Objectives This study aims to fulfill the current research gap pertaining to the lack of a comprehensive systematic mapping study (SMS) regarding the effectiveness of artificial intelligence (AI) machine learning algorithms in the diagnosis of speech and language disorders (SLDs), and the extent to which such AI algorithms can automate this diagnostic process. Methods An SMS has been implemented following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines; 19,774 research papers were screened resulting in 70 studies meeting purposely designed inclusion and exclusion criteria. Results The findings revealed multiple research gaps including substantial divide in the application of AI machine learning algorithms for diagnosing SLDs, where 91.43% versus 8.57% of the studies relating to SLDs, respectively. This is further exacerbated by the absence AI machine learning algorithms for diagnosing prevalent language disorders, such as developmental language disorders in children. Furthermore, most AI machine learning algorithms for diagnosing SLDs are focused on binary classification of these disorders, for example, healthy and pathological voices, but not providing detailed diagnostics, such as the impaired aspects and contextual SLDs severity. Finally, AI machine learning algorithms have predominantly focused on partially automating the SLD assessment phase of the diagnostic process (76%) compared to those that have extended the automation to partially include the diagnosis determination phase (24%). Conclusion The effectiveness of AI machine learning algorithms in automating SLD diagnosis cannot be claimed without larger population datasets, highlighting a research gap for developing AI models to automate the four phases of the SLD diagnostic process and link them to treatment protocols in clinical settings.

Purpose: The current state of the art in process modeling of blood banking and transfusion services is not well grounded; methodological reviews are lacking to bridge the gap between such blood banking and transfusion processes (and their models) and their automation. This research aims to fill this gap with a methodological review. Methods: A systematic mapping study was adopted, driven by five key research questions. Identified research studies were accepted based on fulfilling the following inclusion criteria: 1) research studies should focus on blood banking and transfusion process modeling since the late 1970s; and 2) research studies should focus on process automation in relation to workflow-based systems, with papers classified into categories in line with the analysis undertaken to answer each of the research questions. Results: The search identified 22 papers related to modeling and automation of blood banking and transfusion, published in the period 1979-2022. The findings revealed that only four process modeling languages were reported to visualize process workflows. The preparation of blood components, serologic testing, blood distribution, apheresis, preparation for emergencies, maintaining blood banking and transfusion safety, and documentation have not been reported to have been modeled in the literature. This review revealed the lack of use of Business Process Modeling Notation (BPMN) as the industry standard process modeling language in the domain. The review also indicated a deficiency in modeling specialized processes in blood banking and transfusion, with the majority of reported processes being described as high level, but lacking elaboration. Automation was reported to improve transfusion safety, and to reduce cost, time cycle, and human errors. Conclusion: The work highlights the non-existence of a developed process architectural framework for blood banking and transfusion processes, which is needed to lay the groundwork for identifying and modeling strategic, managerial, and operational processes to bridge the gap with their enactment in healthcare systems. This paves the way for the development of a data-harvesting platform for blood banking and transfusion services.

Goal-oriented requirements engineering (GORE) for Systems of Systems (SoS) includes combining individual operational systems local goals to achieve higher-level goals. GORE offers a structured approach to managing complex requirements, ensuring that strategic goals are translated into operational tasks. This paper provides a review of GORE frameworks, including those incorporating Model-Based Systems Engineering (MBSE), that could be used in the management of complex Systems of Systems. The paper analyzes recent GORE frameworks such as CGS4Adaptation which combines Goals and SysML for managing adaptive Socio-Cyber-Physical Systems (SCPSs); GORE-based approach to Energy Management Systems (EnMS); Model-Based and Goal-Oriented Approach for the conceptual design of smart grid services; GORE and reference architecture approach for microgrid systems; and Adaptation-Oriented Requirement Modeling approach (ADORE). A comparative analysis is conducted to assess the extent of effectiveness these frameworks provide for improving SoS traceability, adaptability, and system design integrity. The paper concludes with key findings on the strengths and limitations of the considered frameworks, on the basis of which, major conclusions on how GORE combined with MBSE can be used for managing SoS requirements in Smart Grid (SG) and socio-cyber-physical systems could be drawn. This review paper also contributes to the requirements engineering domain by outlining effective strategies for designing and managing complex, adaptive Systems of Systems.

Introduction Evidence-based practice is a problem-based solving approach to clinical practice that encourages nurses providing personalized patient care while utilizing the necessary scientific evidence for a better understanding of risks and benefits of diagnostic tests and treatments. Digital transformation of an organization begins with attaining an acceptable digital readiness level. One approach entails specifying and modeling their processes and the respective data models. Objectives In Jordan, at King Hussein Cancer Center—an international and regional accredited cancer care hospital—their nursing practice requires obtaining a standardized specification of evidence-based practice processes and their respective conceptual data model that is currently not specified for digital readiness. Methods The design science research methodology was adopted to deliver two increments. The first was concerned with the design, development, and demonstration of eight evidence-based practice processes specified using BPMN. The second was related to the design and development, demonstration, and evaluation of a respective derived data model of the case study. Both increments involved interviews with domain experts for elicitation and validation. Results Eight evidence-based practice process models were identified and specified using BPMN along with their associated data models, where one representative process model was utilized in this research to demonstrate the effectiveness of process and data modeling towards digital readiness of evidence-based practice in regional cancer center. Conclusions Both deliverables enabled the evidence-based practice management to attain common understanding to identify inefficiencies, redundancies, and areas for improvement that can be addressed through digital solutions. Evidence -based practice BPMN process models were considered as a road map to follow up a project implementation and a rich visualization to perform data analytics to identify evidence-based practice trends, patterns, and insights that can inform strategic data-driven decisions. Both deliverables were concluded necessary for developing respective information systems in the journey towards digital transformation.

Background Blood banks are an important part of healthcare systems. They embrace critical processes that start with donor recruitment and blood collection, followed by blood processing to produce different types of blood components used in transfusions, blood storage, blood distribution, and transfusion. Blood components must be generated at high quality, preserved safely, and transfused in a timely manner. This can be achieved by operating interrelated processes within a complex network. There is no comprehensive blueprint of Blood Banking and Transfusion (BB&T) processes and their relationships; therefore, this study aims to develop and evaluate a BB&T process architecture using the Riva method. Research design This research adopts a design science research methodology process (DSRM) that aims to create artifacts for the purpose of serving humanity through six phases: identifying problems, identifying solutions and objectives, designing and developing artifacts, demonstrating and evaluating the artifacts, and communicating the work. The adapted DSRM process is used to build a process architecture in the BB&T unit to improve the quality and strategic planning of BB&T processes. Applying the adapted DSRM process generated four increments before the outcomes were communicated as a highly comprehensive BB&T process architecture (BB&TPA) blueprint for virtual organizations. Finally, the generated BB&TPA is tested and validated at a reference hospital. Results A Riva-based process architecture diagram was successfully developed, acting as a reference model for virtual BB&T organizations. It is a novel output in the domain of BB&T and can also be considered as a reference model to evaluate the existing processes in BB&T real-world units. This assists domain experts in performing gap analysis in their BB&T units and paths for developing BB&T management information systems and can be incorporated in the inspection workflow of accreditation organizations.

The rising incidence of breast cancer globally highlights the need to focus on quality of life (QoL) as a key outcome in patient care. QoL includes several dimensions, such as physical, psychological, social, and spiritual well-being, all of which can significantly affect treatment outcomes and patient satisfaction. This study aims to develop a comprehensive QoL framework for breast cancer patients to enhance personalized treatment approaches. Utilizing a Design Science Research Methodology (DSRM), we combined established quality of life (QoL) assessment tools, including the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30), the European Organization for Research and Treatment of Cancer Breast-Specific Module (EORTC QLQ-BR23), and the Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale (FACIT-Sp-12), into a comprehensive framework. We conducted a survey of 88 breast cancer patients undergoing treatment at a tertiary hospital in Jordan, collecting demographic, clinical, and QoL data through self-administered questionnaires. Results: Developing a visualized, comprehensive QoL framework for both stakeholders and patients allowed them to examine the various QoL domains that influence patient well-being and their potential impact on treatment outcomes. The analysis revealed that cognitive ability, social support, and physical activity significantly affected patients' QoL. The mean global health score was 65.81, indicating moderate QoL levels. Notably, spirituality, as measured by FACIT-Sp scores, showed a positive correlation with overall QoL, suggesting that spiritual well-being plays an essential role in patient coping and satisfaction. This integrated QoL framework provides valuable insights into the multifaceted experiences of breast cancer patients, emphasizing the need for personalized care that considers physical, functional, psychological, and spiritual dimensions alongside clinical treatment. Future research should further explore the association between these domains and their implications for treatment adherence and long-term survivorship outcomes.

This paper aims to reflect on the extent to which the Design Science Research Method (DSRM) is aligned with orchestrating the development of cancer care data analytics research projects using a data strategy-enabled use-case-driven approach. A fit-for-purpose DSRM research framework has been designed to guide the research design and project-manage the iCanViz research project. The iCanViz project is an intelligent cancer data analytics visualizer aligned to multiple cancer sites, cancer incidences, risk correlations, survivals, and deaths in the Arab world within certain years of reported cancer incidences per cancer site. The DSRM approach has been deemed closely aligned with the iCanViz research project. This is primarily due to its ability to articulate and incorporate cancer care data strategy use-cases as agile software development increments within the iCanViz DSRM research framework. Also, the adaptation of the iCanViz DSRM research framework has contributed to enhancing the even distribution and concurrent execution of cancer care data analytics tasks across the software development and research team. Furthermore, the iCanViz DSRM research design has proven effective in designing research frameworks that bridge the gap between the world of cancer care and the creation of cancer care data analytics systems. With a small research team, the iCanViz project achieved this within a relatively short time frame of less than a year.

The Development of a data model for the iCanViz cancer navigator, a smart cancer incidence analytics project, proved difficult due to the large body of resources to be analyzed. In this paper, we propose an approach to design, develop, and evaluate the data models for projects with large bodies of resources. The proposed approach utilizes topic modeling for data requirement discovery to analyze the semantic structures of thousands of documents and published articles and derive a reference conceptual data model for data strategy use-cases (DS-UCs) for cancer survival and deaths. Using this approach, we are able to process and analyze a great number of resources and map their automatically discovered topics into data model entities. Using the knowledge of a field expert, the derived model was evaluated and adjusted to arrive at a final model that captures the data requirements of the studied DS-UCs. The developed approach proved to be an effective benchmark for data modeling of a complex domain like cancer care, for which the validation of a domain expert is shown to be a critical element.

Adherence to the Universal Health Coverage (UHC) principles in relation to palliative care is a key WHO directive to attain as a right for every citizen. However, UHC principles have been observed to be hindered by several barriers. Moreover, the UNSDGs, and in particular the UNSDG 3, demands “Good Health and Well Being” with the two key indicators UNSDG 3.8.1 and 3.8.2 that can be considered as metrics to assess governance conformance to palliative care. This paper reports on addressing the current research gap in linking the UHC principles to UNSDGs and, in particular, UNSDG3 and the WHO identified Palliative Care Barriers (PCB) using the i* framework Strategic Dependency (SD) and Strategic Rationale (SR) models applied to Home Healthcare Care (HHC) of a regional cancer care organisation, namely King Hussain Cancer Center (KHCC). Building on our i* HHC SD and SR developed models, and for HHC being an essential and critical part of palliative care, an integrated framework has been developed that not only links UHC principles and WHO barriers of palliative care to UNSDG 3, but a full network of dependencies that facilitates observing the linkages and impact of the most critical and strategic actors in HHC on the UHC, barriers to palliative care and UNSDG 3. Furthermore, such highly comprehensive UHC-PCB-UNSDG-i* framework network instantiations have led to identifying patterns of categories or groups of associations between UNSDG3 KPIs, UHC principles, WHO palliative care barriers and HHC actors. Hence, this contributes to healthcare policy and decision makers to revisit their policies, plans, budgets, and constraints for the deficiencies in the qualitative satisfaction of the universal health coverage principles and how palliative care barriers can be alleviated in association with the actors in the i* SD and SR models and associated goals, tasks and resources. A further corollary of this research is that change impact analysis can be timely attained to study the impact of a change driven by updating goals, tasks, and resources of the i* model to improve adherence to the UNSDG3 KPIS and UHC principles. Finally, this work has inspired work in progress to develop a data analytics platform from the evolving instances of applying palliative care processes using the resultant UHC-PCB-UNSDG-i* framework

Palliative care services are rapidly evolving in cancer care organisations. In palliative care processes, administratives are involved without acknowledging that they may hinder rather than facilitate the path of palliative care in respect to cancer patients. Comprehending a palliative care organisation, without looking at current running processes is groundless. Therefore, a critical understanding of palliative care processes is necessary for improving respective cancer care systems. In this research palliative care Business Process Models (BPMs) have been developed to empower palliative care domain experts not only attaining a critical understanding of the currently running palliative care operations, but also in informing further required improvements with associated implications on cancer patients. Amongst the key outcomes of developing and analysing palliative BPMs in a cancer care organisation revealed identifying gaps, limitations, challenges, and opportunities to reflectively improve palliative care processes in cancer care centres. Finally, this research suggests further re-engineering of palliative care processes as reference models that may be instantiated in specific socio-cultural, person-centered, and other contextual settings aimed at improved qualitative palliative care for cancer patients.

Home Health Care (HHC) is an essential and critical part of palliative care and especially for terminal cancer patients. This research is aimed as a first attempt to align with the research gap in modelling the social requirements of palliative care processes and the HHC process in particular. Consequently, this research is a first attempt at developing an $\mathbf{i}^{\ast}$ framework visual goal-oriented and social requirements models of the HHC process of the domain of palliative care with a reflected application using a case study from a leading regional cancer centre in the Middle East, namely KHCC. Furthermore, this research has made it possible for palliative care domain experts in the HHC process and using the associated $\mathbf{i}^{\ast}$ framework strategic dependency and strategic rationale models to visually trace the most critical and strategic actors in the HHC process along with the highly interacting dependers and dependees. Finally, the HHC $\mathbf{i}^{\ast}$ strategic models contribute to bridging the gap between the world of palliative care requirements and their reflective computer-based information systems and $\mathbf{IoT}$ smart devices. Hence, this sheds light towards the realisation of the field of palliative care as being a “systems of systems” virtual organisation with the respective socio-technical systems involvement, for the best care of the palliative patient and especially terminal cancer patients. A further corollary of this research is the insufficiency and less representativeness of palliative care process models to utilise in guiding the development of the HHC $\mathbf{i}^{\ast}$ framework strategic models without linking to the full associated strategic and policy documents of palliative care.

Riva is a business process architecture (BPA) modeling approach that has been used within the software engineering domain to model key business processes for specific organizational boundaries. Also, Riva is mainly a facilitated approach that relies heavily on interactions with stakeholders to determine the key entities that constitute the foundation of modeling a BPA-of-interest, especially in its early stages. Accordingly, Riva is not considered an independent approach (without heavy interaction with stakeholders), nor is it applied to support the derivation of generalized BPA models from multiple heterogeneous sources. Furthermore, while deriving business processes from the identified Riva entities, Riva is anticipated to identify and model Case Processes (CPs), Case Management Processes (CMPs), and Case Strategy Processes (CSPs) and their interrelationships. However, the Riva approach proposed in the literature mainly supports modeling CPs, CMPs, and their interrelationships, and lacks the support of modeling the CSPs, nor their related relationships. To address the limitations mentioned above, this paper presents an adaptation to the Riva-BPA modeling approach that enables modeling of a generalized BPA from multiple heterogeneous sources. In addition, this adaptation facilitates the identification of business processes not only based on the direct interaction with stakeholders but also by utilizing independent elicitation techniques of entities. Moreover, this paper extends Riva to model the corresponding CSPs and their related relationships. The proposed adaptation has been evaluated by experts in the domains of business process modeling and software engineering.

Background: Few ontological attempts have been reported for conceptualizing the bioethics domain. In addition to limited scope representativeness and lack of robust methodological approaches in driving research design and evaluation of bioethics ontologies, no bioethics ontologies exist for pandemics and COVID-19. This research attempted to investigate whether studying the bioethics research literature, from the inception of bioethics research publications, facilitates developing highly agile, and representative computational bioethics ontology as a foundation for the automatic governance of bioethics processes in general and the COVID-19 pandemic in particular. Research Design: The iOntoBioethics agile research framework adopted the Design Science Research Methodology. Using systematic literature mapping, the search space resulted in 26,170 Scopus indexed bioethics articles, published since 1971. iOntoBioethics underwent two distinctive stages: (1) Manually Constructing Bioethics (MCB) ontology from selected bioethics sources, and (2) Automatically generating bioethics ontological topic models with all 26,170 sources and using special-purpose developed Text Mining and Machine-Learning (TM&ML) engine. Bioethics domain experts validated these ontologies, and further extended to construct and validate the Bioethics COVID-19 Pandemic Ontology. Results: Cross-validation of the MCB and TM&ML bioethics ontologies confirmed that the latter provided higher-level abstraction for bioethics entities with well-structured bioethics ontology class hierarchy compared to the MCB ontology. However, both bioethics ontologies were found to complement each other forming a highly comprehensive Bioethics Ontology with around 700 concepts and associations COVID-19 inclusive. Conclusion: The iOntoBioethics framework yielded the first agile, semi-automatically generated, literature-based, and domain experts validated General Bioethics and Bioethics Pandemic Ontologies Operable in COVID-19 context with readiness for automatic governance of bioethics processes. These ontologies will be regularly and semi-automatically enriched as iOntoBioethics is proposed as an open platform for scientific and healthcare communities, in their infancy COVID-19 learning stage. iOntoBioethics not only it contributes to better understanding of bioethics processes, but also serves as a bridge linking these processes to healthcare systems. Such big data analytics platform has the potential to automatically inform bioethics governance adherence given the plethora of developing bioethics and COVID-19 pandemic knowledge. Finally, iOntoBioethics contributes toward setting the first building block for forming the field of "Bioethics Informatics".

Increasingly complex systems of systems (SoS) have to be developed in ever shorter times‐to‐market at reduced costs and with high reliability. In addition, as the life cycle of such SoS frequently spans across several decades, customer expectations and market conditions will evolve. Systems engineering (SE)/model‐based systems engineering (MBSE) and configuration management (CM) need to be ever more closely integrated to appropriately address this situation. CM as a discipline is essential for establishing traceability and controlling baseline evolutions between all the relevant pieces of information resulting from the related system life cycle processes of all constituent systems of such SoS; but for this to work, the scope of CM must be extended both throughout the entire life cycle and across all participating monolithic systems. New frameworks are needed to effectively and efficiently apply CM across SoS arrangements. This paper proposes an ontology‐based, federative approach to managing the inherent complexity of CM in the context of SoS, with particular focus on a change management framework for SoS. Examples from a conceptual system that is concerned with the submarine exploration of Enceladus as part of the “Saturn exploration system” are used to demonstrate typical SE/MBSE artifacts and how CM needs to address them across the involved operational and enabling systems.

This article proposes to use the resource-based view (RBV) to highlight the importance of intangible assets such as knowledge in the design of business process architecture (BPA). This approach, based on knowledge management (KM), implies the need to take into consideration the dynamic aspect of both the internal and the external environment. The article identifies key knowledge management enablers (KMEs) that can affect the successful design of a competitive BPA. Generic identification of KMEs with their main interactions can be useful to drive the development of a dynamic and competitive BPA. Semantic representation using ontologies is a means to accomplish this identification and to specify the necessary abstract level of the KMEs domain. This research utilizes the newly developed abstract KMEs ontology (aKMEOnt) that formally defines an essential pillar of the KM domain to present a knowledge-based approach for BPA modelling, with the novel KMEOntoBPA framework. Thus, one of the anticipated corollaries of this research is the integration between KM and BPA disciplines. The design science research methodology (DSRM) has been used to guide the research phases, which mainly include the design and development, demonstration and evaluation of the research framework. The financing department of a key international bank in Jordan is the case study that has been applied in this research. The research findings show that using a knowledge-based approach with ontologies can provide a dynamic and continuous generation of the BPA elements of the financing department. Furthermore, a knowledge-based BPA approach has several advantages and supports the sources of sustainable competitive advantage (SCA).

This paper discusses a semantic-driven approach to deriving an object-based Business Process Architecture (BPA) using Knowledge Management Enablers (KMEs). The semantic enriched Riva BPA (srBPA) ontology has been selected as an object and ontology based BPA to be derived by the abstract knowledge management enablers’ ontology (aKMEOnt). The aKMEOnt includes six KMEs: information technology, leadership, organisation structure, culture, business repository and knowledge context. The aKMEOnt has been utilised in order to generate the Essential Business Entities (EBEs) of the srBPA ontology. A link between these two artefacts, i.e., the srBPA ontology and aKMEOnt, is demonstrated using a typical example of the deposits department in banking. In conclusion, this new ontology-based approach between KMEs and BPA has informed the effectiveness of using semantic KMEs and Semantic Web Rule Language (SWRL) rules in the semi-automatic identification of representative EBEs. These EBEs characterise the business of deposits in banking and constitute the first essential building block of the Riva BPA method which drives the development of Units of Work and the subsequent 1st and 2nd cut Riva process architectures.

This paper presents a new approach to developing a business process architecture using the design science research methodology. It is also part of the research framework development that generates a business process architecture using semantic knowledge management enablers. The design science research methodology has been adopted to guide the research phases which mainly include problem identification, objectives definition, design and development, demonstration, evaluation and communication. The research framework components are incrementally developed and evaluated according to the design science research methodology phases and its iterative restriction. Sufficient and representative case studies of a bank in Jordan have been applied in order to demonstrate and evaluate the research framework. The bank has been divided into three case studies that reflect the main sectors of business in the bank. Each case has been utilised as an iteration in the design science research methodology. The first iteration applied the treasury case, the second the deposits case and the third the credit case. Following the first and second iterations, feedback was reported and a new iteration conducted. Feedback has been provided according to the evaluation phase of the design science research methodology iterations. The evaluation phase has included tests of verification and validation in the first, second and third iterations of the design science research methodology. These tests are followed by checking dynamism and the mixed methods approach evaluation in the second and third iterations. The mixed methods approach has been used to assess the advantages of the semantic knowledge-based business process architecture and its impact on sources of sustainable competitive advantage; these being core competencies, technical capabilities and social capital. The second and third iterations have shown a successful verification and validation of the research framework and the objectives of dynamism and sustainable competitive advantage have been achieved. The design science research methodology has facilitated the success of the development and evaluation of the research framework and has handled the disadvantages that the bank cases have revealed through the implementation of its iterations.

System of Systems (SoS) results from the integration of a set of independent Constituent Systems (CS) that could be socio or technical, in order to offer unique functionalities. SoS is largely driven by stakeholders' needs and goals taking into consideration SoS-level global goals and CS-level individual goals. It's challenging to manage the satisfaction of these goals in such complex SoS arrangements, where links between these goals may not be clearly known or specified, and competing goals establish a complex stakeholder environment. In this research the i* goal-oriented framework has been utilised in SoS context to identify, model and manage goals of the overall SoS and its constituent systems. This paper discusses a novel Goals Referential Integrity (GRI) model that is intended to maintain the integrity and consistency of both the SoS-level and the CS-level goals, in an attempt to address the current challenges of managing goals in an SoS arrangement. Furthermore, an ontology-based model has been developed to support the GRI model and semantically annotate goals' levels in SoS context, specify the relationships and linkages between the SoS organisation, its constituent systems, global and local goals, and strategic and policy documents. Together the GRI model and its associated ontology model form the Semantic Goals Referential Integrity (SGRI) applied in SoS context, where conflicts between goals at the SoS and the CS-levels can be discovered in an attempt to maintain the semantic integrity of the SoS and CS goals.

Cancer care centers aim at automating the carried out procedures in their labs in order to reduce staff effort and increase test accuracy. This has motivated the researchers undertaking Ig/TCR clonality testing at a cancer care organisation to derive a requirements model for a process-based and service-oriented intelligent framework in diagnosing malignant lymphoproliferative neoplasms. However, eliciting the requirements for such a framework has been challenged by the incomplete specifications of the Ig/TCR testing in the cancer care lab protocols. Workflow models using Business Process Modelling Notation (BPMN) has been developed to deliver a validated set of Ig/TCR workflow models. These BPMN workflow models constituted the base for deriving functional and service requirements for Ig/TCR testing linked with related quality requirements. The triangulation between Ig/TCR process models, the functional/service-oriented requirements and quality requirement formed the foundations to propose an intelligent framework for Ig/TCR clonality testing with process mining and data analytics driven by current and historical Ig/TCR events in a particular lab of a cancer care centre.