Journal of evaluation in clinical practice
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Support for the concept of respect for first-person informed consent and patient autonomy, including the negative right of patients to refuse unwanted interventions has grown, but does not generally include a positive right of patients to receive whatever treatment they request or demand without constraint. Despite this, health-care providers in both Canada and the United States are guilty of providing, in their own opinions, futile or probably futile treatments at the request of patients or their substitute decision-makers. ⋯ The initial hypothesis of the researcher in this study was that SDM is not well understood by physicians, and that this lack of understanding, combined with other factors to be discussed in the full text, may result in patients receiving ethically-inappropriate treatment. Results suggest support for this hypothesis, and that SDM should be more closely examined if it is to be pursued as a method of decision making.
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Inpatient rehabilitation following total knee or hip arthroplasty (TKA, THA) is resource intensive and expensive. Understanding who is referred is integral to the discourse concerning service and cost reform. This study aimed to determine patient prognostic factors associated with referral to inpatient rehabilitation following TKA or THA in a public sector setting. In this setting, surgeon or patient choice does not drive referral. ⋯ In the absence of choice, physical impairment and health factors are associated with referral to inpatient rehabilitation following TKA or THA.
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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.
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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.
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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.