimizing medical error, improving operational efficiency, and eventually improving health equity in one of the most populous countries in Africa.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eEthical approval for this study was obtained from the Health Research Ethics Committee (HREC), University of Benin Teaching Hospital (UBTH), Nigeria (Protocol No: ADM/E22A/VOL.VII/148311664). The study was conducted in accordance with relevant institutional guidelines and the principles of the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e\u003cp\u003eThe questionnaire was administered electronically using Google Forms. An electronic informed consent statement was presented on the first page of the survey, and participants were required to provide consent before proceeding. Participation was voluntary, and no personally identifiable information was collected.\u003c/p\u003e\n\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthors\u0026rsquo; information\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was self-funded.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eOBA conceptualized the research, shaped the research methodology, data collection, and analysis, and wrote the manuscript. The data interpretation was done by HA. The critical revision of the manuscript with respect to intellectual content was carried out by BI. The final version was considered and agreed by all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors give credit to the healthcare professionals and institutions that took part in the research and made this research a possibility by giving valuable information.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmed MM et al (May 2025) Integrating Digital Health Innovations to Achieve Universal Health Coverage: Promoting Health Outcomes and Quality Through Global Public Health Equity. 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Inspired by the global use of EHR, India launched t, \u003cem\u003eLinkedin.com\u003c/em\u003e, Jan. 22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.linkedin.com/pulse/digital-healthcare-india-electronic-health-record-ehr-adoption-wxpdc\u003c/span\u003e\u003cspan address=\"https://www.linkedin.com/pulse/digital-healthcare-india-electronic-health-record-ehr-adoption-wxpdc\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed Jan. 23, 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkat MS, The Evolution of Electronic Health Records in India: Progress, Challenges, and, Future Prospects (2025) (2020\u0026ndash;2025), \u003cem\u003eInternational Journal of Health Technology and Innovation\u003c/em\u003e, vol. 4, no. 02, pp. 24\u0026ndash;30, Aug. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.60142/ijhti.v4i02.06\u003c/span\u003e\u003cspan address=\"10.60142/ijhti.v4i02.06\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBabalola OA, Olufunlayo TF, Akinlawon OO, Odukoya TO, Fagbo OO, Akinlawon D (Sep. 2025) Perception and barriers to electronic medical record use among physicians and nurses in General Hospitals in Lagos State. Afr Health Sci 25(3):154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4314/ahs.v25i3.20\u003c/span\u003e\u003cspan address=\"10.4314/ahs.v25i3.20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuinga N, Magare S, Monda J, English M, Paton C (2018) Survey of Electronic Health Record (EHR) Systems in Kenyan Public Hospitals: A mixed-methods survey (Preprint). 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JMIR Med Inf 14:e73831\u0026ndash;e73831. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/73831\u003c/span\u003e\u003cspan address=\"10.2196/73831\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarerimana A, Wicking K, Biedermann N, Yates K, Pillay JD, Mchunu G (2025) Adoption of electronic health records by nurses in Africa: A scoping review., \u003cem\u003ePubMed\u003c/em\u003e, vol. 11, pp. 20552076251357401\u0026ndash;20552076251357401, Sep. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20552076251357401\u003c/span\u003e\u003cspan address=\"10.1177/20552076251357401\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNgusie HS, Kassie SY, Chereka AA, Enyew EB (Mar. 2022) Healthcare providers\u0026rsquo; readiness for electronic health record adoption: A cross-sectional study during pre-implementation phase. BMC Health Serv Res 22(1):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12913-022-07688-x\u003c/span\u003e\u003cspan address=\"10.1186/s12913-022-07688-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Electronic Health Records (EHR), Cloud Computing, Digital Health, Healthcare Adoption, Nigeria, Low- and Middle-Income Countries (LMICs), Implementation Barriers, Health Informatics, Infrastructure Readiness","lastPublishedDoi":"10.21203/rs.3.rs-8821192/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8821192/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCloud-Based Electronic Health Records (EHRs) are critical to the enhancement of the quality of healthcare, healthcare continuity, and the efficiency of the system. Adoption of them is still low and uneven in Nigeria and other low and middle-income countries (LMICs). There is limited empirical data on the level of adoption, system performance, and the bottlenecks in the implementation process of the systems within the facilities of the healthcare sector in Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimary and secondary data were used in carrying out an exploratory mixed-methods study. The primary data were gathered through an online survey of 26 healthcare facilities operating in 11 states of Nigeria in the period between January and March 2025. A collection of secondary institutional data from 130 facilities comprised records of vendor deployment, government and regulatory reports, hospital web pages, and the peer-reviewed literature, were also used. Quantitative data were undertaken through descriptive statistics and chi-square tests, whereas qualitative responses were undertaken thematically. Pareto analysis, radar charts, box plots, and Ishikawa diagrams were systems-engineering tools that were used to determine significant bottlenecks in performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary survey found that only 23% of the facilities were fully electronic EHR systems, 42% were hybrid systems, and 35% could only use paper-based records. The reported digital implementation (75%), as shown through secondary data, had more reported implementation, but operational maturity was not in line with the institutional claims of implementation. The uptake of the EHR also grew beyond 2019, especially in tertiary and specialist institutions. Approximately 90% of performance bottlenecks that were reported included power instability (76.9%), high implementation costs (69.2%), and poor IT infrastructure (65.4%). The infrastructure reliability score of power (2.3/10) and internet connectivity (3.5/10) was low. System reliability was polarized, with 53.8 percent saying they had rare or no failures and 15.4 percent saying they had daily failures. Staff overall satisfaction was high (57.7%), which was low due to infrastructural constraints.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of cloud-based EHR in Nigeria is still in the process of transition and its implementation is limited by insufficient underlying infrastructure and not by the resistance of users.\u003c/p\u003e","manuscriptTitle":"Cloud-based Electronic Health Records (Ehr) Adoption Rate in Nigeria: Barriers and Performance Bottlenecks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 10:06:33","doi":"10.21203/rs.3.rs-8821192/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-04T19:44:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313879598931755609021621285215963955869","date":"2026-05-04T18:51:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266909428009064045529241324025462546628","date":"2026-03-18T11:13:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178292527078451586540115560868276229990","date":"2026-03-14T13:04:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-02T18:54:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-25T16:58:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-24T16:02:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-24T08:58:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Digital Health","date":"2026-02-24T08:50:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"795ca59f-fafa-427a-a9bc-d60158d6d611","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-04T19:44:41+00:00","index":37,"fulltext":""},{"type":"reviewerAgreed","content":"313879598931755609021621285215963955869","date":"2026-05-04T18:51:48+00:00","index":36,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T10:06:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 10:06:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8821192","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8821192","identity":"rs-8821192","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}