Considerations for the implementation of machine learning into acute care settings

Author:

Bishara Andrew12,Maze Elijah H3,Maze Mervyn14

Affiliation:

1. Department of Anesthesia and Perioperative Care, University of California San Francisco, 1001 Potrero Avenue San Francisco, CA 94110, USA

2. Bakar Computational Health Sciences Institute, University of California San Francisco, 490 Illinois Street, San Francisco, CA 94143, USA

3. Departments of Computer Science and Mathematics, University of Michigan, Bob and Betty Beyster Building, 2260 Hayward Street Ann Arbor, MI 48109, USA

4. Center for Cerebrovascular Research, Building 10, Zuckerberg San Francisco General, 1001 Potrero Avenue, San Francisco, CA 94110, USA

Abstract

Abstract Introduction Management of patients in the acute care setting requires accurate diagnosis and rapid initiation of validated treatments; therefore, this setting is likely to be an environment in which cognitive augmentation of the clinician’s provision of care with technology rooted in artificial intelligence, such as machine learning (ML), is likely to eventuate. Sources of data PubMed and Google Scholar with search terms that included ML, intensive/critical care unit, electronic health records (EHR), anesthesia information management systems and clinical decision support were the primary sources for this report. Areas of agreement Different categories of learning of large clinical datasets, often contained in EHRs, are used for training in ML. Supervised learning uses algorithm-based models, including support vector machines, to pair patients’ attributes with an expected outcome. Unsupervised learning uses clustering algorithms to define to which disease grouping a patient’s attributes most closely approximates. Reinforcement learning algorithms use ongoing environmental feedback to deterministically pursue likely patient outcome. Areas of controversy Application of ML can result in undesirable outcomes over concerns related to fairness, transparency, privacy and accountability. Whether these ML technologies irrevocably change the healthcare workforce remains unresolved. Growing points Well-resourced Learning Health Systems are likely to exploit ML technology to gain the fullest benefits for their patients. How these clinical advantages can be extended to patients in health systems that are neither well-endowed, nor have the necessary data gathering technologies, needs to be urgently addressed to avoid further disparities in healthcare.

Publisher

Oxford University Press (OUP)

Subject

General Medicine

Reference68 articles.

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