INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue I, January 2026  
The Influence of Artificial Intelligence on Hospital Administration in  
Mozambique  
Criscêncio Luís Sande Botão  
Faculty of Health Sciences, Zambezi University  
DOI : https://doi.org/10.51583/IJLTEMAS.2026.150100036  
Received: 14 January 2026; Accepted: 19 January 2026; Published: 28 January 2026  
ABSTRACT  
This article aims to understand the influence of Artificial Intelligence (AI) on Hospital Administration and  
Management in Mozambique (HAM). To this end, the methodology employed was a literature review of articles  
on AI-related topics, conducted through searches in Science Direct, PubMed, Scopus, the website of the  
Mozambican Ministry of Health, and the libraries of the Faculty of Health Sciences at Zambeze University and  
the Faculty of Medicine at Eduardo Mondlane University. The main results concluded that Mozambican HAM  
does not apply AI tools, particularly those related to increasing productivity in sectors such as Predictive  
Maintenance, Bed Management, and Staffing Planning, due to high equipment installation and maintenance  
costs, precarious transportation, connectivity problems, and staff shortages.  
Another important conclusion was that, in the literature review process on the subject, little material was found,  
as little is published, especially in the Mozambican context. Therefore, aware that this article does not cover the  
full scope of AGH, other aspects such as the application of AI in transport (ambulances), medication  
management, hospital logistics, competency-based accounting, and others, may constitute topics of interest  
Keywords: Artificial Intelligence, Hospital Administration and Management in Mozambique  
INTRODUCTION  
Artificial intelligence (AI) began to be debated approximately 70 years ago in developed countries; however, its  
remarkable performance as a topic of scientific, social, and economic interest emerged in the 2000s. Its insertion  
in developing countries, and its factual interest, emerged in the last 15 years. In Mozambique, the study of AI as  
a work tool for improving productivity has been developed frequently in areas such as Engineering. Only in the  
2010s has there been a search to understand a little more about its functionality and applicability in the health  
sciences, especially in the clinical area.  
It is in this sense, with the central objective of understanding the influence of AI on Mozambican Healthcare,  
that this article emerges, since the literature is almost entirely grey. To facilitate understanding of the purpose,  
the article was structured in such a way as to allow the reader to comprehend the topic, primarily organized by  
a set of literature on AI and its connection to understanding AI in healthcare in general, and in particular, its  
influence on Mozambican Healthcare. This organization was made possible through a methodology consisting  
of a review of recent literature, which allowed us to conclude, in general, that Mozambican Healthcare does not  
apply AI tools, especially those related to increasing productivity in sectors such as Predictive Maintenance, Bed  
Management, and Staffing Planning.  
METHODOLOGY  
Considering the objectives of this study, a bibliographic review of articles on the topic of AI was applied  
methodologically. The databases included Science Direct, PubMed, Scopus, the website of the Mozambican  
Ministry of Health, and the libraries of the Faculty of Health Sciences at Zambeze University and the Faculty of  
Medicine at Eduardo Mondlane University. This allowed us to explore articles in the context of AI in for-profit  
business organizations, as well as in health institutions.  
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In general, using inclusion criteria, even without including an exhaustive list of all published studies, we listed  
articles on AI from the years 2016 to 2025. This qualitative approach allowed for a descriptive and exploratory  
analysis, capable of providing an academic critique of the systemic, institutional, and political barriers to the  
adoption of AI in Mozambican healthcare.  
Problem Statement  
Starting from the premise advanced by Graham et al. (2020), according to which we are at a critical point in the  
fourth industrial era (following the mechanical, electrical, and internet eras) known as the "digital revolution,"  
characterized by a fusion of types of technology, elaborating on Artificial Intelligence (AI) in healthcare reveals  
that several studies have been discussing it, focusing on direct medical care, setting aside other assumptions in  
the management process and AGH Parikh et al. (2019), even though the direct medical care process is included  
in the hospital administrative management structure.  
In Mozambique, studies on AI in healthcare also address aspects related to, for example, telemedicine, new  
forms of surgical intervention through AI, and others. However, literature on the use of AI, for example, in the  
management of product stocks, in the management of hospital beds, and other related areas, is almost non-  
existent, judging by the bibliographic review carried out in this study. It is in these terms that it becomes  
fundamental to try to understand: what is the influence of Artificial Intelligence on AGH in Mozambique?  
Concept and theories about Artificial Intelligence  
According to Brunette et al. (2009), the real perception of the existence of AI emerged at a conference in July  
1956 at Dartmouth College, where AI was expressed as the digital revolution. Several leaders in the field were  
present, namely John McCarthy, Marvin Minsky, Oliver Selfridge, Ray Solomonoff, Trenchard More, Claude  
Shannon, Nathan Rochester, Arthur.  
Therefore, as can be seen, AI is not as new as it seems, since it has been around for a long time, to the point that  
Spector (2006) argues that it is an objective that predates the nominal establishment of its field in 1956.  
However, considering the perspectives above, it can be deduced that AI has its foundations dating back  
approximately 70 years.  
It is natural that it evolves over time; therefore, for Graham et al. (2020), AI is broadly defined as a machine or  
computational platform capable of making intelligent decisions.  
Therefore, the fact that it makes intelligent decisions does not necessarily mean that it is the most assertive, so  
its assessment, analysis, and understanding require, in part, human intervention. In this sense, Brunette and  
Flemmer (2009) argue that, throughout the fifty years in which AI has been a defined and active field, there have  
been several bibliographic surveys that have always drawn attention to human intervention to avoid the  
extrapolation of AI tools in organizations.  
It can be inferred that the field is extraordinarily difficult to synthesize, either chronologically or thematically.  
This is why Brunette and Flemmer (2009) state that the reason for this is that there has never been a wave of  
efforts that led to a recognized achievement in the analysis of AI as an antidote to well-being.  
However, despite this, there is a considerable body of literature that the novice must master before attempting to  
deal with what has so far proven to be a multi-headed monster. The incorporation of new knowledge about AI  
still shows the great need to delve deeper into the subject, fundamentally in the field of AGH (Analysis and  
Human-Centered Research).  
Considering the difficulty in proceeding chronologically with AI, and also considering the approach raised by  
Brunette and Flemmer in 2009, it becomes evident that no study will be definitive of the essence of AI. Even so,  
one should never stop seeking new approaches to it, especially for novices.  
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Moreover, alluding to the approach brought by Spector, who stated that, for example, it would have been  
reasonable in the 17th century, when Leibniz wrote about reasoning as a form of calculation, to think that the  
process of creating AI would have to be something like the process of creating a water wheel or a pocket watch:  
first understand the principles, then use human intelligence to conceive a project based on those principles, and  
finally, build a system according to the project.  
That is, it is a process that is densely in process and progress.  
It is worth remembering that, in recent years, AI has become an emerging trend in different areas: science,  
business, medicine, the automotive industry, and education. It has recently become a very popular topic in the  
field of management science and marketing, although, paradoxically, work on its development in other fields of  
science has been continuously underway for more than half a century, as we mentioned earlier regarding its  
genesis.  
Based on the above, Jarek et al. (2019) argue that over the years, AI has appeared and disappeared from the  
spotlight depending on the level of its advancement and the increase in its potential applicability. The interest  
and broad discussion about AI are caused by the first large-scale commercial applications, which demonstrated  
its potential and capabilities.  
It is evident that AI technology has always been an intrusively implicit topic, that is, even if organizations do  
not want to adopt it, they feel obliged to do so at the risk of excluding themselves from the rational and flexible  
decision-making process.  
Therefore, the approaches of Castro and New (2016) are called upon for this reflection, as they unequivocally  
state that AI is already having a major positive impact on many different sectors of the global economy and  
society. For example, humanitarian organizations have been using intelligent chatbots to provide psychological  
support to refugees, and doctors have been using AI to develop personalized treatments for cancer patients.  
However, the benefits of AI, as well as its likely impact in the coming years, are widely underestimated by  
policymakers and the public (Castro & New, 2016). Furthermore, a counter-narrative that AI raises serious  
concerns and justifies a cautious regulatory approach to limit the harm it could cause has gained prominence,  
although it is erroneous and detrimental to social progress.  
Therefore, given that AI has been used in various sectors of society, it is essential to consider all the nuances in  
the application and use of AI, especially in healthcare, which is the next topic.  
Artificial Intelligence in Healthcare  
According to Hosny and Hugo (2019), AI has demonstrated great progress in the detection, diagnosis, and  
treatment of diseases. Deep learning, a subset of machine learning based on artificial neural networks, has  
enabled applications with performance levels close to those of trained professionals in tasks such as the  
interpretation of medical images and the discovery of pharmaceutical compounds.  
Therefore, from a clinical point of view, there are elements that elucidate that AI has indeed revolutionized  
several aspects, which is why, Hosny et al. (2019) argue, that unsurprisingly, most AI developments in healthcare  
meet the needs of high-income countries (HICs), where most research is conducted.  
On the other hand, there is little discussion about what AI can bring to medical practice in low- and middle-  
income countries (LMICs), where labor shortages and limited resources restrict access to and quality of care.  
AI can play an important role in combating global health inequalities at the individual patient, healthcare system,  
and population levels if clinical and administrative aspects are combined. However, challenges in developing  
and implementing AI applications must be addressed before widespread adoption and measurable impact (Hosny  
et al. 2019).  
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However, AI, as measured by the arguments above, highlights the fact that, above all, it must be taken into  
account that in a process of implementing and/or including AI in the institution, it must be oriented towards  
solutions and not towards generating problems, if a widespread adoption process is not observed.  
That is why, according to Sahni and Carrus (2023), it should always be considered that AI in business sectors  
must respect the large amounts of structured and quantitative data, and the computer algorithms, and that the  
essence of AI involves training based on discrete results, because, at the health level, qualitative information,  
such as clinical notes and patient reports, is generally more difficult to interpret, and the multifactorial results  
associated with clinical decision-making make algorithm training more complex. Another challenge is  
integrating AI results into the already complex clinical workflow.  
In the healthcare field, the role of AI in improving clinical judgment has received the most attention, with a  
particular focus on prognosis, diagnosis, treatment, clinical workflow, and the expansion of clinical expertise.  
Specialties such as radiology, pathology, dermatology, and cardiology already use AI in the image analysis  
process. In screening radiological examinations, for example, up to 30% of radiology clinics that responded to a  
survey indicated that they had adopted AI by 2020, and another 20% indicated that they planned to start using  
AI in the near future (Drazen and Kohane, 2023).  
It is shown, from the previous view, that the implementation of an AI process is irreversible at the healthcare  
level, so the monitoring of the implementation must be understood by the entire structure, whether clinical or  
administrative. Naturally, this requires structured monitoring that must also take into account, as Schwalbe and  
Wahl (2020) point out, the simultaneous advances in information technology infrastructure and mobile  
computing power.  
A number of fundamental questions have been raised about AI-driven health interventions and whether the tools,  
methods, and safeguards traditionally used to make ethical and evidence-based decisions about new technologies  
can be applied to AI.  
The above statement is consistent with a study presented by Teeple and Navathe (2019), which concluded that  
AI is gaining ground in clinical practice; however, due to its reliance on historical data, which is based on the  
generation of biased data or biased clinical practices, AI can create or perpetuate biases that can worsen patient  
outcomes. However, by implementing AI strategically and carefully selecting the underlying data, algorithm  
developers can mitigate AI bias. Addressing bias can allow AI to reach its full potential, helping to improve  
diagnosis and prediction while protecting patients.  
Types of AI in Healthcare  
According to Drazen and Kohane (2023), two types of AI have generally been pursued in the healthcare field,  
namely:  
First, machine learning, which involves computational techniques that learn from examples rather than  
operating based on predefined rules, and  
Second, natural language processing, which is the ability of a computer to transform human language and  
unstructured texts into machine-readable structured data that reliably reflects the intent of the language.  
However, regarding the typicality of AI in healthcare, the two elements that stand out justify the need to once  
again seek to understand that the implementation of an AI process necessarily involves the appropriation of the  
entire work team to achieve success, due to the fact that it involves computational techniques that learn from  
examples.  
Underlying this is the interpretation that, when it is intended to associate AI with the management of institutions,  
one must always pay attention to the assumptions involved, and it is in these terms that the following section  
will address AI in AGH.  
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Hospital Administration and Management and Artificial Intelligence  
According to Drazen and Kohane (2023), the environment in which some healthcare organizations operate often  
leads them to focus on short-term financial results, to the detriment of investing in innovative long-term  
technologies, such as AI, as these can ensure better efficiency, effectiveness, and work efficacy.  
Therefore, healthcare organizations that prioritize innovation link investment decisions to the “total mission  
value” (Drazen and Kohane, p. 2, 2023), which includes financial and non-financial factors, such as quality  
improvement, patient safety, patient experience, healthcare professional satisfaction, and increased access to  
healthcare.  
Therefore, AI must be associated with organizational identity or organizational culture, because, as Malik and  
Solaiman (2024) argue, around the world, ‘smart hospitals’ are growing in number, using technology integrated  
with AI in the hospital’s internal network to improve care, outcomes and efficiency, which can only be realistic  
if everyone is involved with the being of the institution.  
At this point, it is important to make a distinction between AI used for AGH and AI used for clinical purposes.  
According to Malik and Solaiman (2024), the former refers to the implementation of AI technologies to optimize  
administrative processes, increase operational efficiency and improve decision-making in non-clinical areas.  
A segunda envolve o uso da IA para apoiar os processos de tomada de decisão e optimizar tarefas administrativas  
directamente relacionadas ao cuidado e tratamento do paciente.  
While both forms of AI are relevant to HGA, this chapter focuses primarily on the former, exploring the  
administrative context in which these technologies exist for healthcare professionals working daily and their  
legal implications.  
From the above, it is clear that AI can be used in HGA in various sectors; however, according to Malik and  
Solaiman (2024), the most notable for HGA are the following:  
Predictive Maintenance  
A medium to large hospital contains between 5,000 and over 10,000 different types of medical equipment, such  
as magnetic resonance imaging (MRI) machines, computed tomography (CT) scanners, X-ray machines, and  
ventilators, which are essential for the diagnosis and treatment of patients.  
Consequently, these devices are expensive and require regular maintenance throughout the product lifecycle,  
contributing to additional costs. Even with maintenance, these medical devices are subject to unexpected failures,  
resulting in delays in patient care, additional costs, and potential damage to the devices themselves.  
However, traditional maintenance and condition monitoring methods for medical devices fail to easily detect  
component performance levels and provide insufficient warnings of impending failures. Current monitoring  
tools lack flexibility, leaving a wide range of equipment integrity issues unresolved.  
Therefore, predictive maintenance involves using large data streams generated by the system to inform decisions  
about preserving a system's capacity and functionality by monitoring and controlling its operation in real time.  
However, collecting and analyzing substantial amounts of time-sensitive data from individual hospital  
equipment separately to monitor equipment integrity and anticipate adverse events can be a rather complex  
exercise. To address this problem, healthcare institutions are exploring big data analytics, including machine  
learning (ML), cloud and edge computing technologies, and advanced data mining algorithms to monitor medical  
equipment.  
Therefore, a predictive maintenance architecture based on AI-powered technologies allows medical devices to  
accumulate data from other devices scattered throughout the hospital, enabling each device to gain self-  
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awareness of its health status by comparing and learning from its own history as well as that of other similar  
devices.  
Hospital Bed Management  
Bed management is another critical aspect of HRM that directly impacts the quality of patient care, staff  
workload, and overall operational efficiency of healthcare. It involves monitoring patient flow in hospital wards  
to coordinate admissions, discharges, and transfers, and allocating beds according to specific patient needs.  
Therefore, efficient and effective bed management improves patient outcomes, ensures prompt admission, and  
timely discharge to free up beds for new admissions. Achieving this efficiency, however, has been a challenge  
for hospitals, as exemplified by Malik and Solaiman (2024), according to whom Humber River Hospital in  
Toronto implemented AI to streamline the Emergency Room (ER) process using software, and was able to  
predict ER admissions two days in advance by processing real-time data from various hospital activities,  
including admissions, wait times, transfers, and discharges.  
Therefore, the introduction of AI in bed management is a decisive factor for significantly improving some of the  
previously mentioned indicators, such as admission forecasting.  
This does not mean that such AI applications are risk-free. Another example involved Kettering General  
Hospital, which conducted a case study to investigate AI techniques for improving bed allocation.  
It was found that, although technology may be a better alternative to current inefficient bed registration and  
tracking systems, it presents its own challenges, including obtaining sufficient and good quality data that  
encompasses the complexity of patients' needs.  
Team Scheduling Planning  
A staff schedule (or rotation) consists of a schedule or list that assigns tasks or responsibilities to individuals in  
a rotating or sequential order.  
To provide quality patient care while controlling costs, particularly in high-pressure environments such as  
intensive care and BS, ensuring adequate staffing levels balanced with the needs and preferences of the medical  
staff is crucial.  
However, medical staff scheduling involves developing work schedules for healthcare professionals so that the  
hospital has sufficient staff to meet patient demand, ensuring that healthcare professionals receive adequate rest  
periods and training opportunities.  
Although scheduling is a common problem for most organizations, the challenge is particularly significant in  
the healthcare sector, which requires its employees to work 24 hours a day. Common scheduling problems in a  
healthcare setting include understaffing, modifications to assigned schedules to accommodate the  
unpredictability inherent in patient care, personal preferences, unexpected last-minute demands, and potential  
role misalignment, such as those resulting from assigning more experienced staff to fill schedule gaps and cover  
for lower-level staff, even if they are not under their direct supervision. Current manual and self-managed  
scheduling methods are tedious and time-consuming, leaving little time for individualized patient care.  
Therefore, integrating AI into existing work scheduling systems can substantially improve their performance,  
allowing hospitals to effectively balance staff availability with patient needs. In intensive care settings, AI-based  
systems are used to anticipate the workload of professionals in Intensive Care Units and BS, ensuring that  
workers with the appropriate skills are allocated to specific shifts.  
Thus, AI-based optimization mechanisms can transform medical scheduling systems by analyzing the complex  
staffing needs across different departments and specialties in hospitals and generating schedules that carefully  
balance staff availability and satisfaction with patient needs.  
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Clinical Decision Support (CDS)  
Considering the vast amount of patient data and their constantly changing needs, making informed clinical  
decisions is a challenge. Hospitals rely on CDS to improve healthcare delivery, assisting clinicians, staff,  
patients, or other individuals with person-specific knowledge and information, intelligently filtered or presented  
at appropriate times, to improve health and healthcare.  
It is in this context that the entire hospital system, segregated into clinical, administrative, and nursing, must be  
aligned for better treatment of patient information, and AI can be one of the tools for the desired alignment.  
A study conducted by Alves et al. (2024) stated that the potential benefits of AI in hospital management are  
balanced with significant challenges and concerns regarding its effective integration, which requires an approach  
to technical, ethical, and cultural issues, focusing on maintaining human elements in decision-making.  
Therefore, AI should be seen as a powerful tool to support, not replace, human judgment in hospital management,  
promising improvements in efficiency, data accessibility, and analytical capacity. Preparing healthcare  
institutions with the necessary infrastructure and providing specialized training for managers is crucial to  
maximizing the benefits of AI while mitigating associated risks.  
Artificial Intelligence and Hospital Administration and Management in Mozambique  
Through the literature review carried out, few studies exist on AI in Mozambique, which leads us to an  
assessment and analysis of AI in low-income countries, which can be cross-referenced and compared with the  
Mozambican reality, obviously glocalizing them.  
In this sense, we find it necessary to bring the argument of Mrazek and O’Neill (2020), who estimate that  
significant investments in health technology, including those that use digital health and AI, contribute to reducing  
the gap in health services in emerging markets, given the potential of these new innovations to reach underserved  
patients.  
The above statement is equated to the Mozambican reality, in which investments in technologies that allow the  
use of AI are still deficient. This is why, according to Matenga and Roda (2024), in Mozambique, AI is still a  
major challenge for all sectors.  
Portanto, a IA, foi sendo estudada a cerca de 70 anos. Sendo algo considerado novo no seio Moçambicano, e  
sobremaneira ao nível da saúde, vale relatar sobre o estudo apresentado por Mahesse (2023), que mostrou que,  
através da IA, os profissionais de saúde melhoraram o diagnóstico precoce e preciso da pneumonia, contribuindo  
para melhores resultados clínicos e uma abordagem mais eficaz no tratamento da doença, pois, alcançou-uma  
precisão de 88% nas imagens extraidas.  
Therefore, based on the above statement, it can be inferred that in the Mozambican national context, AI in AGH  
is still a kind of myth.  
In Paucar et al. (2024), AI transformed AGH, improving operational efficiency and the quality of patient care.  
It improved the optimization of resource allocation, enabled a more efficient distribution of resources, and a  
reduction in waiting times.  
However, it is understood that, for AGH, the application of AI can contribute to greater efficiency, considering  
the advantages listed by Paucar et al. (2024), and Mozambique can replicate this at that level.  
However, Zuhair et al. (2024) argue that there are challenges that contribute to low adoption rates and the  
absence of standardized guidelines in the implementation of AI in health management in emerging countries,  
such as: the high costs of equipment installation and maintenance, poor transportation and connectivity problems,  
and lack of personnel, and that despite these challenges, AI presents a promising future in the area of health  
management.  
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As can be observed, studies related to AI in Mozambique focus primarily on the clinical component and in a  
superficial way. They do not address aspects related to AGH (Authorized Health Management) and the potential  
contributions it can bring.  
A similar approach is seen in Cossa's (2022) statement that the implementation of a chatbot, integrated into a  
current social network, can help to better schedule appointments and improve waiting times, and this is the scope  
of AI.  
Another study by Macamo (2022) concluded that, based on AI, blockchain, being a transparent system (since all  
participants can consult the transactions already carried out and recorded), reliable (since validation occurs  
through cryptographic methods and consensus among members), and highly available (since it operates on a  
peer-to-peer network with a database distributed among the network nodes), provides a new way to protect the  
system for sharing personal clinical records.  
Therefore, it becomes even more evident that there is a lack of studies on AI in AGH in Mozambique; in other  
words, the literature on the subject is grey.  
Concluding Remarks  
After reviewing the relevant articles on AI in AGH in Mozambique, a first conclusion is that AGH Mozambique  
does not apply AI tools, especially those related to increasing productivity in sectors such as Predictive  
Maintenance, as it allows medical devices to accumulate data from other devices dispersed throughout the  
hospital, enabling each device to gain self-awareness of its health status; Bed Management, as the  
implementation of AI allows for faster and more predictive admissions to the emergency room; and Staffing  
Planning, as rotating staff allows for cost control, particularly in high-pressure environments such as intensive  
care. The introduction of AI allows for the reduction of insufficient personnel, adjusts changes in assigned  
schedules to accommodate the unpredictability inherent in patient care, due to the high costs of equipment  
installation and maintenance, precarious transportation, connectivity problems, and staff shortages.  
Another conclusion leads us to reflect that the implementation of an AI process in HGA in Mozambique  
necessarily requires the appropriation of the entire work team in order to achieve success.  
However, despite gaining ground in clinical practice, and due to the dependence on historical data in clinical  
practice, which is based on the generation of biased data or biased clinical practices, AI can create or perpetuate  
biases that can worsen patient outcomes and thus affect HGA.  
Finally, AI should be seen as a powerful tool to support, not replace, human judgment in HGA, promising  
improvements in efficiency, data accessibility, and analytical capacity.  
And, during the literature review, no material related to the topic in question was found, as little is published,  
especially in the Mozambican context. Therefore, aware that this article does not cover the full scope of AGH,  
other aspects such as the application of AI in transport (ambulances), medication management, hospital logistics,  
competency-based accounting, and others, may constitute topics of interest.  
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