A New Argument for No-Fault Compensation in Health Care: The Introduction of Artificial Intelligence Systems

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Abstract

Artificial intelligence (AI) systems advising healthcare professionals will be widely introduced into healthcare settings within the next 5–10 years. This paper considers how this will sit with tort/negligence based legal approaches to compensation for medical error. It argues that the introduction of AI systems will provide an additional argument pointing towards no-fault compensation as the better legal solution to compensation for medical error in modern health care systems. The paper falls into four parts. The first part rehearses the main arguments for and against no-fault compensation. The second explains why it is likely that AI systems will be widely introduced. The third part analyses why it is difficult to fit AI systems into fault-based compensation systems while the final part suggests how no-fault compensation could provide a possible solution to such challenges.

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Most cited references 39

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High-performance medicine: the convergence of human and artificial intelligence

Eric Topol (2019)

The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

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Dissecting racial bias in an algorithm used to manage the health of populations

Ziad Obermeyer, Brian Powers, Christine Vogeli … (2019)

Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.

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Brain tumor segmentation with Deep Neural Networks

Mohammad Havaei, Axel Davy, David Warde-Farley … (2017)

In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster.

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Author and article information

Contributors

Catherine Stanton:

ORCID: http://orcid.org/0000-0003-0405-0665

catherine.stanton@manchester.ac.uk

Journal

Journal ID (nlm-ta): Health Care Anal

Journal ID (iso-abbrev): Health Care Anal

Title: Health Care Analysis

Publisher: Springer US (New York )

ISSN (Print): 1065-3058

ISSN (Electronic): 1573-3394

Publication date (Electronic): 21 March 2021

Publication date PMC-release: 21 March 2021

Publication date (Print): 2021

Volume: 29

Issue: 3

Pages: 171-188

Affiliations

GRID grid.5379.8, ISNI 0000000121662407, Department of Law, School of Social Sciences, , The University of Manchester, ; Manchester, U.K.

Author information

Catherine Stanton http://orcid.org/0000-0003-0405-0665

Article

Publisher ID: 430

DOI: 10.1007/s10728-021-00430-4

PMC ID: 8321978

PubMed ID: 33745121

SO-VID: 89893547-2545-4157-a617-c96ce5783672

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date accepted : 24 February 2021

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ScienceOpen disciplines: Medicine

Keywords: artificial intelligence,deep learning,clinical negligence,no-fault compensation,tort,product liability

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ScienceOpen disciplines: Medicine

Keywords: artificial intelligence, deep learning, clinical negligence, no-fault compensation, tort, product liability

A New Argument for No-Fault Compensation in Health Care: The Introduction of Artificial Intelligence Systems

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Abstract

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Artificial Intelligence in Medicine

Most cited references 39

High-performance medicine: the convergence of human and artificial intelligence

Dissecting racial bias in an algorithm used to manage the health of populations

Brain tumor segmentation with Deep Neural Networks

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