Information Technology

Robotic process automation for identifying missing codes on insurance claims.

OBJECTIVES: This study aimed to develop and implement robotic process automation (RPA) for identifying missing codes during insurance claim post-review at a tertiary hospital and to evaluate its feasibility and effectiveness METHODS: As a single-centre, operational implementation, an RPA system integrated with optical character recognition (OCR) and electronic medical record (EMR) platforms was de...

An inventory of emergency department information systems (EDIS) in Canada: Benchmarking system functionality and technology readiness.

OBJECTIVES: Emergency Department Information Systems (EDIS) are essential digital technology used in Emergency Departments (ED). Modern EDIS provide electronic patient tracking, documentation, order entry and decision support, and are crucial for enabling data analytics and Artificial-Intelligence (AI)-based tools to improve patient care. However, Canada's adoption of digital-health technology is ...

Changes in Clinician Time Expenditure and Visit Quantity With Adoption of Artificial Intelligence-Powered Scribes: A Multisite Study.

IMPORTANCE: Artificial intelligence (AI)-enabled scribes have been proposed to reduce electronic health record (EHR) burden and improve clinician sati...

The Patient Monitoring Roundtable as Catalyst for Health Care Innovation: Case Study.

BACKGROUND: In the field of patient monitoring, there often remains a gap between clinical needs and the monitoring technologies available from indust...

DRSPLUS RETINOGRAPHER IN PATIENTS WITH DIABETES: A Comparison Study by Consensus Grading, Reading Center, and Artificial Intelligence.

PURPOSE: To evaluate the gradable rate of the retinal images acquired with DRSplus retinographer in patients with diabetes and to estimate the diabeti...

A hybrid spiking convolutional neural framework with extreme learning machine for enhanced anomaly detection in network security.

Real-time cybersecurity systems continue to have a significant problem in detecting breaches in dynamic and highly unbalanced network streams. An onli...

Artificial Intelligence in Diabetes Care.

BACKGROUND: Diabetes care requires frequent and high-stakes decisions that must be made in the setting of substantial day-to-day physiologic variabili...

Deep learning-assisted otoscopic screening for paediatric otitis media: feasibility of edge deployment.

BACKGROUND: Otitis media is common in children. Otoscopic differentiation of acute otitis media (AOM), otitis media with effusion (OME) and normal tym...

A unified low-carbon cybersecurity framework integrating energy-efficient intrusion detection, lightweight cryptography, and carbon-aware scheduling for edge-cloud architectures.

The rapid expansion of edge-cloud computing infrastructures has intensified both cybersecurity demands and the associated energy consumption and carbo...

Toward a Harmonized Definition of Digital Health Technologies for Use in Health Technology Assessment: A Scoping Review and Modified Delphi Consensus Study.

OBJECTIVES: The objectives of this study are to map definitions of digital health, eHealth, mHealth, telehealth, telemedicine, and artificial intellig...

Beyond the Hype: Understanding the Limits, Errors and Risk Areas of Artificial Intelligence in Gastroenterology.

Artificial intelligence (AI) has rapidly expanded across gastroenterology, enabling advances in real-time endoscopic detection, radiologic interpretat...

A Comprehensive Review of the Revolutionary Potential of Blockchain in Safe, Secure, and Sustainable Pharmaceutical Operations.

BACKGROUND: Pharmaceutical research and industrial operations generate vast volumes of sensitive data across drug discovery, formulation development, ...

Predicting radiology no-shows with machine learning: Methodological pitfalls and practical considerations.

OBJECTIVE: Predictive machine learning (ML) models may help reduce radiology appointment no-shows and late cancellations, which disrupt care, reduce o...

Legal and Ethical Considerations for Translating Federated Learning into Cross-Border Healthcare Innovation.

Federated Learning (FL) offers a privacy-enhancing architecture for training artificial intelligence on decentralized healthcare data, yet the prevail...

TSM-NIDS: A time-series mixer-based intrusion detection system for IoT networks.

The rapid proliferation of Internet of Things (IoT) devices in healthcare, manufacturing, and smart cities has introduced significant cybersecurity ch...

Temporal Learning with Dynamic Range (TLDR) for modeling recurrent exposure and treatment outcomes.

The temporal sequence of clinical events is crucial in outcomes research, yet standard machine learning (ML) approaches often overlook this aspect in ...

Longitudinal and Multimodal Recording System to Capture Real-World Patient-Clinician Conversations for AI and Encounter Research: Protocol for an Observational Study.

BACKGROUND: The promise of artificial intelligence (AI) in medicine depends on its ability to learn from data that reflect what matters to patients an...

Artificial Intelligence in Neurocritical Care : Multimodal Biosignal Analysis for Prognosis, Monitoring, and Future Pediatric Applications.

Neurocritical care relies on continuous assessment of neurological function and physiology under time pressure, yet bedside teams must interpret high-...

Machine learning models for detecting suicidal ideation in Chinese in-patients with major depressive disorder: A single-centre retrospective study.

UNLABELLED: Suicide claims >720,000 lives annually; major depressive disorder (MDD) carries the highest population-attributable risk. Suicidal ideatio...

Exploring AI Driven Autonomous Vehicles as Mobile Health Units to Expand Access to Healthcare in Underserved Communities.

Access to quality healthcare remains a persistent challenge in many low- and middle-income countries, especially for rural and underserved populations...