AI Medical Compendium Journal:
Journal of patient safety

Showing 1 to 10 of 14 articles

Intelligent Verification Tool for Surgical Information of Ophthalmic Patients: A Study Based on Artificial Intelligence Technology.

Journal of patient safety
OBJECTIVE: With the development of day surgery, the characteristics of "short, frequent and fast" ophthalmic surgery are becoming more prominent. However, nurses are not efficient in verifying patients' surgical information, and problems such as pati...

AI: Promise or Peril for Patient Safety.

Journal of patient safety
Patient safety advocates identify concerns for the impact of AI on patient safety. Patients identified the following 4 main areas that AI developers, regulatory bodies, and clinical users of AI are asked to consider: data integrity and bias, efficacy...

Healthcare Violence and the Potential Promises and Harms of Artificial Intelligence.

Journal of patient safety
Currently, the healthcare workplace is one of the most dangerous in the United States. Over a 3-month period in 2022, two nurses were assaulted every hour. Artificial intelligence (AI) has the potential to prevent workplace violence by developing uni...

Enhancing Pressure Injury Surveillance Using Natural Language Processing.

Journal of patient safety
OBJECTIVE: This study assessed the feasibility of nursing handoff notes to identify underreported hospital-acquired pressure injury (HAPI) events.

Pressure Injury Prediction Model Using Advanced Analytics for At-Risk Hospitalized Patients.

Journal of patient safety
OBJECTIVE: Analyzing pressure injury (PI) risk factors is complex because of multiplicity of associated factors and the multidimensional nature of this injury. The main objective of this study was to identify patients at risk of developing PI.

Machine Learning-Based Mortality Prediction of Patients at Risk During Hospital Admission.

Journal of patient safety
OBJECTIVES: The ability to predict in-hospital mortality from data available at hospital admission would identify patients at risk and thereby assist hospital-wide patient safety initiatives. Our aim was to use modern machine learning tools to predic...

A Keyword Approach to Identify Adverse Events Within Narrative Documents From 4 Italian Institutions.

Journal of patient safety
OBJECTIVES: Existing methods for measuring adverse events in hospitals intercept a restricted number of events. Text mining refers to a range of techniques to extract data from narrative sources. The goal of this study was to evaluate the performance...

Realizing the Power of Text Mining and Natural Language Processing for Analyzing Patient Safety Event Narratives: The Challenges and Path Forward.

Journal of patient safety
Patient safety event (PSE) reports are a useful lens to understand hazards and patient safety risks in healthcare systems. However, patient safety officers and analysts in healthcare systems and safety organizations are challenged to make sense of th...

A Machine Learning Approach to Reclassifying Miscellaneous Patient Safety Event Reports.

Journal of patient safety
BACKGROUND AND OBJECTIVES: Medical errors are a leading cause of death in the United States. Despite widespread adoption of patient safety reporting systems to address medical errors, making sense of the reports collected in these systems is challeng...