Exploring polypharmacy with artificial intelligence: data analysis protocol.

TitleExploring polypharmacy with artificial intelligence: data analysis protocol.
Publication TypeJournal Article
Year of Publication2021
AuthorsSirois, C, Khoury, R, Durand, A, Deziel, P-L, Bukhtiyarova, O, Chiu, Y, Talbot, D, Bureau, A, Després, P, Gagné, C, Laviolette, F, Savard, A-M, Corbeil, J, Badard, T, Jean, S, Simard, M
JournalBMC Med Inform Decis Mak
Volume21
Issue1
Pagination219
Date Published2021 07 20
ISSN1472-6947
KeywordsAged, Artificial Intelligence, Chronic Disease, Data Analysis, Humans, Polypharmacy, Quebec
Abstract

BACKGROUND: Polypharmacy is common among older adults and it represents a public health concern, due to the negative health impacts potentially associated with the use of several medications. However, the large number of medication combinations and sequences of use makes it complicated for traditional statistical methods to predict which therapy is genuinely associated with health outcomes. The project aims to use artificial intelligence (AI) to determine the quality of polypharmacy among older adults with chronic diseases in the province of Québec, Canada.METHODS: We will use data from the Quebec Integrated Chronic Disease Surveillance System (QICDSS). QICDSS contains information about prescribed medications in older adults in Quebec collected over 20 years. It also includes diagnostic codes and procedures, and sociodemographic data linked through a unique identification number for each individual. Our research will be structured around three interconnected research axes: AI, Health, and Law&Ethics. The AI research axis will develop algorithms for finding frequent patterns of medication use that correlate with health events, considering data locality and temporality (explainable AI or XAI). The Health research axis will translate these patterns into polypharmacy indicators relevant to public health surveillance and clinicians. The Law&Ethics axis will assess the social acceptability of the algorithms developed using AI tools and the indicators developed by the Heath axis and will ensure that the developed indicators neither discriminate against any population group nor increase the disparities already present in the use of medications.DISCUSSION: The multi-disciplinary research team consists of specialists in AI, health data, statistics, pharmacy, public health, law, and ethics, which will allow investigation of polypharmacy from different points of view and will contribute to a deeper understanding of the clinical, social, and ethical issues surrounding polypharmacy and its surveillance, as well as the use of AI for health record data. The project results will be disseminated to the scientific community, healthcare professionals, and public health decision-makers in peer-reviewed publications, scientific meetings, and reports. The diffusion of the results will ensure the confidentiality of individual data.

DOI10.1186/s12911-021-01583-x
Alternate JournalBMC Med Inform Decis Mak
PubMed ID34284765
PubMed Central IDPMC8290537
Grant ListCPG—170621 / / Canadian Institute of Health Research /