Hasil untuk "Insurance"

J. Dieleman, Jackie Cao, Abigail Chapin et al.

Importance US health care spending has continued to increase and now accounts for 18% of the US economy, although little is known about how spending on each health condition varies by payer, and how these amounts have changed over time. Objective To estimate US spending on health care according to 3 types of payers (public insurance [including Medicare, Medicaid, and other government programs], private insurance, or out-of-pocket payments) and by health condition, age group, sex, and type of care for 1996 through 2016. Design and Setting Government budgets, insurance claims, facility records, household surveys, and official US records from 1996 through 2016 were collected to estimate spending for 154 health conditions. Spending growth rates (standardized by population size and age group) were calculated for each type of payer and health condition. Exposures Ambulatory care, inpatient care, nursing care facility stay, emergency department care, dental care, and purchase of prescribed pharmaceuticals in a retail setting. Main Outcomes and Measures National spending estimates stratified by health condition, age group, sex, type of care, and type of payer and modeled for each year from 1996 through 2016. Results Total health care spending increased from an estimated $1.4 trillion in 1996 (13.3% of gross domestic product [GDP]; $5259 per person) to an estimated $3.1 trillion in 2016 (17.9% of GDP; $9655 per person); 85.2% of that spending was included in this study. In 2016, an estimated 48.0% (95% CI, 48.0%-48.0%) of health care spending was paid by private insurance, 42.6% (95% CI, 42.5%-42.6%) by public insurance, and 9.4% (95% CI, 9.4%-9.4%) by out-of-pocket payments. In 2016, among the 154 conditions, low back and neck pain had the highest amount of health care spending with an estimated $134.5 billion (95% CI, $122.4-$146.9 billion) in spending, of which 57.2% (95% CI, 52.2%-61.2%) was paid by private insurance, 33.7% (95% CI, 30.0%-38.4%) by public insurance, and 9.2% (95% CI, 8.3%-10.4%) by out-of-pocket payments. Other musculoskeletal disorders accounted for the second highest amount of health care spending (estimated at $129.8 billion [95% CI, $116.3-$149.7 billion]) and most had private insurance (56.4% [95% CI, 52.6%-59.3%]). Diabetes accounted for the third highest amount of the health care spending (estimated at $111.2 billion [95% CI, $105.7-$115.9 billion]) and most had public insurance (49.8% [95% CI, 44.4%-56.0%]). Other conditions estimated to have substantial health care spending in 2016 were ischemic heart disease ($89.3 billion [95% CI, $81.1-$95.5 billion]), falls ($87.4 billion [95% CI, $75.0-$100.1 billion]), urinary diseases ($86.0 billion [95% CI, $76.3-$95.9 billion]), skin and subcutaneous diseases ($85.0 billion [95% CI, $80.5-$90.2 billion]), osteoarthritis ($80.0 billion [95% CI, $72.2-$86.1 billion]), dementias ($79.2 billion [95% CI, $67.6-$90.8 billion]), and hypertension ($79.0 billion [95% CI, $72.6-$86.8 billion]). The conditions with the highest spending varied by type of payer, age, sex, type of care, and year. After adjusting for changes in inflation, population size, and age groups, public insurance spending was estimated to have increased at an annualized rate of 2.9% (95% CI, 2.9%-2.9%); private insurance, 2.6% (95% CI, 2.6%-2.6%); and out-of-pocket payments, 1.1% (95% CI, 1.0%-1.1%). Conclusions and Relevance Estimates of US spending on health care showed substantial increases from 1996 through 2016, with the highest increases in population-adjusted spending by public insurance. Although spending on low back and neck pain, other musculoskeletal disorders, and diabetes accounted for the highest amounts of spending, the payers and the rates of change in annual spending growth rates varied considerably.

Chia‐Chun Tseng, Shun-Jen Chang, W. Tsai et al.

A. Young

D. Schmidt

W. Korpi, Joakim Palme

E. Wenger

Michael Wynne, W. Armstrong

S. Shavell

H. Kunreuther

Teerapong Panboonyuen

This handbook presents a systematic treatment of the foundations and architectures of artificial intelligence for motor insurance, grounded in large-scale real-world deployment. It formalizes a vertically integrated AI paradigm that unifies perception, multimodal reasoning, and production infrastructure into a cohesive intelligence stack for automotive risk assessment and claims processing. At its core, the handbook develops domain-adapted transformer architectures for structured visual understanding, relational vehicle representation learning, and multimodal document intelligence, enabling end-to-end automation of vehicle damage analysis, claims evaluation, and underwriting workflows. These components are composed into a scalable pipeline operating under practical constraints observed in nationwide motor insurance systems in Thailand. Beyond model design, the handbook emphasizes the co-evolution of learning algorithms and MLOps practices, establishing a principled framework for translating modern artificial intelligence into reliable, production-grade systems in high-stakes industrial environments.

Trishul Siddharthan, Sanjay Sethi, Emily Wan et al.

Abstract Background Chronic obstructive pulmonary disease (COPD) is a progressive disease associated with substantial morbidity and mortality. Acute COPD exacerbations are a primary driver of significant burden and contribute to disease progression. Methods This retrospective, observational cohort study used the Optum Clinformatics® Data Mart database to identify patients with COPD who were classified as Global Initiative for Chronic Obstructive Lung Disease (GOLD) A/B0 or A/B1 based on exacerbation history (i.e., they had either 0 [GOLD A/B0] or 1 [GOLD A/B1] moderate exacerbation and 0 severe exacerbations in a 12-month baseline period). Patients were required to be aged ≥ 40 years and to have newly initiated inhaled maintenance therapy for COPD from January 2016 to June 2023. The rates of and time to progression to GOLD E (defined in the claims data as experiencing 2 moderate exacerbations within a 12-month period or 1 severe exacerbation) were estimated using the Kaplain-Meier method. Predictors of progression to GOLD E were analyzed using multivariable Cox proportional hazard models. Results Of the 156,462 included patients, the largest proportion of patients (46.6%) were initiated on long-acting beta-agonists/inhaled corticosteroids. The majority of patients progressed to GOLD E over 5 years. The risk of progressing to GOLD E was approximately 3 times higher in the GOLD A/B1 versus GOLD A/B0 group (hazard ratio [HR] 2.92; 95% CI 2.84–3.00; P < 0.001). The strongest predictor of progressing to GOLD E was history of having a moderate exacerbation. Other independent predictors included older age, having Medicare versus commercial insurance, and the presence of Elixhauser comorbidities. Conclusions Despite use of inhaled maintenance treatments for COPD, most patients still progressed to a frequent or severe exacerbator phenotype. New therapies are needed to modify the disease trajectory in COPD.

Markéta Krùpovà, Nabil Rachdi, Quentin Guibert

In a context of constant increase in competition and heightened regulatory pressure, accuracy, actuarial precision, as well as transparency and understanding of the tariff, are key issues in non-life insurance. Traditionally used generalized linear models (GLM) result in a multiplicative tariff that favors interpretability. With the rapid development of machine learning and deep learning techniques, actuaries and the rest of the insurance industry have adopted these techniques widely. However, there is a need to associate them with interpretability techniques. In this paper, our study focuses on introducing an Explainable Boosting Machine (EBM) model that combines intrinsically interpretable characteristics and high prediction performance. This approach is described as a glass-box model and relies on the use of a Generalized Additive Model (GAM) and a cyclic gradient boosting algorithm. It accounts for univariate and pairwise interaction effects between features and provides naturally explanations on them. We implement this approach on car insurance frequency and severity data and extensively compare the performance of this approach with classical competitors: a GLM, a GAM, a CART model and an Extreme Gradient Boosting (XGB) algorithm. Finally, we examine the interpretability of these models to capture the main determinants of claim costs.

V. Zelizer

H. Varian

Shivani Patel, Heather Armbruster, Gretchen Pardo et al.

Basal cell carcinoma (BCC) is highly curable by surgical excision or radiation. In rare cases, BCC can be locally destructive or difficult to surgically remove. Hedgehog inhibition (HHI) with vismodegib or sonidegib induces a 50-60% response rate. Long-term toxicity includes muscle spasms and weight loss leading to dose decreases. This retrospective chart review also investigates the impact of CoQ10 and calcium supplementation in patients treated with HHI drugs at a single academic medical center from 2012 to 2022. We reviewed the charts of adult patients diagnosed with locally advanced or metastatic BCC treated with vismodegib or sonidegib primarily for progression-free survival (PFS). Secondary objectives included overall survival, BCC-specific survival, time to and reasons for discontinuation, overall response rate, safety and tolerability, use of CoQ10 and calcium supplements, and insurance coverage. Of 55 patients assessable for outcome, 34 (61.8%) had an overall clinical benefit, with 25 (45.4%) having a complete response and 9 (16.3%) a partial response. Stable disease was seen in 14 (25.4%) and 7 (12.7%) progressed. Of the 34 patients who responded to treatment, 9 recurred. Patients who were rechallenged with HHI could respond again. The median overall BCC-specific survival rate at 5 years is 89%. Dose reductions or discontinuations for vismodegib and sonidegib occurred in 59% versus 24% of cases, or 30% versus 9% of cases, respectively. With CoQ10 and calcium supplementation, only 17% required a dose reduction versus 42% without. HHI is highly effective for treating advanced BCC but may require dosing decreases. Sonidegib was better tolerated than vismodegib. CoQ10 and calcium supplementation can effectively prevent muscle spasms.

Worku Chekol Tassew, Yeshiwas Ayale Ferede, Agerie Mengistie Zeleke

Background: In sub-Saharan African nations, there's a documented shortfall in the utilization of statins, despite established clinical guidelines advocating their use for reducing cardiovascular risks and overall mortality among Type 2 diabetes patients aged 40–75 years old. Most clinical guidelines recommend prescribing statins to individuals with type 2 diabetes to reduce the chances of cardiovascular disease. There is currently a lack of extensive research on statin utilization specifically for primary prevention of cardiovascular disease in Africa. Thus, this study aimed to assess the prescription patterns of statins for preventing cardiovascular disease in type 2 diabetes patients. Methods: The findings of the review were presented following the guidelines outlined in the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA-2020) checklist. We conducted searches on electronic databases including PubMed, EMBASE, Cochrane Library, Science Direct, African Journal Online, and Google Scholar. This systematic review and meta-analysis included articles that met specific inclusion criteria: observational studies such as cross-sectional, cohort, and case-control studies focusing on determinants, risk factors, or correlates associated with statin prescription within Africa. Only published articles up to June 2, 2024, published in English, and conducted in either community or healthcare facility settings were considered. Data import was initially conducted using Microsoft Excel, and statistical analysis was performed using STATA software. Cochran's Q test was employed to assess whether there was a significant variance in prevalence among the studies. Additionally, the I2 statistic was utilized to quantify the extent of heterogeneity. A funnel plot, a visual tool, was utilized to evaluate publication bias. Results: The search strategy resulted in 7695 published original articles. The full texts of the 89 papers were assessed for eligibility and quality. Moreover, some articles were rejected due to inaccuracies in the outcome variable. Ultimately, only ten studies focusing on the prevalence of statin prescription were examined. The research suggests that the pooled prevalence of statin prescription among Type 2 diabetic individuals in Africa is found to be 48.82% (95% CI: 35.41–63.24). Age greater than 65 years (AOR = 3.56, 95% CI: 1.70–7.45; I2 = 54.7%), comorbidity (AOR = 1.13, 95% CI: 0.27–4.63, I2 = 96.4%), dyslipidemia (AOR = 3.15, 95% CI: 1.54–6.44, I2 = 61.7%), DM duration greater than ten years (AOR = 1.36, 95% CI: 0.81–2.28, I2 = 77.3%), and government insurance (AOR = 8.85, 95% CI: 2.72–28.76, I2 = 81.5%) were factors associated with statin prescription among type 2 diabetic patients. Conclusions: In general, the extent of statin prescriptions for individuals with type 2 diabetes who are eligible for statin therapy was below the target outlined by clinical practice guidelines. Being over 65 years old, having comorbidities, experiencing dyslipidemia, having type 2 diabetes for more than ten years, and having government insurance were all identified as independent factors predicting the prescription of statins. This finding is concerning and underscores the urgent need to enhance adherence to clinical practice guidelines for the well-being of this vulnerable population at high risk.

Robert Herold, Hayarpi Gevorgyan, Lukas S. Damerau et al.

The usage of smart glasses in goods logistics and order picking has mainly been studied through cross-sectional experimental studies. Our longitudinal field study investigated the effects of smart glasses on the eyesight of 43 employees at two German companies. We combined ophthalmological examinations and questionnaire surveys at two points in time, six months apart. The vision of the employees was examined before and after each work shift. Mixed effects logistic regression was conducted to determine the associations between smart glasses use and effects on visual acuity. In the baseline examination, differences in eyesight before and after shifts were small and not statistically significant. However, some individuals experienced deteriorations, especially in visual acuity at near distances (<i>n</i> = 7 for the right eye, <i>n</i> = 6 for the left). Participants over 40 years of age had 16.1 times higher odds of deterioration compared to those under 40 years (95% CI: 2.7–95.9, <i>p</i> = 0.002). The most commonly reported eye strains were eye fatigue (<i>n</i> = 32), rubbing (<i>n</i> = 25), and burning (<i>n</i> = 24). If smart glasses are to be implemented in logistics companies, it is recommended to offer employees eye tests with an industrial physician in advance.

Tim J. Boonen, Yuyu Chen, Xia Han et al.

This paper explores optimal insurance solutions based on the Lambda-Value-at-Risk ($Λ\VaR$). If the expected value premium principle is used, our findings confirm that, similar to the VaR model, a truncated stop-loss indemnity is optimal in the $Λ\VaR$ model. We further provide a closed-form expression of the deductible parameter under certain conditions. Moreover, we study the use of a $Λ'\VaR$ as premium principle as well, and show that full or no insurance is optimal. Dual stop-loss is shown to be optimal if we use a $Λ'\VaR$ only to determine the risk-loading in the premium principle. Moreover, we study the impact of model uncertainty, considering situations where the loss distribution is unknown but falls within a defined uncertainty set. Our findings indicate that a truncated stop-loss indemnity is optimal when the uncertainty set is based on a likelihood ratio. However, when uncertainty arises from the first two moments of the loss variable, we provide the closed-form optimal deductible in a stop-loss indemnity.

Robin Kühlem, Daniel Otten, Daniel Ludwig et al.

Machine learning (ML) will likely play a large role in many processes in the future, also for insurance companies. However, ML models are at risk of being attacked and manipulated. In this work, the robustness of Gradient Boosted Decision Tree (GBDT) models and Deep Neural Networks (DNN) within an insurance context will be evaluated. Therefore, two GBDT models and two DNNs are trained on two different tabular datasets from an insurance context. Past research in this domain mainly used homogenous data and there are comparably few insights regarding heterogenous tabular data. The ML tasks performed on the datasets are claim prediction (regression) and fraud detection (binary classification). For the backdoor attacks different samples containing a specific pattern were crafted and added to the training data. It is shown, that this type of attack can be highly successful, even with a few added samples. The backdoor attacks worked well on the models trained on one dataset but poorly on the models trained on the other. In real-world scenarios the attacker will have to face several obstacles but as attacks can work with very few added samples this risk should be evaluated.

Roshan Lal Neupane, Ernest Bonnah, Bishnu Bhusal et al.

Insurance claims processing involves multi-domain entities and multi-source data, along with a number of human-agent interactions. Use of Blockchain technology-based platform can significantly improve scalability and response time for processing of claims which are otherwise manually-intensive and time-consuming. However, the chaincodes involved within the processes that issue claims, approve or deny them as required, need to be formally verified to ensure secure and reliable processing of transactions in Blockchain. In this paper, we use a formal modeling approach to verify various processes and their underlying chaincodes relating to different stages in insurance claims processing viz., issuance, approval, denial, and flagging for fraud investigation by using linear temporal logic (LTL). We simulate the formalism on the chaincodes and analyze the breach of chaincodes via model checking.