Journal of insurance medicine (New York, N.Y.)最新文献

The basic definitions of obstructive sleep apnea (OSA), its epidemiology, its clinical features and complications, and the morbidity and mortality of OSA are discussed. Included in this treatise is a discussion of the various symptomatic and polysomnographic phenotypes of COPD that may enable better treatment and impact mortality in persons with OSA. The goal of this article is to serve as a reference for life and disability insurance company medical directors and underwriters when underwriting an applicant with probable or diagnosed sleep apnea. It is well-referenced (133 ref.) allowing for more in-depth investigation of any aspect of sleep apnea being queried.

As the Covid-19 pandemic continues into its 4th year, reports of long-term morbidity and mortality are now attracting attention. Recent studies suggest that Covid-19 survivors are at increased risk of common illnesses, such as myocardial infarction, diabetes mellitus and autoimmune disorders. Mortality may also be increased. This article will review the evidence that supports some of these observations and provide an opinion about their validity and their relevance to insured cohorts.

Applications of Artificial Intelligence (AI) deep-learning models to screening for clinical conditions continue to evolve. Instances provided in this treatise include using a simple one-view PA chest radiograph to screen for Type 2 Diabetes Mellitus (T2DM), congestive heart failure, valvular heart disease, and to assess mortality in asymptomatic persons with respiratory diseases. This technology incorporates hundreds of thousands of CXRs into a convoluted neural network and is generally named AI CXR. As an example, the AUROC (Area Under Receiving Operator Characteristic) of screening for T2DM was 0.84, with sensitivity and specificities that exceed those of the United States Preventative Services Task Force (USPSTF) guidelines for screening with HBA1c or blood glucose studies. The AUROC's for diagnosing ejection fractions less than 40% was 0.92, and for detecting valvular heart diseases was 0.87. The potential implications for underwriting life and disability policies may be significant. A companion article in the Journal of Insurance Medicine addresses this same technology using a simple 12-lead ECG, generally named AI ECGs.

The life insurance industry is transitioning towards precision underwriting driven by increased data availability and access to advanced analytical tools. Effectively utilizing diverse data sources in life insurance underwriting presents an opportunity for medical directors to fully leverage their skillset in this evolving environment. By navigating these changes, balancing the value of data against its limitations, and fostering collaborative approaches to enhance risk assessment and underwriting processes, medical directors can maintain a pivotal role in the life insurance companies of tomorrow.

Context.—: As the Covid-19 pandemic continues into its 4th year, reports of long-term morbidity and mortality are now attracting attention. Recent studies suggest that Covid-19 survivors are at increased risk of common illnesses, such as myocardial infarction, diabetes mellitus and autoimmune disorders. Mortality may also be increased. This article will review the evidence that supports some of these observations and provide an opinion about their validity and their relevance to insured cohorts.

Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancements in diagnosis, prediction, treatment, and outcomes. This article aims to provide a basic understanding of AI enabled ECG technology. Specific conditions and findings will be discussed, followed by reviewing associated terminology and methodology. In the appendix, definitions of AUC versus accuracy are explained. The application of deep learning models enables detecting diseases from normal electrocardiograms at accuracy not previously achieved by technology or human experts. Results with AI enabled ECG are encouraging as they considerably exceeded current screening models for specific conditions (i.e., atrial fibrillation, left ventricular dysfunction, aortic stenosis, and hypertrophic cardiomyopathy). This could potentially lead to a revitalization of the utilization of the ECG in the insurance domain. While we are embracing the findings with this rapidly evolving technology, but cautious optimism is still necessary at this point.

Fetal alcohol spectrum disorder (FASD) and its associated physical and mental conditions is the most prevalent congenital impairment causing developmental and intellectual disability worldwide. Like alcohol abuse, FASD is typically undiagnosed by primary care providers. And like alcohol abuse, life underwriters and medical directors need to be aware of the signs, symptoms, and behaviors associated with FASD to accurately detect, identify, evaluate and assess the mortality risk. Three cases of suspected undiagnosed FASD that were underwritten for life expectancies in legal matters are discussed in this report. Not only were these patients' risks for excess mortality elevated due to their initial neurologic injury due to prenatal exposure to alcohol, but these cases demonstrate the importance of the stability and care needed to make them insurable. The following paper discusses the clinical and social settings at birth that may give underwriters and medical directors some clue to a potential case of the child having FASD and then to assess their statistical and lifestyle mortality risks.