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A Survey on Artificial Intelligence Approaches in Supporting Frontline Workers and Decision Makers for COVID-19 Pandemic.

ABSTRACT: While the world has experience with many different types of infectious diseases, the current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. The transmission of this virus has infected more than 18.92 million people as of August 6, 2020, with over half a million deaths across the globe; the World Health Organization (WHO) has declared this a global pandemic. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. With this in mind, this survey paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. The primary contribution of this paper is in its collation of the current state-of-the-art technological approaches applied to the context of COVID-19, and doing this in a holistic way, covering multiple disciplines and different perspectives. The analysis is widened by investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. Moreover, the impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak.

SUBMITTER: Rasheed J

PROVIDER: S-EPMC7547637 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic.

Rasheed Jawad J Jamil Akhtar A Hameed Alaa Ali AA Aftab Usman U Aftab Javaria J Shah Syed Attique SA Draheim Dirk D

Chaos, solitons, and fractals 20201010

While the world has experience with many different types of infectious diseases, the current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. The transmission of this virus has infected more than 18.92 million people as of August 6, 2020, with over half a million deaths across the globe; the World Health Organization (WHO) has declared this ...[more]

PMID: 33071481

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Project description:BackgroundNew artificial intelligence (AI) tools are being developed at a high speed. However, strategies and practical experiences surrounding the adoption and implementation of AI in health care are lacking. This is likely because of the high implementation complexity of AI, legacy IT infrastructure, and unclear business cases, thus complicating AI adoption. Research has recently started to identify the factors influencing AI readiness of organizations.ObjectiveThis study aimed to investigate the factors influencing AI readiness as well as possible barriers to AI adoption and implementation in German hospitals. We also assessed the status quo regarding the dissemination of AI tools in hospitals. We focused on IT decision makers, a seldom studied but highly relevant group.MethodsWe created a web-based survey based on recent AI readiness and implementation literature. Participants were identified through a publicly accessible database and contacted via email or invitational leaflets sent by mail, in some cases accompanied by a telephonic prenotification. The survey responses were analyzed using descriptive statistics.ResultsWe contacted 609 possible participants, and our database recorded 40 completed surveys. Most participants agreed or rather agreed with the statement that AI would be relevant in the future, both in Germany (37/40, 93%) and in their own hospital (36/40, 90%). Participants were asked whether their hospitals used or planned to use AI technologies. Of the 40 participants, 26 (65%) answered "yes." Most AI technologies were used or planned for patient care, followed by biomedical research, administration, and logistics and central purchasing. The most important barriers to AI were lack of resources (staff, knowledge, and financial). Relevant possible opportunities for using AI were increase in efficiency owing to time-saving effects, competitive advantages, and increase in quality of care. Most AI tools in use or in planning have been developed with external partners.ConclusionsFew tools have been implemented in routine care, and many hospitals do not use or plan to use AI in the future. This can likely be explained by missing or unclear business cases or the need for a modern IT infrastructure to integrate AI tools in a usable manner. These shortcomings complicate decision-making and resource attribution. As most AI technologies already in use were developed in cooperation with external partners, these relationships should be fostered. IT decision makers should assess their hospitals' readiness for AI individually with a focus on resources. Further research should continue to monitor the dissemination of AI tools and readiness factors to determine whether improvements can be made over time. This monitoring is especially important with regard to government-supported investments in AI technologies that could alleviate financial burdens. Qualitative studies with hospital IT decision makers should be conducted to further explore the reasons for slow AI.

Project description:BackgroundRapidly growing coronavirus disease 2019 (COVID-19) pandemic has brought unprecedented challenges to the health system in Nepal. The main objective of this study was to explore the health system preparedness for COVID-19 and its impacts on frontline health-care workers in Nepal.MethodsSemi-structured interviews were conducted among 32 health-care workers who were involved in clinical care of COVID-19 patients and four policy-makers who were responsible for COVID-19 control and management at central and provincial level. Interviews were conducted through telephone or Internet-based tools such as Zoom and Skype. All interviews were audio-recorded, transcribed into English, and coded using inductive and deductive approaches.ResultsBoth health-care workers and policy-makers reported failure to initiate pre-emptive control measures at the early stages of the outbreak as the pivot in pandemic control. Although several measures were rolled out when cases started to appear, the overall health system preparedness was low. The poor governance, and coordination between three tiers of government was compounded by the inadequate personal protective equipment for health-care workers, insufficient isolation beds for patients, and poor engagement of the private sector. Frontline health-care workers experienced various degrees of stigma because of their profession and yet were able to maintain their motivation to continue serving patients.ConclusionPreparedness for COVID-19 was affected by the poor coordination between three tiers of governance. Specifically, the lack of human resources, inadequate logistic chain management and laboratory facilities for testing COVID-19 appeared to have jeopardized the health system preparedness and escalated the pandemic in Nepal. Despite the poor preparedness, and health and safety concerns, health-care workers maintained their motivation. There is an urgent need for an effective coordination mechanism between various tiers of health structure (including private sector) in addition to incentivizing the health-care workers for the current and future pandemics.

Project description:BackgroundArtificial intelligence (AI) has revolutionized various healthcare domains, where AI algorithms sometimes even outperform human specialists. However, the field of clinical ethics has remained largely untouched by AI advances. This study explores the attitudes of anesthesiologists and internists towards the use of AI-driven preference prediction tools to support ethical decision-making for incapacitated patients.MethodsA questionnaire was developed and pretested among medical students. The questionnaire was distributed to 200 German anesthesiologists and 200 German internists, thereby focusing on physicians who often encounter patients lacking decision-making capacity. The questionnaire covered attitudes toward AI-driven preference prediction, availability and utilization of Clinical Ethics Support Services (CESS), and experiences with ethically challenging situations. Descriptive statistics and bivariate analysis was performed. Qualitative responses were analyzed using content analysis in a mixed inductive-deductive approach.ResultsParticipants were predominantly male (69.3%), with ages ranging from 27 to 77. Most worked in nonacademic hospitals (82%). Physicians generally showed hesitance toward AI-driven preference prediction, citing concerns about the loss of individuality and humanity, lack of explicability in AI results, and doubts about AI's ability to encompass the ethical deliberation process. In contrast, physicians had a more positive opinion of CESS. Availability of CESS varied, with 81.8% of participants reporting access. Among those without access, 91.8% expressed a desire for CESS. Physicians' reluctance toward AI-driven preference prediction aligns with concerns about transparency, individuality, and human-machine interaction. While AI could enhance the accuracy of predictions and reduce surrogate burden, concerns about potential biases, de-humanisation, and lack of explicability persist.ConclusionsGerman physicians frequently encountering incapacitated patients exhibit hesitance toward AI-driven preference prediction but hold a higher esteem for CESS. Addressing concerns about individuality, explicability, and human-machine roles may facilitate the acceptance of AI in clinical ethics. Further research into patient and surrogate perspectives is needed to ensure AI aligns with patient preferences and values in complex medical decisions.

Dataset Information

A Survey on Artificial Intelligence Approaches in Supporting Frontline Workers and Decision Makers for COVID-19 Pandemic.

Publications

A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic.

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