Journal of evaluation in clinical practice
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Failure mode and effects analysis (FMEA) is a valuable reliability management tool that can preemptively identify the potential failures of a system and assess their causes and effects, thereby preventing them from occurring. The use of FMEA in the healthcare setting has become increasingly popular over the last decade, being applied to a multitude of different areas. The objective of this study is to review comprehensively the literature regarding the application of FMEA for healthcare risk analysis. ⋯ FMEA has high practicality for healthcare quality improvement and error reduction and has been prevalently employed to improve healthcare processes in hospitals. This research supports academics and practitioners in effectively adopting the FMEA tool to proactively reduce healthcare risks and increase patient safety, and provides an insight into its state-of-the-art.
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Within competency-based medical education, self-regulated learning (SRL) requires residents to leverage self-assessment and faculty feedback. We sought to investigate the potential for competency-based assessments to foster SRL by quantifying the relationship between faculty feedback and entrustment ratings as well as the congruence between faculty assessment and resident self-assessment. ⋯ Minimal and inconsistent associations were found between narrative and numerical feedback. Performance monitoring accuracy and feedback should be included in assessment validation.
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Effective treatment of depression involves collaboration with informed patients and families and appropriate knowledge sharing. We describe here our experience, as a case example, of a collaboration to "translate" a clinical guideline designed for practitioners into an accessible, plainlanguage version that patients and families can use during the care process, both to provide basic educational information and to foster informed discussions with their treatment providers. Content experts in knowledge translation, patient advocacy, patient-oriented research, and psychiatry guided overall project design. ⋯ Both during and at the end of the project, writers provided additional recommendations for improving the process, including more in-person meetings, distribution of step-by-step instructions on the writing task, and a key terms glossary of technical terms to support their role. In conclusion, we describe a process with practical tips and reflective feedback on important considerations for engaging persons with lived experience as leaders in the codesign and writing process of lay treatment guidelines. These methods may serve as a model for similar projects in other areas of healthcare.
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A clinical decision support system (CDSS) is a computerized system using case-based reasoning to assist clinicians in assessing disease status, in selecting appropriate therapy or in making other clinical decisions. Previous randomized controlled trials (RCTs or trials) have shown that CDSSs have the potential to improve the insulin use, but the evidence was conflicting and uncertain. The purpose of our study was to determine whether a CDSS improves the use of insulin. ⋯ CDSSs have the potential to improve the insulin use and blood glucose control in a clinical setting. The methodologies in these studies were of mixed quality. Better designed and longer-term studies are required to ensure a larger and more reliable evidence base on the effects of CDSS intervention on insulin use.
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In the context of the gradual development of artificial intelligence in health care, the clinical decision support systems (CDSS) play an increasing crucial role in improving the quality of the therapeutic and diagnostic efficiency in health care. The fuzzy logic (FL) provides an effective means for dealing with uncertainties in the health decision-making process; therefore, FL-based CDSS becomes a very powerful tool for data and knowledge management, being able to think like an expert clinician. This work proposes an FL-based CDSS for the evaluation of renal function in posttransplant patients. ⋯ We propose that the CDSSs for the assessment and follow-up of kidney-transplanted patients built in this study are applicable to clinical practice.