Daily Eastern News: June 24, 1992

https://thekeep.eiu.edu/den_1992_jun/1003/thumbnail.jp

Daily Eastern News: June 24, 1992

https://thekeep.eiu.edu/den_1992_jun/1003/thumbnail.jp

Evaluating the Legality of Age-Based Criteria in Health Care: From Nondiscrimination and Discretion to Distributive Justice

Recent disputes over whether older people should pay more for health insurance, or receive lower priority for transplantable organs, highlight broader disagreements regarding the legality of using age-based criteria in health care. These debates will likely intensify given the changing age structure of the American population and the turmoil surrounding the financing of American health care. This Article provides a comprehensive examination of the legality and normative desirability of age-based criteria. In the Article, I defend a distributive justice approach to age-based criteria. Rather than viewing age as a personal characteristic akin to race or religion, the distributive justice approach regards age as relevant in two ways to the distribution of an extremely valuable and widely desirable good, namely years of life. First, age establishes how much life someone has already enjoyed. Second, age indicates (though imperfectly) how much more life a person is likely to gain from treatment. A distributive justice approach also differentiates justifications grounded in distributive considerations — such as the higher predicted costs of treating older patients — from justifications grounded in animus or false stereotypes about older patients. Distributive justice approaches are therefore aligned with emerging animus-focused approaches to antidiscrimination law. The distributive justice approach I defend contrasts with two prevailing theoretical approaches to age-based criteria, which I call nondiscrimination and discretion. On the nondiscrimination approach, age-based criteria are viewed with great skepticism, analogous to race-based criteria: they are permissible — if at all — only when they advance the interests of disadvantaged groups, and they are subject to exacting judicial scrutiny. In contrast, the discretion approach, often identified with highly deferential versions of the “rational basis” test in equal protection law, views age-based criteria as broadly permissible, and defers to medical professionals’ judgments. Part I of the Article explains that age discrimination statutes, as well as the Equal Protection Clause and similar state constitutional provisions, permit the use of age-based criteria when those criteria have a rational grounding and do not appeal to animus or bias. They therefore leave room open for the use of a distributive justice approach. Part II goes on to argue that the conceptual underpinnings of antidiscrimination law do not support the enactment of new law, or the adoption of new interpretations of existing law, that would reject the use of age-based criteria. Part III proposes a detailed normative framework for the use of age-based criteria in health care, the lifetime justice approach, that considers the future life patients can gain from treatment and the past years of life they already have experienced. The lifetime justice approach also includes a principle of nonabandonment, which supports the continued provision of supportive medical care to older people in need. It then defends this framework against objections — most prominently, the objection that it disregards the moral equality of older people. Part IV applies the analysis offered in the earlier Parts to age-based criteria employed in various areas of medical practice and health policy, including the examples of transplantation and health insurance discussed at the outset

Reverse Informed Consent: The Unreasonably Dangerous Patient

Latrogenic injuries\u27-those caused by health care professionals (HCPs) in the course of treating patients-raise significant ethical, legal, and public policy issues.\u27 With the advent of the AIDS epidemic, these issues become even more difficult when the iatrogenic injury results not from the patient\u27s having received treatment below the professional standard of care (which is the usual grist for the malpractice mill) but from an infectious condition of the HCP. Considerable public attention has been directed to patients who have been exposed to the risk of AIDS by HIV-positive HCPs.6 It is difficult to be unmoved by the tragic example of Kimberly Bergalis and five other patients who contracted AIDS after being treated by a dentist who died of AIDS shortly thereafter.\u27 The public outcry of Physician, heal thyself\u27 was immediate, as were governmental proposals for dealing with the perceived problem. Responses from various interest groups were equally swift and effective.\u27 Despite the flood of proposals and recommendations, however, there appears to be an absence of political will to address the problem; the task therefore is thrown to the legal system. In this instance, the law of torts will be asked to provide the theories of recovery for patients who are infected with HIV by HCPs.\u2

Assessing workers’ decision quality with scarce ground truth data

Accurately assessing workers’ decision quality is fundamental for management, and the efficiency of expert and crowd-sourcing markets. This paper establishes novel ML and AI methods to accurately evaluate workers’ decision accuracy and bias with scarce ground truth (GT or gold standard GS) data, and to further improve accuracy assessment through costly effectively acquiring GT if given an acquisition budget. Without the proposed methods, assessing workers’ decision quality typically requires GT data to compare with workers’ noisy decisions. However, GT is often prohibitively costly to acquire for even a small fraction of each worker’s decisions. For example, physicians may determine a diagnosis and initiate a treatment, yet the correct decision, such as the one that can be established by a panel of physicians. Consequently, in practice, there is often poor transparency regarding physicians’ decision quality. In my dissertation, I collaborating with my coauthors developed the groundwork for achieving scalable and inexpensive assessments of workers’ decision accuracy and bias. The empirical results show that the decision accuracy assessment with very limited GT improves the best available approach by 60% to 93%; my bias assessment produces either comparable to or outperforms the commonly used existing approach; my cost-effective GT acquisition strategy applied in Amazon Mechanical Workers’ accuracy assessment achieves the same performance only using 1/3 of the GT or improve the assessment by 24%. All proposed methods have significant implications in many impactful domains including health care, fraud detection, fact checking, and online labor markets. The methods proposed in this dissertation address the problem of estimating workers’ decision accuracy and bias from historical data with scarcely available ground truth, and achieve the state of the art performance. This dissertation lays the groundwork towards increasing transparency in workers’ (sources’) decision quality.Information, Risk, and Operations Management (IROM