Triaging Patients With Artificial Intelligence for Respiratory Symptoms in Primary Care to Improve Patient Outcomes: A Retrospective Diagnostic Accuracy Study.

Purpose: Respiratory symptoms are the most common presenting complaint in primary care. Often these symptoms are self resolving, but they can indicate a severe illness. With increasing physician workload and health care costs, triaging patients before in-person consultations would be helpful, possibly offering low-risk patients other means of communication.

Artificial intelligence in the GPs office: a retrospective study on diagnostic accuracy.

Objective: Machine learning (ML) is expected to play an increasing role within primary health care (PHC) in coming years. No peer-reviewed studies exist that evaluate the diagnostic accuracy of ML models compared to general practitioners (GPs). The aim of this study was to evaluate the diagnostic accuracy of an ML classifier on primary headache diagnoses in PHC, compare its performance to GPs, and examine the most impactful signs and symptoms when making a prediction.

[Effectiveness of an online cognitive behavioral therapy for insomnia].

Introduction: Insomnia is a common health problem with serious mental and physical consequences as well as increased economical costs. The use of hypnotics in Iceland is immense in spite of cognitive behavioral therapy for insomnia (CBT-I) being recommended as the first choice treatment of chronic insomnia. To meet the needs of more individuals suffering from insomnia, online CBT-I was established at betrisvefn.