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The objective of this study is to determine whether primary care electronic health record (EHR) data are sufficiently complete and plausible to support automated audits of antimicrobial prescribing quality.Cross-sectional descriptive assessment of antimicrobial auditing-related fields in Patron, a large Australian primary care EHR dataset with 3.5 million patients from 129 consenting general practices. Data from 2018 to 2022 were evaluated using the Harmonized Data Quality Assessment Terminology and Framework, covering [...]

Guidelines recommend measuring glycated hemoglobin (HgbA1c) levels on all inpatients with diabetes mellitus, if untested in the prior 3 months. In response, our health system applies default HgbA1c orders in the electronic insulin orders for qualifying patients. However, recent red blood cell transfusions may cause falsely low HgbA1c values, leading to inappropriate management. Clinicians often forget to deselect default HgbA1c orders after transfusion, leading to erroneous results. We evaluated a [...]

Estimating readmission risk for intensive care unit (ICU) patients is critical for clinicians to optimize resource allocation and prevent premature discharges. Machine learning models currently applied to this task either lack interpretability or cannot identify patient subgroups with distinctive readmission risks and characteristics. We addressed this gap by introducing a cutting-edge rule-based model, namely truly unordered rule sets (TURS), to reveal heterogeneous readmission risks and subgroup-level patient characteristics.

The National Cancer Institute (NCI), part of the National Institutes of Health (NIH) supports efforts to address critical challenges in advancing cancer research. As part of this effort, NCI sponsored the development of a privacy-preserving record linkage (PPRL) software that transforms identifying patient information into multiple tokens through a set of cryptographically secure keyed hash functions. This project aims to evaluate the PPRL software in the perspective of re-identification risks [...]

Large language models' (LLMs') performance in high-stakes, compliance-driven settings such as drafting clinical research documents remains underexplored. This study aims to build a benchmark and an evaluation framework for assessing LLMs' compliance and factuality in generating informed consent forms (ICFs) from clinical trial protocols.

To explore the relationship between ambulatory physician electronic health record (EHR) use characteristics and proxies for physician efficiency.

To assess the interpretability and acceptance of Shapley values for making artificial intelligence/machine learning (AI/ML) tools more transparent, interpretable, and useful to clinicians.

Traditional clinical trial enrollment relies on manual screening and coordinator-led recruitment, creating scalability barriers in high-volume perioperative environments. This study evaluated whether a fully automated, electronic health record (EHR)-integrated clinical decision support (CDS) system could identify eligible patients and engage clinicians in real time without manual screening or dedicated research staff.

To develop and validate an agent-based Large Language Model (LLM) system for extracting structured data from breast cancer synoptic pathology reports and assess the performance gap between synthetic and real-world validation.

This study evaluates the usefulness of explicit syntactic knowledge, integrated via a neural mechanism, in improving the accuracy of named entity recognition in the domain of biomedical text processing.