Achieving good-quality consent: review of literature, case law and guidance

Comparative study on informed consent regulation in health care among Italy, France, United Kingdom, Nordic Countries, Germany, and Spain

The information and subsequent expression of will, so-called informed consent, have become the essential element of health right, understood as the right to autonomous choice in health, based on the fiduciary relationship between physician and patient. This gradually leads European Countries to adopt special legislations and to issue frequent judgments on the subject. However, new challenges in daily clinical practice call for further study of legal solutions. The authors analyse and compare the regulations on informed consent in health care of Italy, France, the United Kingdom, the Nordic Countries, Germany, and Spain. The health and legal contexts, existence of special regulations on informed consent and their characteristics are discussed. Informed consent resulted a mandatory requirement. Clear communication about treatment, therapeutic alternatives, and major risks, discussed in conversation, but preferably documented in writing, are agreed upon. The possibility of dissent and withdrawal of consent are also included. There is a growing interest in involving and regulating the entire health team in information and consent. Lowering the age of consent for minors or analysing the maturity of minors are attempts to increase their participation in health decisions. On another side, the protection of adult incapables persons requires greater involvement of family and fiduciaries to better adapt to changing health needs. Health policy must take responsibility for training health professionals and citizens about the value of health information and communication as a shared choice in care planning, to strengthen the bond of trust with the healthcare system and users.

Integrating Shared Decision-Making into Undergraduate Oncology Education: A Pedagogical Framework

The integration of shared decision-making (SDM) into undergraduate oncology education represents a critical evolution in medical pedagogy, reflecting the growing complexity and patient-centric focus of contemporary healthcare. This paper introduces a comprehensive pedagogical framework designed to embed SDM within the undergraduate medical curriculum, particularly in oncology, where the multiplicity of treatment options and their profound impact on patient life underscore the necessity of this approach. Grounded in a systematic literature review and aligned with established educational theories, this framework proposes twelve strategic approaches to cultivate future physicians proficient in both clinical acumen and patient-collaborative decision-making. The framework emphasizes real-world clinical experience, role-playing, case studies, and decision aids to deepen students' understanding of SDM. It advocates for the development of communication skills, ethical deliberation, and cultural competence, recognizing the multifaceted nature of patient care. The inclusion of patient narratives and evidence-based decision-making further enriches the curriculum, offering a holistic view of patient care. Additionally, the integration of digital tools within the SDM process acknowledges the evolving technological landscape in healthcare. The paper also addresses challenges in implementing this framework, such as curricular constraints and the need for educator training. It underscores the importance of continual evaluation and adaptation of these strategies to the dynamic field of medical education and practice. Overall, this comprehensive approach aims not only to enhance the quality of oncological care but also to prepare medical students for the complexities of modern medicine, where patient involvement in decision-making is both a necessity and an expectation.

Socioeconomic disparities and regional environment are associated with cervical lymph node metastases in children and adolescents with differentiated thyroid cancer: developing a web-based predictive model

Purpose

To establish an online predictive model for the prediction of cervical lymph node metastasis (CLNM) in children and adolescents with differentiated thyroid cancer (caDTC). And analyze the impact between socioeconomic disparities, regional environment and CLNM.

Methods

We retrospectively analyzed clinicopathological and sociodemographic data of caDTC from the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2019. Risk factors for CLNM in caDTC were analyzed using univariate and multivariate logistic regression (LR). And use the extreme gradient boosting (XGBoost) algorithm and other commonly used ML algorithms to build CLNM prediction models. Model performance assessment and visualization were performed using the area under the receiver operating characteristic (AUROC) curve and SHapley Additive exPlanations (SHAP).

Results

In addition to common risk factors, our study found that median household income and living regional were strongly associated with CLNM. Whether in the training set or the validation set, among the ML models constructed based on these variables, the XGBoost model has the best predictive performance. After 10-fold cross-validation, the prediction performance of the model can reach the best, and its best AUROC value is 0.766 (95%CI: 0.745-0.786) in the training set, 0.736 (95%CI: 0.670-0.802) in the validation set, and 0.733 (95%CI: 0.683-0.783) in the test set. Based on this XGBoost model combined with SHAP method, we constructed a web-base predictive system.

Conclusion

The online prediction model based on the XGBoost algorithm can dynamically estimate the risk probability of CLNM in caDTC, so as to provide patients with personalized treatment advice.

Harnessing Machine Learning to Personalize Web-Based Health Care Content

Web-based health care content has emerged as a primary source for patients to access health information without direct guidance from health care providers. The benefit of this approach is dependent on the ability of patients to access engaging high-quality information, but significant variability in the quality of web-based information often forces patients to navigate large quantities of inaccurate, incomplete, irrelevant, or inaccessible content. Personalization positions the patient at the center of health care models by considering their needs, preferences, goals, and values. However, the traditional methods used thus far in health care to determine the factors of high-quality content for a particular user are insufficient. Machine learning (ML) uses algorithms to process and uncover patterns within large volumes of data to develop predictive models that automatically improve over time. The health care sector has lagged behind other industries in implementing ML to analyze user and content features, which can automate personalized content recommendations on a mass scale. With the advent of big data in health care, which builds comprehensive patient profiles drawn from several disparate sources, ML can be used to integrate structured and unstructured data from users and content to deliver content that is predicted to be effective and engaging for patients. This enables patients to engage in their health and support education, self-management, and positive behavior change as well as to enhance clinical outcomes.