Psychometric Properties of a Machine Learning-Based Patient-Reported Outcome Measure on Medication Adherence: Single-Center, Cross-Sectional, Observational Study.

Journal: Journal of medical Internet research
PMID: 37843891

Abstract

BACKGROUND: Medication adherence plays a critical role in controlling the evolution of chronic disease, as low medication adherence may lead to worse health outcomes, higher mortality, and morbidity. Assessment of their patients' medication adherence by clinicians is essential for avoiding inappropriate therapeutic intensification, associated health care expenditures, and the inappropriate inclusion of patients in time- and resource-consuming educational interventions. In both research and clinical practices the most extensively used measures of medication adherence are patient-reported outcome measures (PROMs), because of their ability to capture subjective dimensions of nonadherence. Machine learning (ML), a subfield of artificial intelligence, uses computer algorithms that automatically improve through experience. In this context, ML tools could efficiently model the complexity of and interactions between multiple patient behaviors that lead to medication adherence.

Authors

  • Virginie Korb-Savoldelli
    Pharmacy Department, Hôpital Européen Georges Pompidou, APHP, 20 Rue Leblanc, Paris, France.
  • Yohann Tran
    Clinical Research Unit, Université Paris Cité, Hôpital européen Georges Pompidou, Assistance Publique - Hôpitaux de Paris (APHP), Paris, France.
  • Germain Perrin
    Pharmacy Department, Hôpital européen Georges Pompidou, Assistance Publique - Hôpitaux de Paris (APHP), Paris Cedex 15, France.
  • Justine Touchard
    Hôpital Universitaire Necker-Enfants Malades, Paris, France.
  • Jean Pastre
    Pulmonary Medecine and Intensive Care Department, Hôpital européen Georges Pompidou, Assistance Publique - Hôpitaux de Paris (APHP), Paris, France.
  • Adrien Borowik
    Pharmacy Department, Hôpital européen Georges Pompidou, Assistance Publique - Hôpitaux de Paris (APHP), Paris Cedex 15, France.
  • Corine Schwartz
    Pharmacy Department, Hôpital européen Georges Pompidou, Assistance Publique - Hôpitaux de Paris (APHP), Paris Cedex 15, France.
  • Aymeric Chastel
    Pharmacy Department, Hôpital européen Georges Pompidou, Assistance Publique - Hôpitaux de Paris (APHP), Paris Cedex 15, France.
  • Eric Thervet
    Nephrology Department, Hôpital européen Georges Pompidou, Assistance Publique - Hôpitaux de Paris (APHP), Paris, France.
  • Michel Azizi
    Clinical Investigation Center (CIC) 1418 Clinical Epidemiology, Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris (APHP), Paris, France.
  • Laurence Amar
    Clinical Investigation Center (CIC) 1418 Clinical Epidemiology, Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris (APHP), Paris, France.
  • Benjamin Kably
    Clinical Investigation Center (CIC) 1418 Clinical Epidemiology, Institut National de la Santé et de la Recherche Médicale (INSERM), Hôpital européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris (APHP), Paris, France.
  • Armelle Arnoux
    Informatics and Clinical Research Unit, Department of Biostatistics, Hôpital européen Georges Pompidou, AP-HP, Université de Paris, INSERM CIC1418-EC Clinical Epidemiology Team, Paris, France.
  • Brigitte Sabatier
    Pharmacy Department, Hôpital Européen Georges Pompidou, APHP, 20 Rue Leblanc, Paris, France.

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