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Background: Down syndrome is the most common liveborn genetic condition. However, there are no surveys measuring societal services and supports for people with Down syndrome. We developed a questionnaire so that initiatives could be targeted towards countries most in need of assistance. Method: We formed a geographically diverse group of physicians, family members of people with Down syndrome, and members of Down syndrome not-for-profit organisations to create a survey of societal services and supports. We used a modified Delphi method and disseminated the survey to Down syndrome non-profit organisations worldwide. Results: Our survey consists of 61 items categorised within five domains: Education, Community Inclusion, Independence, Healthcare, and Social and Policy Issues. Conclusions: We developed a survey to measure societal services and supports available to people with Down syndrome as perceived by organisational leaders. Our methods might serve as a blueprint for other populations of people with intellectual and developmental disabilities.

Introduction: There is mounting interest in the use of risk prediction models to guide lung cancer screening. Electronic health records (EHRs) could facilitate such an approach, but smoking exposure documentation is notoriously inaccurate. While the negative impact of inaccurate EHR data on screening practices reliant on dichotomized age and smoking exposure-based criteria has been demonstrated, less is known regarding its impact on the performance of model-based screening.Aims and Methods: Data were collected from a cohort of 37 422 ever-smokers between the ages of 55 and 74, seen at an academic safety-net healthcare system between 1999 and 2018. The National Lung Cancer Screening Trial (NLST) criteria, PLCOM2012 and LCRAT lung cancer risk prediction models were validated against time to lung cancer diagnosis. Discrimination (area under the receiver operator curve [AUC]) and calibration were assessed. The effect of substituting the last documented smoking variables with differentially retrieved "history conscious" measures was also determined.Results: The PLCOM2012 and LCRAT models had AUCs of 0.71 (95% CI, 0.69 to 0.73) and 0.72 (95% CI, 0.70 to 0.74), respectively. Compared with the NLST criteria, PLCOM2012 had a significantly greater time-dependent sensitivity (69.9% vs. 64.5%, p < .01) and specificity (58.3% vs. 56.4%, p < .001). Unlike the NLST criteria, the performances of the PLCOM2012 and LCRAT models were not prone to historical variability in smoking exposure documentation.Conclusions: Despite the inaccuracies of EHR-documented smoking histories, leveraging model-based lung cancer risk estimation may be a reasonable strategy for screening, and is of greater value compared with using NLST criteria in the same setting.Implications: EHRs are potentially well suited to aid in the risk-based selection of lung cancer screening candidates, but healthcare providers and systems may elect not to leverage EHR data due to prior work that has shown limitations in structured smoking exposure data quality. Our findings suggest that despite potential inaccuracies in the underlying EHR data, screening approaches that use multivariable models may perform significantly better than approaches that rely on simpler age and exposure-based criteria. These results should encourage providers to consider using pre-existing smoking exposure data with a model-based approach to guide lung cancer screening practices. [ABSTRACT FROM AUTHOR]

Background: The public launch of OpenAI's ChatGPT platform generated immediate interest in the use of large language models (LLMs). Health care institutions are now grappling with establishing policies and guidelines for the use of these technologies, yet little is known about how health care providers view LLMs in medical settings. Moreover, there are no studies assessing how pediatric providers are adopting these readily accessible tools., Objective: The aim of this study was to determine how pediatric providers are currently using LLMs in their work as well as their interest in using a Health Insurance Portability and Accountability Act (HIPAA)-compliant version of ChatGPT in the future., Methods: A survey instrument consisting of structured and unstructured questions was iteratively developed by a team of informaticians from various pediatric specialties. The survey was sent via Research Electronic Data Capture (REDCap) to all Boston Children's Hospital pediatric providers. Participation was voluntary and uncompensated, and all survey responses were anonymous., Results: Surveys were completed by 390 pediatric providers. Approximately 50% (197/390) of respondents had used an LLM; of these, almost 75% (142/197) were already using an LLM for nonclinical work and 27% (52/195) for clinical work. Providers detailed the various ways they are currently using an LLM in their clinical and nonclinical work. Only 29% (n=105) of 362 respondents indicated that ChatGPT should be used for patient care in its present state; however, 73.8% (273/368) reported they would use a HIPAA-compliant version of ChatGPT if one were available. Providers' proposed future uses of LLMs in health care are described., Conclusions: Despite significant concerns and barriers to LLM use in health care, pediatric providers are already using LLMs at work. This study will give policy makers needed information about how providers are using LLMs clinically., (©Susannah Kisvarday, Adam Yan, Julia Yarahuan, Daniel J Kats, Mondira Ray, Eugene Kim, Peter Hong, Jacob Spector, Jonathan Bickel, Chase Parsons, Naveed Rabbani, Jonathan D Hron. Originally published in JMIR Formative Research (https://formative.jmir.org), 12.09.2024.)