Home // IARIA Congress 2024, The 2024 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications // View article
Authors:
Daniela Zanni
Joshua Hinton
Ejike Nwokoro
Keywords: Patient Support Program; medication adherence, artificial intelligence; predictive analysis.
Abstract:
Patient adherence to medication has been a long- sought health outcome measure, with the demonstrable benefits of reduced disease-related complications, improved quality of life, and reduced mortality. However, when not adequately supported, chronic disease patients often experience a downward trend in their adherence over time. Patient Support Programs are designed to address this issue by keeping patients engaged throughout their chronic disease management journey and supporting them in developing increasing accountability for their own health and wellbeing. Although these Patient Support Programs have been shown to be a viable tool in supporting treatment retention and adherence, there remains sparse evidence relating to quantifiable economic benefit, both from a pharmaceutical revenue and a health system cost-saving perspective. To this end, this study sought to explore the impact of Patient Support Programs on treatment retention as well as the medicine revenue implication of any observed impact of these programs.. We found that adoption of Patient Support Programs reduces patient drop-off from treatment (1.1% drop off compared to 2.8% when patients are not enrolled in the program). Additionally, the observed impact translates to economic benefits, in terms of medicine revenue, of between £2,156,561 and £4,714,787, over a 6-month period. Furthermore, our analysis suggests that complementing these Patient Support Programs with prior prediction of patient risk of poor adherence (through machine learning)can result in the generation of additional medicine revenue of almost £500,000 over a 6-month period. We opine that this enhancement is made possible through early deployment of the programs, as well as their deployment in a manner that is guided by a better understanding of individual patient risk of poor adherence.
Pages: 7 to 9
Copyright: Copyright (c) IARIA, 2024
Publication date: June 30, 2024
Published in: conference
ISBN: 978-1-68558-180-0
Location: Porto, Portugal
Dates: from June 30, 2024 to July 4, 2024