Big Data and Big Data Analytics for Improved Healthcare Service and Management

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.

Ontological Approach to Holistic Healthcare Systems Simulation

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.

Modeling Human Physiology Coupled With Hyperbaric Plant Simulation for Oil and Gas

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.

Image-Based PSi Signal Acquisition From a Patient Monitor During a Medical Procedure

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.

Comparing the Perception of Privacy for Medical Devices and Devices With Medical Functionality

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.

Bring Your Own Device in Healthcare

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.

Converging Semantic Knowledge and Deep Learning for Medical Coding

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.

A Proof of Concept of a Business Intelligence Platform to Support the Decision-Making Process of Health Professionals in Waiting Lists

In the last years, the increase of the average waiting times in waiting lists has been an issue felt in several health institutions worldwide. Therefore, this problematic situation creates the need to define and implement new administrative measures in order to improve the management of these organizations. In this context, this research project arose in an attempt to support the decision-making process in waiting lists, namely medical appointments and surgeries, in a hospital located in the north of Portugal. Hereupon, a pervasive business intelligence platform was designed and developed using recent technologies such as React, Node.js, and MySQL. The proposed information technology artifact allows the efficient and easy identification in real-time of average waiting times outside the outlined patterns. Thus, the aim is to enable the reduction of average waiting times through the analysis of business intelligence indicators in order to ensure patients' satisfaction by taking necessary and adequate measures.

Data Driven Symbiotic Machine Learning for the Identification of Motion-Based Action Potentials

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.

Introductory Analysis of Human Upper Extremity After Stroke

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).