The Added Value of Medical Testing in Underwriting Life Insurance

Abstract

BackgroundIn present-day life-insurance medical underwriting practice the risk assessment starts with a standard health declaration (SHD). Indication for additional medical screening depends predominantly on age and amount of insured capital. From a medical perspective it is questionable whether there is an association between the level of insured capital and medical risk in terms of mortality. The aim of the study is to examine the prognostic value of parameters from the health declaration and application form on extra mortality based on results from additional medical testing.MethodsA history register-based cohort study was conducted including about 15.000 application files accepted between 2007 and 2010. Blood pressure, lipids, cotinine and glucose levels were used as dependent variables in logistic regression models. Resampling validation was applied using 250 bootstrap samples to calculate area under the curves (AUC’s). The AUC was used to discriminate between persons with and without at least 25% extra mortality.ResultsBMI and the overall assessment of the health declaration by an insurance physician or medical underwriter showed the strongest discrimination in multivariable analysis. Including all variables at minimum cut-off levels resulted in an AUC of 0.710 while by using a model with BMI, the assessment of the health declaration and gender, the AUC was 0.708. Including all variables at maximum cut-off levels lead to an AUC of 0.743 while a model with BMI, the assessment of the health declaration and age resulted in an AUC of 0.741.ConclusionsThe outcome of this study shows that BMI and the overall assessment of the health declaration were the dominant variables to discriminate between applicants for life-insurance with and without at least 25 percent extra mortality. The variable insured capital set by insurers as factor for additional medical testing could not be established in this study population. The indication for additional medical testing at underwriting life-insurance can possibly be done on limited variables instead of the obligatory medical testing based on age and the amount of insured capital.

Teasing out Artificial Intelligence in Medicine: An Ethical Critique of Artificial Intelligence and Machine Learning in Medicine

“…The potential for Big Data to personalize preferences and direct consumers' attention into or out of what has been described as a locus of self-resonance-the "echo chamber" or "filter bubble"-also permits the possibility of "Big Nudging" (Souto-Otero and Beneito-Montagut 2016) by employing personalized strategies to operationalize health and other governmental policies, affecting an individual's autonomy through coercion, particularly when data from health devices linked to the "internet of things" covertly reporting to (for instance) health insurance decision algorithms (Bronsema et al 2015;Helbing et al 2019).…”