Genomics, Insurance and Human Rights: Is there a Place for Regulatory Frameworks in Africa?

2006 ◽  
Vol 2 (1) ◽  
pp. 20-34
Author(s):  
Vincent O. Nmehielle
Keyword(s):  
Human Rights ◽  
Health Insurance ◽  
Genetic Information ◽  
Insurance Industry ◽  
Genetic Discrimination ◽  
Insurance Companies ◽  
Genetic Profile ◽  
Regulatory Frameworks ◽  
Health Care Context ◽  
Insurance Decisions

AbstractThis article examines the human rights dimension of genetic discrimination in Africa, exploring the place of regulatory frameworks while taking into account the disadvantaged position of the average African. This is in response to the tendency of insurance companies toward making health insurance decisions on the basis of individual genetic information, which could result in genetic discrimination or health insurance discrimination based on a person's genetic profile. The author considers such questions as the intersection between human rights (right to life, health, privacy, human dignity and against genetic discrimination) in relation to the insurance industry, as well as the obligations of state and non-state actors to promote, respect, and protect the enjoyment of these rights. The article argues that African nations should not stand aloof in trying to balance the competing interests (scientific, economic and social) presented by the use of genetic information in the health care context and that ultimately it is the responsibility of states to develop domestic policies to protect their most vulnerable citizens and to prevent entrenched private discrimination based on an individual's genes.

scholarly journals Genetic Discrimination in Access to Life Insurance: Does Ukrainian Legislation Offer Sufficient Protection against the Adverse Consequences of the Genetic Revolution to Insurance Applicants?

Laws ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 2
Author(s):  
Mykhailo Arych ◽  
Yann Joly
Keyword(s):  
Human Rights ◽  
Family History ◽  
Life Insurance ◽  
Genetic Information ◽  
Legal Framework ◽  
Genetic Discrimination ◽  
Insurance Companies ◽  
European Convention ◽  
Genetic Characteristics ◽  
Application Forms

This paper presents an inter-disciplinary study of the risk for, and protections against, genetic discrimination in access to life insurance in Ukraine. It aims (i) to review questions related to genetic information, health status, and family history currently included in Ukrainian life insurance application forms; (ii) to analyze the Ukrainian legislation related to equity and nondiscrimination and to determine whether it provides adequate protection against genetic discrimination (GD). Research findings of our insurance application forms review show that Ukrainian life insurance companies ask broad questions about health and family history that may be perceived by applicants as requiring the disclosure of their genetic information. Our legal analysis shows that today there are no genetic specific law protecting Ukrainians people against GD in insurance. However, Ukrainian human rights legislation provides some protection against multiple grounds of discrimination and given the ratification by Ukraine of the European Convention on Human Rights it is possible that these grounds could be interpreted by tribunals as also including genetic characteristics. As a next step, Ukrainian researchers should develop a survey to obtain much needed data on the incidence and impact of GD in Ukraine. Following this it will be possible for policymakers to better assess whether there is a need for an explicit non-GD law in this country. Such a law would have the benefit of explicitly aligning Ukraine’s legal framework with that of many of its European partners.

scholarly journals Potential for Genetic Discrimination in Access to Insurance: Is There a Dark Side to Increased Availability of Genetic Information?

2013 ◽  
Vol 50 (3) ◽  
pp. 577 ◽  
Author(s):  
Elizabeth Adjin-Tettey
Keyword(s):  
Human Rights ◽  
General Population ◽  
Health Professionals ◽  
Predictive Value ◽  
Genetic Information ◽  
Insurance Industry ◽  
Private Insurance ◽  
Genetic Discrimination ◽  
Dark Side ◽  
Legislative Framework

This article addresses the reliance on genetic information as part of the private insurance industry’s practice of risk segmentation whereby underwritingdecisions are based on risk information about individuals and groups as compared to the general population. The author argues that there are a number of concerns regarding reliance on genetic information in insurance underwriting, including uncertainty about what constitutes genetic information and the predictive value thereof, possible conflicts with human rights values, potential reductions in access to insurance, and the legal and ethical obligations of individuals who undergo testing, health professionals, and insurers. This article reviews the solutions that have been adopted in other jurisdictions and concludes that the use of genetic information is consistent with standard insurance industry practices. However, it is recommended that a legislative framework be established in Canada to regulate the use of genetic information.

scholarly journals Health Insurance as a Healthcare Financing Mechanism in India: Key Strategic Insights and a Business Model Perspective

2021 ◽  
pp. 025609092110270
Author(s):  
Rohit Kumar ◽  
Aditya Duggirala
Keyword(s):  
Health Insurance ◽  
Business Model ◽  
Critical Success Factors ◽  
Success Factors ◽  
Insurance Industry ◽  
Insurance Companies ◽  
Healthcare Financing ◽  
Strategic Choices ◽  
Critical Success ◽  
Health Insurance Industry

This study provides strategic insights and a business model perspective on health insurance as a vehicle for financing healthcare. It uses both primary (expert interview) and secondary data to investigate the overall disease burden and healthcare industry trends and track healthcare financing through the health insurance mechanism in India. To identify the critical success factors and to gain a business model perspective within the health insurance industry, telephonic and face-to-face interviews were held with 27 experts in the healthcare, insurance, and strategic management field. The study’s findings suggest that the growth of health insurance as a healthcare financing mechanism in India has been challenged continuously and impacted by multiple changes in the health insurance and healthcare industry over the last decade. One of the critical challenges faced by insurance companies is the high incurred claim ratio. We find the Indian health insurance industry to be very competitive and that the focus on critical success factors can help insurance companies gain a competitive advantage. The health insurance business model is unique, with varying configurations, and broadly comprises strategic choices and consequences. In this article, drawing from the strategic management literature on the resource-based view (RBV) and insights gained from the interviews of healthcare and health insurance experts, we highlight the six critical success factors relevant for competing in the health insurance business. We also list five strategic choices that can help health insurance companies improve their profitability and gain a sustained competitive advantage. We recommend that the insurance companies design and develop an innovative business model centred around lowering the claim ratio and simultaneously increasing the customer willingness to pay. To increase the customer willingness to pay and reduce the claim ratio, the insurance companies should focus on the six critical success factors and invest in the five strategic choices.

scholarly journals Genetic discrimination: emerging ethical challenges in the context of advancing technology

2019 ◽  
Author(s):  
Carolyn Riley Chapman ◽  
Kripa Sanjay Mehta ◽  
Brendan Parent ◽  
Arthur L Caplan
Keyword(s):  
Health Insurance ◽  
Genetic Testing ◽  
Genetic Information ◽  
Predictive Power ◽  
The United States ◽  
Legal Framework ◽  
Genetic Discrimination ◽  
Ethical Challenges ◽  
Health Related ◽  
Discrimination In Employment

Abstract Genetic testing is becoming more widespread, and its capabilities and predictive power are growing. In this paper, we evaluate the ethical justifications for and strength of the US legal framework that aims to protect patients, research participants, and consumers from genetic discrimination in employment and health insurance settings in the context of advancing genetic technology. The Genetic Information Nondiscrimination Act (GINA) and other laws prohibit genetic and other health-related discrimination in the United States, but these laws have significant limitations, and some provisions are under threat. If accuracy and predictive power increase, specific instances of use of genetic information by employers may indeed become ethically justifiable; however, any changes to laws would need to be adopted cautiously, if at all, given that people have consented to genetic testing with the expectation that there would be no genetic discrimination in employment or health insurance settings. However, if our society values access to healthcare for both the healthy and the sick, we should uphold strict and broad prohibitions against genetic and health-related discrimination in the context of health insurance, including employer-based health insurance. This is an extremely important but often overlooked consideration in the current US debate on healthcare.

Beyond “Genetic Discrimination”: Toward the Broader Harm of Geneticism

1995 ◽  
Vol 23 (4) ◽  
pp. 345-353 ◽  
Author(s):  
Susan M. Wolf
Keyword(s):  
Health Insurance ◽  
Genetic Information ◽  
Genetic Discrimination ◽  
Genetic Knowledge ◽  
Analytic Framework ◽  
Genetic Tests ◽  
Race And Gender ◽  
And Gender ◽  
Almost All ◽  
As If

The current explosion of genetic knowledge and the rapid proliferation of genetic tests has rightly provoked concern that we are approaching a future in which people will be labeled and disadvantaged based on genetic information. Indeed, some have already suffered harm, including denial of health insurance. This concern has prompted an outpouring of analysis. Yet almost all of it approaches the problem of genetic disadvantage under the rubric of “genetic discrimination.”This rubric is woefully inadequate to the task at hand. It ignores years of commentary on race and gender demonstrating the limits of antidiscrimination analysis as an analytic framework and corrective tool. Too much discussion of genetic disadvantage proceeds as if scholars of race and gender had not spent decades critiquing and developing antidiscrimination theory.Indeed, there are multiple links among race, gender, and genetics. Dorothy Roberts has discussed the historical links between racism and genetics, while she and others have begun to map connections between gender and genetics.

Genetic Exceptionalism and Legislative Pragmatism

2007 ◽  
Vol 35 (S2) ◽  
pp. 59-65 ◽  
Author(s):  
Mark A. Rothstein
Keyword(s):  
Health Insurance ◽  
Genetic Information ◽  
Medical Information ◽  
Genetic Discrimination ◽  
Genome Project ◽  
Genetic Privacy ◽  
State Laws ◽  
Genetic Exceptionalism ◽  
Discrimination In Employment ◽  
The Human Genome Project

One of the most important and contentious policy issues surrounding genetics is whether genetic information should be treated separately from other medical information. The view that genetics raises distinct issues is what Thomas Murray labeled “genetic exceptionalism,” borrowing from the earlier term “HIV exceptional-ism.” The issue of whether the use of genetic information should be addressed separately from other health information is not merely an academic concern, however. Since the Human Genome Project began in 1990, nearly every state has enacted legislation prohibiting genetic discrimination in health insurance; two-thirds of the states have enacted laws prohibiting genetic discrimination in employment, and other state laws have been enacted dealing with genetic discrimination in life insurance, genetic privacy, and genetic testing. Bills in Congress also would prohibit genetic discrimination in health insurance and employment.

scholarly journals Comparison of Machine Learning Algorithms for Processing of Health Insurance Claim

2020 ◽  
Vol 5 (2) ◽  
pp. 01-19
Author(s):  
Paridhi Saxena ◽  
◽  
Abhishek Seth ◽  
Gangesh Chawla ◽  
Ranganath Singari
Keyword(s):  
Machine Learning ◽  
Health Insurance ◽  
Consumer Satisfaction ◽  
Insurance Industry ◽  
Machine Learning Algorithms ◽  
Insurance Companies ◽  
Insurance Claim ◽  
Lifestyle Behaviour ◽  
Customer Lifetime ◽  
Health Insurance Industry

The health insurance industry protects against financial losses resulting from various health conditions. Since a long, it has relied on statistics and data to calculate risks and thereby, centre attention more profoundly on a particular target audience for increasing the operational efficiency of the industry. Technologies like Machine Learning and Artificial Intelligence prove to be an efficient tool for enabling insurance companies to predict the Customer Lifetime Value (CLV). This can be done using customer lifestyle behaviour data allowing to assess the customer's potential profitability for insurance companies. This creates a more personalised marketing offer within the audience. The insurance industry and its components constitute a dynamic and competitive sector representing approximately 2.7 percent of the US Gross Domestic Product (GDP). As customers have become progressively scrupulous about narrowing down their specific requirements, insurers and insurance companies are scrutinizing techniques for improving business operations and consumer satisfaction. An attempt in this regard has been made to analyse the “sample insurance claim prediction dataset" using various machine learning models including Decision tree, Random Forest algorithms, Naïve Bayes, K-nearest neighbour algorithm, Supper Vector machines and Neural Networks. A comparative analysis is performed to generate reports.

Genetic Discrimination in Health Insurance: Current Legal Protections and Industry Practices

2007 ◽  
Vol 44 (3) ◽  
pp. 350-368 ◽  
Author(s):  
Karen Pollitz ◽  
Beth N. Peshkin ◽  
Eliza Bangit ◽  
Kevin Lucia
Keyword(s):  
Health Insurance ◽  
Genetic Information ◽  
Genetic Test ◽  
Genetic Discrimination ◽  
Health Condition ◽  
Future Health ◽  
Individual Health ◽  
Increased Risk ◽  
The Individual ◽  
Test Result

Most states have enacted genetic nondiscrimination laws in health insurance, and federal legislation is pending in Congress. Scientists worry fear of discrimination discourages some patients from participating in clinical trials and hampers important medical research. This paper describes a study of medical underwriting practices in the individual health insurance market related to genetic information. Underwriters from 23 companies participated in a survey that asked them to underwrite four pairs of hypothetical applicants for health insurance. One person in each pair had received a positive genetic test result indicating increased risk of a future health condition—breast cancer, hemochromatosis, or heart disease—for a total of 92 underwriting decisions on applications involving genetic information. In seven of these 92 applications, underwriters said they would deny coverage, place a surcharge on premiums, or limit covered benefits based on an applicant's genetic information.

scholarly journals An Empirical Study on the Equity Performance of China's Health Insurance Companies During the COVID-19 Pandemic—Based on Cases of Dominant Listed Companies

2021 ◽  
Vol 9 ◽  
Author(s):  
Rendao Ye ◽  
Na An ◽  
Yichen Xie ◽  
Kun Luo ◽  
Ya Lin
Keyword(s):  
Neural Network ◽  
Health Insurance ◽  
Stock Price ◽  
Short Term Memory ◽  
Insurance Industry ◽  
Back Propagation ◽  
Listed Companies ◽  
Insurance Companies ◽  
Neural Network Models ◽  
Health Insurance Companies

The health insurance industry in China is undergoing great shocks and profound impacts induced by the worldwide COVID-19 pandemic. Taking for instance the three dominant listed companies, namely, China Life Insurance, Ping An Insurance, and Pacific Insurance, this paper investigates the equity performances of China's health insurance companies during the pandemic. We firstly construct a stock price forecasting methodology using the autoregressive integrated moving average, back propagation neural network, and long short-term memory (LSTM) neural network models. We then empirically study the stock price performances of the three listed companies and find out that the LSTM model does better than the other two based on the criteria of mean absolute error and mean square error. Finally, the above-mentioned models are used to predict the stock price performances of the three companies.

scholarly journals Application of Machine Learning Techniques to Predict the Impact of Health Insurance on the Wellbeing of an Individual

2019 ◽  
Vol 9 (2) ◽  
pp. 3065-3070
Keyword(s):  
Machine Learning ◽  
Health Insurance ◽  
Large Scale ◽  
Insurance Industry ◽  
Financial Burden ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Insurance Companies ◽  
The Government ◽  
The Impact

The healthcare domain in India has suffered considerably despite the advancement in technology. Several financing schemes are endorsed by the insurance companies to lessen the financial burden faced by the government and people. Nonetheless, Health Insurance segment in India remains underdeveloped due to various complexities that it faces. This paper exploits a heuristic sampling approach combined with the ensemble Machine Learning algorithms on the large-scale insurance business data to realize the current shape of the Health Insurance industry in India. Through the courtesy of Data Mining and Data Analytics, it is plausible to furnish insights that assist the common people in acquiring closure that helps in the process of decision making.

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