Recently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. ...In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML.
This study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems.
To gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care-specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process.
With the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process.
Many clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Abstract Background Machine learning and robotics technologies are increasingly being used in the healthcare domain to improve the quality and efficiency of surgeries and to address challenges such ...as staff shortages. Robotic scrub nurses in particular offer great potential to address staff shortages by assuming nursing tasks such as the handover of surgical instruments. Methods We introduce a robotic scrub nurse system designed to enhance the quality of surgeries and efficiency of surgical workflows by predicting and delivering the required surgical instruments based on real-time laparoscopic video analysis. We propose a three-stage deep learning architecture consisting of a single frame-, temporal multi frame-, and informed model to anticipate surgical instruments. The anticipation model was trained on a total of 62 laparoscopic cholecystectomies. Results Here, we show that our prediction system can accurately anticipate 71.54% of the surgical instruments required during laparoscopic cholecystectomies in advance, facilitating a smoother surgical workflow and reducing the need for verbal communication. As the instruments in the left working trocar are changed less frequently and according to a standardized procedure, the prediction system works particularly well for this trocar. Conclusions The robotic scrub nurse thus acts as a mind reader and helps to mitigate staff shortages by taking over a great share of the workload during surgeries while additionally enabling an enhanced process standardization.
Within the last decades, corporate information technology (IT) environments have approached considerable degrees of complexity. As a consequence, IT has become increasingly difficult to manage ...resulting in high costs and poor flexibility. Today, it is generally acknowledged that the sustainability of corporate IT environments can only be ensured through a continuous and long-term management on the level of the Enterprise (IT) Architecture (EA). To address this, many firms have implemented a dedicated Enterprise (IT) Architecture Management (EAM) function. However, little is known yet on the effectiveness of such functions and the factors influencing EAM success. Within this research, we thus seek to answer two main questions: (1) do firms adopting EAM perform better with regard to high-level information management objectives like IT flexibility and IT efficiency, and if so, (2) what are the critical success factors in attaining these goals? To answer these questions, a field survey was conducted within the international financial services industry. The results provide evidence that the implementation of an EAM function is in fact supportive in the creation and sustainment of IT efficiency and IT flexibility. Several factors are shown to be of critical importance for achieving these goals with architectural governance being the most important one.
•Effects of formal and self-control on mobile app developers are compared.•Self-control has more positive effects on continuance intention and app quality.•Perceived autonomy mediates the effect of ...self-control on continuance intentions.•Embracing softer governance instruments in platform ecosystems is recommended.
Although control modes have been studied extensively in traditional IS contexts, minimal attention has been directed toward understanding how different control modes operate in platform ecosystems. Drawing on the IS control and self-determination literatures, we examined the differential effects of formal and self-control on third-party developers’ continuance intentions and application quality on mobile software platforms. Two studies from a laboratory experiment (N=138) and a follow-up field survey with Android app developers (N=230) show that self-control is superior to formal control because it allows for higher perceived autonomy that in turn promotes continuance intentions and application quality.
Given the rising popularity of social networking sites (SNSs), the influence of these platforms on the subjective well-being (SWB) of their users is an emerging topic in information systems research. ...Building on the norm of reciprocity and the social functional approach to positive emotions, we posit that targeted reciprocity-evoking forms of SNS activities are best suited to promote users' positive emotions. The favourable potential of these activities is likely to be particularly pronounced among adolescents who pay special attention to social acceptance, which can be channelled with the help of reciprocal communication. Therefore, we conducted a quantitative 7-day diary study of 162 adolescent Facebook users attending German schools, looking at the impact of their daily SNS activities on their SWB. Based on a linear mixed model analysis, our results confirm a positive link between targeted reciprocity-evoking activities - such as chatting, giving and receiving feedback - and adolescents' positive emotions. Our findings provide a reassuring perspective on the implications of the sociotechnical design of SNS communication channels. Specifically, by encouraging targeted activities, providers, users, and other stakeholders can ensure the beneficial impact of this technology on users' SWB.
Unlocking AI’s Potential Peter Buxmann; Sara Ellenrieder
Weizenbaum journal of the digital society,
05/2024, Letnik:
4, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Rapid advances in artificial intelligence (AI) have fueled high expectations for the technology’s potential to fundamentally transform our economy and society through automation. However, given the ...inscrutability and, sometimes, susceptibility to error of AI systems, we argue that the focus should shift towards fostering effective human-AI collaboration rather than pursuing automation alone. In this context, system decisions must be made available to decision-makers in an explainable and understandable manner, as further required by the EU’s recently passed AI Act. Research shows that there is potential for humans to learn from explainable AI systems and improve their own performance over time. Meanwhile, in addition to enabling humans to benefit from working with AI systems on various everyday tasks, such collaboration ensures the safe and reliable use of AI systems, especially in high-risk areas such as medicine, where human oversight remains paramount.
Data-centric approaches such as big data and related approaches from business intelligence and analytics (BI&A) have recently attracted major attention due to their promises of huge improvements in ...organizational performance based on new business insights and improved decision making. Incorporating data-centric approaches into organizational decision processes is challenging, even more so with big data, and it is not self-evident that the expected benefits will be realized. Previous studies have identified the lack of a research focus on the context of decision processes in data-centric approaches. By using a multiple case study approach, the paper investigates different types of BI&A-supported decision processes, and makes three major contributions. First, it shows how different facets of big data and information processing mechanism compositions are utilized in different types of BI&A-supported decision processes. Second, the paper contributes to information processing theory by providing new insights about organizational information processing mechanisms and their complementary relationship to data-centric mechanisms. Third, it demonstrates how information processing theory can be applied to assess the dynamics of mechanism composition across different types of decisions. Finally, the study’s implications for theory and practice are discussed.
Lagging IT security investments in small and medium-sized enterprises (SME) point towards a security divide between SME and large enterprises, yet our structured literature review shows that ...organizational IT security research has largely neglected the SME context. In an effort to expose reasons for this divide, we build on extant research to conceptualize SME-specific characteristics in a framework and suggest propositions regarding their influence on IT security investments. Based on 25 expert interviews, emerging constraints are investigated and validated. Our findings imply that several widely held assumptions in extant IT security literature should be modified if researchers claim generalizability of their results in an SME context. Exemplary assumptions include the presence of skilled workforce, documented processes or IT-budget planning which are often un(der) developed in SME. Additionally, our study offers context-specific insights regarding particular effects of identified constraints on IT security investments for all involved stakeholders (researchers, SME, large enterprises, governments).
•Online social networks that rely on secondary data use face a challenging trade-off.•Providers need to evaluate consequences of their privacy policies’ contents.•We integrate conflicting interests ...of providers and users of online social networks.•Privacy policies need to be reconceptualized beyond the e-commerce domain.•Privacy risks transfer the effects of a privacy policy’s contents on user behavior.
Privacy policies determine online social network providers’ options to monetize user data. However, these statements also intrude on users’ privacy and, thus, might reduce their willingness to disclose personal information, which in turn limits the data available for monetization. Given these conflicting interests, we conducted an experimental survey to investigate the relationship between privacy policies and users’ reactions. We show that users’ privacy risk perceptions mediate the effect that changes in policies’ monetization options have on users’ willingness to disclose information. Our findings emphasize privacy policies as a delicate managerial concept for companies relying on data monetization.
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