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Prediction and pattern analysis of medication refill adherence through electronic health records and dispensation data
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7453-9186
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
2020 (English)In: Journal of Biomedical Informatics: X, E-ISSN 2590-177X, Vol. 6-7, article id 100075Article in journal (Refereed) Published
Abstract [en]

Background and purpose

Low adherence to medication in chronic disease patients leads to increased morbidity, mortality, and healthcare costs. The widespread adoption of electronic prescription and dispensation records allows a more comprehensive overview of medication utilization. In combination with electronic health records (EHR), such data provides new opportunities for identifying patients at risk of nonadherence and provide more targeted and effective interventions. The purpose of this article is to study the predictability of medication adherence for a cohort of hypertensive patients, focusing on healthcare utilization factors under various predictive scenarios. Furthermore, we discover common proportion of days covered patterns (PDC-patterns) for patients with index prescriptions and simulate medication-taking behaviours that might explain observed patterns.

Procedures

We predict refill adherence focusing on factors of healthcare utilization, such as visits, prescription information and demographics of patient and prescriber. We train models with machine learning algorithms, using four different data splits: stratified random, patient, temporal forward prediction with and without index patients. We extract frequent, two-year long PDC-patterns using K-means clustering and investigate five simple models of medication-taking that can generate such PDC-patterns.

Findings

Model performance varies between data splits (AUC test set: 0.77–0.89). Including historical information increases the performance slightly in most cases (approx. 1–2% absolute AUC uplift). Models show low predictive performance (AUC test set: 0.56–0.66) on index-prescriptions and patients with sudden drops in PDC (Recall: 0.58–0.63). We find 21 distinct two-year PDC-patterns, ranging from good adherence to intermittent gaps and early discontinuation in the first or second year. Simulations show that observed PDC-patterns can only be explained by specific medication consumption behaviours.

Conclusions

Prediction models developed using EHR exhibit bias towards patients with high healthcare utilization. Even though actual medication-taking is not observable, consumption patterns may not be as arbitrary, provided that medication refilling and consumption is linked.  © 2020 The Authors. Published by Elsevier Inc.

Place, publisher, year, edition, pages
New York, NY: Elsevier, 2020. Vol. 6-7, article id 100075
Keywords [en]
Medication refill adherence, Electronic health records, Simulation, Prediction, Refill patterns
National Category
Signal Processing Pharmacology and Toxicology Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-43529DOI: 10.1016/j.yjbinx.2020.100075Scopus ID: 2-s2.0-85087509892OAI: oai:DiVA.org:hh-43529DiVA, id: diva2:1504082
Part of project
iMedA: Improving MEDication Adherence through Person Centered Care and Adaptive Interventions, Vinnova
Funder
Vinnova
Note

Funding: Vinnova, Health Technology Center and CAISR at Halmstad University and Hallands Hospital for financing the research work under the project iMedA [Grant No.: 2017-04617]. 

Available from: 2020-11-26 Created: 2020-11-26 Last updated: 2023-11-29Bibliographically approved
In thesis
1. Data-driven personalized healthcare: Towards personalized interventions via reinforcement learning for Mobile Health
Open this publication in new window or tab >>Data-driven personalized healthcare: Towards personalized interventions via reinforcement learning for Mobile Health
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Medical and technological advancement in the last century has led to the unprecedented increase of the populace's quality of life and lifespan. As a result, an ever-increasing number of people live with chronic health conditions that require long-term treatment, resulting in increased healthcare costs and managerial burden to the healthcare provider. This increase in complexity can lead to ineffective decision-making and reduce care quality for the individual while increasing costs. One promising direction to tackle these issues is the active involvement of the patient in managing their care. Particularly for chronic diseases, where ongoing support is often required, patients must understand their illness and be empowered to manage their care. With the advent of smart devices such as smartphones, it is easier than ever to provide personalised digital interventions to patients, help them manage their treatment in their daily lives, and raise awareness about their illness. If such new approaches are to succeed, scalability is necessary, and solutions are needed that can act autonomously without costly human intervention. Furthermore, solutions should exhibit adaptability to the changing circumstances of an individual patient's health, needs and goals. Through the ongoing digitisation of healthcare, we are presented with the unique opportunity to develop cost-effective and scalable solutions through Artificial Intelligence (AI).

This thesis presents work that we conducted as part of the project improving Medication Adherence through Person-Centered Care and Adaptive Interventions (iMedA) that aims to provide personalised adaptive interventions to hypertensive patients, supporting them in managing their medication regiment. The focus lies on inadequate medication adherence (MA), a pervasive issue where patients do not take their medication as instructed by their physician. The selection of individuals for intervention through secondary database analysis on Electronic Health Records (EHRs) was a key challenge and is addressed through in-depth analysis of common adherence measures, development of prediction models for MA and discussions on limitations of such approaches for analysing MA. Furthermore, providing personalised adaptive interventions is framed in the contextual bandit setting and addresses the challenge of delivering relevant interventions in environments where contextual information is significantly corrupted.       

The contributions of the thesis can be summarised as follows: (1) Highlighting the issues encountered in measuring MA through secondary database analysis and providing recommendations to address these issues, (2) Investigating machine learning models developed using EHRs for MA prediction and extraction of common refilling patterns through EHRs and (3) formal problem definition for a novel contextual bandit setting with context uncertainty commonly encountered in Mobile Health and development of an algorithm designed for such environments.  

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2021. p. 55
Series
Halmstad University Dissertations ; 79
Keywords
Information Driven Care, Electronic Health Records, Machine Learning, Reinforcement Learning
National Category
Health Care Service and Management, Health Policy and Services and Health Economy Signal Processing
Identifiers
urn:nbn:se:hh:diva-44091 (URN)9789188749666 (ISBN)9789188749673 (ISBN)
Presentation
2021-04-19, Wigforss, Visionen, Kristian IV:s väg 3, Halmstad, 14:00 (English)
Opponent
Supervisors
Available from: 2021-04-08 Created: 2021-04-01 Last updated: 2022-03-11Bibliographically approved
2. Mobile Health Interventions through Reinforcement Learning
Open this publication in new window or tab >>Mobile Health Interventions through Reinforcement Learning
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis presents work conducted in the domain of sequential decision-making in general and Bandit problems in particular, tackling challenges from a practical and theoretical perspective, framed in the contexts of mobile Health. The early stages of this work have been conducted in the context of the project ``improving Medication Adherence through Person-Centred Care and Adaptive Interventions'' (iMedA) which aims to provide personalized adaptive interventions to hypertensive patients, supporting them in managing their medication regimen. The focus lies on inadequate medication adherence (MA), a pervasive issue where patients do not take their medication as instructed by their physician. The selection of individuals for intervention through secondary database analysis on Electronic Health Records (EHRs) was a key challenge and is addressed through in-depth analysis of common adherence measures, development of prediction models for MA, and discussions on limitations of such approaches for analyzing MA. Providing personalized adaptive interventions is framed in several bandit settings and addresses the challenge of delivering relevant interventions in environments where contextual information is unreliable and full of noise. Furthermore, the need for good initial policies is explored and improved in the latent-bandits setting, utilizing prior collected data to optimal selection the best intervention at every decision point. As the final concluding work, this thesis elaborates on the need for privacy and explores different privatization techniques in the form of noise-additive strategies using a realistic recommendation scenario.         

The contributions of the thesis can be summarised as follows: (1) Highlighting the issues encountered in measuring MA through secondary database analysis and providing recommendations to address these issues, (2) Investigating machine learning models developed using EHRs for MA prediction and extraction of common refilling patterns through EHRs, (3) formal problem definition for a novel contextual bandit setting with context uncertainty commonly encountered in Mobile Health and development of an algorithm designed for such environments. (4) Algorithmic improvements, equipping the agent with information-gathering capabilities for active action selection in the latent bandit setting, and (5) exploring important privacy aspects using a realistic recommender scenario.   

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2023. p. 56
Series
Halmstad University Dissertations ; 102
National Category
Computer Sciences
Research subject
Health Innovation, Information driven care
Identifiers
urn:nbn:se:hh:diva-52139 (URN)978-91-89587-17-5 (ISBN)978-91-89587-16-8 (ISBN)
Public defence
2023-12-15, S1002, Kristian IV:s väg 3, Halmstad, 13:00 (English)
Opponent
Supervisors
Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2024-01-03Bibliographically approved

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Galozy, AlexanderNowaczyk, Sławomir

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