Cost effectiveness review of text messaging, smartphone application, and website interventions targeting T2DM or hypertension.
Ruben Willems et al. NPJ Digit Med 2023 8 (1) 150
Digital health interventions have been shown to be clinically-effective for type 2 diabetes mellitus (T2DM) and hypertension prevention and treatment. This study synthesizes and compares the cost-effectiveness of text-messaging, smartphone application, and websites. We found that digital interventions are cost-effective without substantial differences between the different delivery modes. Future health economic studies should increase transparency, conduct sufficient sensitivity analyses, and appraise the ICUR more critically in light of a reasoned willingness-to-pay threshold.
An AI-Enhanced Electronic Health Record Could Boost Primary Care Productivity.
Jeffrey E Harris et al. JAMA 2023 8
More than a few commentators have seriously inquired whether artificial intelligence (AI) could ultimately replace many clinicians. The far likelier prospect, however, is that the newly emerging technology will enhance clinical productivity. To be sure, AI-based pattern recognition software can already scan retinal photos for complications of diabetes, detect tuberculosis on chest x-rays, and evaluate screening mammograms. And some AI applications have been found to be comparable if not superior to human clinical judgment.
Are You Up to Date on Your Preventive Care?
CDC, July 2023
Family health history is a record of the diseases and health conditions in your family. You and your family members share genes. You may also have behaviors in common, like what you do for physical activity and what you like to eat. You may live in the same area and come into contact with similar harmful things in the environment. Family history includes all of these factors, any of which can affect your health. If you have a family history of a chronic disease, like cancer, heart disease, diabetes, or osteoporosis, you’re more likely to get that disease yourself.
A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records.
Megan M Shuey et al. EBioMedicine 2023 7 104674
We developed and tested a genetically-informed drug-repurposing pipeline for diabetes management. This approach mapped genetically-predicted gene expression signals from the largest genome-wide association study for type 2 diabetes mellitus to drug targets using publicly available databases to identify drug–gene pairs. These drug–gene pairs were then validated using a two-step approach: 1) a self-controlled case-series (SCCS) using electronic health records from a discovery and replication population, and 2) Mendelian randomization (MR).