Journal of medical Internet research
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J. Med. Internet Res. · Jun 2020
Virtual Management of Patients With Cancer During the COVID-19 Pandemic: Web-Based Questionnaire Study.
During the coronavirus disease (COVID-19) pandemic, patients with cancer in rural settings and distant geographical areas will be affected the most by curfews. Virtual management (telemedicine) has been shown to reduce health costs and improve access to care. ⋯ Oncologists have a high level of awareness of virtual management. Although their survey responses indicated that second- and third-line palliative treatments should be interrupted, they stated that neoadjuvant, adjuvant, perioperative, and first-line palliative treatments should continue. Our results confirm that oncologists' views on the priority of anticancer treatments are consistent with the evolving literature during the COVID-19 pandemic. Challenges to virtual management should be addressed to improve the care of patients with cancer.
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J. Med. Internet Res. · Jun 2020
A Double Triage and Telemedicine Protocol to Optimize Infection Control in an Emergency Department in Taiwan During the COVID-19 Pandemic: Retrospective Feasibility Study.
Frontline health care workers, including physicians, are at high risk of contracting coronavirus disease (COVID-19) owing to their exposure to patients suspected of having COVID-19. ⋯ The implementation of the double triage and telemedicine protocol in the ED during the COVID-19 pandemic has high potential to improve infection control.
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J. Med. Internet Res. · Jun 2020
Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists.
Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. ⋯ AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that "affect" the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.
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J. Med. Internet Res. · Jun 2020
Improvements in Patient Monitoring in the Intensive Care Unit: Survey Study.
Due to demographic change and, more recently, coronavirus disease (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be due to the lack of user involvement in research and development. ⋯ This survey study of ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm standard operating procedures, introducing wireless sensors, preparing for the use of AI, and enhancing the digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine.
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J. Med. Internet Res. · Jun 2020
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians.
Artificial intelligence (AI) can transform health care practices with its increasing ability to translate the uncertainty and complexity in data into actionable-though imperfect-clinical decisions or suggestions. In the evolving relationship between humans and AI, trust is the one mechanism that shapes clinicians' use and adoption of AI. Trust is a psychological mechanism to deal with the uncertainty between what is known and unknown. ⋯ However, assessing the magnitude and impact of human trust on AI technology demands substantial attention. Will a clinician trust an AI-based system? What are the factors that influence human trust in AI? Can trust in AI be optimized to improve decision-making processes? In this paper, we focus on clinicians as the primary users of AI systems in health care and present factors shaping trust between clinicians and AI. We highlight critical challenges related to trust that should be considered during the development of any AI system for clinical use.