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A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring

Nature Biomedical Engineering (2023)Cite this article

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Abstract

The complex relationships between continuously monitored health signals and therapeutic regimens can be modelled via machine learning. However, the clinical implementation of the models will require changes to clinical workflows. Here we outline ClinAIOps (‘clinical artificial-intelligence operations’), a framework that integrates continuous therapeutic monitoring and the development of artificial intelligence (AI) for clinical care. ClinAIOps leverages three feedback loops to enable the patient to make treatment adjustments using AI outputs, the clinician to oversee patient progress with AI assistance, and the AI developer to receive continuous feedback from both the patient and the clinician. We lay out the central challenges and opportunities in the deployment of ClinAIOps by means of examples of its application in the management of blood pressure, diabetes and Parkinson’s disease. By enabling more frequent and accurate measurements of a patient’s health and more timely adjustments to their treatment, ClinAIOps may substantially improve patient outcomes.

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Fig. 1: ClinAIOps connects stakeholders via feedback loops.
Fig. 2: Timeline of the application of ClinAIOps to CTM, and the roles of patients, clinicians and AI developers.
Fig. 3: Interactions involved in the application of ClinAIOps to the monitoring of patients with PD, atrial fibrillation or hypertension.
Fig. 4: Feedback loops in the application of ClinAIOps to the management of blood pressure.

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Acknowledgements

The authors thank A. Karargyris for helpful feedback.

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  1. These authors contributed equally: Vijay Janapa Reddi, David Kim, Pranav Rajpurkar.

Authors and Affiliations

  1. Harvard John A. Paulson School of Engineering and Applied Sciences, Boston, MA, USA

    Emma Chen, Shvetank Prakash & Vijay Janapa Reddi

  2. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Emma Chen & Pranav Rajpurkar

  3. Department of Emergency Medicine, Stanford School of Medicine, Stanford, CA, USA

    David Kim

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Chen, E., Prakash, S., Janapa Reddi, V. et al. A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring. Nat. Biomed. Eng (2023). https://doi.org/10.1038/s41551-023-01115-0

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