An Ostensive Information Architecture to Enhance Semantic Interoperability for Healthcare Information Systems

Semantic interoperability establishes intercommunications and enables data sharing across disparate systems. In this study, we propose an ostensive information architecture for healthcare information systems to decrease ambiguity caused by using signs in different contexts for different purposes. The ostensive information architecture adopts a consensus-based approach initiated from the perspective of information systems re-design and can be applied to other domains where information exchange is required between heterogeneous systems. Driven by the issues in FHIR (Fast Health Interoperability Resources) implementation, an ostensive approach that supplements the current lexical approach in semantic exchange is proposed. A Semantic Engine with an FHIR knowledge graph as the core is constructed using Neo4j to provide semantic interpretation and examples. The MIMIC III (Medical Information Mart for Intensive Care) datasets and diabetes datasets have been employed to demonstrate the effectiveness of the proposed information architecture. We further discuss the benefits of the separation of semantic interpretation and data storage from the perspective of information system design, and the semantic reasoning towards patient-centric care underpinned by the Semantic Engine.

OpenEHR modeling: improving clinical records during the COVID-19 pandemic

The COVID-19 pandemic had put pressure on various national healthcare systems, due to the lack of health professionals and exhaustion of those avaliable, as well as lack of interoperability and inability to restructure their IT systems. Therefore, the restructuring of institutions at all levels is essential, especially at the level of their information systems. Furthermore, the COVID-19 pandemic had arrived in Portugal at March 2020, with a breakout on the northern region. In order to quickly respond to the pandemic, the CHUP healthcare institution, known as a research center, has embraced the challenge of developing and integrating a new approach based on the openEHR standard to interoperate with the institution’s existing information and its systems. An openEHR clinical modelling methodology was outlined and adopted, followed by a survey of daily clinical and technical requirements. With the arrival of the virus in Portugal, the CHUP institution has undergone through constant changes in their working methodologies as well as their openEHR modelling. As a result, an openEHR patient care workflow for COVID-19 was developed.

vhMentor: An Ontology Supported Mobile Agent System for Pervasive Health Care Monitoring

Healthcare provision is a set of activities that demands the collaboration of several stakeholders (e.g. physicians, nurses, managers, patients etc.) who hold distinct expertise and responsibilities. In addition, medical knowledge is diversely located and often shared under no central coordination and supervision authority, while medical data flows remain mostly passive regarding the way data is delivered to both clinicians and patients. In this paper, we propose the implementation of a virtual health Mentor (vhMentor) which stands as a dedicated ontology schema and FIPA compliant agent system. Agent technology proves to be ideal for developing healthcare applications due to its distributed operation over systems and data sources of high heterogeneity. Agents are able to perform their tasks by acting pro-actively in order to assist individuals to overcome limitations posed during accessing medical data and executing non-automatic error-prone processes. vhMentor further comprises the Jess rules engine in order to implement reasoning logic. Thus, on the one hand vhMentor is a prototype that fills the gap between healthcare systems and the care provision community, while on the other hand allows the blending of next generation distributed services in healthcare domain.

Cognitive workload reduction in hospital information systems : Decision support for order set optimization

Order sets are a critical component in hospital information systems that are expected to substantially reduce physicians' physical and cognitive workload and improve patient safety. Order sets represent time interval-clustered order items, such as medications prescribed at hospital admission, that are administered to patients during their hospital stay. In this paper, we develop a mathematical programming model and an exact and a heuristic solution procedure with the objective of minimizing physicians' cognitive workload associated with prescribing order sets. Furthermore, we provide structural insights into the problem which lead us to a valid lower bound on the order set size. In a case study using order data on Asthma patients with moderate complexity from a major pediatric hospital, we compare the hospital's current solution with the exact and heuristic solutions on a variety of performance metrics. Our computational results confirm our lower bound and reveal that using a time interval decomposition approach substantially reduces computation times for the mathematical program, as does a K -means clustering based decomposition approach which, however, does not guarantee optimality because it violates the lower bound. The results of comparing the mathematical program with the current order set configuration in the hospital indicates that cognitive workload can be reduced by about 20.2% by allowing 1 to 5 order sets, respectively. The comparison of the K -means based decomposition with the hospital's current configuration reveals a cognitive workload reduction of about 19.5%, also by allowing 1 to 5 order sets, respectively. We finally provide a decision support system to help practitioners analyze the current order set configuration, the results of the mathematical program and the heuristic approach.

Analyzing privacy requirements: A case study of healthcare in Saudi Arabia

Developing legally compliant systems is a challenging software engineering problem, especially in systems that are governed by law, such as healthcare information systems. This challenge comes from the ambiguities and domain-specific definitions that are found in governmental rules. Therefore, there is a significant business need to automatically analyze privacy texts, extract rules and subsequently enforce them throughout the supply chain. The existing works that analyze health regulations use the U.S. Health Insurance Portability and Accountability Act as a case study. In this article, we applied the Breaux and Antón approach to the text of the Saudi Arabian healthcare privacy regulations; in Saudi Arabia, privacy is among the top dilemmas for public and private healthcare practitioners. As a result, we extracted and analyzed 2 rights, 4 obligations, 22 constraints, and 6 rules. Our analysis can assist requirements engineers, standards organizations, compliance officers and stakeholders by ensuring that their systems conform to Saudi policy. In addition, this article discusses the threats to the study validity and suggests open problems for future research.