Understanding pathways from implementation to sustainment: a longitudinal, mixed methods analysis of promising practices implemented in the Veterans Health Administration.

BACKGROUND: The Veterans Health Administration (VHA) is the United States largest learning health system. The Diffusion of Excellence (DoE) program is a large-scale model of diffusion that identifies and diffuses evidence-informed practices across VHA. During the period of 2016-2021, 57 evidence-informed practices were implemented across 82 VHA facilities. This setting provides a unique opportunity to understand sustainment determinants and pathways. Our objective was to characterize the longitudinal pathways of practices as they transition from initial implementation to long-term sustainment at each facility. METHODS: A longitudinal, mixed-methods evaluation of 82 VHA facilities. Eighty-two facility representatives, chosen by leadership as points-of-contact for 57 DoE practices, were eligible for post-implementation interviews and annual sustainment surveys. Primary outcomes (implementation, sustainment), and secondary outcomes (institutionalization, effectiveness, anticipated sustainment) at four time-points were collected. We performed descriptive statistics and directed content analysis using Hailemariam et al.'s factors influencing sustainment. RESULTS: After approximately five years post-implementation (e.g., 2021 sustainment outcomes), of the 82 facilities, about one-third fully sustained their practice compared to one-third that did not fully sustain their practice because it was in a "liminal" stage (neither sustained nor discontinued) or permanently discontinued. The remaining one-third of facilities had missing 2021 sustainment outcomes. A higher percentage of facilities (70%) had inconsistent primary outcomes (changing over time) compared to facilities (30%) with consistent primary outcomes (same over time). Thirty-four percent of facilities with sustained practices reported resilience since they overcame implementation and sustainment barriers. Facilities with sustained practices reported more positive secondary outcomes compared to those that did not sustain their practice. Key factors facilitating practice sustainment included: demonstrating practice effectiveness/benefit, sufficient organizational leadership, sufficient workforce, and adaptation/alignment with local context. Key factors hindering practice sustainment included: insufficient workforce, not able to maintain practice fidelity/integrity, critical incidents related to the COVID-19 pandemic, organizational leadership did not support sustainment of practice, and no ongoing support. CONCLUSIONS: We identified diverse pathways from implementation to sustainment, and our data underscore that initial implementation outcomes may not determine long-term sustainment outcomes. This longitudinal evaluation contributes to understanding impacts of the DoE program, including return on investment, achieving learning health system goals, and insights into achieving high-quality healthcare in VHA.

Endohepatology: Arrival at the frontier of interventional endosonography.

Endohepatology describes the emerging field where diagnostic and therapeutic endoscopic ultrasound (EUS) are used for the diagnosis and management of liver disease and its sequelae. In this editorial we comment on the article by Gadour et al. The spectrum of EUS-guided procedures includes liver parenchymal and lesional biopsy, abscess drainage, treatment of focal liver lesions, diagnosis of portal hypertension and management of gastric varices. The data suggest that the application of EUS to hepatology is technically feasible and safe, heralding the arrival at a new frontier for EUS. More data, specifically randomised trials comparing EUS to interventional radiology techniques, and continued partnership between endoscopy and hepatology are required to see this field establish itself outside expert tertiary centres.

Exploring the Role of Complexity in Health Care Technology Bottom-Up Innovations: Multiple-Case Study Using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability Complexity Assessment Tool.

BACKGROUND: New digital technology presents new challenges to health care on multiple levels. There are calls for further research that considers the complex factors related to digital innovations in complex health care settings to bridge the gap when moving from linear, logistic research to embracing and testing the concept of complexity. The nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework was developed to help study complexity in digital innovations. OBJECTIVE: This study aims to investigate the role of complexity in the development and deployment of innovations by retrospectively assessing challenges to 4 digital health care innovations initiated from the bottom up. METHODS: A multicase retrospective, deductive, and explorative analysis using the NASSS complexity assessment tool LONG was conducted. In total, 4 bottom-up innovations developed in Region Västra Götaland in Sweden were explored and compared to identify unique and shared complexity-related challenges. RESULTS: The analysis resulted in joint insights and individual learning. Overall, the complexity was mostly found outside the actual innovation; more specifically, it related to the organization's readiness to integrate new innovations, how to manage and maintain innovations, and how to finance them. The NASSS framework sheds light on various perspectives that can either facilitate or hinder the adoption, scale-up, and spread of technological innovations. In the domain of condition or diagnosis, a well-informed understanding of the complexity related to the condition or illness (diabetes, cancer, bipolar disorders, and schizophrenia disorders) is of great importance for the innovation. The value proposition needs to be clearly described early to enable an understanding of costs and outcomes. The questions in the NASSS complexity assessment tool LONG were sometimes difficult to comprehend, not only from a language perspective but also due to a lack of understanding of the surrounding organization's system and its setting. CONCLUSIONS: Even when bottom-up innovations arise within the same support organization, the complexity can vary based on the developmental phase and the unique characteristics of each project. Identifying, defining, and understanding complexity may not solve the issues but substantially improves the prospects for successful deployment. Successful innovation within complex organizations necessitates an adaptive leadership and structures to surmount cultural resistance and organizational impediments. A rigid, linear, and stepwise approach risks disregarding interconnected variables and dependencies, leading to suboptimal outcomes. Success lies in embracing the complexity with its uncertainty, nurturing creativity, and adopting a nonlinear methodology that accommodates the iterative nature of innovation processes within complex organizations.

Artificial intelligence and illusions of understanding in scientific research.

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists' visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community's ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

Working Toward Recommended Terminology in the Canadian Preschool Speech-Language Pathology Context.

PURPOSE: This quality improvement project aimed to address the inconsistent use of clinical labels across a preschool speech and language program in Ontario, Canada. The study investigated whether a multicomponent knowledge translation (KT) intervention could increase speech-language pathologists' (SLPs') knowledge about the recommended clinical labels, motivate their intentions to use the labels, and facilitate practice change during a 3-month pilot period. METHOD: The diffusion of innovations theory was utilized to identify and address known and suspected barriers and facilitators that could influence the adoption of consistent terminology. The intervention was evaluated using a pre-experimental study design (with pre, post, and follow-up testing) and included two phases: Phase 1 involved the pretraining survey, KT intervention, and posttraining survey, and Phase 2 included an exit survey after a 3-month pilot period. RESULTS: Five hundred twenty-nine SLPs in Phase 1 and 387 SLPs in Phase 2 participated. Following the web-based intervention, SLPs demonstrated improved knowledge about the recommended labels with most indicating intentions to communicate the labels going forward. SLPs also reported increased comfort using labels and positive views on their importance and value. After the 3-month pilot period, SLPs' reported use of most recommended labels decreased, as did ratings of comfort, value, and importance. However, most SLPs reported intentions to use the labels going forward. CONCLUSIONS: Despite having intentions to adopt the recommended labels, the lack of implementation by SLPs suggests the presence of additional barriers impacting their use of the recommended clinical labels in practice. Future work should investigate clinician-identified barriers to inform future implementation efforts. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25254940.

Clinical Research in Renal Transplantation: A Bibliometric Perspective on a Half-century of Innovation and Progress.

BACKGROUND: Groundbreaking biomedical research has transformed renal transplantation (RT) into a widespread clinical procedure that represents the mainstay of treatment for end-stage kidney failure today. Here, we aimed to provide a comprehensive bibliometric perspective on the last half-century of innovation in clinical RT. METHODS: The Web of Science Core Collection was used for a comprehensive screening yielding 123 303 research items during a 50-y period (January 1973-October 2022). The final data set of the 200 most-cited articles was selected on the basis of a citation-based strategy aiming to minimize bias. RESULTS: Studies on clinical and immunological outcomes (n = 63 and 48), registry-based epi research (n = 38), and randomized controlled trials (n = 35) dominated the data set. Lead US authors have signed 110 of 200 articles. The overall level of evidence was high, with 84% of level1 and -2 reports. Highest numbers of these articles were published in New England Journal of Medicine , Transplantation , and American Journal of Transplantation. Increasing trend was observed in the number of female authors in the postmillennial era (26% versus 7%). CONCLUSIONS: This study highlights important trends in RT research of the past half-century. This bibliometric perspective identifies the most intensively researched areas and shift of research interests over time; however, it also describes important imbalances in distribution of academic prolificacy based on topic, geographical aspects, and gender.

Exploring Nurse' Use of Digital Nursing Technology.

This is a quantitative cross-sectional study using the characteristics of innovation diffusion theory to evaluate nurse' acceptance and adoption of digital nursing technology (DNT). Data were collected through questionnaires based on innovation diffusion theory in the wards of a regional hospital in Taiwan from March 21 to May 31, 2022. Results indicated that the higher the innovative characteristics of DNT, the higher the DNT acceptance. Difficulties with network connections contributed to negative experiences and led to recommendations for future system improvement.

Innovation Diffusion Across 13 Specialties and Associated Clinician Characteristics.

Diffusion of innovations, defined as the adoption and implementation of new ideas, processes, products, or services in health care, is both particularly important and especially challenging. One known problem with adoption and implementation of new technologies is that, while organizations often make innovations immediately available, organizational actors are more wary about adopting new technologies because these may impact not only patients and practices but also reimbursement. As a result, innovations may remain underutilized, and organizations may miss opportunities to improve and advance. As innovation adoption is vital to achieving success and remaining competitive, it is important to measure and understand factors that impact innovation diffusion. Building on a survey of a national sample of 654 clinicians, our study measures the extent of diffusion of value-enhancing care delivery innovations (i.e., technologies that not only improve quality of care but has potential to reduce care cost by diminishing waste, Faems et al., 2010) for 13 clinical specialties and identifies healthcare-specific individual characteristics such as: professional purview, supervisory responsibility, financial incentive, and clinical tenure associated with innovation diffusion. We also examine the association of innovation diffusion with perceived value of one type of care delivery innovation - artificial intelligence (AI) - for assisting clinicians in their clinical work. Responses indicate that less than two-thirds of clinicians were knowledgeable about and aware of relevant value-enhancing care delivery innovations. Clinicians with broader professional purview, more supervisory responsibility, and stronger financial incentives had higher innovation diffusion scores, indicating greater knowledge and awareness of value-enhancing, care delivery innovations. Higher levels of knowledge of the innovations and awareness of their implementation were associated with higher perceptions of the value of AI-based technology. Our study contributes to our knowledge of diffusion of innovation in healthcare delivery and highlights potential mechanisms for speeding innovation diffusion.

Researching models of innovation and adoption in health professions education.

BACKGROUND: Despite the constant presence of change and innovation in health professions education (HPE), there has been relatively little theoretical modelling of such change, the experiences of change, the ideology associated with change or the unexpected consequences of change. In this paper, the authors explore theoretical approaches to the adoption of innovations in HPE as a way of mapping a broader theoretical landscape of change. METHOD: The authors, HPE researchers with an interest in technology adoption and systemic change, present a narrative review of the literature based on a series of thought experiments regarding how communities and individuals respond to the introduction of new ideas or methods. This research investigates the stages of innovation adoption, from the emergence and hype around new ideas to the concrete experiences of early adopters. RESULTS: When an innovation first emerges, there is often little concrete information available to inform potential adopters, leaving it susceptible to hype, both positive and negative. This can be described using the Gartner Hype Cycle model, albeit with important caveats. Once the adoption of an innovation gets underway, early adopter user experiences can inform those that follow. This can be described using Rogers' diffusion of innovation model, again with caveats. Notably, neither model goes beyond the point of single point-in-time, yes/no, individual adoption. Other approaches, such as learning curve theory, are needed to track uptake and maintenance by individuals over time. SIGNIFICANCE: This expanded theoretical base, while still somewhat instrumentalist, combined with complementary theoretical perspectives can afford opportunities to better explore reasons for variance, volunteerism and resistance to change. In summary, change is complicated and nuanced, and better models and theories are needed to understand and work meaningfully with change in HPE. To that end, the authors seek to encourage richer and more thoughtful research and scholarly thinking about change and a more nuanced approach to the pursuit of change in HPE as a whole.

Will Generative Artificial Intelligence Deliver on Its Promise in Health Care?

Importance: Since the introduction of ChatGPT in late 2022, generative artificial intelligence (genAI) has elicited enormous enthusiasm and serious concerns. Observations: History has shown that general purpose technologies often fail to deliver their promised benefits for many years ("the productivity paradox of information technology"). Health care has several attributes that make the successful deployment of new technologies even more difficult than in other industries; these have challenged prior efforts to implement AI and electronic health records. However, genAI has unique properties that may shorten the usual lag between implementation and productivity and/or quality gains in health care. Moreover, the health care ecosystem has evolved to make it more receptive to genAI, and many health care organizations are poised to implement the complementary innovations in culture, leadership, workforce, and workflow often needed for digital innovations to flourish. Conclusions and Relevance: The ability of genAI to rapidly improve and the capacity of organizations to implement complementary innovations that allow IT tools to reach their potential are more advanced than in the past; thus, genAI is capable of delivering meaningful improvements in health care more rapidly than was the case with previous technologies.

Pivotal Trial Demographic Representation and Clinical Development Times for Oncology Therapeutics.

This study evaluates whether FDA-approved novel cancer therapeutics supported by pivotal trials with adequate representation of minoritized groups were associated with slower clinical development times than those with inadequate representation.

The determinants of public acceptance of telemedicine apps: an innovation diffusion perspective.

With the rapid advancement of information technology, telemedicine apps have gradually become an indispensable tool for providing patients with more convenient, efficient, and accessible healthcare services. However, the successful implementation of these apps largely depends on widespread acceptance among the public. To thoroughly investigate the factors influencing the public's acceptance of these apps and the relationships between these factors, this study developed a theoretical model based on the Diffusion of Innovation theory and the Theory of Perceived Value. To validate this model, we conducted a survey of 387 residents in Beijing, China, and employed structural equation modeling to analyze the collected data. The research findings indicate that attributes of innovation diffusion, including relative advantage, compatibility, complexity, trialability, and observability, significantly and positively influence the public's perceived value. Particularly noteworthy is that perceived value partially mediates the relationship between innovation attributes and public acceptance, emphasizing the crucial role of perceived value in the public decision-making process. This study employed a theory-driven approach to elucidate the acceptance of telemedicine apps and offers fresh insights into the existing literature. By integrating the research paradigms of innovation diffusion and customer perceived value, we provide a coherent explanation of how individual cognitive processes lead to acceptance behavior. In summary, this research enriches the existing theoretical studies on the acceptance of telemedicine apps and holds positive implications for healthcare practice.