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To assess large language models on their ability to accurately infer cancer disease response from free-text radiology reports.

The COVID-19 pandemic was associated with significant changes to the delivery of ambulatory care, including a dramatic increase in patient messages to physicians. While asynchronous messaging is a valuable communication modality for patients, a greater volume of patient messages is associated with burnout and decreased well-being for physicians. Given that women physicians experienced greater electronic health record (EHR) burden and received more patient messages pre-pandemic, there is concern that COVID [...]

Clinical decision support (CDS) can prevent medical errors and improve patient outcomes. Electronic health record (EHR)-based CDS, designed to facilitate prescription drug monitoring program (PDMP) review, has reduced inappropriate opioid prescribing. However, the pooled effectiveness of CDS has exhibited substantial heterogeneity and current literature does not adequately detail why certain CDS are more successful than others. Clinicians regularly override CDS, limiting its impact. No studies recommend how to help nonadopters [...]

Little is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series risk model for predicting ED visits and hospitalizations in patients with HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various time windows.

Clinical decision support (CDS) systems powered by predictive models have the potential to improve the accuracy and efficiency of clinical decision-making. However, without sufficient validation, these systems have the potential to mislead clinicians and harm patients. This is especially true for CDS systems used by opioid prescribers and dispensers, where a flawed prediction can directly harm patients. To prevent these harms, regulators and researchers have proposed guidance for validating predictive [...]

Researchers at New York University (NYU) Grossman School of Medicine contacted the Health Sciences Library for help with locating large datasets for reuse. In response, the library developed and maintained the NYU Data Catalog, a public-facing data catalog that has supported not only faculty acquisition of data but also the dissemination of the products of their research in various ways.

Health organizations and systems rely on increasingly sophisticated informatics infrastructure. Without anti-racist expertise, the field risks reifying and entrenching racism in information systems. We consider ways the informatics field can recognize institutional, systemic, and structural racism and propose the use of the Public Health Critical Race Praxis (PHCRP) to mitigate and dismantle racism in digital forms. We enumerate guiding questions for stakeholders along with a PHCRP-Informatics framework. By focusing on [...]

To describe experiences and activities of Clinical Informatics (CI) fellows since the first fellowships were accredited in 2014.

Incorporating artificial intelligence (AI) into clinics brings the risk of automation bias, which potentially misleads the clinician's decision-making. The purpose of this study was to propose a potential strategy to mitigate automation bias.

Foundational domains are the building blocks of educational programs. The lack of foundational domains in undergraduate health informatics (HI) education can adversely affect the development of rigorous curricula and may impede the attainment of CAHIIM accreditation of academic programs.