Adherence to long-term treatment is a key strategy in the management of chronic diseases. Yet according to the World Health Organisation (WHO), even in developed markets, only 50% of patients who suffer from chronic diseases adhere to treatment recommendations. According to the US Food and Drug Administration (FDA), adherence to medicines is defined as the extent to which patients take medication as prescribed by their doctors. This involves factors such as getting prescriptions filled, remembering to take medication on time and following the directions given by their doctors. Poor adherence is a critical challenge for the healthcare industry as it is closely associated with suboptimal health outcomes, increased morbidity and mortality and augmented healthcare utilisation and costs. For example, a 2017 retrospective cohort study using a large US claims database explored why clinical outcomes are not keeping pace with the availability of new treatment options. The study found that real-life HbA1c reductions fell far short of those reported in randomised clinical trials (RCTs), with poor medication adherence emerging as the key driver behind the disconnect.¹ The Healthcare Effectiveness Data and Information Set (HEDIS) 2019 report shows that only about 40% of commercially insured health maintenance organisation (HMO) patients and 30% of Medicaid patients achieve the HbA1c target goal of less than 7.0%.² One of the major contributory factors for failure to achieve the target HbA1c goals is poor medication adherence.³

It therefore becomes critical for insurers and healthcare providers to track and monitor medication nonadherence. What strategies and models can we implement that are reliable, practical and cost-effective to accurately measure medication nonadherence? To understand this better we conducted a limited literature search to identify medical publications between January 2015 and December 2020 that focused on best practices adopted by healthcare stakeholders across the globe to measure medication adherence.

The table in Figure 1 summarises different methodologies adopted by healthcare stakeholders to measure medication adherence.

Figure 1: Assessment of Different Methodologies to Measure Medication Adherence

We found the multiple methodologies to measure and monitor drug adherence in these studies used by different stakeholders have their own advantages and limitations. For example:

• Direct drug measurement in patient’s body fluids is a highly accurate method. However, it is invasive and expensive. It does have a limitation in generating understanding of any specific patterns of nonadherence.⁷ This methodology may be particularly relevant in markets where electronic medical records or standardised claims data systems or other monitoring mechanisms are not feasible.
• Measures like pharmacy and insurance claims-based analytics for medication adherence are very popular in markets with digital data transactions and standardised codes. This method is already commonly used as a part of disease management initiatives of provider profiling. While administratively manageable if data is available, it has certain limitations as it does not allow details of potential barriers or factors associated with nonadherence. It does allow exploring the relationship between demographic variables with nonadherence but relies on availability of accurate and detailed claims line-level data.
• Emerging insurtech innovations, e.g., Medication Events Monitoring System (MEMS), where a microprocessor placed in the cap of the medication container records the time and date every time the patient removes the cap. MEMS gives additional information about dosing intervals and partial adherence unlike the manual counting method.⁴ However, this option is expensive and not yet universally available.
• Patient’s self-reported measure is the simplest and most common method for measuring medication adherence. Carefully designed approaches can be expanded beyond measuring percentage adherence to evaluate attitudes and behaviours governing adherence. Like any self-reported information, this method is prone to overestimation due to recall, reporting and social desirability bias.

During the literature review we came across some interesting emerging practices to manage this challenge. The table in Figure 2 summarises some advancements in standardising medication adherence measures. For emerging markets where disease management approaches by providers or payer offerings are still at an early stage of development, this highlights the importance of monitoring mechanisms in the programme planning and information collection processes.

Figure 2: Other Advancements to Measure Medication Adherence

Choosing and implementing the measure or combination of measures best meeting a particular organization’s requirements will help frame the most competent and personalised intervention to recognise the medication-taking behaviours in patients with chronic diseases. For a payer, using the most appropriate measure would help improve disease management effectiveness and service profiling. Accurate assessment of medication adherence will help in designing both optimal and cost-effective strategies to improve medication adherence. Each measurement approach has certain dependencies for systems, data and/or personnel and may not be feasible in all contexts. Consequently, a healthcare provider or payer would have to carefully consider these approaches to find the most appropriate solution. For some agencies, a multimodal approach would seem to be an appropriate solution.⁷

According to the US Centers for Disease Control and Prevention (CDC), nonadherence has been identified as the cause of approximately USD 100 billion to USD 300 billion worth of avoidable annual healthcare spending.^8,,⁹ This potential wastefulness can be avoided by carefully investing in strategies, with the availability of data analytics tools, that can assess and improve a patient’s medication adherence. Increased medication adherence can result in improvements in disease control and other aspects of self-management among members.

¹Carls GS, Tuttle E, Tan R-D, et al. Understanding the gap between efficacy in randomized controlled trials and effectiveness in real-world use of GLP-1 RA and DPP-4 therapies in patients with type 2 diabetes. Diabetes Care 2017;40:1469–1478. Retrieved June 14, 2021, from https://care.diabetesjournals.org/content/40/11/1469.long.

²National Committee for Quality Assurance. Comprehensive diabetes care. Retrieved June 14, 2021, from https://www.ncqa.org/hedis/measures/comprehensive-diabetes-care/.

³Polonsky WH, Henry RR. Poor medication adherence in type 2 diabetes: recognizing the scope of the problem and its key contributors. Dovepress Open. 2016 ;1299–1307. Retrieved June 14, 2021, from https://www.dovepress.com/poor-medication-adherence-in-type-2-diabetes-recognizing-the-scope-of--peer-reviewed-fulltext-article-PPA.

⁴Lam WY, Fresco P. Medication Adherence Measures: An Overview. Biomed Res Int. 2015;2015:217047. Retrieved June 14, 2021, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4619779/.

⁵Kardas et al . The Need to Develop Standard Measures of Patient Adherence for Big Data: Viewpoint. J Med Internet Res 2020;22(8):e18150. Retrieved June 14, 2021, from https://www.jmir.org/2020/8/e18150/PDF.

⁶Hutchins DS, Zeber JE, Roberts CS, Williams AF, Manias E, Peterson AM, IPSOR Medication Adherence Persistence Special Interest Group. Initial Medication Adherence-Review and Recommendations for Good Practices in Outcomes Research: An ISPOR Medication Adherence and Persistence Special Interest Group Report. Value Health 2015 Jul;18(5):690-699. Retrieved June 14, 2021, from https://www.valueinhealthjournal.com/action/showPdf?pii=S1098-3015%2815%2901844-6.

⁷Anghel LA, Farcas AM, Oprean RN. An overview of the common methods used to measure treatment adherence. Med Pharm Rep. 2019;92(2):117-122. doi:10.15386/mpr-1201. Retrieved June 14, 2021, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6510353/.

⁸Cutler RL, Fernandez-Llimos F, Frommer M, Benrimoj C, Garcia-Cardenas V. Economic impact of medication non-adherence by disease groups: a systematic review. BMJ Open. 2018;8(1):e016982. Retrieved June 14, 2021, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5780689/.

⁹CDC Grand Rounds: Improving Medication Adherence for Chronic Disease Management — Innovations and Opportunities.2017. Retrieved June 14, 2021, from https://onlinelibrary.wiley.com/doi/full/10.1111/ajt.14649.