Are student nurses ready for new technologies in mental health? Mixed-methods study

Trust and acceptability of data-driven clinical recommendations in everyday practice: A scoping review

BACKGROUND

Increasing attention is being given to the analysis of large health datasets to derive new clinical decision support systems (CDSS). However, few data-driven CDSS are being adopted into clinical practice. Trust in these tools is believed to be fundamental for acceptance and uptake but to date little attention has been given to defining or evaluating trust in clinical settings.

OBJECTIVES

A scoping review was conducted to explore how and where acceptability and trustworthiness of data-driven CDSS have been assessed from the health professional's perspective.

METHODS

Medline, Embase, PsycInfo, Web of Science, Scopus, ACM Digital, IEEE Xplore and Google Scholar were searched in March 2022 using terms expanded from: "data-driven" AND "clinical decision support" AND "acceptability". Included studies focused on healthcare practitioner-facing data-driven CDSS, relating directly to clinical care. They included trust or a proxy as an outcome, or in the discussion. The preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) is followed in the reporting of this review.

RESULTS

3291 papers were screened, with 85 primary research studies eligible for inclusion. Studies covered a diverse range of clinical specialisms and intended contexts, but hypothetical systems (24) outnumbered those in clinical use (18). Twenty-five studies measured trust, via a wide variety of quantitative, qualitative and mixed methods. A further 24 discussed themes of trust without it being explicitly evaluated, and from these, themes of transparency, explainability, and supporting evidence were identified as factors influencing healthcare practitioner trust in data-driven CDSS.

CONCLUSION

There is a growing body of research on data-driven CDSS, but few studies have explored stakeholder perceptions in depth, with limited focused research on trustworthiness. Further research on healthcare practitioner acceptance, including requirements for transparency and explainability, should inform clinical implementation.

Insights into medical students' perceptions of work culture during the COVID-19 pandemic: a mixed method study

BACKGROUND

The COVID-19 pandemic brought about profound social changes that affected students worldwide. These changes had both psychological and economic consequences, and also led to the adoption of new teaching methods. It can also have an impact on work culture, which is the collective set of values, norms, and practices within a specific profession, shaping how individuals in that field behave, communicate, and identify with their work. The aim of the study was to examine medical students' perception of professional culture during the COVID-19 crisis when they voluntarily participated in the healthcare network established, outside of university placements, for the management of COVID patients.

METHODS

A questionnaire study based on the vignette methodology was conducted among third-year medical students. Drawing from three scenarios in which students were variably engaged in crisis management, it included questions about their perceptions of the medical profession, their motivation, and their sense of belonging to the profession.

RESULTS

352 students responded to the survey. The pandemic had both a positive and a negative impact on students' perceptions of the medical profession. Cluster analysis using a k-means algorithm and principal component analysis revealed three clusters of students with different perceptions of the medical profession. The first cluster, which represented the majority of students, corresponded to a relatively positive perception of the profession that was reinforced during the pandemic. In the second cluster, students' perceptions were reinforced still further, and particular importance was attached to field experience. Students in the third cluster had the most negative perceptions, having been shaken the most by the pandemic, and they attached little importance to field experience.

CONCLUSIONS

The analysis highlighted the importance of students being able to adapt and draw on a range of resources during the COVID-19 pandemic. This underscores the need for work cultures that support adaptability and coping. Further research is needed to understand its long-term effects on students' perceptions of the medical profession and to identify interventions that could support students in the aftermath of this difficult period.

Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review

The application of machine learning (ML) and artificial intelligence (AI) in healthcare domains has received much attention in recent years, yet significant questions remain about how these new tools integrate into frontline user workflow, and how their design will impact implementation. Lack of acceptance among clinicians is a major barrier to the translation of healthcare innovations into clinical practice. In this systematic review, we examine when and how clinicians are consulted about their needs and desires for clinical AI tools. Forty-five articles met criteria for inclusion, of which 24 were considered design studies. The design studies used a variety of methods to solicit and gather user feedback, with interviews, surveys, and user evaluations. Our findings show that tool designers consult clinicians at various but inconsistent points during the design process, and most typically at later stages in the design cycle (82%, 19/24 design studies). We also observed a smaller amount of studies adopting a human-centered approach and where clinician input was solicited throughout the design process (22%, 5/24). A third (15/45) of all studies reported on clinician trust in clinical AI algorithms and tools. The surveyed articles did not universally report validation against the “gold standard” of clinical expertise or provide detailed descriptions of the algorithms or computational methods used in their work. To realize the full potential of AI tools within healthcare settings, our review suggests there are opportunities to more thoroughly integrate frontline users’ needs and feedback in the design process.

Telepsychiatry to Provide Mental Health Support to Healthcare Professionals during the COVID-19 Crisis: A Cross-Sectional Survey among 321 Healthcare Professionals in France

Pandemics are difficult times for the mental health of healthcare professionals, who are more likely to present with PTSD-like symptoms. In the context of a highly contagious communicable disease, telemedicine is a useful alternative to usual care, and should be considered as a means to support healthcare professionals’ mental health. This is a multicenter (n = 19), cross-sectional study, based on a 27-item questionnaire, aiming to investigate the acceptability to healthcare workers of a telepsychiatry service as a means of providing mental health support during the COVID-19 pandemic. Between October and December 2020, 321 responses were received, showing that women, caregiving staff, and those directly involved in the care of COVID-19 patients are less favorable to the idea of receiving remote support. In our population, barriers were related to the clinical setting or ethics, and most of the respondents would not accept a drug prescription by telepsychiatry. Although telepsychiatry should be a part of the armamentarium of mental health management, it is not suitable as a stand-alone approach, and should be combined with conventional face-to-face consultations.

Improving clinical decision-making in psychiatry: implementation of digital phenotyping could mitigate the influence of patient’s and practitioner’s individual cognitive biases

High stake clinical choices in psychiatry can be impacted by external irrelevant factors. A strong understanding of the cognitive and behavioural mechanisms involved in clinical reasoning and decision-making is fundamental in improving healthcare quality. Indeed, the decision in clinical practice can be influenced by errors or approximations which can affect the diagnosis and, by extension, the prognosis: human factors are responsible for a significant proportion of medical errors, often of cognitive origin. Both patient’s and clinician’s cognitive biases can affect decision-making procedures at different time points. From the patient’s point of view, the quality of explicit symptoms and data reported to the psychiatrist might be affected by cognitive biases affecting attention, perception or memory. From the clinician’s point of view, a variety of reasoning and decision-making pitfalls might affect the interpretation of information provided by the patient. As personal technology becomes increasingly embedded in human lives, a new concept called digital phenotyping is based on the idea of collecting real-time markers of human behaviour in order to determine the ‘digital signature of a pathology’. Indeed, this strategy relies on the assumption that behaviours are ‘quantifiable’ from data extracted and analysed through connected tools (smartphone, digital sensors and wearable devices) to deduce an ‘e-semiology’. In this article, we postulate that implementing digital phenotyping could improve clinical reasoning and decision-making outcomes by mitigating the influence of patient’s and practitioner’s individual cognitive biases.