In most Western nations, stroke remains the single greatest cause of disability, including communication and swallowing disabilities. Although adherence to stroke clinical practice guidelines improves stroke patient outcomes, guidelines continue to be underutilised, and the reasons for this are not well understood. This is the first in-depth qualitative study identifying the complex barriers and facilitators to guideline implementation as experienced by speech pathologists in stroke care. Suggested implementation strategies include local monitoring of guideline implementation (e.g. team meetings, audits), increasing collaboration on implementation projects (e.g. managerial involvement, networking), and seeking speech pathologist input into guideline development.

Background The Social Brain Toolkit, conceived and developed in partnership with stakeholders, is a novel suite of web-based communication interventions for people with brain injury and their communication partners. To support effective implementation, the developers of the Social Brain Toolkit have collaborated with people with brain injury, communication partners, clinicians, and individuals with digital health implementation experience to coproduce new implementation knowledge. In recognition of the equal value of experiential and academic knowledge, both types of knowledge are included in this study protocol, with input from stakeholder coauthors. Objective This study aims to collaborate with stakeholders to prioritize theoretically based implementation targets for the Social Brain Toolkit, understand the nature of these priorities, and develop targeted implementation strategies to address these priorities, in order to support the Social Brain Toolkit’s implementation. Methods Theoretically underpinned by the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework of digital health implementation, a maximum variation sample (N=35) of stakeholders coproduced knowledge of the implementation of the Social Brain Toolkit. People with brain injury (n=10), communication partners (n=11), and clinicians (n=5) participated in an initial web-based prioritization survey based on the NASSS framework. Survey completion was facilitated by plain English explanations and accessible captioned videos developed through 3 rounds of piloting. A speech-language pathologist also assisted stakeholders with brain injury to participate in the survey via video teleconference. Participants subsequently elaborated on their identified priorities via 7 web-based focus groups, in which researchers and stakeholders exchanged stakeholder perspectives and research evidence from a concurrent systematic review. Stakeholders were supported to engage in focus groups through the use of visual supports and plain English explanations. Additionally, individuals with experience in digital health implementation (n=9) responded to the prioritization survey questions via individual interview. The results will be deductively analyzed in relation to the NASSS framework in a coauthorship process with people with brain injury, communication partners, and clinicians. Results Ethical approval was received from the University of Technology Sydney Health and Medical Research Ethics Committee (ETH20-5466) on December 15, 2020. Data were collected from April 13 to November 18, 2021. Data analysis is currently underway, with results expected for publication in mid-2022. Conclusions In this study, researchers supported individuals with living experience of acquired brain injury, of communicating with or clinically supporting someone post injury, and of digital health implementation, to directly access and leverage the latest implementation research evidence and theory. With this support, stakeholders were able to prioritize implementation research targets, develop targeted implementation solutions, and coauthor and publish new implementation findings. The results will be used to optimize the implementation of 3 real-world, evidence-based interventions and thus improve the outcomes of people with brain injury and their communication partners. International Registered Report Identifier (IRRID) DERR1-10.2196/35080

We sought (a) an inductive understanding of patient and clinician perspectives and experiences of the communication of diagnostic test information and (b) a normative understanding of the management of uncertainty that occurs during the clinical encounter in emergency care. Between 2016 and 2018, 58 interviews were conducted with patients and nursing, medical, and managerial staff. Interview data were sequentially analyzed through an inductive thematic analysis, then a normative theory of uncertainty management. Themes of “Ideals,” “Service Efficiency,” and “Managing Uncertainty” were inductively identified as influencing the communication of diagnostic test information. A normative theory of uncertainty management highlighted (a) how these themes reflected the interaction’s sociocultural context, encapsulated various criteria by which clinicians and patients evaluated the appropriateness and effectiveness of their communication, and represented competing goals during the clinical encounter, and (b) how systemic tensions between themes accounted for when diagnostic test information communication occurred, was deferred or avoided.

Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.