Deciphering Factors Contributing to Cost-Effective Medicine Using Machine Learning.
Bowen Long1, Jinfeng Zhou2, Fangya Tan1
1Department of Analytics, Harrisburg University of Science and Technology, Harrisburg, PA 17101, USA.
Bioengineering (Basel, Switzerland)
|August 29, 2024
Summary
Machine learning identified key factors for over-the-counter (OTC) medication cost-effectiveness. Factors like FSA/HSA eligibility, symptom range, and packaging size significantly influence perceived value for consumers.
Area of Science:
- Pharmacoeconomics
- Health Informatics
- Consumer Health
Background:
- Over-the-counter (OTC) medications are widely used, but their cost-effectiveness is not always clear to consumers.
- Identifying factors that influence the perceived value of OTC medications is crucial for informed purchasing decisions.
- Existing research often overlooks the interplay between product attributes, pricing, and consumer financial accounts.
Purpose of the Study:
- To identify critical factors influencing the cost-effectiveness of OTC medications using machine learning.
- To develop a novel cost-effectiveness rating (CER) model incorporating user ratings and prices.
- To analyze how specific medication characteristics impact cost-effectiveness across different therapeutic categories.
Main Methods:
- Utilized machine learning algorithms to analyze a large dataset of OTC medications from Amazon.
- Developed a proprietary cost-effectiveness rating (CER) metric based on user reviews and product pricing.
- Examined the influence of factors including Flexible Spending Account (FSA)/Health Savings Account (HSA) eligibility, symptom treatment range, safety warnings, special effects, active ingredients, and packaging size.
Main Results:
- FSA/HSA eligibility, broader symptom treatment range, and smaller packaging size were positively correlated with higher cost-effectiveness.
- Cold medicines with safety warnings, featuring ingredients like phenylephrine and acetaminophen, demonstrated cost-effectiveness due to lower prices.
- Allergy medications with child-friendly attributes and digestion medicines containing calcium, famotidine, or magnesium showed enhanced cost-effectiveness.
Conclusions:
- Machine learning can effectively identify key drivers of OTC medication cost-effectiveness.
- Consumer perception of value is influenced by a combination of financial accessibility (FSA/HSA), therapeutic breadth, and product attributes.
- These findings provide actionable insights for consumers, manufacturers, and retailers to optimize OTC medication choices and market strategies.
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