How social networks influence the local implementation of initiatives developed in quality improvement collaboratives in health care: A qualitative process study.

BACKGROUND: Quality improvement collaboratives (QICs) have facilitated cross-organizational knowledge exchange in health care. However, the local implementation of many quality improvement (QI) initiatives continues to fail, signaling a need to better understand the contributing factors. Organizational context, particularly the role of social networks in facilitating or hindering implementation within organizations, remains a potentially critical yet underexplored area to addressing this gap. PURPOSE: We took a dynamic process perspective to understand how QI project managers' social networks influence the local implementation of QI initiatives developed through QICs. METHODOLOGY: We explored the case of a QIC by triangulating data from an online survey, semistructured interviews, and archival documents from 10 organizations. We divided implementation into four stages and employed qualitative text analysis to examine the relationship between three characteristics of network structure (degree centrality, network density, and betweenness centrality) and the progress of each QI initiative. RESULTS: The progress of QI initiatives varied considerably among organizations. The transition between stages was influenced by all three network characteristics to varying degrees, depending on the stage. Project managers whose QI initiatives progressed to advanced stages of implementation had formed ad hoc clusters of colleagues passionate about the initiatives. CONCLUSION: Implementing QI initiatives appears to be facilitated by the formation of clusters of supportive individuals within organizations; this formation requires high betweenness centrality and high network density. PRACTICE IMPLICATIONS: Flexibly modifying specific network characteristics depending on the stage of implementation may help project managers advance their QI initiatives, achieving more uniform results from QICs.

Are the European reference networks for rare diseases ready to embrace machine learning? A mixed-methods study.

BACKGROUND: The delay in diagnosis for rare disease (RD) patients is often longer than for patients with common diseases. Machine learning (ML) technologies have the potential to speed up and increase the precision of diagnosis in this population group. We aim to explore the expectations and experiences of the members of the European Reference Networks (ERNs) for RDs with those technologies and their potential for application. METHODS: We used a mixed-methods approach with an online survey followed by a focus group discussion. Our study targeted primarily medical professionals but also other individuals affiliated with any of the 24 ERNs. RESULTS: The online survey yielded 423 responses from ERN members. Participants reported a limited degree of knowledge of and experience with ML technologies. They considered improved diagnostic accuracy the most important potential benefit, closely followed by the synthesis of clinical information, and indicated the lack of training in these new technologies, which hinders adoption and implementation in routine care. Most respondents supported the option that ML should be an optional but recommended part of the diagnostic process for RDs. Most ERN members saw the use of ML limited to specialised units only in the next 5 years, where those technologies should be funded by public sources. Focus group discussions concluded that the potential of ML technologies is substantial and confirmed that the technologies will have an important impact on healthcare and RDs in particular. As ML technologies are not the core competency of health care professionals, participants deemed a close collaboration with developers necessary to ensure that results are valid and reliable. However, based on our results, we call for more research to understand other stakeholders' opinions and expectations, including the views of patient organisations. CONCLUSIONS: We found enthusiasm to implement and apply ML technologies, especially diagnostic tools in the field of RDs, despite the perceived lack of experience. Early dialogue and collaboration between health care professionals, developers, industry, policymakers, and patient associations seem to be crucial to building trust, improving performance, and ultimately increasing the willingness to accept diagnostics based on ML technologies.

We're implementing AI now, so why not ask us what to do? - How AI providers perceive and navigate the spread of diagnostic AI in complex healthcare systems.

Despite high expectations of artificial intelligence (AI) in medical diagnostics, predictions of its extensive and rapid adoption have so far not been matched by reality. AI providers seeking to promote and perpetuate the use of this technology are faced with the complex reality of embedding AI-enabled diagnostics across variable implementation contexts. In this study, we draw upon a complexity science approach and qualitative methodology to understand how AI providers perceive and navigate the spread of AI in complex healthcare systems. Using semi-structured, one-to-one interviews, we collected qualitative data from 14 providers of AI-enabled diagnostics. We triangulated the data by complementing the interviews with multiple sources, including a focus group of physicians with experience using these technologies. The notion of embedding allowed us to connect local implementation efforts with systemic diffusion. Our study reveals that AI providers self-organise to increase their adaptability when navigating the variable conditions and unpredictability of complex healthcare contexts. In addition to the tensions perceived by AI providers within the sociocultural, technological, and institutional subsystems of healthcare, we illustrate the practices emerging among them to mitigate these tensions: stealth science, agility, and digital ambidexterity. Our study contributes to the growing body of literature on the spread of AI in healthcare by capturing the view of technology providers and adding a new theoretical perspective through the lens of complexity science.

Genetic newborn screening and digital technologies: A project protocol based on a dual approach to shorten the rare diseases diagnostic path in Europe.

Since 72% of rare diseases are genetic in origin and mostly paediatrics, genetic newborn screening represents a diagnostic "window of opportunity". Therefore, many gNBS initiatives started in different European countries. Screen4Care is a research project, which resulted of a joint effort between the European Union Commission and the European Federation of Pharmaceutical Industries and Associations. It focuses on genetic newborn screening and artificial intelligence-based tools which will be applied to a large European population of about 25.000 infants. The neonatal screening strategy will be based on targeted sequencing, while whole genome sequencing will be offered to all enrolled infants who may show early symptoms but have resulted negative at the targeted sequencing-based newborn screening. We will leverage artificial intelligence-based algorithms to identify patients using Electronic Health Records (EHR) and to build a repository "symptom checkers" for patients and healthcare providers. S4C will design an equitable, ethical, and sustainable framework for genetic newborn screening and new digital tools, corroborated by a large workout where legal, ethical, and social complexities will be addressed with the intent of making the framework highly and flexibly translatable into the diverse European health systems.