Telemedicine as a tool for bridging geographical inequity: insights in geospatial interactions from a study on chronic heart failure patients.

Introduction: Chronic heart failure patients experience large disparities in quality of and access to treatment, with rural populations receiving lower levels of care. Telemonitoring of patients is increasingly being used as an important tool for improving patient management and care and might reduce geographical inequities in healthcare.

Methods: We investigate the presence and magnitude of a geospatial interaction effect on the health benefit of a supplementary telemedicine intervention, by analyzing the relationship between distance to regular place of treatment and the benefit of telemedicine in a secondary analysis of data from a previously conducted RCT.

Pulmonary Artery Calcification in a 57-Year-Old Man.

A 57-year-old man was admitted to our hospital via the ED presenting in reduced general condition because of an infection of unknown origin, generalized edema, and dyspnea at rest (peripheral capillary oxygen saturation, 89%) that required 2 L/min intranasal oxygen. Anamnesis was complicated by an infection-triggered delirium, but his wife reported an increasing physical decay that had led to bed confinement. The BP was reduced at 88/55 mm Hg with a normal heart rate of 86 beats/min.

Economic evaluation alongside a randomized controlled trial of blended cognitive-behavioral therapy for patients suffering from major depressive disorder.

Objective: This study aimed to investigate the cost-effectiveness of blended cognitive-behavioral therapy (CBT) compared to standard CBT for adult patients suffering from major depressive disorder (MDD).

Design: A cost-utility analysis alongside the randomized controlled ENTER trial.

Setting: Center for Telepsychiatry, Mental Health Services in the Region of Southern Denmark, Denmark.

A Conditional Generative Adversarial Network for Synthesis of Continuous Glucose Monitoring Signals.

This report describes how a Conditional Generative Adversarial Network (CGAN) was used to synthesize realistic continuous glucose monitoring systems (CGM) from healthy individuals and individuals with type 1 diabetes over a range of different HbA1c levels. The results showed that even though the CGAN generated data, did not perfectly reflect real world CGM, many of the important features were captured and reflected in the synthetic signals. It is briefly discussed how heterogenous data sources constitutes a challenge for comparison of predictive CGM models.