Publications by authors named "J M Facelli"
While phone location data is widely collected by providers and shared under special agreements with data aggregators, it is not available for research or routine surveillance purposes. Moreover, raw phone data may expose personal travel patterns which would not be ethically or lawful to use. One large data aggregator, Advan ( https://advanresearch.
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- Type 2 diabetes (T2D) is a significant health issue in the U.S., and this study focuses on how racial and ethnic differences affect the prescription of effective diabetes medications.
- The research analyzed data from 57,320 patients, revealing notable disparities in the prescription rates of GLP-1 medications like tirzepatide, semaglutide, and dulaglutide among different racial and ethnic groups compared to White patients.
- The findings suggest that these disparities could lead to worse health outcomes for underrepresented groups, signaling a need for targeted interventions to ensure equitable access to diabetes care.
Article Synopsis
- This research explores whether structured clinical data can predict dementia diagnoses, using a machine learning model on a population-based cohort.
- The study linked healthcare data and sociodemographic information, finding that 12.4% of participants were diagnosed with dementia, with Random Forest models yielding an Area Under the Curve (AUC) of 0.67 for overall predictions.
- While structured clinical data showed some predictive capability, using ICD codes improved accuracy to 0.77, indicating a need for further research to ensure these models accurately identify true dementia cases.
Objective: Understanding and quantifying biases when designing and implementing actionable approaches to increase fairness and inclusion is critical for artificial intelligence (AI) in biomedical applications.
Methods: In this Special Communication, we discuss how bias is introduced at different stages of the development and use of AI applications in biomedical sciences and health care. We describe various AI applications and their implications for fairness and inclusion in sections on 1) Bias in Data Source Landscapes, 2) Algorithmic Fairness, 3) Uncertainty in AI Predictions, 4) Explainable AI for Fairness and Equity, and 5) Sociological/Ethnographic Issues in Data and Results Representation.
Background: Increased workload, including workload related to electronic health record (EHR) documentation, is reported as a main contributor to nurse burnout and adversely affects patient safety and nurse satisfaction. Traditional methods for workload analysis are either administrative measures (such as the nurse-patient ratio) that do not represent actual nursing care or are subjective and limited to snapshots of care (eg, time-motion studies). Observing care and testing workflow changes in real time can be obstructive to clinical care.
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