Assessing the economic impact of digital endpoints on medication adherence

KEYWORDS:

• Medication adherence
• digital endpoint
• economic impact
• clinical outcomes
• digital health technology

1. Introduction

Adherence to medication is crucial for patient care and indispensable for reaching clinical outcomes and treatment efficacy. Poor adherence to medications affects approximately 50% of the patient population [1]. This is particularly true in chronic conditions such as diabetes, asthma, chronic obstructive pulmonary disease (COPD), and hypertension with long-term therapies, leading to exacerbated health condition, increased risk of disease progression, premature deaths, prolonged use of healthcare services and significant healthcare expenditure. One systematic review [2] indicated that the annual adjusted disease-specific economic cost of non-adherence per person ranged from $949 to $44,190 (in 2015 US$). Appropriate interventions are therefore necessary to improve medication adherence and reduce the economic burden.

However, the adherence monitoring is contingent on the ability to detect the medication adherence during treatment and is quite challenging given objective and robust endpoints (measurements) are lacking in most diseases. Traditional measurements in clinics such as concentration of drug/metabolic measurement, self-reporting and pill count [3] are often insufficient because they rely on in-clinic measurements and subjective evaluations, leading to suboptimal assessment reliability, sensitivity and accuracy. In addition, clinical studies are typically long, costly and require large numbers of participants when using these traditional endpoints, which motivates stakeholders to develop functional endpoints that are less susceptible to these pitfalls, to provide an objective and accurate reflection of real-world medication adherence, and to decrease the time and costs related to outcome measurement [4,5]. Recently, with the growing trend of digitalization across the healthcare ecosystem, the implementation of digital health technology offers unique opportunity to develop digital endpoints. These include wearable devices (e.g. with sensors) that allow continuous assessment of functional ability during normal daily life and collect meaningful patient data (sensor-generated data) in a natural environment, offer a potential solution to the limitations of traditional endpoints. Wearable devices measure medication adherence endpoints through sensors and digital platforms that use real-time verification methods to track, verify, and measure medication intake and patient health in real-time. For example, ingestible sensors communicate directly with external devices to confirm ingestion and provide physical evidence of compliance with treatment regimens. Wearable devices provide data that reveals physiological responses and patient activities, offering insights into patient adherence patterns and treatment effectiveness. This technology not only allows healthcare providers to monitor adherence accurately but also facilitates personalized interventions by providing real-time feedback to patients about consistent medication use and making adjustments in treatment plans based on collected data.

The clinical meaningfulness of applying digital endpoints to monitor medication adherence and improving medication adherence is well-established. A literature review reported that an ingestible sensor for a serious mental illness could help provide real-time objective medication-taking adherence information for clinicians [6]. The review revealed that in small-scale studies, adherence rates using ingestible sensors varied from 73.9% to 88.6% for patients with serious mental disorders (SMD) and were over 80% for those with cardiac conditions and transplant recipients, reaching up to 99.4%. The studies involving SMD patients noted that participants were clinically stable, with the majority assessed as having ‘mild disease’ severity. Minor dermatological issues were the most common side effects associated with the wearable sensors that accompany the ingestible sensors.

The review concludes that ingestible sensors offer a promising tool for providing clinicians with real-time, objective data on medication adherence. Nonetheless, more research is necessary to fully understand their impact on adherence rates and treatment outcomes within the context of SMI and other conditions.

In addition, a randomized controlled trial conducted between 2015 and 2020 assessed the effectiveness of digital adherence monitoring in asthma Participants in the active group, using digital endpoints, showed lower increases in treatment requirements and higher adherence to medication compared to the control group [7]. Moreover, this strategy was proven to be cost-effective on the long-term [8]. Another systematic review spanning 2008 to 2018 (incorporated 11 RCTs) indicated that mobile applications potentially enhance medication adherence, and nine of these RCTs exhibited enhancements in clinical outcomes or patient-related outcome measures (PROMs) [9,10]. These objective data provided by digital endpoint could complement in-clinic assessment and provide support for decisions regarding disease management, which could in turn reduce unnecessary healthcare expenditures.

However, the economic implications of digital endpoints in medication adherence are multifaceted and warrant a detailed exploration. This editorial aims to shed light on the economic implications of digital endpoints utilization in medication adherence.

2. Economic implications of digital endpoint on medication adherence

Due to a limited body of evidence demonstrating the cost-saving potential of digital endpoints for medication adherence, its financial benefits remain uncertain. Economic implications associated with their usage can can be direct and indirect, each with its own justifications.

2.1. Direct economic implications

Digital medication adherence systems, including mobile apps, wearable devices, and other digital tools, are transforming healthcare by enhancing patient adherence to treatment regimen [11]. These systems do more than just track medication usage; they facilitate vital and effective communication between patients and healthcare providers through reminders and alerts, leading to direct healthcare cost savings as adherence improves.

The role of digital endpoints in medication adherence is foundational to achieving cost efficiency in healthcare. For chronic conditions like diabetes, asthma, COPD, and hypertension, improved adherence prevents complications requiring hospitalization, thus directly reducing healthcare expenses. Empirical evidence supports that enhanced medication adherence leads to fewer emergency hospital visits and lower disease-related medical costs. Higher medication costs are offset by reduced medical expenses, resulting in net healthcare savings [12,13].

Digital medicine, offering continuous monitoring, behavioral modification, and personalized interventions, emerges as a cost-effective solution for chronic disease management. With chronic diseases impacting about half of adult Americans and significant healthcare spending, digital medicine facilitates extensive, personalized interventions outside traditional healthcare settings, thereby playing a key role in preventing and managing chronic disorders at lower costs [14].

2.2. Indirect economic implications

In the United States, around half of the 3.8 billion prescriptions issued annually are not taken as prescribed, leading to an estimated $100–300 billion increase in healthcare costs and over 100,000 preventable deaths each year [15]. Digital tools enhance adherence, reduce waste, and have the potential to lower healthcare expenses indirectly by influencing market dynamics, demand, and pricing.

Moreover, insurance companies and payers experience indirect economic benefits from improved medication adherence, as healthier patients usually entail fewer healthcare claims and expenses, leading to potentially lower premiums. This aligns with the shift toward value-based care models, which are indirectly cost-effective for insurers and healthcare providers by emphasizing patient outcomes. From the patient perspective, effective digital medication management can lead to lower personal healthcare costs, fewer emergency visits, and a reduction in the need for advanced treatments. Healthier individuals also indirectly contribute to societal economic benefits by reducing costs related to illness, such as lost workdays [16].

Furthermore, digital endpoints allow healthcare providers to extend their reach into remote or underserved areas, expanding the patient pool while potentially leading to increased patient volumes and revenue generation for healthcare organizations.

3. Conclusion

Digital endpoints in medication adherence demonstrate immense potential to improve patient outcomes and lower healthcare costs, but there remains little empirical evidence regarding their direct economic impacts. As the field of digital health continues to develop, such analyses will become ever more vital for ascertaining true cost-effectiveness of these interventions through longitudinal studies, integration into healthcare systems and taking advantage of advances in AI/ML to achieve both improved patient care and cost efficiency.

4. Expert opinion

Research on digital endpoints for medication adherence reveals key findings and potential, yet also presents certain limitations. A notable strength is the effective integration of technology to increase medication adherence, particularly for chronic conditions. This integration has demonstrated direct cost-saving impacts, such as reduced hospitalizations and emergency visits, as well as indirect economic benefits like decreased healthcare spending by patients and insurance companies. However, the scarcity of empirical evidence regarding the financial impact of digital endpoints in medication adherence remains a significant gap. Challenges in quantifying their cost-effectiveness include the early stage of digital health development, the need for long-term studies with extensive data collection, and variability across healthcare systems and patient demographics.

The goal of seamlessly integrating digital tools into healthcare systems to enhance medication adherence, improve patient outcomes, and reduce healthcare costs is ambitious. Overcoming challenges such as ensuring the privacy and security of patient data, enhancing the user-friendliness of digital tools, and demonstrating their cost-effectiveness across healthcare settings is crucial to achieve this goal [17–19].

Future research should focus on longitudinal studies to establish the long-term benefits and sustainability of digital medication adherence systems. It should also explore their integration into existing healthcare infrastructure and assess their impact across different demographics. For example, cost-effectiveness analyses could be conducted to reveal the health-economic value of digital endpoints utilization. In turn, this information would support regulatory decision-making regarding reimbursement and pricing of digital endpoint or digital health technology.

In the coming years, advancements in healthcare technology are likely to make digital tools more accessible, personalized, and integrated with other healthcare systems, potentially leading to broader adoption and a deeper understanding of the transformative potential of digital medication adherence in healthcare delivery.

The integration of artificial intelligence (AI) and machine learning (ML) into digital medication adherence research is closely linked with the concept of digital endpoints. AI and ML can analyze vast amounts of data generated by digital health tools, leading to more personalized treatment plans and accurate predictions of patient adherence patterns. This capability enhances the effectiveness of digital endpoints, allowing for more precise and responsive medication management. For example, wrist band sensors track the unique motion of taking medication from its packaging, providing a non-intrusive method of monitoring adherence and sending reminders when doses are missed [20,21].

Declaration of interests

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

The paper was not funded.