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1.
Crit Rev Clin Lab Sci ; 61(4): 254-274, 2024 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-38809116

RESUMEN

No standard tool to measure pathologist workload currently exists. An accurate measure of workload is needed for determining the number of pathologists to be hired, distributing the workload fairly among pathologists, and assessing the overall cost of pathology consults. Initially, simple tools such as counting cases or slides were used to give an estimate of the workload. More recently, multiple workload models, including relative value units (RVUs), the Royal College of Pathologists (RCP) point system, Level 4 Equivalent (L4E), Work2Quality (W2Q), and the University of Washington, Seattle (UW) slide count method, have been developed. There is no "ideal" model that is universally accepted. The main differences among the models come from the weights assigned to different specimen types, differential calculations for organs, and the capture of additional tasks needed for safe and timely patient care. Academic centers tend to see more complex cases that require extensive sampling and additional testing, while community-based and private laboratories deal more with biopsies. Additionally, some systems do not account for teaching, participation in multidisciplinary rounds, quality assurance activities, and medical oversight. A successful workload model needs to be continually updated to reflect the current state of practice.Awareness about physician burnout has gained attention in recent years and has been added to the World Health Organization's International Classification of Diseases (World Health Organization, WHO) as an occupational phenomenon. However, the extent to which this affects pathologists is not well understood. According to the WHO, burnout syndrome is diagnosed by the presence of three components: emotional exhaustion, depersonalization from one's work (cynicism related to one's job), and a low sense of personal achievement or accomplishment. Three drivers of burnout are the demand for productivity, lack of recognition, and electronic health records. Prominent consequences of physician burnout are economic and personal costs to the public and to the providers.Wellness is physical and mental well-being that allows individuals to manage stress effectively and to thrive in both their professional and personal lives. To achieve wellness, it is necessary to understand the root causes of burnout, including over-work and working under stressful conditions. Wellness is more than the absence of stress or burnout, and the responsibility of wellness should be shared by pathologists themselves, their healthcare organization, and governing bodies. Each pathologist needs to take their own path to achieve wellness.


Asunto(s)
Agotamiento Profesional , Patólogos , Carga de Trabajo , Humanos
2.
Clin Chem Lab Med ; 62(11): 2148-2155, 2024 Oct 28.
Artículo en Inglés | MEDLINE | ID: mdl-38646706

RESUMEN

The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health. We are thus moving into a different model of "computer-assisted diagnostics", where AI is there to provide support, rather than replacing, the pathologist. Herein we focus on the practical aspects of CP, from a pathologist perspective. There is a wide range of potential applications where CP can enhance precision of pathology diagnosis, tailor prognostic and predictive information, as well as save time. There are, however, a number of potential limitations for CP that currently hinder their wider adoption in the clinical setting. We address the key necessary steps towards clinical implementation of computational pathology, discuss the significant obstacles that hinders its adoption in the clinical context and summarize some proposed solutions. We conclude that the advancement of CP in the clinic is a promising resource-intensive endeavour that requires broad and inclusive collaborations between academia, industry, and regulatory bodies.


Asunto(s)
Inteligencia Artificial , Humanos , Algoritmos , Inteligencia Artificial/tendencias , Biología Computacional/métodos , Biología Computacional/tendencias , Diagnóstico por Computador/métodos , Diagnóstico por Computador/tendencias , Aprendizaje Automático , Patología Clínica/métodos , Patología Clínica/tendencias
3.
PLoS One ; 16(11): e0260075, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34843517

RESUMEN

BACKGROUND: Current data indicates 70% of adults with obesity report experiencing bias and stigmatisation when engaging with healthcare. Most studies to date, have focused on weight bias from a healthcare professional's perspective. Few have explored weight bias from the perspective of the individual living with obesity and no study has conducted this research in the Irish context. AIMS: This study explored, the lived-in experience of individuals afflicted with obesity, when interacting with the Irish healthcare system. It examined whether participants encountered weight bias and stigma, if so, how it may have impacted them and gathered their suggestions on how it could be best addressed. METHODS: Employing a phenomenological approach, purposive sampling and semi-structured interviews were conducted with 15 individuals living with class II (BMI 35.0-39.9) or III obesity (BMI ≥40kg/m2) who reported regular and consistent engagement with the Irish healthcare system. Predominant emergent themes were categorised using the interview domains; (1) experiences of obesity bias and stigma, (2) impact of this bias and stigma and (3) suggested avenues to reduce bias and stigma. FINDINGS: Participants reported experiencing high levels of weight bias and stigmatisation. Relating to experiences, three themes were identified; interpersonal communication, focus of care and physical environment. In terms of its impact, there were two emergent themes; negativity towards future healthcare and escalation of unhealthy behaviours. Suggested avenues to eliminate bias and stigma included the introduction of a timely and clear clinical pathway for obesity management and a focus on HCPs education in relation to obesity causes and complexity. CONCLUSIONS: Outside of specialist obesity tertiary care, weight bias and stigmatisation is commonly reported in the Irish healthcare system. It is a significant issue for those living with obesity, detrimental to their physiological and psychological health. A concerted effort by HCPs across clinical, research and educational levels is required to alleviate its harmful effects.


Asunto(s)
Atención a la Salud/tendencias , Pacientes/psicología , Prejuicio de Peso/tendencias , Adulto , Femenino , Instituciones de Salud , Humanos , Irlanda , Masculino , Persona de Mediana Edad , Obesidad , Brechas de la Práctica Profesional/tendencias , Estigma Social , Estereotipo , Encuestas y Cuestionarios , Prejuicio de Peso/psicología
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