Journal of evaluation in clinical practice
-
Heart failure (HF) clinics are highly effective, yet not optimally utilized. A realist review was performed to identify contexts (eg, health system characteristics, clinic capacity, and siting) and underlying mechanisms (eg, referring provider knowledge of clinics and referral criteria, barriers in disadvantaged patients) that influence utilization (provider referral [ie, of all appropriate and no inappropriate patients] and access [ie, patient attends ≥1 visit]) of HF clinics. ⋯ Given the burden of HF and benefit of HF clinics, more research is needed to understand, and hence overcome sub-optimal use of HF clinics. In particular, an understanding from the perspective of referring providers is needed.
-
Although mental health clinics are under increasing pressure to demonstrate value and routine outcome monitoring (ROM) has become a mandated component of care, providers have been slow to adopt ROM into practice, with some estimating that less than 20% of mental health clinicians use it consistently in the United States. This article explores perceived barriers and facilitators to integrating ROM into practice among clinicians and administrators in a large urban US community psychiatry clinic. ⋯ In order for psychiatry clinics to successfully implement ROM into practice, they must diagnose organization-side barriers and translate this knowledge into actionable quality improvement initiatives ranging from the infrastructural to the cultural.
-
This paper examines the use of artificial intelligence (AI) for the diagnosis of autism spectrum disorder (ASD, hereafter autism). In so doing we examine some problems in existing diagnostic processes and criteria, including issues of bias and interpretation, and on concepts like the 'double empathy problem'. We then consider how novel applications of AI might contribute to these contexts. We're focussed specifically on adult diagnostic procedures as childhood diagnosis is already well covered in the literature.
-
Despite the great promises that artificial intelligence (AI) holds for health care, the uptake of such technologies into medical practice is slow. In this paper, we focus on the epistemological issues arising from the development and implementation of a class of AI for clinical practice, namely clinical decision support systems (CDSS). We will first provide an overview of the epistemic tasks of medical professionals, and then analyse which of these tasks can be supported by CDSS, while also explaining why some of them should remain the territory of human experts. ⋯ In practice, this means that the system indicates what factors contributed to arriving at an advice, allowing the user (clinician) to evaluate whether these factors are medically plausible and applicable to the patient. Finally, we defend that proper implementation of CRSS allows combining human and artificial intelligence into hybrid intelligence, were both perform clearly delineated and complementary empirical tasks. Whereas CRSSs can assist with statistical reasoning and finding patterns in complex data, it is the clinicians' task to interpret, integrate and contextualize.
-
According to an influential taxonomy of varieties of uncertainty in health care, existential uncertainty is a key aspect of uncertainty for patients. Although the term "existential uncertainty" appears across a number of disciplines in the research literature, its use is diffuse and inconsistent. To date there has not been a systematic attempt to define it. The aim of this study is to generate a theoretically-informed conceptualisation of existential uncertainty within the context of an established taxonomy. ⋯ Humans rely on identity, worldview, and a sense of meaning in life as ways of managing the ineradicable uncertainty of our being-in-the-world, and these can be challenged by a serious diagnosis. It is important that medical professionals acknowledge issues around existential uncertainty as well as issues around scientific uncertainty, and recognise when patients might be struggling with these. Further research is required to identify ways of measuring existential uncertainty and to develop appropriate interventions, but it is hoped that this conceptualisation provides a useful first step towards that goal.