The usability of ventilator maintenance user interface: A comparative evaluation of user task performance, workload, and user experience

Enhancing user experience in the digital service environment: A comprehensive study on the design and evaluation of internet-based healthcare products

RATIONALE

In the era of burgeoning digital technology, healthcare is a challenging transformative change towards virtual and digital platforms. Internet-based healthcare services are emerging as a popular trend within the medical area. User experience (UX) is paramount for the healthcare service, as it significantly influences experience satisfaction and fosters user viscosity. Gaining a profound understanding of users' demands and crafting services that align with their expectations is essential.

METHODS

Consequently, exploring an effective design approach for the digital healthcare service that prioritizes UX along with utilizing a comprehensive evaluation methodology to handle UX data, is of profound importance. This study introduces a design methodology for Internet-based healthcare products grounded in the UX and mental (UX-M) model. Aiming to refine the Internet-based healthcare product design by integrating insights from the experience data, it employs the Delphi-ANP and the fuzzy comprehensive evaluation to determine evaluation indexes and conduct experiential assessments.

RESULTS

The UX evaluation results of existing schemes are compared with the proposed design scheme of the intelligent guidance and internet hospital. The findings indicate that the UX evaluation of Internet-based medical services with the proposed method outperforms the existing schemes.

CONCLUSIONS

On the one hand, UX research of Internet-based healthcare products can significantly enhance service satisfaction for patients utilizing online medical treatments. On the other hand, the analysis of experience-based evaluation empowers designers to refine and improve UX design of Internet-based medical services. Such research endeavors are critical for enhancing the overall quality of service offerings and elevating user satisfaction in the digital healthcare landscape.

Critical Device Reliability Assessment in Healthcare Services

Medical device reliability is the ability of medical devices to endure functioning and is indispensable to ensure service delivery to patients. Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) technique was employed in May 2021 to evaluate existing reporting guidelines on medical device reliability. The systematic searching is conducted in eight different databases, including Web of Science, Science Direct, Scopus, IEEE Explorer, Emerald, MEDLINE Complete, Dimensions, and Springer Link, with 36 articles shortlisted from the year 2010 to May 2021. This study aims to epitomize existing literature on medical device reliability, scrutinize existing literature outcomes, investigate parameters affecting medical device reliability, and determine the scientific research gaps. The result of the systematic review listed three main topics on medical device reliability: risk management, performance prediction using Artificial Intelligence or machine learning, and management system. The medical device reliability assessment challenges are inadequate maintenance cost data, determining significant input parameter selection, difficulties accessing healthcare facilities, and limited age in service. Medical device systems are interconnected and interoperating, which increases complexity in assessing their reliability. To the best of our knowledge, although machine learning has become popular in predicting medical device performance, the existing models are only applicable to selected devices such as infant incubators, syringe pumps, and defibrillators. Despite the importance of medical device reliability assessment, there is no explicit protocol and predictive model to anticipate the situation. The problem worsens with the unavailability of a comprehensive assessment strategy for critical medical devices. Therefore, this study reviews the current state of critical device reliability in healthcare facilities. The present knowledge can be improved by adding new scientific data emphasis on critical medical devices used in healthcare services.

The development of input-monitoring system on biofuel economics and social welfare analysis

Biofuel production relies on stable supply of biomass which would be significantly influenced by climate-induced impacts. Since the actual agricultural outputs are relatively unpredictable in the face of uncertain environmental conditions and can only be realized in the harvest season, providing useful information regarding the stability of biomass supply to the downstream biofuel industry is crucial. This study firstly illustrates a theoretical framework to explore the resultant market equilibrium and optimal conditions of agricultural and bioenergy production in the face of highly uncertain environmental risks and then employs a two-stage stochastic programming model to investigate the optimal biofuel development and associated economic and environmental effects. The results show that total welfare may not always increase because the loss of other agricultural commodities induced by climate impacts may be greater than the gains received by biofuel production and emission reduction. This study provides insights into the area where artificial intelligence monitoring system can be implemented to analyze the input data associated with agricultural activities and help the biofuel industry to improve its production possibilities.

Unravelling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks

Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just "when" but also "what" to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments.

Causes of use errors in ventilation devices - Systematic review

A systematic review according to the PRISMA reporting standard was performed to identify causes of use errors in mechanical ventilators described in the literature. The PubMed search resulted in the inclusion of 16 papers. The errors described were systematically analyzed with regard to their causes and categorized in an adapted cause-and-effect diagram. The causes of use errors were related to specific usability issues and to the general condition that medical staff often work with different ventilators. When many devices are used, the different user interfaces are a source of use errors, since, for example, the same ventilation modes have different names. In order to avoid the identified causes for use errors in the future, this work offers manufacturers of ventilation devices design recommendations and the possibility to include the results in their risk management. In addition, standardizing user interface content across all ventilators, as in ISO 19223, can help reduce use errors.