Artificial Intelligence, Technostress and Work Outcomes in Healthcare: A Power Perspective on AI Characteristics

2023 | International Conference on Information Systems | Citations: 0

Authors: Grüning, Maximilian; Henkenjohann, Richard; Santos, Daniel Pinto dos; Trenz, Manuel

Abstract: Artificial intelligence (AI) has emerged as a powerful tool for complex decisio ... Expand

Abstract: Artificial intelligence (AI) has emerged as a powerful tool for complex decision-making. However, little is known about how AI-based systems should be integrated into clinical practice to improve patient and provider outcomes. We argue that AI-based systems are no longer passive tools and can assume certain responsibilities for tasks. A novel aspect of our study is the exploration of the power relationship between AI and physicians, which can significantly impact medical care. Therefore, we aim to shed light on how configurations of power in AI interactions influence technostress and work outcomes. We outline a lab-in-the-field experiment on diagnosis and reporting by physicians using AI. Our study aims to uncover (1) the differential effects of power configurations in AI on physicians' positive and negative technostress levels, and (2) their psychological and behavioral outcomes. We aim to contribute to information systems research and practice by highlighting the power implications of AI. Collapse

Topics: artificial intelligence technostress decision making explainable artificial intelligence health information system

Methods: experiment field experiment survey statistical hypothesis test partial least squares path modeling

LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Joshi, Amogh Manoj; Nayak, Deepak Ranjan; Das, Dibyasundar; Zhang, Yudong

Abstract: Recent years have witnessed a rise in employing deep learning methods, especiall ... Expand

Abstract: Recent years have witnessed a rise in employing deep learning methods, especially convolutional neural networks (CNNs) for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a lightweight multi-scale CNN called LiMS-Net is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in the presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model. Collapse

Topics: covid 19

Methods: computed tomography ImageNet ablation analysis experiment machine learning

Same but Different: Variations in Reactions to Digital Transformation Within an Organizational Field

2023 | Journal of the Association for Information Systems | Citations: 3

Authors: Noesgaard, Mette; Nielsen, Jeppe; Jensen, Tina Blegind; Mathiassen, Lars

Abstract: Researchers and practitioners are increasingly interested in understanding how o ... Expand

Abstract: Researchers and practitioners are increasingly interested in understanding how organizations transform their value propositions and practices using digital technologies. While the extant literature offers important empirical and theoretical insights into digital transformation in individual organizations, we know little about how adopting organizations within an organizational field react differently over time to the same digital transformation initiative. This is unfortunate, as such insights could help scholars and managers understand option repertoires and constraints in handling digital transformation ideas that travel into organizations. Against this backdrop, we had access to a unique case over an eighteen-year period, which shows how organizations within the Danish home care field reacted differently to a nationwide digital transformation initiative on mobile technology use. To analyze this case, we applied virus theory as a promising perspective for examining how and why the same digital technology and transformation idea occasions different reactions in similar contexts. Our analysis highlights the emerging, fluctuating, and consequential nature of digital transformation within the Danish home care field, which has led to very different reactions across adopting organizations. Drawing on this analysis, we contribute to the expanding literature on digital transformation by providing theoretical and practical knowledge about variations regarding how organizations within an organizational field react over time to digital transformation ideas. Collapse

Topics: digital transformation mobile system information system use diffusion of innovation business topic

Methods: qualitative interview longitudinal research longitudinal case study case study qualitative content analysis

Designing User-Centric Explanations for Medical Imaging with Informed Machine Learning

2023 | DESRIST | Citations: 0

Authors: Oberste, Luis; Rüffer, Florian; Aydingül, Okan; Rink, Johann; Heinzl, Armin

Abstract: A flawed algorithm released in clinical practice can cause unintended harm to pa ... Expand

Abstract: A flawed algorithm released in clinical practice can cause unintended harm to patient health. Risks, regulation, responsibility, and ethics shape the demand of clinical users to understand and rely on the outputs made by artificial intelligence. Explainable artificial intelligence (XAI) offers methods to render a model’s behavior understandable from different perspectives. Extant XAI, however, is mainly data-driven and designed to meet developers’ demands to correct models rather than clinical users’ expectations to reflect clinically relevant information. To this end, informed machine learning (IML) utilizes prior knowledge jointly with data to generate predictions, a promising paradigm to enrich XAI with medical knowledge. To explore how IML can be used to generate explanations that are congruent to clinical users’ demands and useful to medical decision-making, we conduct Action Design Research (ADR) in collaboration with a team of radiologists. We propose an IML-based XAI system for clinically relevant explanations of diagnostic imaging predictions. With the help of ADR, we reduce the gap between implementation and user evaluation and demonstrate the effectiveness of the system in a real-world application with clinicians. While we develop design principles of using IML for user-centric XAI in diagnostic imaging, the study demonstrates that an IML-based design adequately reflects clinicians’ conceptions. In this way, IML inspires greater understandability and trustworthiness of AI-enabled diagnostic imaging. Collapse

Topics: explainable artificial intelligence artificial intelligence usability knowledge base decision making

Methods: design artifact design principle qualitative interview design science action design research

Explanatory Interactive Machine Learning

2023 | Business & Information Systems Engineering | Citations: 1

Authors: Pfeuffer, Nicolas; Baum, Lorenz; Stammer, Wolfgang; Abdel-Karim, Benjamin M.; Schramowski, Patrick; Bucher, Andreas M.; Hügel, Christian; Rohde, Gernot; Kersting, Kristian; Hinz, Oliver

Abstract: The most promising standard machine learning methods can deliver highly accurate ... Expand

Abstract: The most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper the creation of new knowledge on the part of humans and the broader acceptance of this technology. Explainable Artificial Intelligence attempts to overcome this problem by making the results more interpretable, while Interactive Machine Learning integrates humans into the process of insight discovery. The paper builds on recent successes in combining these two cutting-edge technologies and proposes how Explanatory Interactive Machine Learning (XIL) is embedded in a generalizable Action Design Research (ADR) process – called XIL-ADR. This approach can be used to analyze data, inspect models, and iteratively improve them. The paper shows the application of this process using the diagnosis of viral pneumonia, e.g., Covid-19, as an illustrative example. By these means, the paper also illustrates how XIL-ADR can help identify shortcomings of standard machine learning projects, gain new insights on the part of the human user, and thereby can help to unlock the full potential of AI-based systems for organizations and research. Collapse

Topics: explainable artificial intelligence database system crowdsourcing artificial intelligence cloud computing

Methods: machine learning action design research case study computational algorithm design science

Bringing Machine Learning Systems into Clinical Practice: A Design Science Approach to Explainable Machine Learning-Based Clinical Decision Support Systems

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Pumplun, Luisa; Peters, Felix; Gawlitza, Joshua; Buxmann, Peter

Abstract: Clinical decision support systems (CDSSs) based on machine learning (ML) hold g ... Expand

Abstract: Clinical decision support systems (CDSSs) based on machine learning (ML) hold great promise for improving medical care. Technically, such CDSSs are already feasible but physicians have been skeptical about their application. In particular, their opacity is a major concern, as it may lead physicians to overlook erroneous outputs from ML-based CDSSs, potentially causing serious consequences for patients. Research on explainable AI (XAI) offers methods with the potential to increase the explainability of black-box ML systems. This could significantly accelerate the application of MLbased CDSSs in medicine. However, XAI research to date has mainly been technically driven and largely neglects the needs of end users. To better engage the users of ML-based CDSSs, we applied a design science approach to develop a design for explainable ML-based CDSSs that incorporates insights from XAI literature while simultaneously addressing physicians' needs. This design comprises five design principles that designers of ML-based CDSSs can apply to implement user-centered explanations, which are instantiated in a prototype of an explainable ML-based CDSS for lung nodule classification. We rooted the design principles and the derived prototype in a body of justificatory knowledge consisting of XAI literature, the concept of usability, and an online survey study involving 57 physicians. We refined the design principles and their instantiation by conducting walk-throughs with six radiologists. A final experiment with 45 radiologists demonstrated that our design resulted in physicians perceiving the ML-based CDSS as more explainable and usable in terms of the required cognitive effort than a system without explanations. He earned his doctorate in medicine in 2018 and his Habilitation in medicine in 2021, with research focusing on perfusion imaging in computed tomography and quantitative computed tomography in COPD. An accomplished researcher, Joshua has authored and co-authored numerous articles in renowned journals and serves as a reviewer for multiple scientific journals, such as the European Journal of Radiology and the Journal of Thoracic Imaging. He has also been recognized with several prestigious awards in the field of radiology. Collapse

Topics: explainable artificial intelligence usability knowledge representation decision making knowledge base

Methods: design artifact design principle design science design requirement survey

The Force of Habit: Examining the Status Quo Bias for Using Mixed Reality in Patient Education

2022 | HICSS | Citations: 0

Authors: Anton, Eduard; Schuir, Julian; Teuteberg, Frank

Abstract: Using mixed reality (MR) glasses for preoperative patient education (PPE) can he ... Expand

Abstract: Using mixed reality (MR) glasses for preoperative patient education (PPE) can help patients understand the purpose and risks of surgical procedures through informative visualizations. However, patients tend to be critical regarding the use of MR glasses in healthcare and often prefer the status quo of healthcare services provided. This study explores the resistance to MR technologies in PPE through the lens of the status quo bias theory by surveying n = 171 participants. We conducted a fuzzy set qualitative comparative analysis revealing configurations that provide a typological understanding of patient resistance. This allows healthcare stakeholders to take more targeted interventions to promote MR adoption. Notably, the results indicate that healthcare providers need to be transparent in communicating the benefits and drawbacks of using MR, as uncertainty costs are the main driver of resistance to MR glasses in PPE. Collapse

Topics: decision making individual characteristic health information system technology adoption perceived ease of use

Methods: survey fuzzy set qualitative comparative analysis robustness check confirmatory factor analysis descriptive statistic

Theories: technology acceptance model unified theory of acceptance and use of technology complexity theory status quo bias theory loss aversion

Coalitional Strategies for Efficient Individual Prediction Explanation

2022 | Information Systems Frontiers | Citations: 0

Authors: Ferrettini, Gabriel; Escriva, Elodie; Aligon, Julien; Excoffier, Jean-Baptiste; Soulé-Dupuy, Chantal

Abstract: As Machine Learning (ML) is now widely applied in many domains, in both research ... Expand

Abstract: As Machine Learning (ML) is now widely applied in many domains, in both research and industry, an understanding of what is happening inside the black box is becoming a growing demand, especially by non-experts of these models. Several approaches had thus been developed to provide clear insights of a model prediction for a particular observation but at the cost of long computation time or restrictive hypothesis that does not fully take into account interaction between attributes. This paper provides methods based on the detection of relevant groups of attributes -named coalitions- influencing a prediction and compares them with the literature. Our results show that these coalitional methods are more efficient than existing ones such as SHapley Additive exPlanation (SHAP). Computation time is shortened while preserving an acceptable accuracy of individual prediction explanations. Therefore, this enables wider practical use of explanation methods to increase trust between developed ML models, end-users, and whoever impacted by any decision where these models played a role. Collapse

Topics: shapley additive explanation healthcare data legal context General Data Protection Regulation Python

Methods: data transformation principal component analysis random forest classification support vector machine experiment

Integrated Declarative Process and Decision Discovery of the Emergency Care Process

2022 | Information Systems Frontiers | Citations: 0

Authors: Mertens, Steven; Gailly, Frederik; Van Sassenbroeck, Diederik; Poels, Geert

Abstract: Deviations and variations are the norm rather than the exception in medical diag ... Expand

Abstract: Deviations and variations are the norm rather than the exception in medical diagnosis and treatment processes. Physicians must leverage their knowledge and experience to choose an appropriate variation for each patient. However, this knowledge and experience is often tacit. Process modeling offers a way to convert tacit to explicit knowledge. Many process mining techniques have been developed due to the difficulty of doing this manually, yet, they often neglect the decisions themselves, and these proposed techniques are just one piece of a comprehensive process discovery method. In this paper, we use the Action Design Research methodology to develop a method for process and decision discovery of medical diagnosis and treatment processes. The method was iteratively improved and validated by applying it to a practical setting, which was the emergency medicine department of a hospital. An analysis of the resulting model shows that previously tacit knowledge was successfully made explicit. Collapse

Topics: process mining data quality explicit knowledge business process automation IT project

Methods: event log business process modeling design science conceptual modelling action design research

From Lock-In to Transformation: A Path-Centric Theory of Emerging Technology and Organizing

2022 | Organization Science | Citations: 0

Authors: Pentland, Brian T.; Yoo, Youngjin; Recker, Jan; Kim, Inkyu

Abstract: We offer a path-centric theory of emerging technology and organizing that addres ... Expand

Abstract: We offer a path-centric theory of emerging technology and organizing that addresses a basic question. When does emerging technology lead to transformative change? A path-centric perspective on technology focuses on the patterns of actions afforded by technology in use. We identify performing and patterning as self-reinforcing mechanisms that shape patterns of action in the domain of emerging technology and organizing. We use a dynamic simulation to show that performing and patterning can lead to a wide range of trajectories, from lock-in to transformation, depending on how emerging technology in use influences the pattern of action. When emerging technologies afford new actions that can be flexibly recombined to generate new paths, decisive transformative effects are more likely. By themselves, new affordances are not likely to generate transformation. We illustrate this theory with examples from the practice of pharmaceutical drug discovery. The path-centric perspective offers a new way to think about generativity and the role of affordances in organizing. Collapse

Topics: affordance technology adoption marketing management information system use user experience

Methods: simulation theory development cluster analysis field study time series analysis

Theories: theory of affordance actor network theory sociomaterialism theory