Polypharmacy in an Elderly Population: Enhancing Medication Management Through the Use of Clinical Decision Support Software Platforms

Abstract

Introduction Polypharmacy is a growing problem in the United States. The use of multiple medications increases the likelihood that a patient will experience potential drug interactions and adverse drug reactions (ADRs). Those individuals with dementia or Alzheimer’s disease (AD) are at greater risk, due to age, comorbidities, and an increased likelihood of being on a greater number of neuroactive medications. Methods uMETHOD Health (uMH) has developed a precision medicine platform to address dementia and mild AD through the creation of personalized, multidomain treatment plans. Many interactions and ADRs may be observed, such as drug–drug interactions (DDIs), drug–gene interactions (DGIs), anticholinergic cognitive burden (ACB), and depression-inducing drugs (DIDs). uMH’s algorithms can parse these interactions, rate them based on input from open-source databases, and then record all these interactions in a generated treatment plan. A total of 295 individuals aged 65 and older were included in this analysis. Results Of 295 individuals, 97.59% were on at least one medication, with an overall mean of 11.5 medications per person; 83.66% were on five or more medications. A total of 102 DGIs, 3642 DDIs, and one high-priority DDI were found in this population. There was a significant increase in the number of DDIs as medications per person increased ( P value < 0.0001). Of the population, 65.86% were on one or more anticholinergic drugs. There was a significant difference in the ACB score between individuals with cognitive decline and those without. In total, 60.98% of the overall population were on DIDs, with a mean of 1.19 medications per person. Conclusions The results of this work show that older populations have a high medication burden. With the growing elderly and AD populations, medication management for polypharmacy is a need that grows direr every year. uMH’s platform was able to identify a multitude of polypharmacy problems that individuals are currently facing. Funding uMETHOD Health.

Big data analysis techniques to address polypharmacy in patients – a scoping review

“…This approach has to be examined over a longer period of time and further validated, including a broader variety of medications. Two studies [24,26] were identified as focusing on uncovering drug-drug interactions in patients on multiple medications. The developed scoring system from Duke et al 2010 [26] was evaluated in a pilot study and the results showed that using the application is 60% faster in searching for adverse events compared to traditional drug information resources.…”

“…The third study [24] included in this review also addresses interactions in medication plans. The results of the evaluation showed that older populations have a high medication burden and therefore medication management for polypharmacy is a challenging task.…”

“…Furthermore, physicians were very satisfied with the developed tool and rated its usability as very high [28]. Another decision support system was evaluated by Keine et al 2019 [24]. The aim of this system was to identify problems of polypharmacy in individuals, such as drug-drug interactions (DDIs), drug-genome interactions (DGIs), and drug-diet interactions.…”

“…Of the 295 individuals, 97.59% were on at least one medication, with an overall mean of 11.5 medications per person, with 83.66% of them on five or more medications. Additionally, many interactions were identified [24].…”

Leveraging machine learning and big data for optimizing medication prescriptions in complex diseases: a case study in diabetes management

“…The complexity of disease management in the presence of comorbidity and polypharmacy has been the focus of studies based on data analytics using ML/AI. For example, Keine and colleagues [19] investigated the effect of polypharmacy on older adults with dementia or Alzheimer's disease and predicted the medication burden using ML. Other investigators [20][21][22] raised computational challenges with predicting the effects of drug combination (both synergism and antagonism) and suggested ML as an effective method for tackling the challenge when data are available.…”