AIMC Topic: Patient Safety

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Evaluating eligibility criteria of oncology trials using real-world data and AI.

Nature Apr 7, 2021
There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes ...
Artificial Intelligence Patient Safety Electronic Health Records Reproducibility of Results Humans Datasets as Topic Clinical Trials as Topic Proportional Hazards Models Clinical Laboratory Techniques Lung Neoplasms Carcinoma, Non-Small-Cell Lung Patient Selection Medical Oncology
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Assessing Drug Development Risk Using Big Data and Machine Learning.

Cancer research Dec 22, 2020
Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard ...
Drug Development Drug-Related Side Effects and Adverse Reactions Humans Risk Assessment Machine Learning Female Neoplasms Patient Safety Drug Delivery Systems Antineoplastic Agents Male Big Data
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Intelligent checklists improve checklist compliance in the intensive care unit: a prospective before-and-after mixed-method study.

British journal of anaesthesia Nov 17, 2020
BACKGROUND: We examined whether a context and process-sensitive 'intelligent' checklist increases compliance with best practice compared with a paper checklist during intensive care ward rounds.
Teaching Rounds Checklist Practice Patterns, Physicians' Attitude to Computers Health Status Paper Benchmarking Critical Care Quality Improvement Quality Indicators, Health Care Practice Guidelines as Topic Intensive Care Units Length of Stay Decision Support Systems, Clinical Guideline Adherence Artificial Intelligence Patient Safety Prospective Studies Humans
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Development and Validation of a Deep Learning Model for Detection of Allergic Reactions Using Safety Event Reports Across Hospitals.

JAMA network open Nov 2, 2020
IMPORTANCE: Although critical to patient safety, health care-related allergic reactions are challenging to identify and monitor.
Humans Algorithms Patient Safety Cross-Sectional Studies Male Middle Aged Diagnosis, Computer-Assisted Female Reproducibility of Results Aged Adult Boston Deep Learning ROC Curve Hypersensitivity
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BMIVPOT, a Fully Automated Version of the Intravenous Pole: Simulation, Design, and Evaluation.

Journal of healthcare engineering Aug 10, 2020
Robotic intravenous poles are automated supportive instrument that needs to be triggered by patients to hold medications and needed supplies. Healthcare engineering of robotic intravenous poles is advancing in order to improve the quality of health s...
Equipment Design Cost-Benefit Analysis Automation Therapy, Computer-Assisted Artificial Intelligence Humans Systems Integration Mobile Applications Patient Safety Infusions, Intravenous Microcomputers Computer Simulation Reproducibility of Results Robotics Equipment and Supplies, Hospital Computer-Aided Design User-Computer Interface Image Processing, Computer-Assisted
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Ethical thinking machines in surgery and the requirement for clinical leadership.

American journal of surgery Jul 8, 2020
Decision Support Systems, Clinical Machine Learning Humans Thinking Patient Safety Specialties, Surgical Clinical Decision-Making Leadership
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What are the main patient safety concerns of healthcare stakeholders: a mixed-method study of Web-based text.

International journal of medical informatics May 4, 2020
OBJECTIVES: Various healthcare stakeholders define quality of care in different ways. Public policy could advocate all these concerns. This study was conducted to identify the main themes on patient safety of stakeholders expressed before and after t...
Natural Language Processing Internet Health Services Quality Improvement Government Patient Safety Republic of Korea Hospitals Longitudinal Studies Humans Research Design
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A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan.

BMC health services research Apr 15, 2020
BACKGROUND: Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with 'look-alike and sound-alike' (LASA) being the lead error. Existing solutions to preven...
Humans Patient Safety Medication Errors Deep Learning Drug Labeling Models, Theoretical Medication Systems, Hospital Taiwan
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Opportunities for machine learning to improve surgical ward safety.

American journal of surgery Feb 26, 2020
BACKGROUND: Delayed recognition of decompensation and failure-to-rescue on surgical wards are major sources of preventable harm. This review assimilates and critically evaluates available evidence and identifies opportunities to improve surgical ward...
Surgery Department, Hospital Machine Learning Humans Patient Safety Quality Improvement Surgical Procedures, Operative
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Medication-rights detection using incident reports: A natural language processing and deep neural network approach.

Health informatics journal Dec 10, 2019
Medication errors often occurred due to the breach of medication rights that are the right patient, the right drug, the right time, the right dose and the right route. The aim of this study was to develop a medication-rights detection system using na...
Support Vector Machine Humans Natural Language Processing Neural Networks, Computer Risk Management Patient Safety
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