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

{"title":"Biological Clocks: Ready for Prime Time?","authors":"Timothy Meagher","doi":"10.17849/insm-53-2-1-5.1B","DOIUrl":"10.17849/insm-53-2-1-5.1B","url":null,"abstract":"<p><p>Interest in the concept of \"biological age\" has grown substantially in recent years. Longevity clinics and direct-to-consumer companies now promote biological age testing, often with the promise that targeted interventions can slow the aging process. This article examines the scientific validity of testing for biological age and evaluates its potential utility in life insurance underwriting.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"53 2","pages":"221-225"},"PeriodicalIF":0.0,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Benzodiazepine Abuse: A Report of 3 Cases.","authors":"Vera F Dolan","doi":"10.17849/insm-53-2-1-5.1","DOIUrl":"10.17849/insm-53-2-1-5.1","url":null,"abstract":"<p><p>Benzodiazepines are a class of tranquilizers that, like prescription opiates, are commonly subject to abuse. Abusers of benzodiazepines commonly malinger to sustain access to these prescriptions from their health care providers by creating and enhancing medically necessary conditions requiring treatment with benzodiazepines. Such behavior may involve displays of unrelenting extreme anxiety that are resolved only by the administration of benzodiazepines. Typical substance abuse behaviors also include noncompliance with treatment, lack of self-care, and lack/failure of counseling. Three cases underwritten for life expectancies in legal matters provide insight as to what kinds of benzodiazepine abuse behaviors can arise, given the conditions and resources available. For life underwriters and medical directors, such benzodiazepine abuse behaviors can serve as an underwriting red flag at time of application. Signs and symptoms of additional substance abuse, such as alcohol and prescription opiates, are likely to be found among those who abuse benzodiazepines and can serve as underwriting red flags.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"53 2","pages":"182-186"},"PeriodicalIF":0.0,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Between Fear and Adoption: The AI Paradox in Medico-Legal Practice Among National Insurance Physicians.","authors":"Eytan Ellenberg, David Amsalem, Eldad Katorza","doi":"10.17849/insm-53-2-1-10.1","DOIUrl":"10.17849/insm-53-2-1-10.1","url":null,"abstract":"<p><strong>Objectives.—: </strong>To assess artificial intelligence adoption patterns, trust levels, and perceived utility among physicians conducting disability assessments for a national insurance system, with a focus on the paradox between widespread use and limited trust in artificial intelligence for medico-legal decisions.</p><p><strong>Background.—: </strong>Artificial intelligence is rapidly entering clinical workflows, yet its integration into insurance medicine-where physicians must translate complex clinical findings into legally defensible administrative decisions-remains unexplored. Physicians conducting disability assessments operate at the intersection of clinical judgment, administrative burden, and legal accountability, making them a critical population for evaluating artificial intelligence adoption and its epistemic limits.</p><p><strong>Methods.—: </strong>A cross-sectional survey was conducted in February 2026 among 428 physicians working in medical committees, appeals boards, and advisory roles at the Israeli National Insurance Institute (response rate 85.6%). The questionnaire assessed artificial intelligence usage frequency, trust in artificial intelligence-supported decisions (5-point scale), perceived risks, and preferred applications.</p><p><strong>Results.—: </strong>Artificial intelligence adoption was widespread (76% regular or occasional use; 27% daily use), yet trust remained low (mean 2.7 of 5). Daily users showed higher trust than non-daily users (odds ratio 1.81; 95% confidence interval 1.14-2.88). Administrative burden was the dominant adoption driver. Preferred applications included medical record summarization (85%), inconsistency detection (45%), and protocol drafting (35%). Most use was informal, relying on personal tools. Senior physicians expressed relational concerns; less experienced physicians emphasized technical risks.</p><p><strong>Conclusions.—: </strong>Insurance medicine physicians distinguish between instrumental trust for administrative efficiency and epistemic trust for legally accountable judgment. Artificial intelligence integration should emphasize administrative augmentation while preserving human interpretative authority.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"53 2","pages":"226-235"},"PeriodicalIF":0.0,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Trinity of Risk Selection: Servicing Our Customers.","authors":"John R Iacovino","doi":"10.17849/insm-53-2-1-2.1","DOIUrl":"10.17849/insm-53-2-1-2.1","url":null,"abstract":"<p><p>Risk selection and underwriting are more than reviewing requirements and making a final decision. It is servicing our customers, the applicant, producer, and our company. Meeting the needs of all three assures the profitability of our company.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"53 2","pages":"236-237"},"PeriodicalIF":0.0,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Are There Types of Early-Stage Adenocarcinomas That Are Insurable?","authors":"Wei Li","doi":"10.17849/insm-53-1-1-7.2","DOIUrl":"10.17849/insm-53-1-1-7.2","url":null,"abstract":"<p><p>Lung cancer is the most common cause of cancer-related mortality worldwide. With the introduction of low-dose computed tomography (LDCT), detection of adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are increasingly being found in young, never-smoking females in Asia. There are survival studies suggesting that these early cancers, with resection, have no recurrence in 5 and even 10 years and therefore might be considered cured, allowing for favorable underwriting consideration for life insurance. However, other studies have shown incidences of secondary primary lung cancers (SPLCs) occurring within 10 years after surgical resections of AIS and MIA tumors, but with their clinical course and response to treatment appear to be better than original primary lung cancers, potentially still allowing for insurance with rating. The goal of this article is to review the evidence both for and against insuring these populations of lung cancer patients.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":" ","pages":"98-104"},"PeriodicalIF":0.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146150829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI in Healthcare: How Relevant to Medical Risk Selection?","authors":"Timothy Meagher","doi":"10.17849/insm-53-1-1-9.2","DOIUrl":"10.17849/insm-53-1-1-9.2","url":null,"abstract":"<p><p>The role of artificial intelligence (AI) in biologic discovery, and in the practice of medicine is rapidly increasing. AI-assisted analyses of large databases are leading to impressive biologic discoveries and AI-diagnostics are beginning to change clinical practice. With time, AI-generated content will become a substantive component of electronic health records. By extension, more risk-relevant information will be available to life insurers. Further, as AI-assisted biomedical discovery provides new foundational knowledge, clinical medicine will benefit, and human mortality should improve. This article explains why AI will become indispensable to healthcare, describes its current role, and predicts the expanded role it will have in the future. It also outlines the abundance of barriers to its implementation. Finally, it describes the relevance of this evolution to medical risk selection, on which it will have a considerable impact.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":" ","pages":"81-89"},"PeriodicalIF":0.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146150824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retinal Coronary Artery Calcium Scoring - A Scalable Tool for Life Insurers.","authors":"Muhammed Adil Khan","doi":"10.17849/insm-53-1-1-3.2","DOIUrl":"10.17849/insm-53-1-1-3.2","url":null,"abstract":"<p><strong>Background.—: </strong>Cardiovascular risk estimation for life insurance underwriting relies on risk estimation from conventional metrics: age, sex, smoking status, body mass index, systolic and diastolic blood pressure, total and high-density lipoprotein cholesterol and stress electrocardiogram. Coronary artery calcium (CAC) scoring via CT is a validated predictor of cardiovascular risk but remains costly, invasive, and unsuitable for large-scale underwriting. A novel artificial intelligence (AI) model, RetiCAC, predicts CAC scores from retinal photographs, offering a non-invasive and scalable alternative.</p><p><strong>Objective.—: </strong>To assess the potential role of RetiCAC in life insurance underwriting for improved cardiovascular risk stratification and pricing accuracy.</p><p><strong>Methods.—: </strong>This review draws on evidence from The Lancet Digital Health study of RetiCAC and evaluates its accuracy to and prognostic value compared with traditional CAC scoring. Potential underwriting applications were considered, including risk stratification, replacement of costly diagnostics, predictive augmentation, improvement in customer experience and integration with dynamic underwriting models and wellness programs.</p><p><strong>Results.—: </strong>RetiCAC demonstrated incremental predictive prognostic value, particularly in borderline and intermediate-risk groups, and showed comparable performance to CT-derived CAC scoring in external cohorts. For insurers, RetiCAC could enable scalable, non-invasive cardiovascular risk assessment, refine mortality predictions, and improve classification of substandard applicants. Its digital nature supports remote underwriting models and wellness integration.</p><p><strong>Conclusion.—: </strong>RetiCAC has potential as a non-invasive adjunct to traditional underwriting, enhancing cardiovascular risk prediction while reducing reliance on invasive testing. Broader adoption will require further validation, regulatory approval, and ethical safeguards, but integration could provide insurers with competitive advantages and align risk assessment with preventive health strategies.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":" ","pages":"78-80"},"PeriodicalIF":0.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146012838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast Actual/Expected Data Processing.","authors":"David Wesley","doi":"10.17849/insm-53-1-1-10.1","DOIUrl":"10.17849/insm-53-1-1-10.1","url":null,"abstract":"<p><p>A common problem with mortality analyses on company or registry data is that the processing time on large datasets can be an impediment to the interactive process of the analysis. The following paper delineates an approach using the Polars dataframe library and the programming language Python to speed up the processing time considerably.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"53 1","pages":"105-114"},"PeriodicalIF":0.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bipolar Disorders: Converting Years of Life Lost to Mortality Ratios.","authors":"John R Iacovino","doi":"10.17849/insm-53-1-1-2.1","DOIUrl":"https://doi.org/10.17849/insm-53-1-1-2.1","url":null,"abstract":"<p><p>A publication in the British Journal of Psychiatry reviews years of potential life lost (YPLL) and life expectancy in bipolar disorders.1 YPLL is useful in some insurance products but not in life insurance underwriting, where the magnitude of risk is derived from the mortality ratio and subsequent debits, eg, a mortality ratio of 200% is approximately 100 debits.2,3 A method to convert YPLL into mortality ratios and appropriate debits for medical risk underwriting of BPD is discussed.4.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":"53 1","pages":"3-4"},"PeriodicalIF":0.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147310900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Cardiovascular Disease Risk.","authors":"Rodney C Richie","doi":"10.17849/insm-53-1-1-8.2","DOIUrl":"10.17849/insm-53-1-1-8.2","url":null,"abstract":"<p><p>There was a steady decrease in cardiovascular disease (CVD ischemic heart disease and stroke) mortality from 1960 to 2020, but since then, this decline has reversed. There have been over 228,000 excess CVD deaths through 2022,1 undoubtedly partially due to the COVID-19 pandemic, but the mortality rate continues to rise (arguably due to the rising epidemic of obesity and diabetes). CVD remains the leading cause of death in developed countries, accounting for over 30% of deaths, and risk estimation is a cornerstone approach to guiding CVD prevention in clinical medicine. Data from the CDC reveal that 36% of US adults have no CVD risk factors, 35% have 1, and 29% have 2 or more risk factors. The age-adjusted percentage of adults with 2 or more CVD risk factors has increased between 2013-2014 to August 2021-August 2023, especially in older age groups.2 Assessing the risk for CVD mortality is essential for the disability and life insurance industry required to assess that risk at a single point in time (at the issuance of an insurance policy). Evaluating this risk requires careful attention to modifiable and non-modifiable factors, including hypertension and other co-morbidities, abnormal lipid profiles, and lifestyle inequalities. The goal of this treatise is to evaluate the various CVD calculators, but also to review other risk factors that may not be routinely sought in estimating CVD risk. The importance of apolipoproteinB (apoB) and lipoprotein A (LpA) as better risk predictors than just elevated LDL levels will be emphasized, and evidence of systemic inflammation and insulin resistance will be proposed as essential early indicators of future cardiovascular disease.</p>","PeriodicalId":39345,"journal":{"name":"Journal of insurance medicine (New York, N.Y.)","volume":" ","pages":"90-97"},"PeriodicalIF":0.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146012854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}