The lack of interoperability in healthcare refers to the inability of different healthcare systems, devices, and applications to effectively exchange and use health information [19] . It is a significant challenge that hinders seamless communication and collaboration among healthcare entities, leading to fragmented care and inefficient processes. Several studies have highlighted the consequences and underlying issues associated with the lack of interoperability in healthcare [20] .

Access control is a critical feature of healthcare information systems. It is about enforcing rules to ensure that only authorized users get access to resources and information in a system. Such information sharing however raises security and privacy concerns that require appropriate access control mechanisms to ensure Health Insurance Portability and Accountability Act (HIPAA) compliance [21] [22] investigated access control constraints and their implications in healthcare information systems. They highlighted the significance of access control models, policies, and mechanisms in protecting sensitive patient information and maintaining data confidentiality.

However, the top priority is always to provide the best possible care for a patient. User Authentication is one major Access constraint to health information systems, often restricted to authorized users. User authentication mechanisms, such as usernames, passwords, and multi-factor authentication, ensure that only authenticated individuals can access the system. In emergency or otherwise unexpected situations, clinicians need to be able to bypass access control. In a crisis, availability of information takes precedence over privacy concerns [23] . Protecting patient privacy is important, but the most important goal is to provide the best possible care for patients, which depends on the clinicians having access to information. Hence, access control is a balance between confidentiality and availability [24] . This is what makes access control in healthcare systems so challenging and interesting as a research subject.

Furthermore Consent and Privacy Settings, involving obtaining patient consent for accessing their health information. Patients may have the ability to specify who can access their data and set privacy preferences, such as restricting access to certain healthcare providers or specifying the purposes for which their data can be used [25] .

Lastly, there are, physical access controls constraints, such as locked server rooms, access card systems, and biometric authentication, can prevent unauthorized physical access to health information systems. It has become clear that many access control mechanisms implemented in existing systems are too static, in the sense that access rules rarely change or are updated after a patient is admitted [26] . Access usually depends on what ward a patient is admitted to, where a user is working and the profession (doctor, nurse etc.) of the user. However, the care process for a patient is dynamic and individual.

To improve access control, and minimize the use of exception access mechanisms, access control should be process-based tailored to workflows in healthcare and dynamic in the sense that access rules change or are updated as the care process changes [16] . It is important for healthcare organizations to implement robust access controls to prevent unauthorized access, data breaches, and privacy violations. These constraints help ensure that health information is accessed only by authorized individuals and that patient privacy and data security are maintained [21] .

Access control in healthcare information systems ensures only authorized users access sensitive patient information, balancing confidentiality and availability. Key mechanisms include usernames, passwords, multi-factor authentication, and physical controls like locked server rooms. In emergencies, clinicians may bypass controls for patient care. Consent and privacy settings manage patient permissions. Dynamic, workflow-based access controls are crucial to minimize exceptions, prevent unauthorized access, and maintain data security and patient privacy.

Clinical decision-making is essential for effective patient care and has been studied for decades but research on how EHR use influences clinical decision-making is comparatively limited. Prior researches demonstrates that physicians, irrespective of professional training, have significant, non-overlapping blind spots in EHR use, manifesting as an inability to recognize critical patient safety issues [22] . Failure to see blind spots during EHR use partially explains why medical errors are among the leading cause of death [23] . In the context of health information systems, cognitive overload can be a significant constraint that affects healthcare professionals’ ability to use the systems effectively and efficiently [24] .

Health information systems often contain vast amounts of patient data, clinical guidelines, research papers, and other medical information across the medical domain. Healthcare professionals may feel overwhelmed by the volume and complexity of information they need to process and interpret. Too much information presented simultaneously can lead to cognitive overload, making it challenging for users to locate relevant information or make decisions efficiently [25] . Furthermore, it might take more time in accessing the information and may be a challenge especially in cases of emergencies.

Clinical decision-making is crucial for providing quality patient care, although the impact of electronic health record (EHR) use on decision-making has limited research. Studies indicate that regardless of professional training, physicians exhibit blind spots in EHR use, leading to potential patient safety issues and contributing to medical errors. Cognitive overload, caused by the vast amount of patient data and medical information within health information systems, can hinder healthcare professionals’ ability to effectively utilize the systems. Overwhelming amounts of information can lead to difficulty in processing and interpreting data, ultimately affecting decision-making efficiency and timely access to critical information, particularly in emergency situations.

Usability and skills constraints in interoperability refer to the challenges and limitations faced by users, such as healthcare professionals and IT staff, when interacting with interoperable health information systems [26] . These constraints encompass difficulties in using the systems effectively, lack of necessary skills or training, and inadequate user experience design. Here are some references that discuss usability and skills constraints in interoperability. Health information systems (HIS) with complex user interfaces can be difficult to navigate and use efficiently. A cluttered or confusing interface, unclear labels, and non-intuitive workflows can lead to user frustration and errors.

It therefore becomes of paramount importance for HIS to require users to have a certain level of technological literacy. Users who lack familiarity with computers, software applications, or specific technologies may find it challenging to navigate and utilize the systems effectively. In addition, Systems that do not prioritize user-centered design principles may not align with the needs, preferences, and workflows of the end-users [27] . This can result in systems that are difficult to learn and use effectively. This can also be because of Insufficient Training. Inadequate training programs or resources can impede users’ ability to acquire the necessary skills to effectively utilize health information systems [28] . Users may struggle to understand system functionalities, interpret data, or perform specific tasks, leading to reduced efficiency and suboptimal system utilization.

Usability and skills constraints in health information systems (HIS) hinder healthcare professionals and IT staff due to complex interfaces, lack of training, and poor design. Cluttered, non-intuitive workflows cause frustration and errors, while inadequate technological literacy makes systems hard to use. Systems that ignore user-centered design principles and insufficient training further reduce efficiency and effective utilization

Many low-income countries have limited infrastructure and network. In rural communities, the foundational infrastructure and network are even more limited compared to major cities and towns. Infrastructure constraints and network connectivity are crucial aspects of interoperability that can affect the seamless exchange and sharing of health information between different systems and stakeholders [29] .

These constraints encompass limitations in hardware, software, network infrastructure, and connectivity issues that can hinder the effective transmission and accessibility of data. HIS rely on robust and reliable infrastructure to function effectively. As defined by [30] , an IT infrastructure as, “the composite of hardware, software, network resources and service components that support the delivery of business systems and IT-enabled processes”. In this context, it is perceived that concepts like configuration management, version control, monitoring, logging, storing, backup, inventory, documentation and task manager are the infrastructure services that support the operation and management of the IT environment of an enterprise. Reliable and high-speed internet connectivity is also essential for the seamless operation of health information systems [31] .

However, in some regions or rural areas, internet access may be limited, unstable, or expensive. Poor internet connectivity can hinder real-time data exchange, remote access to systems, and interoperability with external interfaces. Health information systems often require specific hardware components, such as computers, tablets, or mobile devices, to access and utilize the system. Limited availability or outdated hardware can impact the user experience, system performance, and overall efficiency of healthcare professionals [32] .

Low-income countries, especially rural areas, often face significant infrastructure and network connectivity challenges, which are crucial for the interoperability of health information systems (HIS). These challenges include limitations in hardware, software, network infrastructure, and connectivity, which hinder effective data transmission and access. Robust and reliable infrastructure, comprising hardware, software, network resources, and service components, is essential for HIS to function effectively. Key infrastructure services include configuration management, version control, monitoring, and backup. Reliable high-speed internet is also crucial, but many rural regions have limited, unstable, or expensive access, impeding real-time data exchange and remote access. Additionally, HIS require specific hardware like computers and mobile devices, and limited or outdated hardware can negatively impact user experience, system performance, and healthcare efficiency.

Standards and protocols play a crucial role in ensuring interoperability in health information systems. There are over 40 different SDOs in the health IT arena. Interoperability utilizes standards, interfaces and protocols to connect systems using appropriate techniques, methodologies and all associated issues such as legislation, agreements, governance, workflows and privacy concerns [33] . It can be achieved at various “levels” (technical, syntactic, semantic, organisational and legal).

Interoperability standards and protocols constraints refer to the challenges and limitations associated with the adoption and implementation of standard formats, protocols, and specifications for seamless data exchange and interoperability between different health information systems [34] . These constraints encompass issues related to the compatibility, consistency, and adherence to interoperability standards. One of the primary constraints is the lack of standardized data formats, coding systems, and terminologies. Different health information systems may use different standards, making it challenging to exchange and interpret data accurately. Without consistent and widely adopted standards, interoperability becomes difficult to achieve [35] .

The presence of multiple standards and versions within the same domain also create interoperability challenges. Health information systems may be designed based on different versions of standards, leading to incompatible data formats and structures. This can result in data discrepancies and hinder effective data exchange. Some standards may have limited scope, focusing on specific data elements or use cases [36] . This limited scope can hinder comprehensive interoperability as different systems may require additional data elements or functionalities not covered by the standards. Incomplete or insufficient standards can impede seamless data exchange. Furthermore, Standards and protocols continuously evolve to keep pace with technological advancements and changing healthcare needs. However, the rapid evolution of standards can create challenges for interoperability [37] . Systems built on older versions of standards may face difficulties in integrating and exchanging data with systems using the latest versions.

Even when standards exist, there may be gaps in their implementation or interpretation across different systems. Variations in how standards are implemented or understood can lead to inconsistencies and hinder interoperability [38] . Lack of adherence to standards or non-compliance with specifications can impede effective data exchange. When different systems use different standards or versions, mapping and translating data between them can be complex. The need for extensive mapping and translation processes adds complexity, cost, and potential for errors [39] . Inefficient mapping methodologies or lack of automated tools can hinder seamless interoperability.

Standards and protocols are crucial for interoperability in health information systems, but face challenges such as lack of standardization, multiple standards and versions, limited scope, and evolving standards. These issues create compatibility problems, data discrepancies, and complex mapping needs, making seamless data exchange difficult. Implementation gaps and inconsistent adherence further hinder effective interoperability.

Healthcare practitioners were invited to address questions giving their responses according to the effect that various constraints posed to them in carrying out digital healthcare collaboration amongst service providers. The question was, “Please select from the options below which score factor (from 1 to 5) ranking the effect of the given constraints in eHealth systems interoperability. Where 1 represents less effect and 5 represents most effect”. The question listed 19 factors as constraints which needed practitioners to rate the extent of effect on a Likert scale with values ranging from 1 to 5, where 1 stood for Least effect and 5 stood for Most effect. The analysis described the frequencies of suggested responses as displayed graphically in “ Figure 1 ”.

From the results, it can be withdrawn that the constraints the topped the list with frequencies of 100% are availability of ICT infrastructure, healthcare providers’ cooperation, access to data costs, Security, privacy and integrity of data, Inter-organisation engagement and Usability and skills of healthcare practitioners.

However, some healthcare practitioners felt that there are some factors that had least effect though with small but significant frequencies which are cognitive overload (29.58%) and patient socio-demographic variables (5.63%). Generally healthcare practitioners coincided to consider all the other 18 factors to be constraints apart from the cognitive overload factor which showed mixed views to the factor by the evenly spread frequencies of most effect (4.23%), high effect (18.31%), moderate (29.58%), less effect (18.31%) and least effect (29.58%). This shows that healthcare practitioners do not see cognitive overload or the abundance in information availability as a constraint but rather as a resource that enriches healthcare service collaboration.

For the “Interoperability Constraints” theme, Reliability test was carried out and the Cronbach’s alpha is α = 0.372, which indicates a very low level of internal consistency for the scale in this specific theme as shown in Table 1 .

Table 2 shows that we can remove questions INC_5, and INC_18 and that would increase the Cronbach’s alpha. Therefore, removal of the abovementioned questions would lead to an improvement in Cronbach’s alpha. We therefore consider removing these 2 variables in subsequent analysis.

Factor analysis of HIS Interoperability Constraints shall be analyzed.

Table 3 and Table 4 show labels that were used in place of actual variable names to run the analysis. The analysis tables will display the labels in place of the actual variables.

We observe 6 components whose Eigenvalue is at least 1 according to Kaiser’s criterion. Therefore, the 19 variables/questions under the “Interoperability Constraints” theme seem to measure 6 underlying factors. This is because only our first 6 components have an Eigenvalue of at least 1. The other components having low quality scores are not assumed to represent real traits underlying our 19 variables/questions. Such components are considered “scree” as shown by the line chart below.

A scree plot visualizes the Eigenvalues (quality scores). Again, we see that the first 6 components have Eigenvalues over 1. We consider these “strong factors”. After that, component 7 and onwards, the Eigenvalues start to drastically fall.

Our rotated component matrix (above) shows that our first component is measured by the following variables:

We observe that these variables all relate to Protocol standards, leadership management and broadband quality. Therefore, we interpret component 1 as “Leadership management through broadband quality and protocol standards”. This is the underlying trait measured by INC_1, INC_11 and INC_17.

Our rotated component matrix ( Table 5 ) shows that our second component is measured by the following variables:

We observe that these variables all relate to Practitioner competence and institutional architectures. Therefore, we interpret component 2 as “Practitioner competence in institutional architectures”. This is the underlying trait measured by INC_8 and INC_19.

Our rotated component matrix (above) shows that our third component is measured by the following variables:

We observe that these variables all relate to demographics and existing systems. Therefore, we interpret component 3 as “Demographics for existing systems”. This is the underlying trait measured by INC_6 and INC_9.

Our rotated component matrix ( Table 5 ) shows that our fourth component is measured by the following variables:

We observe that these variables all relate to Cognitive overload and Facilities. Therefore, we interpret component 4 as “Cognitive overload from facilities”. This is the underlying trait measured by INC_18 and INC_10.

Our rotated component matrix (above) shows that our fifth component is measured by INC_2-Lack Of Training variable. Therefore, we interpret component 5 as “Lack of training”.

Our rotated component matrix (above) shows that our sixth component is measured by the – INC_14-Government Funding variable. Therefore, we interpret component 6 as “Government funding”.

After interpreting all components in a similar fashion, we arrived at the following descriptions:

Component 1 - “Leadership management through broadband quality and protocol standards”.

Component 2 - “Practitioner competence in institutional architectures”.

Component 3 - “Demographics for existing systems”.

Component 4 - “Cognitive overload from facilities”.

Component 5 - “Lack of training”.

Component 6 - “Government funding”.

Adding factor scores to our data.

We compute factor scores as means over variables measuring these underlying factors.

The Cronbach’s alpha for the “Infrastructure Constraints” theme, is α = 0.068, which indicates a very low level of internal consistency for the scale in this specific theme as shown in Table 6 .

We can see that removal of questions IC_1, IC_3 and IC_4 would increase the Cronbach’s alpha ( Table 7 ). Therefore, removal of the above-mentioned questions would lead to an improvement in Cronbach’s alpha. We therefore consider removing these 3 variables in subsequent Factor analysis.

Factor analysis results for HIS Infrastructure Constraints are then oresented:

Table 8 and Table 9 show labels that were used in place of actual variable names to run the analysis. The analysis tables will display the labels in place of the actual variables.

We observe that there is only 1 component whose Eigenvalue is at least 1 as shown in Table 9 . Therefore, the 5 variables/questions under the “HIS Infrastructure constraints” theme seem to measure 1 underlying factor. This is because only our 1st component has an Eigenvalue of at least 1. The other components having low quality scores are not assumed to represent real traits underlying our 5 variables/questions. Such components are considered “scree” as shown by the line chart in “ Figure 3 ”.

A scree plot visualizes the Eigenvalues (quality scores). Again, we see that the first 5 components have Eigenvalues over 1. We consider these “strong factors”. After that, component 4 and onwards, the Eigenvalues drop off significantly. Table 10 shows the Rotated component matrix for variables IC_8 and IC_7.

Our rotated component matrix (above) shows that our component is measured by the following variables:

We observe that these variables all relate to existing systems and institutional architectures. Therefore, we interpret component 1 as “Existing system architectures from institutions”. This is the underlying trait measured by IC_7 and IC_8.

Adding factor scores to our data.

We compute factor scores as means over variables measuring these underlying factors.