The doctor will see (all of) you now: Ethical dilemmas in double-booking patients

Beltrami, Eric J.; Grant‐Kels, Jane M.

doi:10.1016/j.jaad.2022.11.046

Cited by 1 publication

(2 citation statements)

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“…Moreover, executive leadership argues that the hospital's precarious financial position demands that all potential sources of revenue loss be mitigated as much as possible and that the financial survival of the hospital trumps the inconvenience of a longer wait time for certain patients. [55][56][57] Case 3. A mortality prediction model has come into widespread use across the United States.…”

Section: Moral Character and Fairness In ML Developmentmentioning

confidence: 99%

“…Alternatively, if the clinical entity's financial survival is truly at stake, developers and clinician users might collaborate on modifying the model. For example, the model might be reconfigured to double-book likely no shows who only require short visits or visits that might not require a physician's attention [56]. Or the model might "examine" the clinic's weekly workflow and double-book patients during predictably light periods or down-times.…”

Section: Moral Character and Fairness In ML Developmentmentioning

confidence: 99%

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Fairness as an afterthought: An American perspective on fairness in model developer-clinician user collaborations

Banja,

Gichoya,

Martinez-Martin

et al. 2023

PLOS Digit Health

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Numerous ethics guidelines have been handed down over the last few years on the ethical applications of machine learning models. Virtually every one of them mentions the importance of “fairness” in the development and use of these models. Unfortunately, though, these ethics documents omit providing a consensually adopted definition or characterization of fairness. As one group of authors observed, these documents treat fairness as an “afterthought” whose importance is undeniable but whose essence seems strikingly elusive. In this essay, which offers a distinctly American treatment of “fairness,” we comment on a number of fairness formulations and on qualitative or statistical methods that have been encouraged to achieve fairness. We argue that none of them, at least from an American moral perspective, provides a one-size-fits-all definition of or methodology for securing fairness that could inform or standardize fairness over the universe of use cases witnessing machine learning applications. Instead, we argue that because fairness comprehensions and applications reflect a vast range of use contexts, model developers and clinician users will need to engage in thoughtful collaborations that examine how fairness should be conceived and operationalized in the use case at issue. Part II of this paper illustrates key moments in these collaborations, especially when inter and intra disagreement occurs among model developer and clinician user groups over whether a model is fair or unfair. We conclude by noting that these collaborations will likely occur over the lifetime of a model if its claim to fairness is to advance beyond “afterthought” status.

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Section: Moral Character and Fairness In ML Developmentmentioning

confidence: 99%

Section: Moral Character and Fairness In ML Developmentmentioning

confidence: 99%