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Although many aspects of nursing documentation are considered an essential part of clinical communication and care coordination, other types of nursing documentation have been implemented to meet compliance and secondary uses. Adding required documentation without carefully assessing its association with patient outcomes adds excessive documentation burden on nurses. There is a gap in the evidence of the association between additional required nursing documentation and improvements in patient outcomes.This study aimed [...]

Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) is particularly difficult to identify using existing approaches. This study aims to develop and evaluate a Large Language Model (LLM)-based framework for detecting PEAE from unstructured narrative data in EMRs.

Artificial Intelligence (AI)-based approaches for extracting Social Drivers of Health (SDoH) from clinical notes offer healthcare systems an efficient way to identify patients' social needs, yet we know little about the acceptability of this approach to patients and clinicians. We investigated patient and clinician acceptability through interviews.

While performance drift of clinical prediction models is well-documented, the potential for algorithmic biases to emerge post-deployment has had limited characterization. A better understanding of how temporal model performance may shift across subpopulations is required to incorporate fairness drift into model maintenance strategies.

Electronic health records (EHRs) have significantly impacted healthcare improving access to patient information and enhancing communication among the health care team. However, the lack of usability and increased documentation burden has greatly contributed to clinician burnout. Improvements in EHR design that include physician input are critical to developing specific changes that make EHRs more intuitive and less cumbersome to use; however, it can be challenging to gather input from physicians [...]

Although efforts to effectively govern AI continue to develop, relatively little work has been done to systematically measure and include patient perspectives or expectations of AI in governance. This analysis is designed to understand patient expectations of healthcare AI.

This study aimed to assess the efficacy of a biomedical informatics boot camp with regard to improving the skill sets of its participants.The University at Buffalo hosts a free, virtual biomedical informatics boot camp annually. Lectures covering various subject matters are offered, for example, general programming, machine learning, natural language processing, and clinical decision support. Once the 2023 boot camp had concluded, an anonymous voluntary survey was distributed.Seventy percent of [...]

Effective postoperative care is crucial for the success of total joint arthroplasty (TJA) and prevention of unnecessary emergency department (ED) visits. We explore the feasibility and acceptability of utilizing an Interactive Voice Response System (IVRS) to enhance postoperative monitoring in primary TJA patients.This pilot study was conducted with 24 participants undergoing primary TJA randomly assigned to an intervention group receiving IVRS calls (n = 12) or a control group (n = 12) receiving standard [...]

The Enhanced EHR-facilitated Cancer Symptom Control (E2C2) Trial is a pragmatic trial testing a collaborative care approach for managing common cancer symptoms. There were challenges in identifying cancer site and metastatic status.This study compares three different approaches to determine cancer site and six strategies for identifying the presence of metastasis using EHR and cancer registry data.The E2C2 cohort included 50,559 patients seen in the medical oncology clinics of a large [...]