ABSTRACT
Healthcare systems worldwide are confronted with numerous challenges such as an aging population, an increasing number of chronically ill patients, innovations as cost drivers and growing cost pressure. The COVID-19 pandemic causes additional burden for healthcare systems. In order to overcome these challenges, digital technologies are increasingly used. Especially the past decade witnessed a tremendous boom of artificial intelligence (AI) within the healthcare sector. AI has the potential to revolutionize healthcare and to mitigate the challenges healthcare systems are confronted with. The existing literature has frequently examined specific benefits of AI within the healthcare sector. However, there are still research gaps according to different application areas in healthcare. For this reason, an empirical study design has been conducted to investigate the potentials of AI in healthcare and to consequently identify its role. Based on a Systematic Literature Review (SLR), the following application areas for key determinants in healthcare have been identified: management tasks, medical diagnostics, medical treatment and drug discovery. By means of structural equation modeling (SEM), the study confirmed medical diagnostics and drug discovery as positive and significant influencing factors on the potential benefits of AI in healthcare. The other determinants didn't prove a significant influence. Based on the findings of the study, various recommendations have been derived to further exploit the potentials of AI in healthcare. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Demographic change, shortage of qualified employees and increasing cost pressure—the healthcare sector has to deal with various challenges. Coping with the current COVID-19 pandemic is an additional issue. All these barriers contribute to the fact that digitalization in the healthcare sector is moving forward more and more. Without the application of advanced technologies, healthcare organizations would reach their limits. In this context, the use of AI is becoming increasingly important. The potentials are wide-ranging and include applications in diagnostics and therapy, as well as the development of pharmaceuticals. But what challenges are associated with the use of AI in healthcare? Within the framework of a qualitative empirical study according to Mayring, this question has been investigated. Based on a systematic literature review, the following barriers of AI in healthcare have been identified and examined: Disagreement in data protection, lack of compatibility with ethical aspects, quality of training data, knowledge, and trust of physicians in AI-supported systems. The next step in the research design have been expert interviews among medical staff as well as AI developers with focus on AI in the healthcare sector mainly in Germany. According to these interviews, the data are analyzed and evaluated. Based on the results of the study, potential activities have been derived in order to be able to successfully overcome the barriers of AI in the healthcare sector in the future. Finally, the opinions of physicians and developers on the identified barriers are compared and discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Demographic change, shortage of qualified employees and increasing cost pressure—the healthcare sector has to deal with various challenges. Coping with the current COVID-19 pandemic is an additional issue. All these barriers contribute to the fact that digitalization in the healthcare sector is moving forward more and more. Without the application of advanced technologies, healthcare organizations would reach their limits. In this context, the use of AI is becoming increasingly important. The potentials are wide-ranging and include applications in diagnostics and therapy, as well as the development of pharmaceuticals. But what challenges are associated with the use of AI in healthcare? Within the framework of a qualitative empirical study according to Mayring, this question has been investigated. Based on a systematic literature review, the following barriers of AI in healthcare have been identified and examined: Disagreement in data protection, lack of compatibility with ethical aspects, quality of training data, knowledge, and trust of physicians in AI-supported systems. The next step in the research design have been expert interviews among medical staff as well as AI developers with focus on AI in the healthcare sector mainly in Germany. According to these interviews, the data are analyzed and evaluated. Based on the results of the study, potential activities have been derived in order to be able to successfully overcome the barriers of AI in the healthcare sector in the future. Finally, the opinions of physicians and developers on the identified barriers are compared and discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Healthcare systems worldwide are confronted with numerous challenges such as an aging population, an increasing number of chronically ill patients, innovations as cost drivers and growing cost pressure. The COVID-19 pandemic causes additional burden for healthcare systems. In order to overcome these challenges, digital technologies are increasingly used. Especially the past decade witnessed a tremendous boom of artificial intelligence (AI) within the healthcare sector. AI has the potential to revolutionize healthcare and to mitigate the challenges healthcare systems are confronted with. The existing literature has frequently examined specific benefits of AI within the healthcare sector. However, there are still research gaps according to different application areas in healthcare. For this reason, an empirical study design has been conducted to investigate the potentials of AI in healthcare and to consequently identify its role. Based on a Systematic Literature Review (SLR), the following application areas for key determinants in healthcare have been identified: management tasks, medical diagnostics, medical treatment and drug discovery. By means of structural equation modeling (SEM), the study confirmed medical diagnostics and drug discovery as positive and significant influencing factors on the potential benefits of AI in healthcare. The other determinants didn’t prove a significant influence. Based on the findings of the study, various recommendations have been derived to further exploit the potentials of AI in healthcare. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
This chapter examines the metaphorical conceptualizations of the coronavirus (Sars-CoV-2) by analyzing and comparing public discourse from the spring of 2020 in two languages, English and German. Firstly, the chapter discusses pragmalinguistic knowledge from health communication research, which has demonstrated how metaphorical conceptualizations help patients make sense of illnesses. This illuminates how the appearance of Covid-19 in 2020 demanded new ways of speaking about the impact of illness on the individual but, in this case, also on a societal, even global scale. For a full understanding of the metaphorical construals of Sars-CoV-2 as a target domain, it proves helpful to recapitulate the history of infection and contagion as metaphoric source domains, mapped on various domains of practice in many cultural and societal spheres. The empirical part of this chapter then presents the results of a cognitive and pragmatic analysis of metaphors framing Sars-CoV-2 and its ensuing illness, Covid-19. This shows how, during the first wave of 2020, fairly conventional metaphoric mappings of infection and contagion became the basis for new, ad hoc conceptualizations in both journalistic and political discourse, blurring the boundaries between, and sometimes reversing, source and target domains. Our analysis demonstrates how the meaning-making qualities of metaphors have helped journalists, politicians, and other social actors to make the ambiguous biological threat intelligible. Necessary social and political actions and reactions to the pandemic were often communicated to the public through metaphors, alluding to domains such as war, natural disasters, journeys, and religion. On a final note, the chapter discusses how different metaphor choices (by different actors, in different sociocultural contexts) may reveal different ideological and philosophical perspectives on society, nature, and human agency. © 2022 Walter de Gruyter GmbH, Berlin/Boston.
ABSTRACT
Nowadays, digital technologies become more and more indispensable on a personal and business level. New innovations accelerate processes and disrupt the markets even in the healthcare sector. A wide range of studies have demonstrated the effectiveness of digital technologies for numerous application areas like diagnostics or treatment, but there is no research about the general potential that experts from the healthcare sector see in the implementation of digital business models. In addition to technological developments and low research depth in this area, pandemics like Covid-19 demonstrate the importance of the healthcare industry. Through this motivation a research project on the topic “Potential benefits of digital business models in the healthcare industry” was developed to answer this concern. The authors could identify key performance indicators (KPIs), individualization, efficiency and communication channels as central potentials. These determinants were evaluated by means of structural equation modelling, whereby KPIs and communication channels show a significant influence on the potential of digital business models and their processes in healthcare. In order to address the rapid developments in the field of Artificial Intelligence (AI), an outlook on its potential benefits and challenges in healthcare is given finally. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.