Health data privacy is essential for the acceptance of digital health applications. Hence, privacy is a precondition for future healthcare delivery. This study compares the perception of the current state of health data privacy in officially registered and therefore regulated health applications (medical devices) according to the medical product act as well as non-regulated health applications (devices with medical functionality) in Germany. To this end, an empirical study based on a questionnaire is conducted (n=53). The results show that there are significant differences between the analysed health applications with respect to perceived data privacy. In particular, there is a significant difference of the levels of perceived security between both types of devices. Low privacy for one type of device may hamper trust in digital health applications in general as there are spill-over effects regarding the perception of data privacy. Thus, the study suggests that legal regulations for devices with medical functionality should be adapted to protect health data adequately.
Underwater activities are an essential part of different industrial fields, and if in some cases autonomous and remotely controlled solutions could be used, often intervention of human divers is pivotal. In order to operate at depths of tens to hundreds of meters, the main technique that allows safe and efficient operation is called saturation diving, which foresees gradual adaptation of divers to harsh underwater conditions by means of hyperbaric chambers. This type of facility requires highly qualified personnel for management; however, nowadays, most training is done on empty industrial plants, which is costly and limits the possibility to take into account vital parameters of personnel inside them as well as making it practically impossible to reproduce emergency situations. This paper proposes an innovative approach in M&S for the hyperbaric plants devoted to support training and certification of life support supervisors (LSS) as well as other operators involved in diving activities.
Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.
Depth of anesthesia (DoA) is determined by assessment of relevant clinical signs, interpretation of hemodynamic measurements, and EEG measurements. The induction and proper dosing of anesthetic agents is an essential task of the anesthesiologist during a diagnostic procedure or surgery under general anesthesia. Therefore, DoA control seems to be a suitable problem to tackle with a closed loop control approach. One must be able to acquire the relevant signals online and in real time, but patient monitors are intentionally not able to connect to an external device during a procedure for safety reasons. The article introduces a universal image-based system for signal acquisition from a patient monitor that operates in the Matlab-Simulink environment for convenient integration into DoA modelling, simulation, and control. In addition, it provides the anesthesiologist with a simple dashboard that displays key acquired signal values and trends. The system has been tested on a Masimo Root with SedLine patient monitor. The results show that the PSi signal can be reliably acquired.
This article aims at developing a new ontology for healthcare systems (HS) simulation. The ontology includes various classes that represent major components of HS simulation and their relationships as an integrated whole. It is formally expressed using system entity structure language with links to basic models developed in various formalisms and stored in a model base repository. Entities are mapped into web ontology language (OWL) classes and can be visualized in Protégée and queried with SPARQL. Classes are built based on agreed-upon concepts in HS simulation domain and serve to document and formalize knowledge while providing notable benefits such as common representation of healthcare models from different simulation platforms, model reuse, querying simulation models, and browsing. The paper also presents an illustrative case study to showcase the use of the ontology while capturing successfully within its scope an outbreak of cholera disease and its mitigation plan.
In this article, the authors develop and analyze a linear programming model to obtain an ideal diet for individuals with diabetes by setting the glycemic load as the objective function. Additionally, a standardized system is used in order to facilitate the substitutability of foods present in a diet since those are classified according to their macronutrient content (proteins, lipids, and carbohydrates) and these values are, on average, very similar. Finally, the diet glycemic index is calculated with the model's outcome to corroborate that it is indeed a diet with low glycemic index and that, at the same time, it complies with the nutrient restrictions, which proves that the model can be a useful tool both to generate low glycemic index diets and to restrict certain nutrients from the diet.
Within an environment of parallel objects, an approach of structured parallel programming and the paradigm of the orientation to objects show a programming method based on high level parallel compositions or HLPCs to solve two problems of combinatorial optimization: grouping fragments of DNA sequences and the parallel exhaustive search (PES) of RNA strings that help the sequence and the assembly of DNAs. The pipeline and farm models are shown as HLPCs under the object orientation paradigm and with them it is proposed the creation of a new HLPCs that combines and uses the previous ones to solve the cited problems. Each HLPC proposal contains a set of predefined synchronization constraints between processes, as well as the use of synchronous, asynchronous and asynchronous future modes of communication. This article shows the algorithms that solve the problems, their design and implementation as HLPCs and the performance metrics in their parallel execution using multicores and video accelerator card.
Bring your own device (BYOD) policies have become a very popular topic in information technology, as this approach allows employees to bring their devices into their organizations and use them to access information. This trend has some benefits both for the organization and to employees. This paper aims to identify those benefits as well as the advantages and disadvantages of BYOD usage in organizations. In addition, SWOT analysis of BYOD usage is presented and discussed. Finally, it is introduced as an approach to BYOD in healthcare. Utilizing personal devices at work is beneficial to organizational employees as they are in some way satisfied, and they have more freedom and choice to use their devices. This can easily lead the employees to be more productive and flexible. Organizations who embrace BYOD policies have noticed that their employees are happier, more productive, and more collaborative.
The adoption of business processes (BP) can help healthcare providers in structuring the way information systems and people have to interact. Business process management (BPM) is a methodology that structures a way of representing system processes. At the same time, the human resources are organized in identified or implicit structures that allows individuals to exchange information either related to their work function or not. Nevertheless, the human organizations structure and communication channels are not, up to now, fully captured by the information systems. It may lead to losing part of the useful information exchanged by participants. Accordingly, this article focuses on multi-agent solutions representing social networks in the healthcare domain associated with BPM of patient pathways. The purpose is to study the feasibility of combining BP with agent-based models in order to better improve performance, manage resources, and ensure coordination between them.
Controlling access to sensitive personal information is a primary concern in healthcare. Regardless of whether access control policies are determined by patients, healthcare professionals, institutions, legal and regulatory authorities, or some combination of these, assuring the strict enforcement of policies across all systems that store personal health information is the overriding, essential goal of any healthcare security solution. While a comprehensive healthcare security architecture may need to impose specific controls on individual data items, most access control decisions will be based on sensitivity levels automatically assigned to information classes by a “sensitivity profile,” combined with the authorization level of the user. This article proposes the use of multi-level security, defined by lattice-based sensitivity profiles, to ensure compliance with data access restrictions between systems. This security approach accommodates the complexities needed for health data access and benefits from existing, proven tools that are used for defense and national security applications.
The field of medical coding enables to assign codes of medical classifications such as the international classification of diseases (ICD) to clinical notes, which are medical reports about patients' conditions written by healthcare professionals in natural language. These texts potentially include medical terms that define diagnosis, symptoms, drugs, treatments, etc., and the use of spontaneous language is challenging for automatic processing. Medical coding is usually performed manually by human medical coders becoming time-consuming and prone to errors. This research aims at developing new approaches that combine deep learning elements together with traditional technologies. A semantic-based proposal supported by a proprietary knowledge graph (KG), neural network implementations, and an ensemble model to resolve the medical coding are presented. A comparative discussion between the proposals where the advantages and disadvantages of each one is analysed. To evaluate approaches, two main corpus have been used: MIMIC-III and private de-identified clinical notes.
Understanding and modelling technical and biological processes is one of the basic prerequisites for the management and control of such processes. With the help of identification, the interdependencies of such processes can be deciphered and thus a model can be achieved. The verification of the models enables the quality of the models to be assessed. This article focuses on the identification and verification of motion and sensory feedback-based action potentials in peripheral nerves. Based on the acquisition of action potentials, the identification process correlates physiological and motion-based parameters to match movement trajectories and the corresponding action potentials. After a brief description of a prototype of a biosignal acquisition and identification system, this article introduces a new identification method, the symbiotic cycle, based on the well-known term symbiotic simulation. As an example, this article presents a data-driven method to create a human readable model without using presampled data. The closed-loop identification method is integrated into this symbiotic cycle.
The most reliable prognostic factors associated with upper extremity (UE) recovery are localized motor impairments, especially in the musculature of the hand and abduction of the shoulder in the first days after a stroke. An evaluation of the biomechanics of the hand allows an accurate identification of the motion arcs of the digital joints. This article includes an assess the prognostic value of the range of motion of the finger joints using an instrumental glove (CyberGlove II®) for the time one week after stroke for UE functional recovery. A prospective, longitudinal, observational study is made with follow-ups at 3-4 days, 1 week, 3 and 6 months of the patients with UE motor impairment. Variables collected included: demographic data, level of stroke severity (NIHSS), deep sensitivity, sphincter incontinence, Fugl Meyer assessment of UE (FM-UE), muscle balance with the Medical Research Council (MRC), muscle tone (Modified Ashworth Scale) and pre- and post-stroke functional ability (Barthel Index and Modified Rankin Scale).
In recent years, the increase of average waiting times in waiting lists is an issue that has been felt in health institutions. Thus, the implementation of new administrative measures to improve the management of these organizations may be required. Hereupon, the aim of this present work is to support the decision-making process in appointments and surgeries waiting lists in a hospital located in the north of Portugal, through a pervasive Business Intelligence platform that can be accessed anywhere and anytime by any device connected within the hospital's private network. By representing information that facilitate the analysis of information and knowledge extraction, the Web tool allows the identification in real-time of average waiting times outside the outlined patterns. Thereby, the developed platform permits their identification, enabling their further understanding in order to take the necessary measures. Thus, the main purpose is to enable the reduction of average waiting times through the analysis of information in order to, subsequently, ensure the satisfaction of patients.
A potential new generation computing environment is emerging which combines wiki technology with semantic web concepts. This has brought about the fusion of the wiki execution ecosystem, a semantic web for model-driven applications, and a high-level language as an extension to wiki text for accelerated development. Semantic MediaWiki provides this platform and a fragment of a health record, including allergy intolerance as structured in HL7 FHIR with terminology bindings to SNOMED CT and to HL7 terminologies was developed by the author in a short timeframe (approximately 10 hours). The system navigates around the health record and controls the entry of terms in the record from controlled ValueSets. All terminologies and ValueSets are integrated into the prototype.
A potential new generation computing environment is emerging which combines wiki technology with semantic web concepts. This has brought about the fusion of the wiki execution ecosystem, a semantic web for model-driven applications, and a high-level language as an extension to wiki text for accelerated development. Semantic MediaWiki provides this platform and a fragment of a health record, including allergy intolerance as structured in HL7 FHIR with terminology bindings to SNOMED CT and to HL7 terminologies was developed by the author in a short timeframe (approximately 10 hours). The system navigates around the health record and controls the entry of terms in the record from controlled ValueSets. All terminologies and ValueSets are integrated into the prototype.
This article presents SmartSOP, a framework for IT support of clinical laboratory standards. Adoption of laboratory standards and good practices is critical for ensuring high-quality health services, but clinical labs are dealing with many challenges in following lab standards. The proposed IT framework facilitates an easy access to standardised procedures, monitoring their execution and recording laboratory test results. The SmartSOP framework has been positively evaluated by clinical practitioners from a hospital in Nigeria. The results of the evaluation indicate that lab scientists are likely to adopt SmartSOP if they are provided with relevant training and equipment.
Fujitsu HIKARI is an artificial intelligence solution to assist clinicians in medical decision making, developed in the context of a joint collaboration project between Fujitsu Laboratories of Europe and Hospital Clínico San Carlos. This decision support system leverages on data analytics combined with healthcare semantic information to provide health estimations for patients, improving care quality and personalized treatment. Fujitsu HIKARI stands on the shoulders of biomedical knowledge, which includes (i) theoretical knowledge extracted from scientific literature, domain expert knowledge, and health standards; and (ii) empirical knowledge extracted from real patient electronic health records. The theoretical knowledge combines a theoretical knowledge graph (TKG) and a biomedical document repository (BDR). The empirical knowledge is encoded in an empirical knowledge graph (EKG). One of the main functionalities of Fujitsu HIKARI is the patient mental health risks assessment, which is based on the exploitation of its underlying Biomedical Knowledge.
Mobile health (mHealth) has emerged as a tool to enhance efficiency of healthcare service delivery in developing countries especially in hard-to-reach areas. The purpose of this article was to review the mHealth initiatives implemented in Malawi as a developing country, since 2010-2017 and their impact on health outcomes. Data was gathered through published reports, peer-reviewed papers, grey-literature on electronic health (eHealth), telemedicine and mHealth. The findings reveal that although the majority of mHealth projects have registered positive impacts, implementation challenges still exist. The study also revealed that the majority of mHealth projects are driven and funded by Non-Governmental Organizations (NGO) and have not moved beyond pilot phase. There is also lack of funding model on part of the Malawi Government to scale-up the mHealth programmes. Based on the challenges, the article makes some recommendations.
