A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 OutbreakRead the full article
Journal of Healthcare Engineering provides a vehicle for the exchange of advanced knowledge, emerging technologies, and innovative ideas related to all aspects of engineering involved in healthcare delivery processes and systems.
Chief Editor, Professor Zollo, has research expertise in neuro-robotics and biomedical technologies for neuroscience, rehabilitation and assistance robotics, and robotic and mechatronic devices for personal assistance and service robotics.
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Comparison of Presurgical Dental Models Manufactured with Two Different Three-Dimensional Printing Techniques
Three-dimensional printing is a rapidly developing area of technology and manufacturing in the field of oral surgery. The aim of this study was comparison of presurgical models made by two different types of three-dimensional (3D) printing technology. Digital reference models were printed 10 times using fused deposition modelling (FDM) and digital light processing (DLP) techniques. All 3D printed models were scanned using a technical scanner. The trueness, linear measurements, and printing time were evaluated. The diagnostic models were compared with the reference models using linear and mean deviation for trueness measurements with computer software. Paired t-tests were performed to compare the two types of 3D printing technology. A value < 0.05 was considered statistically significant. For FDM printing, all average distances between the reference points were smaller than the corresponding distances measured on the reference model. For the DLP models, the average distances in the three measurements were smaller than the original. Only one average distance measurement was greater. The mean deviation for trueness was 0.1775 mm for the FDM group and 0.0861 mm for the DLP group. Mean printing time for a single model was 517.6 minutes in FDM technology and 285.3 minutes in DLP. This study confirms that presurgical models manufactured with FDM and DLP technologies are usable in oral surgery. Our findings will facilitate clinical decision-making regarding the best 3D printing technology to use when planning a surgical procedure.
Text Messaging-Based Medical Diagnosis Using Natural Language Processing and Fuzzy Logic
The use of natural language processing (NLP) methods and their application to developing conversational systems for health diagnosis increases patients’ access to medical knowledge. In this study, a chatbot service was developed for the Covenant University Doctor (CUDoctor) telehealth system based on fuzzy logic rules and fuzzy inference. The service focuses on assessing the symptoms of tropical diseases in Nigeria. Telegram Bot Application Programming Interface (API) was used to create the interconnection between the chatbot and the system, while Twilio API was used for interconnectivity between the system and a short messaging service (SMS) subscriber. The service uses the knowledge base consisting of known facts on diseases and symptoms acquired from medical ontologies. A fuzzy support vector machine (SVM) is used to effectively predict the disease based on the symptoms inputted. The inputs of the users are recognized by NLP and are forwarded to the CUDoctor for decision support. Finally, a notification message displaying the end of the diagnosis process is sent to the user. The result is a medical diagnosis system which provides a personalized diagnosis utilizing self-input from users to effectively diagnose diseases. The usability of the developed system was evaluated using the system usability scale (SUS), yielding a mean SUS score of 80.4, which indicates the overall positive evaluation.
Surface Electromyography as a Method for Diagnosing Muscle Function in Patients with Congenital Maxillofacial Abnormalities
Electromyography (EMG) is the most objective and reliable method available for imaging muscle function and efficiency, which is done by identifying their electrical potentials. In global surface electromyography (sEMG), surface electrodes are located on the surface of the skin, and it detects superimposed motor unit action potentials from many muscle fibers. sEMG is widely used in orthodontics and maxillofacial orthopaedics to diagnose and treat temporomandibular disorders (TMD) in patients, assess stomatognathic system dysfunctions in patients with malocclusions, and monitor orthodontic therapies. Information regarding muscle sEMG activity in subjects with congenital maxillofacial abnormalities is limited. For this reason, the aim of this review is to discuss the usefulness of surface electromyography as a method for diagnosing muscle function in patients with congenital malformations of the maxillofacial region. Original papers on this subject, published in English between 1995 until 2020, are located in the MEDLINE/PubMed database.
How to Enhance Digital Support for Cross-Organisational Health Care Teams? A User-Based Explorative Study
Health care service provision of individualised treatment to an ageing population prone to chronic conditions and multimorbidities is threatened. There is a need for digitally supported care, that is, (1) person-centred, (2) integrated, and (3) proactive. The research project 3P, Patients and Professionals in Productive Teams, aimed to validate and verify the prerequisites for health care systems run with patient-centred service models. This paper presents an explorative study of the digital support of a cross-organisational health care team in Norway, providing services to elderly frail people with multimorbidities in hospital discharge transition. Qualitative research methods were employed, with interviews and observations to map and evaluate the information flow and the digital support of collaborative work across organisations. The evaluation showed a lacking interoperability between the digital systems and a limited support for cross-organisational teamwork, causing raised manual efforts to maintain the information flow. Tools for coordination and planning across organisations were lacking. To enhance the situation, principles for a cloud-based health portal are proposed with a shared workspace, teamwork functionality for cross-organisational health care teams, and automatic back-end synchronisation of stored information. The main implications of this paper lie in the proposed principles which are transferable to a multitude of clinical contexts, where ad-hoc based access to shared medical information is of importance for decision-making and life-saving treatment.
Variation of the Penetration Effort in an Artificial Tissue by Hypodermic Needles
Fear of injection-related pain is a drawback to injectable therapy. Hypodermic injections are a cause for great anxiety and reduced adherence to the subcutaneous application of insulin for glycemic control in diabetics or in the treatment of multiple sclerosis, increasing the risk of complications and mortality. Injured or sick people have to undergo several daily injections, forcing them to rotate the veins and regions used to recover from the trauma caused by the perforation of the skin, tissue, muscles, veins, and arteries. People who suffer from type 1 diabetes mellitus (DM1) need to have their glycemic control 3 to 5 times a day and to take insulin up to 3 times a day. In both cases, the patient needs to perforate the skin. To quantify the pain perceived by the patients depends on the evaluation of each patient and therefore is subjective. This study aims to understand the application and self-application of hypodermic injections and decrease pain during its application and the phobia of the patient, following the reasoning that the lower the effort to penetrate the needle, the less trauma in the tissue and therefore the pain provoked. For that, it was analyzed how some of the characteristics of the needle can influence the sensation of pain in the injection. The needle penetration effort was measured in an artificial tissue (substitute skin model) for different cannula diameters, roughness, depth of penetration, lubrication, and angles of the perforating tip bevel. This study aimed to find alternatives to facilitate the application and self-application of hypodermic injections, increase safety and comfort, and reduce the pain intensity perceived by the patient. To do this, the bevel of needles used repeatedly was analyzed in the profile projector and SEM to verify the loss of the profile or the formation of burrs that could hamper the penetration or traumatize the tissue during the reuse of needles. It has also been mechanically analyzed, which can be done to prevent that the needles used in the subcutaneous application do not inadvertently reach the muscle. The greater penetration effort observed in the needles with greater angle of the bevel is responsible for the patient’s perception of pain.
Frontal Alpha Complexity of Different Severity Depression Patients
Depression is a leading cause of disability worldwide, and objective biomarkers are required for future computer-aided diagnosis. This study aims to assess the variation of frontal alpha complexity among different severity depression patients and healthy subjects, therefore to explore the depressed neuronal activity and to suggest valid biomarkers. 69 depression patients (divided into three groups according to the disease severity) and 14 healthy subjects were employed to collect 3-channel resting Electroencephalogram signals. Sample entropy and Lempel–Ziv complexity methods were employed to evaluate the Electroencephalogram complexity among different severity depression groups and healthy group. Kruskal–Wallis rank test and group t-test were performed to test the difference significance among four groups and between each two groups separately. All indexes values show that depression patients have significantly increased complexity compared to healthy subjects, and furthermore, the complexity keeps increasing as the depression deepens. Sample entropy measures exhibit superiority in distinguishing mild depression from healthy group with significant difference even between nondepressive state group and healthy group. The results confirm the altered neuronal activity influenced by depression severity and suggest sample entropy and Lempel–Ziv complexity as promising biomarkers in future depression evaluation and diagnosis.