Literature DB >> 26906556

Fragility, uncertainty, and healthcare.

Wendy A Rogers1, Mary J Walker2.   

Abstract

Medicine seeks to overcome one of the most fundamental fragilities of being human, the fragility of good health. No matter how robust our current state of health, we are inevitably susceptible to future illness and disease, while current disease serves to remind us of various frailties inherent in the human condition. This article examines the relationship between fragility and uncertainty with regard to health, and argues that there are reasons to accept rather than deny at least some forms of uncertainty. In situations of current ill health, both patients and doctors seek to manage this fragility through diagnoses that explain suffering and provide some certainty about prognosis as well as treatment. However, both diagnosis and prognosis are inevitably uncertain to some degree, leading to questions about how much uncertainty health professionals should disclose, and how to manage when diagnosis is elusive, leaving patients in uncertainty. We argue that patients can benefit when they are able to acknowledge, and appropriately accept, some uncertainty. Healthy people may seek to protect the fragility of their good health by undertaking preventative measures including various tests and screenings. However, these attempts to secure oneself against the onset of biological fragility can cause harm by creating rather than eliminating uncertainty. Finally, we argue that there are good reasons for accepting the fragility of health, along with the associated uncertainties.

Entities:  

Keywords:  Fragility; Healthcare; Overdiagnosis; Screening; Uncertainty; Vulnerability

Mesh:

Year:  2016        PMID: 26906556     DOI: 10.1007/s11017-016-9350-3

Source DB:  PubMed          Journal:  Theor Med Bioeth        ISSN: 1386-7415


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Review 10.  The benefits and harms of breast cancer screening: an independent review.

Authors: 
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  4 in total

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Journal:  Theor Med Bioeth       Date:  2017-08

3.  A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction.

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  4 in total

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