Remote Patient Monitoring and Machine Learning in Acute Exacerbations of Chronic Obstructive Pulmonary Disease: Dual Systematic Literature Review and Narrative Synthesis.

Journal: Journal of medical Internet research
PMID: 39250789

Abstract

BACKGROUND: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with high mortality, morbidity, and poor quality of life and constitute a substantial burden to patients and health care systems. New approaches to prevent or reduce the severity of AECOPD are urgently needed. Internationally, this has prompted increased interest in the potential of remote patient monitoring (RPM) and digital medicine. RPM refers to the direct transmission of patient-reported outcomes, physiological, and functional data, including heart rate, weight, blood pressure, oxygen saturation, physical activity, and lung function (spirometry), directly to health care professionals through automation, web-based data entry, or phone-based data entry. Machine learning has the potential to enhance RPM in chronic obstructive pulmonary disease by increasing the accuracy and precision of AECOPD prediction systems.

Authors

  • Henry Mark Granger Glyde
    EPSRC Centre for Doctoral Training in Digital Health and Care, University of Bristol, Bristol, United Kingdom.
  • Caitlin Morgan
    Academic Respiratory Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
  • Tom M A Wilkinson
    Clinical and Experimental Science, University of Southampton, Southampton, United Kingdom.
  • Ian T Nabney
    School of Engineering and Mathematics, University of Bristol, Bristol, United Kingdom.
  • James W Dodd
    Academic Respiratory Unit, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.

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