Abstract
BackgroundPolypharmacy 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.MethodsWe 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.DiscussionThe 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.
Highlights
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
While demonstrating better predictive performance, the results obtained with neural networks are difficult to explain, which is a limiting factor when one needs to justify why a treatment is being recommended to a patient
This research program will be performed by a multi-disciplinary team consisting of specialists in artificial intelligence (AI), health data, statistics, pharmacy, public health, law, and ethics
Summary
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. Polypharmacy, which is the simultaneous use of multiple medications by the same individual, has been associated with a plethora of Sirois et al BMC Med Inform Decis Mak (2021) 21:219 harmful health consequences, such as frailty, falls, cognitive problems, hospitalizations, and mortality [9,10,11]. It represents a potential harm for the patient and a financial burden for the health care system [7, 12, 13]. The concept of polypharmacy is often studied as a static exposure to medications without considering past medication use or subsequent changes, which may limit the conclusions about the consequences of polypharmacy [17]
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