Enhancing patients adherence with AI
Introduction
The life sciences sector is at the forefront of innovation, particularly in the healthcare industry where AI applications are implemented to revolutionize
patient care. Despite advancements, the pharmaceutical industry has never really tackled improving patient adherence without concrete solutions.
Partnering with Novellas Healthcare since 2023, CROPLAND has taken on the challenge of improving patient adherence with the help of AI.
Our goals
With the support of VLAIO a feasibility study was initiated focusing on 2 primary questions:
1. Can therapy adherence be accurately measured?
2. Is it possible to predict patient therapy adherence?
Understanding therapy adherence
First things first. What is the definition of therapy adherence. Through extensive research and expert consultations, we created a tailored definition that considers:
- WHO’s categories affecting adherence: health system, condition, treatment regimen, socioeconomic environment, and the patient.
- AMA’s threshold: adherence is taking at least 80% of prescribed medication.
- APA’s factors: education, medication cost, belief in treatment, and fear of side effects.
Combining these insights, the study proposed that therapy adherence refers to a patient’s commitment to follow the agreed treatment plan, including medication dosage and timing, while considering factors such as symptoms, side effects, medication cost, and disease knowledge.
This definition was refined with expert input, emphasizing the importance of the therapeutic area and medication administration mode.
Data analysis journey
CROPLAND analyzed Novellas Healthcare’s datasets, focusing on adherence programs and factors like age, location, disease, and dropout rates, while Novellas Healthcare developed Dynamic Forms to capture structured patient data securely.
Add sensitive data and an initial vague description of therapy adherence to the equation and it is clear that analyzing this was no piece of cake.
With a custom data pipeline, CROPLAND data engineers were able to transform unstructured patient surveys into workable data. This data was then used to test the correlation between various factors and therapy adherence. Interpretation of this data was supported by Novellas Healthcare experts who were continuously involved in the project. This iterative approach created a synergy between both partners, leading to better results than either could have achieved separately.
Key Insights
- Face-to-Face (F2F) Visits:
- Regular F2F visits significantly improve adherence compared to digital-only interactions.
- Optimal frequency of visits is crucial, as too many visits have an equally negative impact on therapy adherence as too few
- Other Factors:
- Age, location, and gender showed less significant impact on adherence.
Conclusion
We’ve successfully quantified and measured therapy adherence, enabling personalized actions to maximize adherence. By combining AI and healthcare expertise, Cropland and Novellas Healthcare are paving the way for innovative solutions.
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