1,720,978 research outputs found
OHIO - Odin Hospital Indoor Compass for Empowering the Management of Hospitals
No full textOHIO is a project that received funding from the European Union’s Horizon 2020 research and innovation action programme, via the ODIN – Open Call issued and executed under the ODIN project (GA 101017331), focused on the enhancement of hospital safety, productivity, and quality. The main objective of OHIO is to provide a solution to integrate the ODIN Platform with an informative system able to empower the management of hospital facilities in terms of clinical engineering, logistics, and disaster preparedness. It will enhance the existing Computer Aided Facility Management system and Indoor Positioning System mobile application, improving the process of maintenance of medical equipment, streamlining logistics, and supporting the top management in designing effective responses to disasters. OHIO will also be fully integrated within the ODIN Platform exploiting the offered services and features
An integrated custom decision-support computer aided facility management informative system for healthcare facilities and analysis
Full text linkThis article presents a Computer Aided Facility Management informative system which can output Key Performance Indicators and quantitative parameters about the analysed healthcare facility. The designed system is a self-sufficient application able to manage and analyse digital plans of hospital buildings with no need of third-party plugins or licenses. The system maps hospital’s inner organisation, destinations of use and environmental comforts giving quantitative, qualitative and graphical reports. The core database is linked to other existing hospital databases, so that the system can act as a central control cockpit. Outputs can be used by top-management and decisional staff as a decision-support tool in order to improve hospital’s structure and organisation and to reduce the major workflow risks. Furthermore, many plug-ins and modules have been developed through the years which can be easily linked to the main application thanks to its REST architecture, and which contribute to a complete analysis and management of the healthcare facilities
Standardization of Failure Codes and Nomenclature of Medical Devices for Evidence-Based Maintenance
No full textBiomedical technologies should be managed and maintained starting from the evidence provided by data. In this way, Clinical Engineering and Health Technology Management professionals can keep medical equipment safe and reliable with the optimal use of their resources. Evidence-Based Maintenance is grounded on the analysis of Real-World Evidence to monitor the maintenance effectiveness and plan any necessary changes to improve it. The lack of global standards contributes to the scarcity of accessible and shareable data to extract evidence. The purpose of this study is to draw attention to and describe the issues which can emerge during Evidence-Based Maintenance when dealing with the lack of standardization for naming and coding medical devices, with an emphasis on the nomenclature of medical equipment and the standardization of fault codes
Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing
Full text linkThe primary goal of this project is to create a framework to extract Real-World Evidence to support Health Technology Assessment, Health Technology Management, Evidence-Based Maintenance, and Post Market Surveillance (as outlined in the EU Medical Device Regulation 2017/745) of medical devices using Natural Language Processing (NLP) and Artificial Intelligence. An initial literature review on Spontaneous Reporting System databases, Health Information Technologies (HIT) fault classification, and Natural Language Processing has been conducted, from which it clearly emerges that adverse events related to HIT are increasing over time. The proposed framework uses NLP techniques and Explainable Artificial Intelligence models to automatically identify HIT-related adverse event reports. The designed model employs a pre-trained version of ClinicalBERT that has been fine-tuned and tested on 3,075 adverse event reports extracted from the FDA MAUDE database and manually labelled by experts
Health Information Technology Adverse Events Identification and Classification with Natural Language Processing and Deep Learning
No full textThe main topic of this work is to develop a framework to extract Real-World Evidence through Natural Language Processing (NLP) and Neural Networks. An initial literature analysis has been performed, from which it clearly emerges that adverse events concerning Health Information Technology (HIT) are gradually growing over time. The goal of the proposed framework is to automatically identify adverse event reports related to HIT, aiming to support Health Technology Assessment and Post Market Surveillance as outlined in European Regulation 2017/745 on Medical Devices. The designed model uses a pre-trained version of ClinicalBERT, additionally fine-tuned on 3, 705 adverse events reports extracted from the FDA MAUDE database, which had been previously manually labelled by experts. Results show better metrics than other existing HIT adverse events reports text classifiers based on non-BERT models, performing with an accuracy of 0.9906, precision of 0.9840, recall of 0.9973, and F1 score of 0.9906
Evidence based management of medical devices: a follow-up experiment
No full textEnsuring the optimal operation and longevity of medical devices is essential for maintaining high safety standards in healthcare. This study presents a significant advancement in the field by enhancing an existing evidence-based maintenance (EBM) framework, which is crucial for the effective management of medical equipment. Building upon previous methodologies, this research introduces a novel, comprehensive taxonomy for categorizing maintenance reports by revising and updating existing failure codes. This refinement addresses gaps in previous models, enabling more precise and effective maintenance strategies. A key innovation of this study is the development of a standard XML protocol, which addresses the current lack of standardization in the field, providing a consistent and unified approach to data management. This standardization is critical for improving communication and data exchange across different healthcare systems and technologies. To validate the enhanced EBM framework, the study applies it to corrective maintenance work orders related to Digital Angiography Systems, Magnetic Resonance Imaging, and Computed Tomography, using data from the Pisa University Hospital's Computerized Maintenance Management System from 2021 to 2022. This validation demonstrates the reproducibility and adaptability of the framework across different medical equipment, healthcare contexts, and timeframes. The findings represent a significant advancement in healthcare technology management, offering a robust, standardized approach that optimizes the safety, functionality, and longevity of medical devices. This research not only expands maintenance standards in the healthcare sector but also provides a critical tool for addressing the current challenges in medical device maintenance, paving the way for more reliable and effective healthcare technologies
Bed management in hospital systems
No full textThe paper presents a design for a bed management web-application to efficiently provide for the allocation of beds inside hospitals to reduce the diversions (transfer of patient in other ward or hospital) and thus the number of outliers (patient admitted in not-right ward) which may cause a longer length of stay. Information system helps the role of Bed Manager to improve the performances of the hospital-care flow optimizing the clinical paths. The system itself analyzes the interaction between patients, admission status and personnel in order to reduce the length of stay and the cost of care for hospitals. The application is designed to be linked to an existing facility manager system to gather information about the number of beds and their physical location in each room
A collaborative RESTful cloud-based tool for management of chromatic pupillometry in a clinical trial
No full textChromatic Pupillometry represents a novel approach for the assessment of Inherited Retinal Diseases. A multi-centric pilot study with a sample of 40 paediatric patients has been designed, involving physicians and engineers. In this paper, the Electronic Medical Record, named ORÁO and specifically developed to collect ophthalmologic and pupillometric data, is presented. The platform is a cloud- based application, with a RESTful and three-tier architecture. These features make it available via web for the ophthalmologists involved in the project and working in two different University centres. The platform has been designed by the whole team and developed by the Department of Information Engineering of the University of Florence. The interfaces of the medical record have been evaluated in term of Usability, according to standards. An Heuristic Evaluation has been performed in the first stage of the design of the platform and the main severe usability issues have been addressed. The outcome of the project is a customized software solution. Moreover, the physicians have an excellent attitude toward the use of ORÁO and they perceive it as a useful tool to gather the data they collect with the aim of evaluating the overall progression of the pilot study
A Region-Specific GAN-Based Solution for Data Augmentation in Dermatology
No full textMalignant melanoma (MM) remains a leading cause of skin cancer mortality despite accounting for only 1% of skin cancers. Early detection and accurate classification are essential to improve outcomes. However, the necessity of relying on big datasets to obtain clinical-ready performing deep-learning models makes their training difficult. Generative Adversarial Networks (GANs) offer a promising solution by creating high-quality synthetic data for augmentation. This preliminary study focuses on developing a GAN-based framework for MM body lesion images, explicitly excluding areas like the face, palms, and soles due to their unique dermoscopic patterns. Utilising a StyleGAN3-t architecture with adaptive discriminator augmentation, the model generated synthetic images at a resolution of 512×512 pixels, achieving a Fréchet Inception Distance (FID) score of 31.73 after 1,740 iterations. These results highlight the model’s ability to produce diverse, high-quality images comparable to real-world data. Further research will investigate different GAN models, improved metrics, and subjective validation via physician assessments. This development has the potential to minimize overdiagnosis and enhance clinical outcomes in melanoma treatment significantly
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