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Quality Assurance, Health Care

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Ripe for Disruption? Adopting Nurse-Led Data Science and Artificial Intelligence to Predict and Reduce Hospital-Acquired Outcomes in the Learning Health System.

Nursing administration quarterly
Nurse leaders are dually responsible for resource stewardship and the delivery of high-quality care. However, methods to identify patient risk for hospital-acquired conditions are often outdated and crude. Although hospitals and health systems have b...

Artificial Intelligence in Radiation Oncology.

Hematology/oncology clinics of North America
The integration of artificial intelligence in the radiation oncologist's workflow has multiple applications and significant potential. From the initial patient encounter, artificial intelligence may aid in pretreatment disease outcome and toxicity pr...

Enabling Data-Driven Clinical Quality Assurance: Predicting Adverse Event Reporting in Clinical Trials Using Machine Learning.

Drug safety
INTRODUCTION: Adverse event (AE) under-reporting has been a recurrent issue raised during health authorities Good Clinical Practices (GCP) inspections and audits. Moreover, safety under-reporting poses a risk to patient safety and data integrity. The...

CAD and AI for breast cancer-recent development and challenges.

The British journal of radiology
Computer-aided diagnosis (CAD) has been a popular area of research and development in the past few decades. In CAD, machine learning methods and multidisciplinary knowledge and techniques are used to analyze the patient information and the results ca...

Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth.

Physics in medicine and biology
Enabling automated pipelines, image analysis and big data methodology in cancer clinics requires thorough understanding of the data. Automated quality assurance steps could improve the efficiency and robustness of these methods by verifying possible ...

[Potential of methods of artificial intelligence for quality assurance].

Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft
BACKGROUND: Procedures with artificial intelligence (AI), such as deep neural networks, show promising results in automatic analysis of ophthalmological imaging data.

Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.

The journal of trauma and acute care surgery
BACKGROUND: Incomplete prehospital trauma care is a significant contributor to preventable deaths. Current databases lack timelines easily constructible of clinical events. Temporal associations and procedural indications are critical to characterize...