Actuarial Technique of Modelling the Out of Pocket with Deductible and Stop Loss in Health Care Insurance

The paper is intended to numerically estimate health insurance out of pocket spending from the perspective of the enrollees in order to inform whether health insurance scheme pricing policy delivers value to the enrollees. We study the implications of financing health care insurance using a classification model for health care insurance under the framework of deductibles and stop loss. The paper is designed for health underwriting professionals, particularly those who consult for health insurance schemes. This is essential given the form and emergence of treating enrollees fairly, based on regulations that impact on actuarial consultants advising on health insurance scheme product lines. The objectives of this paper are to demonstrate actuarial methods for estimating (i) total health expenses (ii) out of pocket costs (iii) reimbursements by third party insurance. (iv) examine the actuarial implications of the chosen model. In computing the out of pocket and reimbursement, the Pitacco’s model was adopted because of its relatively computational superiority and the fact that both deductible and stop loss form its core parameters. Our results show that there is a positive relationship between total health expense and the share funded by out of pocket spending. As the health expenditure increases, the share funded by out of pocket progressively increases.

Analysis of Health Care Billing via Quantile Variable Selection Models

Fraudulent billing of health care insurance programs such as Medicare is in the billions of dollars. The extent of such overpayments remains an issue despite the emerging use of analytical methods for fraud detection. This motivates policy makers to also be interested in the provider billing characteristics and understand the common factors that drive conservative and/or aggressive behavior. Statistical approaches to tackling this problem are confronted by the asymmetric and/or leptokurtic distributions of billing data. This paper is a first attempt at using a quantile regression framework and a variable selection approach for medical billing analysis. The proposed method addresses the varying impacts of (potentially different) variables at the different quantiles of the billing aggressiveness distribution. We use the mammography procedure to showcase our analysis and offer recommendations on fraud detection.

Development of the Dutch Structure for Integrated Children’s Palliative Care

Children’s palliative care (CPC) is gaining attention worldwide, facilitated by the exchange of knowledge during regular specialised congresses. This article describes the developments in the Netherlands over the past 15 years. The Foundation for Children’s Palliative Expertise (PAL) was established as a nationwide initiative committed to improving palliative care for children countrywide. This led to the development of the first hospital-based CPC team in 2012, which expanded to a total of seven teams adjacent to children’s university hospitals. Regional networks for CPC were developed in parallel to these teams from 2014 onwards. The networks are a collaboration of professionals from different disciplines and organisations, from hospital to homecare, and have covered the aspects of CPC nationally from 2019 onwards. They are connected through the Dutch Knowledge Centre for CPC. This centre was established in 2018 by the PAL Foundation in collaboration with the Dutch Association for Pediatrics. In 2013, the first evidence-based guideline, ‘palliative care for children’, provided access to knowledge for parents and healthcare providers, and in 2017, a format for an individual palliative care plan was established. Within the Knowledge Centre for CPC, a physician’s support centre for dilemma’s regarding the end of life of children was set up. The efforts to have children’s palliative care embedded in the regular Dutch health care insurance are ongoing.

Health Care Insurance and Moral Hazard

In this article, we discuss how moral hazard affects health care insurance, the social benefit and the harmfulness of moral hazard in health care insurance. We also discuss the relationship between copayment rate and decease risks and indicate that it is necessary to reduce copayment ratio for serious decease in social health care insurance in order to generate social gain. Finally, we carry out sensitivity analysis to illustrate the effect of the change of important parameters on optimal copayment rate.

THE IMPORTANCE OF BIG DATA AND ARTIFICIAL INTELLIGENCE FOR EFFECTIVE HEALTHCARE INSURANCE PLANS

Big data is a complex noun that marks sets of data in various formats. Th ere are a lot of challenges in dealing with them, including how to store, search, analyze and share them. In this paper, co-authors deal with relation of big data and artifi cial intelligence and eff ective healthcare insurance plans. In the analysis is taken into account that insurance as a business activity is critically connected to managing risk. In the paper is tested hypothesis: the quality of understanding risks in health care insurance is directly connected to the quality of information. Th is subject requires multidisciplinary approach that includes: informatics, legal and organizational science as well as insurance in health care.