This research is an interdisciplinary work between data scientists, innovation management researchers, and experts from a Swedish hygiene and health company. Based on this collaboration, we have developed a novel package for automatic idea detection to control and prevent healthcare-associated infections (HAI). The principal idea of this study is to use machine learning methods to extract informative ideas from social media to assist healthcare professionals in reducing the rate of HAI. Therefore, the proposed package offers a corpus of data collected from Twitter, associated expert-created labels, and software implementation of an annotation framework based on the Active Learning paradigm. We employed Transfer Learning and built a two-step deep neural network model that incrementally extracts the semantic representation of the collected text data using the BERTweet language model in the first step and classifies these representations as informative or non-informative using a multi-layer perception (MLP) in the second step. The package is AID4HAI (Automatic Idea Detection for controlling and preventing Healthcare-Associated Infections) and is made fully available (software code and the collected data) through a public GitHub repository (https://github.com/XaraKar/AID4HAI). We believe that sharing our ideas and releasing these ready-to-use tools contributes to the development of the field and inspires future research.

Artificial intelligence (AI) is a promising generation of digital technologies. Recent applications and research suggest that AI can not only influence but also accelerate innovation in organizations. However, as the field is rapidly growing, a common understanding of the underlying theoretical capabilities has become increasingly vague and fraught with ambiguity. In view of the centrality of innovation capabilities in making innovation happen, we bring together these scattered perspectives in a systematic and multidisciplinary literature review. The aim of this literature review is to summarize the role of AI in influencing innovation capabilities and provide a taxonomy of AI applications based on empirical studies. Drawing on the technological–organizational–environmental (TOE) framework, our review condenses the research findings of 62 studies. The results of our study are twofold. First, we identify a dichotomous view of innovation capabilities triggered by AI adoption: enabling and enhancing. The enabling capabilities are those that research identifies as enablers of AI adoption, underscoring the competencies and routines needed to implement AI. The enhancing capabilities denote the role that AI adoption has in transforming or creating innovation capabilities in organizations. Second, we propose a taxonomy of AI applications that reflects the practical adoption of AI in relation to three underlying reasons: replace, reinforce, and reveal. Our study makes three main contributions. First, we identify the innovation capabilities that are either required for or generated by AI adoption. Second, we propose a taxonomy of AI applications. Third, we use the TOE framework to track trends in the theoretical contributions of recent articles and propose a research agenda. © 2023 The Authors.

Innovations that employ novel technologies can be problematic, particularly when sources of uncertainty can lead to severe financial, social, and reputational losses. Nowhere is this more evident than in innovations across Medical Device (MD) developments. In healthcare, described by stricter safety requirements, adapting the MD process to ongoing regulatory principles intended to balance benefits and risks is often elusive. We investigate a single case study comprising four medical device technology developments implemented in products and services. Does this study explore how a healthcare firm changes its medical device development process to adaptive regulations? Our study offers three contributions. First, we contribute to the innovation literature by proposing a flexible MD process in which safety standards are continuously revised, and development stages are regulated differently. For example, when the legal and regulatory aspects of emerging technologies are unpredictable, unknown firms are encouraged to de-risk the early-stage potential problem in adopting the emerging technology. Second, we contribute to the literature on Technology Development by showing how introducing digital technologies innovation requires a significant change in the culture and mindset of the organization. In the healthcare industry, where rules and procedures are hindering risky and uncertain investment, nurturing the culture of people towards risk-taking and learning from failure is a crucial dimension of digital transformation. Third, we propose a combined process that leverages traditional MD phases. The change management theory suggests a way to enact a digital transformation in a hyper-regulated environment.

Based on the results of a multiple case study of seven manufacturing firms, a contingency framework for harnessing fuzziness in the front end of innovation is proposed by delineating two discrete capability paths through which new product ideas are developed into corroborated product definitions. The study illustrates that ideas characterized by high levels of fuzziness benefit from following an exploratory path, where the creative potential of fuzziness is embraced by deploying problem-formulation and problem-solving capabilities. In contrast, ideas at low levels of fuzziness benefit from following an exploitative path, where fuzziness is tolerated by drawing upon idea-refinement and process-management capabilities. When the fuzziness level of the idea and the set of capabilities to develop the idea are poorly aligned, the idea-development process is either inefficient or runs the risk of stalling. These findings have theoretical and practical implications for the front end of innovation and new product idea development. © 2021 The Authors. Published by Elsevier Ltd.

Disruptive innovation is widely recognised as a bold enterprise. Risks and uncertainties drive incumbent firms to seek alternative solutions to find disruptive ideas. Machine learning emerges as a powerful tool to reduce uncertainties while processing vast amounts and types of information. However, incumbents encounter immense difficulty in codifying tacit knowledge into effective algorithms and often end up with incremental or tactical outcomes despite bold aspirations. Using the literature on problem finding, we explore the development process of machine learning for ideation. Our action research conducted on a healthcare firm provides theoretical and managerial contributions. First, this study suggests that ideation for disruptive innovation benefits from machine learning by facilitating a heuristic search in which a group of actors evaluate plausible hypotheses rather than seek logically accurate conclusions. Previous studies on ideation stress directional search. Second, we propose an ideation process centred on problem formulation to identify disruptive innovation based on its inherent characteristics (e.g., radical functionality and discontinuous technical standard). Third, we discuss the challenges of adopting algorithm-based systems in the ideation — a process well known for being fuzzy.

Background: Significant efforts have been made to develop artificial intelligence (AI) solutions for health care improvement. Despite the enthusiasm, health care professionals still struggle to implement AI in their daily practice.

Objective: This paper aims to identify the implementation frameworks used to understand the application of AI in health care practice.

Methods: A scoping review was conducted using the Cochrane, Evidence Based Medicine Reviews, Embase, MEDLINE, and PsycINFO databases to identify publications that reported frameworks, models, and theories concerning AI implementation in health care. This review focused on studies published in English and investigating AI implementation in health care since 2000. A total of 2541 unique publications were retrieved from the databases and screened on titles and abstracts by 2 independent reviewers. Selected articles were thematically analyzed against the Nilsen taxonomy of implementation frameworks, and the Greenhalgh framework for the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) of health care technologies.

Results: In total, 7 articles met all eligibility criteria for inclusion in the review, and 2 articles included formal frameworks that directly addressed AI implementation, whereas the other articles provided limited descriptions of elements influencing implementation. Collectively, the 7 articles identified elements that aligned with all the NASSS domains, but no single article comprehensively considered the factors known to influence technology implementation. New domains were identified, including dependency on data input and existing processes, shared decision-making, the role of human oversight, and ethics of population impact and inequality, suggesting that existing frameworks do not fully consider the unique needs of AI implementation.

Conclusions: This literature review demonstrates that understanding how to implement AI in health care practice is still in its early stages of development. Our findings suggest that further research is needed to provide the knowledge necessary to develop implementation frameworks to guide the future implementation of AI in clinical practice and highlight the opportunity to draw on existing knowledge from the field of implementation science. ©Fábio Gama, Daniel Tyskbo, Jens Nygren, James Barlow, Julie Reed, Petra Svedberg. 

Firms increasingly use artificial intelligence (AI) to innovate. Yet, the current literature offers fragmented guidance regarding its use in innovation processes. This study argues that the exponential use of AI has resulted in a mass of disorganised knowledge, creating confusion and frustration surrounding how managers navigate from conventional to digital innovation processes. A systematic literature review was carried out to examine studies investigating AI innovation published over the last decade (2011–2021). The results suggest that AI is present across all innovation phases and that firms have created three unique capabilities: AI-enabled ideation, AI-enabled development, and AI-enabled commercialisation. This article enriches the innovation management literature, and it equips managers with practical guidance in the use of AI.

Purpose: Facilitation activities support implementation of evidence-based interventions within healthcare organizations. Few studies have attempted to understand how facilitation activities are performed to promote the uptake of evidence-based interventions in hospitals from resource-poor countries during crises such as pandemics. This paper aims to explore facilitation activities by infection prevention and control (IPC) professionals in 16 hospitals from 9 states in Brazil during the COVID-19 pandemic.

Design/methodology/approach: Primary and secondary data were collected between March and December 2020. Semi-structured interviews were conducted with 21 IPC professionals in Brazilian hospitals during the COVID-19 pandemic. Public and internal documents were used for data triangulation. The data were analyzed through thematic analysis technique.

Findings: Building on the change response theory, this study explores the facilitation activities from the cognitive, behavioral and affective aspects. The facilitation activities are grouped in three overarching dimensions: (1) creating and sustaining legitimacy to continuous and rapid changes, (2) fostering capabilities for continuous changes and (3) accelerating individual commitment. Practical implications: During crises such as pandemics, facilitation activities by IPC professionals need to embrace all the cognitive, behavioral and affective aspects to stimulate positive attitudes of frontline workers toward continuous and urgent changes.

Originality/value: This study provides unique and timely empirical evidence on the facilitation activities that support the implementation of evidence-based interventions by IPC professionals during crises in hospitals in a resource-poor country.

© 2021, Luís Irgang, Magnus Holmén, Fábio Gama and Petra Svedberg

Small‐ and medium‐sized enterprises (SMEs) largely depend on proficient idea generation activities to improve their front‐end innovation performance, yet the liabilities of newness and smallness often hamper SMEs' ability to benefit from systematic idea generation. To compensate for these liabilities, many SMEs adopt an open innovation approach by collaborating with market‐based partners such as customers and suppliers. This study investigates the relationship between SMEs' systematic idea generation and front‐end performance and investigates the moderating role of market‐based partnership for SMEs. Drawing on a survey of 146 Swedish manufacturing SMEs, this study provides two key contributions. First, the systematic idea generation and front‐end performance relationship in SMEs is non‐linear. Accordingly, higher levels of front-end performance are achieved when idea generation activities are highly systematic. Second, the returns from higher levels of systematic idea generation are positively moderated by market‐based partnerships. Thus, external cooperation with customers and suppliers pays off most toward front‐end performance when SMEs have highly systematic idea generation processes. These results indicate a contingency perspective on the role of external partnerships. They also have implications for research into the front‐end of innovation and open innovation in the context of SMEs. © 2018 John Wiley & Sons Ltd

Background: Health care-associated infections (HAIs) are among the major causes of death of hospitalized patients. The ability to find and solve problems relating to HAIs is heavily contingent on infection prevention and control (IPC) teams’ practices. Previous studies provide detailed insights into how IPC teams find appropriate solutions, but they do not addressed how problems are found. This limitation is a severe drawback as IPC teams may focus their attention on the wrong problems that leads to inefficient or suboptimal solutions.

Purpose: This study aims to understand how IPC teams find problems with HAIs from a Problem-Finding and Problem-Solving perspective.

Method: We performed a multiple case study of three hospitals located in Brazil and Sweden. We collected data from 3 exploratory interviews and 13 semi-structured interviews with nurses and physicians enrolled in IPC teams. Supplemented documents were used for data triangulation. Data were analyzed using a thematic approach.

Findings: Results from this study suggest an approach based on two different sets of activities for finding problems: practices for HAI prevention and for HAI control. Practices for HAI prevention comprises routinely and elective actions whereas practices for HAI control involve ad hoc and emergent actions. The practices are organized into problem-detection, -framing, and -formulation activities.

Conclusion: We identified and detailed practices that guide IPC teams’ attention to find valuable problems relating to HAIs. By detecting, framing and formulating problems, IPC teams can find appropriate solutions.

Practice Implications: A range of practices are detailed in our study to guide IPC teams’ attention in how to find relevant problems relating to HAI prevention and control as well as the criteria on how to prioritize latent problems to obtain needed solutions. Our study provides a basis for supporting decision makers on how to identify opportunities for improve IPC policies and practices.