Charting a new course in healthcare:early-stage AI algorithm registration to enhance trust and transparency

AI holds the potential to transform healthcare, promising improvements in patient care. Yet, realizing this potential is hampered by over-reliance on limited datasets and a lack of transparency in validation processes. To overcome these obstacles, we advocate the creation of a detailed registry for AI algorithms. This registry would document the development, training, and validation of AI models, ensuring scientific integrity and transparency. Additionally, it would serve as a platform for peer review and ethical oversight. By bridging the gap between scientific validation and regulatory approval, such as by the FDA, we aim to enhance the integrity and trustworthiness of AI applications in healthcare.</p

Charting a new course in healthcare:early-stage AI algorithm registration to enhance trust and transparency

AI holds the potential to transform healthcare, promising improvements in patient care. Yet, realizing this potential is hampered by over-reliance on limited datasets and a lack of transparency in validation processes. To overcome these obstacles, we advocate the creation of a detailed registry for AI algorithms. This registry would document the development, training, and validation of AI models, ensuring scientific integrity and transparency. Additionally, it would serve as a platform for peer review and ethical oversight. By bridging the gap between scientific validation and regulatory approval, such as by the FDA, we aim to enhance the integrity and trustworthiness of AI applications in healthcare.</p

Charting a new course in healthcare: early-stage AI algorithm registration to enhance trust and transparency

AI holds the potential to transform healthcare, promising improvements in patient care. Yet, realizing this potential is hampered by over-reliance on limited datasets and a lack of transparency in validation processes. To overcome these obstacles, we advocate the creation of a detailed registry for AI algorithms. This registry would document the development, training, and validation of AI models, ensuring scientific integrity and transparency. Additionally, it would serve as a platform for peer review and ethical oversight. By bridging the gap between scientific validation and regulatory approval, such as by the FDA, we aim to enhance the integrity and trustworthiness of AI applications in healthcare

Optimal screening for increased risk for adverse outcomes in hospitalised older adults

Background: screening for frailty might help to prevent adverse outcomes in hospitalised older adults. Objective: to identify the most predictive and efficient screening tool for frailty. Design and setting: two consecutive observational prospective cohorts in four hospitals in the Netherlands. Subjects: patients aged ≥70 years, electively or acutely hospitalised for ≥2 days. Methods: screening instruments included in the Dutch Safety Management Programme [VeiligheidsManagementSysteem (VMS)] on four geriatric domains (ADL, falls, undernutrition and delirium) were used and the Identification of Seniors At Risk, the 6-item Cognitive Impairment Test and the Mini-Mental State Examination were assessed. Three months later, adverse outcomes including functional decline, high-healthcare demand or death were determined. Correlation and regression tree analyses were performed and predictive capacities were assessed. Results: follow-up data were available of 883 patients. All screening instruments were similarly predictive for adverse outcome ( predictive power 0.58–0.66), but the percentage of positively screened patients (13–72%), sensitivity (24–89%) and specificity (35–91%) highly differed. The strongest predictive model for frailty was scoring positive on ≥3 VMS domains if aged 70–80 years; or being aged ≥80 years and scoring positive on ≥1 VMS domains. This tool classified 34% of the patients as frail with a sensitivity of 68% and a specificity of 74%. Comparable results were found in the validation cohort. Conclusions: the VMS-tool plus age (VMS+ ) offers an efficient instrument to identify frail hospitalised older adults at risk for adverse outcome. In clinical practice, it is important to weigh costs and benefits of screening given the rather low-predictive power of screening instruments

Women’s beliefs about medicines and adherence to pharmacotherapy in pregnancy: Opportunities for community pharmacists?

Background During pregnancy women might weigh benefits of treatment against potential risks to the unborn child. However, non-adherence to necessary treatment can adversely affect both mother and child. To optimize pregnant women’s beliefs and medication adherence, community pharmacists are ideally positioned to play an important role in primary care. Objective This narrative review aimed to summarize the evidence on 1) pregnant women’s beliefs, 2) medication adherence in pregnancy, and 3) community pharmacists’ counselling during pregnancy. Method Three search strategies were used in Medline and Embase to find original studies evaluating women’s beliefs, medication adherence and community pharmacists’ counselling during pregnancy. All original descriptive and analytic epidemiological studies performed in Europe, North America and Australia, written in English and published from 2000 onwards were included. Results We included 14 studies reporting on women’s beliefs, 11 studies on medication adherence and 9 on community pharmacists’ counselling during pregnancy. Women are more reluctant to use medicines during pregnancy and tend to overestimate the teratogenic risk of medicines. Risk perception varies with type of medicine, level of health literacy, education level and occupation. Furthermore, low medication adherence during pregnancy is common. Finally, limited evidence showed current community pharmacists’ counselling is insufficient. Barriers hindering pharmacists are insufficient knowledge and limited access to reliable information. Conclusion Concerns about medication use and non-adherence are widespread among pregnant women. Community pharmacists’ counselling during pregnancy is insufficient. Further education, training and research are required to support community pharmacists in fulfilling all the opportunities they have when counselling pregnant women

Pre-existing chromatin accessibility and gene expression differences among naive CD4+ T cells influence effector potential

CD4+ T cells have a remarkable potential to differentiate into diverse effector lineages following activation. Here, we probe the heterogeneity present among naive CD4+ T cells before encountering their cognate antigen to ask whether their effector potential is modulated by pre-existing transcriptional and chromatin landscape differences. Single-cell RNA sequencing shows that key drivers of variability are genes involved in T cell receptor (TCR) signaling. Using CD5 expression as a readout of the strength of tonic TCR interactions with self-peptide MHC, and sorting on the ends of this self-reactivity spectrum, we find that pre-existing transcriptional differences among naive CD4+ T cells impact follicular helper T (TFH) cell versus non-TFH effector lineage choice. Moreover, our data implicate TCR signal strength during thymic development in establishing differences in naive CD4+ T cell chromatin landscapes that ultimately shape their effector potential