Conectar
To explore the use, parameters, safety and outcomes of physical rehabilitation for adults with sepsis.
We conducted a scoping review following the Joanna Briggs Institute framework.
Studies were eligible for inclusion in the study if they included: (1) adults 18 and older, (2) with a previous diagnosis of sepsis, (3) using a physical rehabilitation intervention at any point of sepsis management, (4) published in English or French.
We searched seven databases and screened titles and abstracts, reviewed full texts and performed data extraction independently and in duplicate. We summarised findings narratively using the "population, context, concept" framework and used descriptive statistics where appropriate. End-users reviewed and commented on study findings.
We included 58 studies, representing 77 434 participants, with the majority (79%) being published in the last decade. A large proportion (36%) of physical rehabilitation interventions included exercise and were overseen by a physical therapist (41%). The parameters of the interventions varied widely. However, all interventions (100%) were hospital based and the interventions implemented appeared safe. Of the 28 studies evaluating effectiveness of the intervention, function improved in most studies (78%) following physical rehabilitation.
Research addressing physical rehabilitation for patients with sepsis is increasing. Physical rehabilitation appears safe and may improve functional outcomes in those with sepsis. Future research should report details of intervention parameters and evaluate rehabilitation post-hospital discharge to maximise impact on function and quality of life for sepsis survivors.
The protocol was registered on Open Science Framework Registries (Registration DOI: https://doi.org/10.17605/OSF.IO/2EPJ6).
Our study was designed to assess whether paired normal-tumour testing increased access to targeted therapy, clinical trials and influenced cancer screening recommendations given to patients and their families.
Prospective cohort study.
Academic cancer centre in the Pacific Northwest region of the USA.
Patients newly diagnosed between 01 January 2021 and 31 December 2022 with cancers of the oesophagus, gastro-oesophageal junction and stomach (CEGEJS) were included. All other cancer diagnoses such as head and neck, duodenal and lower gastrointestinal tract cancers were excluded.
Paired germline and tumour genetic test within 90 days of new patient visit.
Number of targeted therapies received (or not) when eligible, follow-up treatment data and number of inherited predispositions to cancers identified. No secondary outcome measures.
Of 42 patients, 32 (76.2%) were eligible for at least one targeted therapy. 19 patients received immunotherapy, when 16 had a biomarker predicting immunotherapy benefit, and benefit of immunotherapy was unclear for 3. Another 11 did not have this biomarker, and 6 of them received immunotherapy. Six pathogenic variants were identified in four high-risk genes. By 01 January 2024, 18 patients (42.9%) had died of complications of cancer.
More than 75% of patients who received tumour testing were eligible for a targeted therapy regardless of their stage at diagnosis, emphasising the need to expand access to testing with staging workup to improve survival outcomes. Six families received personalised screening recommendations, thanks to this study.
The concept of health equity by design encompasses a multifaceted approach that integrates actions aimed at eliminating biased, unjust, and correctable differences among groups of people as a fundamental element in the design of algorithms. As algorithmic tools are increasingly integrated into clinical practice at multiple levels, nurses are uniquely positioned to address challenges posed by the historical marginalization of minority groups and its intersections with the use of “big data” in healthcare settings; however, a coherent framework is needed to ensure that nurses receive appropriate training in these domains and are equipped to act effectively.
We introduce the Bias Elimination for Fair AI in Healthcare (BE FAIR) framework, a comprehensive strategic approach that incorporates principles of health equity by design, for nurses to employ when seeking to mitigate bias and prevent discriminatory practices arising from the use of clinical algorithms in healthcare. By using examples from a “real-world” AI governance framework, we aim to initiate a wider discourse on equipping nurses with the skills needed to champion the BE FAIR initiative.
Drawing on principles recently articulated by the Office of the National Coordinator for Health Information Technology, we conducted a critical examination of the concept of health equity by design. We also reviewed recent literature describing the risks of artificial intelligence (AI) technologies in healthcare as well as their potential for advancing health equity. Building on this context, we describe the BE FAIR framework, which has the potential to enable nurses to take a leadership role within health systems by implementing a governance structure to oversee the fairness and quality of clinical algorithms. We then examine leading frameworks for promoting health equity to inform the operationalization of BE FAIR within a local AI governance framework.
The application of the BE FAIR framework within the context of a working governance system for clinical AI technologies demonstrates how nurses can leverage their expertise to support the development and deployment of clinical algorithms, mitigating risks such as bias and promoting ethical, high-quality care powered by big data and AI technologies.
As health systems learn how well-intentioned clinical algorithms can potentially perpetuate health disparities, we have an opportunity and an obligation to do better. New efforts empowering nurses to advocate for BE FAIR, involving them in AI governance, data collection methods, and the evaluation of tools intended to reduce bias, mark important steps in achieving equitable healthcare for all.
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