Digital Technologies for Public Health Services after the COVID-19 Pandemic: A Risk Management Analysis

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Abstract

Digitalization has become an important part of human lives that occurs in many fields, ranging from education to labor. Artificial intelligence is one of the most important disruptive technologies, which has produced massive changes in current medical practices, such as MRI, X-ray, and surgeries. AI-based surgeries present lower risks to patients and support medical specialists when it comes to burnout and more challenging operations, which can be more easily performed with the help of robots. The COVID-19 pandemic had a huge impact on healthcare systems due to the large number of patients that overburdened medical healthcare professionals and the medical capacities of hospitals. In this paper, we approach AI-based tools, which have a significant impact on various specializations in medicine under the form of robots, based on an extensive literature review. The research methods consist of a quantitative study conducted on a sample of 50 nurses with the purpose of assessing the awareness of nurses regarding digital technologies used in the medical field, focusing mainly on their capacity to classify digital technological risks that may occur in a public healthcare system. The results show that most of the respondents (62%) are aware of digital applications used in hospitals and are able to classify and manage the risks that may occur. After conducting our research, we found that nurses have a certain degree of reluctance when it comes to the introduction of digital technologies in the medical field.

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Several coronavirus disease 2019 (COVID-19) vaccines are currently in human trials. In June 2020, we surveyed 13,426 people in 19 countries to determine potential acceptance rates and factors influencing acceptance of a COVID-19 vaccine. Of these, 71.5% of participants reported that they would be very or somewhat likely to take a COVID-19 vaccine, and 61.4% reported that they would accept their employer’s recommendation to do so. Differences in acceptance rates ranged from almost 90% (in China) to less than 55% (in Russia). Respondents reporting higher levels of trust in information from government sources were more likely to accept a vaccine and take their employer’s advice to do so.

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Rahul Deo (2015)

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.