Pharmacoequity measurement framework: A tool to reduce health disparities
Publication: Journal of Managed Care & Specialty Pharmacy
Preprint
Abstract
Pharmacoequity is a health system and policy goal of ensuring equitable access to high-quality medications for all individuals, regardless of factors such as race, ethnicity, socioeconomic status, or resource availability to reduce health disparities. Although measurement frameworks have been widely used in health equity contexts, a focused framework for pharmacoequity remains a critical gap. In this article, we introduce a novel pharmacoequity measurement framework anchored in the patient medication-use journey. The framework includes the following domains: (1) access to health care services, (2) prescription generation, (3) primary medication nonadherence, (4) secondary medication nonadherence, and (5) medication monitoring. For each domain, we provide examples of outcome measures and potential data sources that can be used for evaluation. We also outline an implementation workflow of the pharmacoequity measurement framework that population health stakeholders can use across various settings (eg, health systems, health plans). The framework provides a structured approach to identify existing gaps in the path toward achieving pharmacoequity and lay the foundation for targeted interventions. Additionally, it enables ongoing monitoring of progress toward achieving pharmacoequity while identifying interventions that are effective, scalable, and sustainable.
Plain language summary
Frameworks help organizations and researchers structure how they evaluate and track progress. In this paper, we propose a framework to measure pharmacoequity, which is a goal for ensuring that all people have access to high-quality medications regardless of race, ethnicity, gender, or other socioeconomic factors. Organizations that are interested in achieving pharmacoequity can apply this framework to assess their efforts and monitor progress toward achieving equitable access to medications.
Implications for managed care pharmacy
With well-documented disparities in medication use, managed care organizations must strive to achieve pharmacoequity—ensuring equitable access to high-quality medications for all. The proposed pharmacoequity measurement framework, embedded in the patient medication-use journey, helps population health stakeholders identify gaps, drive targeted interventions, and track progress. By taking a holistic approach, rather than focusing on individual domains like secondary nonadherence, the framework can help uncover organization-specific challenges in the pursuit of achieving pharmacoequity.
Pharmacoequity describes a goal of ensuring equitable access to high-quality medications for all individuals, regardless of factors such as race, ethnicity, gender, socioeconomic status, or resource availability.1 Achieving pharmacoequity is fundamental to reducing health disparities, and thus must be a health system and policy priority. Disparities in medication use and access can arise from access-related factors like gaps in insurance coverage, limited health care provider and pharmacy access, and out-of-pocket prescription costs; as well as social determinants of health factors like transportation challenges, housing insecurity, and systemic racism; and, research factors like differential rates of clinical trial participation.1-5 These multifaceted factors often work collectively, resulting in disparities in medication use and access.
Achieving pharmacoequity requires measuring existing gaps, implementing targeted interventions, and monitoring progress toward desired outcomes. Although health equity frameworks exist,6-8 they do not adequately capture the complexities of medication use and access. A dedicated pharmacoequity framework is needed for several reasons. First, broad disparities exist in medication use and access, highlighting the importance of a framework to systematically measure and address pharmaco-inequities.1-5 Additionally, pharmacoequity is an emerging field lacking a comprehensive structure to guide future research and scalable interventions. Finally, the complexities of addressing disparities across the pharmacotherapeutic treatment continuum demands a structured approach.
To address this gap, we propose a pharmacoequity measurement framework, anchored in the patient medication-use journey. This framework is intended for use by health systems, health plans, and other population health stakeholders to systematically identify gaps and implement targeted interventions, ultimately advancing equitable medication access for all. Moreover, the framework can be adapted to assess disparities across various subgroups—race, ethnicity, gender, insurance type, and residence type (eg, rural vs urban)—based on organizational priorities, patient demographics, and patient/community needs.
Domains of the Pharmacoequity Measurement Framework
The patient journey of accessing and utilizing medications is multifaceted. The Medication Access Patient Journey conceptual framework, developed by the Pharmacy Quality Alliance (PQA) and the National Pharmaceutical Council (NPC), illustrates this complex process.9 It outlines a cyclical journey that begins with the patient recognizing a need for medication and seeking help, progresses to interactions with health care providers that may result in a prescription, continues through the process of filling the prescription, and culminates in the patient adhering to the medication regimen. Our proposed framework highlights key domains along the journey where pharmacoequity can be measured and interventions can be targeted to ensure equitable access to medications. The domains that form the structure of the pharmacoequity measurement framework are summarized in Table 1 and include:
1.
Access to Health Care Services – Access broadly refers to the ability to obtain health care services, including pharmacies and related services. Limited access to health care services is associated with several adverse outcomes including delays in diagnosis and treatment, poor disease state management, and increased health care costs.10-13 It also hinders the initiation step of obtaining necessary medications, potentially contributing to pharmaco-inequities. There are several documented disparities in access to health care services among certain population groups. For example, people with lower incomes and Black and Hispanic/Latino populations often lack a usual source of care and face delays in accessing health care.14,15 Similarly, Black and Hispanic/Latino communities are disproportionately impacted by lack of access to pharmacies.16,17 Increasing use of telemedicine, particularly following the COVID-19 pandemic, has the potential to increase access to health care providers; however, telemedicine use has been shown to vary by patient race, patient ethnicity, distance to clinics, income, and insurance type.18-20 Disparities in access to health care services are associated with barriers such as transportation,21,22 geography (eg, rural vs urban),23-25 medical mistrust,26-28 insurance coverage,15 and lack of staff diversity.29,30
Commonly used access-related outcome measures include patient reports of a usual source of care, delays in care,9,10 time to specialist referrals,31,32 frequency of primary care and specialist visits,31-33 and telemedicine visit usage rates.18-20 Visit and referral patterns can be assessed using administrative claims data and/or electronic medical records (EMRs). However, patient surveys may be required to identify whether a patient has a usual source of care or experience delays in care, as well as information regarding patient-specific barriers to accessing health care services. Geospatial mapping can help uncover potential disparities in access to health care providers related to distance.34,35
2.
Prescription Generation: Once a patient visits a prescriber, the next step involves discussing treatment options, which may include prescription medications. Shared decision-making has been shown to positively impact medication prescribing in certain settings and can serve as a measurable goal.36,37 Measuring patient-provider discussions is challenging because of the lack of systematic data capture. Consequently, there is limited evidence on the reasons and outcomes associated with such discussions. However, documentation of goals-of-care discussions in oncology within EMR-systems using standardized templates can be adapted to capture pharmacotherapy-related discussions.38 Large language models can be trained to analyze EMR notes and generate structured data related to the frequency and timing of pharmacotherapy-related discussions, including shared decision-making.39 Lastly, traditional methods like recordings and surveys are also valuable tools to measure the occurrence of such discussions.40,41
Next, treatment initiation hinges on the generation of a prescription or order following a discussion. However, factors such as bias/stigma,31,42 medical mistrust,43,44 insurance coverage,45-47 provider specialty,35 and cost42 can contribute to varying rates of treatment initiation; and treatment initiation may vary by age and race.38 Depending on the disease state, failure to initiate appropriate treatment can lead to disease progression,48 worsened health outcomes,48 and increased health care costs.49
Measuring appropriate prescription generation first requires defining appropriate treatment. Clinical guidelines are often used to define appropriate treatment.50 Outcome measures include the rates of guideline-concordant prescribing, for example. Additionally, newer treatments may be superior to older treatments in some cases; and outcome measures can assess the rates of novel therapy prescribing.45,51,52 Prescription generation can be difficult to measure because claims and dispensing data do not capture prescriptions that are written but never filled. However, prescription generation measures can be ideally derived from EMRs, or established e-prescribing databases. These sources provide a comprehensive view of prescribed medications and can be supplemented with claims data for further analysis. Point-of-care systems with e-prescribing functionality can also be used if they capture the relevant data elements associated with prescription generation.
3.
Primary Medication Nonadherence – Primary medication nonadherence is the failure to fill an initial prescription.49 Disparities in primary medication nonadherence have been shown among age groups, racial and ethnic groups, and by gender.53,54 Factors such as poor insurance coverage, high prescription drug costs, geography (eg, proximity to pharmacies), limited transportation access, prescriber specialty, complexity of the medication regimen, and health literacy can contribute to high rates of primary medication nonadherence.2,54,55 Poor patient-provider communication is frequently reported as a reason for primary medication nonadherence.56-60 Although, there is extensive evidence documenting associations of adherence with outcomes, the consequences of the subset of primary medication nonadherence has not been widely reported.
The Pharmacy Quality Alliance (PQA) has developed a consensus-based definition and endorsed a primary medication nonadherence quality metric, defined as the percentage of prescriptions for chronic medications prescribed but not obtained by the patient within 30 days.61 Additionally, the time from prescription generation to prescription fill can provide information relevant to primary medication nonadherence. Measuring primary medication nonadherence requires use of EMR or e-prescribing databases in conjunction with pharmacy dispensing or claims data.
4.
Secondary Medication Nonadherence – Secondary medication nonadherence is when patients do not continue to take their prescribed medications as directed after initially filling their prescription. There is extensive evidence showing that secondary medication nonadherence is associated with adverse outcomes, including increased mortality, suboptimal outcomes, and increased health care costs.62-64 However, studies have also shown disparities in nonadherence among racial and ethnic subgroups and by gender.65-68 There is an extensive body of research identifying a wide range of factors influencing secondary medication nonadherence across various conditions. These factors can be grouped as patient-specific barriers (eg, medication regimen knowledge, health literacy, language), illness-specific barriers (eg, symptoms, severity), medication-specific barriers (eg, polypharmacy, side effects), health system–related barriers (eg, multiple prescribers, access), social barriers (eg, food insecurity, housing instability, transportation, stigma), and logistical barriers (eg, drug costs, insurance coverage, drug shortage).64,69-71
There are 2 measures of secondary medication nonadherence that rely upon administrative claims data and are commonly used in research: medication possession ratio (MPR) and proportion of days covered (PDC).72 Both assess the proportion of days during which a patient has medication available over a specific time frame; however, MPR uses the days supply of medication, whereas PDC uses the days covered.72 In this way, PDC is more conservative because it accounts for overlapping days supply.72 Quality measures generally use PDC as a measurement of adherence. Both measures can be calculated using administrative claims data or dispensing data. Because patient adherence is dynamic in nature, PDC or MPR can be measured longitudinally to reflect changes over time. A notable limitation of both measures is that neither account for whether a patient ingested their medication, only that they filled the prescription.
5.
Medication Monitoring – Health care providers routinely follow patients taking prescription medications to monitor for therapeutic outcomes and adverse events and may adjust, discontinue, or switch medications as a result. However, disparities in medication monitoring have been reported by age group, race and ethnicity, and by gender.73-82, For example, medication therapy monitoring (MTM) services have been shown to improve medication regimens,83 yet Black and Hispanic populations are less likely to receive MTM services.77,78 Similarly, prescribers are less likely to intensify blood pressure treatment in Black patients, resulting in poor blood pressure control.76 On the flipside, patients with low socioeconomic status are more likely to be on potentially inappropriate medications, highlighting disparities in deprescribing that need to be addressed.84 Several factors have been shown to be associated with variations in medication monitoring including duration of illness, geography, presence of comorbidities, provider specialty, and number of concomitant medications.79,80,85
Medication monitoring measures vary by disease and treatment. Example measures include guideline-concordant monitoring rates,74 adverse event management rates,86,87 MTM completion rates,77,78 measurements of clinical inertia,76 and treatment discontinuation rates.81,82 The example measures can largely be measured using EMRs or administrative claims data. Improved collection of these measures represents a strategy to improving not only pharmacoequity but also achieving deprescribing equity.88
| Access to health care services | Prescription generationa | Primary medication nonadherence | Secondary medication nonadherence | Medication monitoring | |
|---|---|---|---|---|---|
| Possible factors associated with disparities in the respective domain | - Bias/stigma - Cost/drug price - Insurance coverage - Medical mistrust - Provider specialty | - Cost/drug price - Insurance coverage - Geographyb - Health literacy - Medication regimen - Patient - provider communication - Provider specialty - Transportation | - Cost/drug price - Disease symptoms and severity - Insurance coverage - Geographyb - Health literacy - Medication regimen - Transportation | ||
| Examples of outcome measures | - Usual source of care - Delays in care - Time to specialist referrals - Frequency of PCP and specialist visits - Telemedicine visit usage rates | - Rates of guideline - concordant prescribing - Rates of novel therapy prescribing | - Primary nonadherence rates - Time to fill first prescription | - Medication possession ratio - Proportion of days covered | - Adverse event management rates - Clinical inertia measures - Guideline-concordant monitoring rates - MTM completion rates - Treatment discontinuation rates |
| Possible data sources | - EMR - Claims data - Geospatial mapping data - Patient surveys | - EMR or e-prescribing data, which may be supplemented with claims data | - EMR or e-prescribing data AND - Claims or pharmacy dispensing data | - Claims data - Pharmacy dispensing data | - EMR - Claims data |
aThis domain can include the occurrence of patient-provider discussions of pharmacotherapeutic options. Measuring patient-provider discussions is challenging because of the lack of systematic data capture. Consequently, there is limited evidence on the reasons and outcomes associated with these discussions. However, metrics like frequency and timing of discussions related to pharmacotherapy options and shared decision-making can be assessed using data sources like EMR notes, patient recordings, and surveys.
bFor example: rural vs urban; geographic proximity to pharmacies, health care providers, health systems.
EMR = electronic medical record; MTM = medication therapy management; PCP = primary care provider.
Implementation of Pharmacoequity Measurement Framework by Population Health Stakeholders
Population health stakeholders can adapt the pharmacoequity measurement framework to their specific preferences, priorities, and capabilities. Implementing the framework is akin to other quality improvement projects; yet, its distinctiveness lies in its holistic approach, rooted in the patient medication-use journey. The implementation flowchart serves as a resource to measure and monitor progress toward achieving pharmacoequity (Figure 1). By selecting a specific population and condition, identifying appropriate outcome measures for each domain of the patient medication-use journey, and obtaining baseline data, organizations can prioritize domains with the greatest disparities. This approach enables focus on factors driving disparities within the prioritized domains and implementation of quality improvement projects to address these factors. Monitoring and tracking results are essential for evaluating the effectiveness of interventions and refining strategies over time.
In the hypothetical example shown in Figure 1, the workflow evaluates and addresses racial disparities in the use of antiobesity medications within a health system affiliated with a health plan. The next step involves identifying relevant outcome measures for each domain such as frequency of primary care provider visits, antiobesity medication prescribing rates, primary medication nonadherence rate, PDC, and medication discontinuation rates. Data are then collected from EMR and/or claims to establish baseline metrics. The hypothetical analysis reveals disparities in antiobesity medication prescribing rates, while noting limited disparities in primary medication nonadherence rates. Prescription generation is identified as a priority domain for improvement. Potential factors driving these disparities are identified from the literature, prescriber surveys, community/patient engagement, and organization experience. Based on the findings, interventions such as health care provider education, EMR alerts, and reduced copays are implemented. The impact of these interventions is monitored by reassessing racial disparities in antiobesity medication prescribing rates. The process is then repeated to address other conditions (eg, patients with multiple myeloma), other domains (eg, primary medication nonadherence), or other factors in the prioritized domain (eg, medical mistrust related to antiobesity medication initiation), ensuring a holistic approach to achieving pharmacoequity.
It is important to note that implementation of the pharmacoequity framework moves beyond looking at individual domains in isolation, and instead adopts a holistic approach grounded in the patient medication-use journey. This approach can uncover organization-specific challenges to pharmacoequity that may not be apparent when focusing on 1 domain alone, such as secondary medication nonadherence.
Incorporating patient and community voices is foundational to achieving pharmacoequity, as it ensures that the interventions are accounting for the lived experiences and needs of those most impacted by disparities. Engaging patients and communities fosters trust and helps uncover nuanced barriers that may not be readily evident through the literature or provider experience alone. This framework offers multiple opportunities to integrate patient and community voices meaningfully. Patients and community voices can play an active role in early stages, such as identifying priority conditions to address, as well as later phases, like prioritizing domains and factors for targeted interventions. In doing so, organizations can better address root causes of disparities and cocreate initiatives that are meaningful to the communities impacted by disparities.
Consideration for Data Sources
Implementing a pharmacoequity measurement framework is challenging without an established, robust data-capture infrastructure. Data limitation such as availability, quality, and completeness can pose obstacles to comprehensive implementation. However, organizations can mitigate these challenges by taking a phased approach, building incrementally upon existing data-aggregation processes like community health needs assessment surveys, drug utilization evaluation reports, and regional research collaborations. Collaboration among internal stakeholders from population health, health equity, informatics, and analytics is critical to ensuring an efficient and balanced framework rollout that avoids overwhelming existing systems. Over time, organizations can broaden this foundation, potentially by partnering with external data aggregators to enhance data completeness and insights. This gradual expansion supports an adaptable framework that can evolve as data capture and reporting capacity increase, ultimately driving more effective pharmacoequity efforts. As organizations adopt and refine their approaches, sharing lessons learned related to data capture will be vital in the path toward achieving pharmacoequity.
Next Steps
Although the framework is initially designed to empower population health stakeholders within health systems and health plans, it holds the potential to evolve into a broader tool with the help of the research community. Evidence generated by health service researchers, quality improvement champions, and other stakeholders through the framework’s intended use can reveal common themes contributing to pharmaco-inequities. The evidence, when available, can potentially inform the development of curated, evidence-based pharmacoequity scorecards. These scorecards, tailored to specific conditions or medications, can be created by quality associations and the broader scientific community. This adaptability is crucial because patient medication-use journeys can vary significantly depending on the condition or medication class. For instance, anticancer therapy administered in a hospital outpatient setting presents a distinct journey compared with that of antibiotics prescribed in a community setting. This adaptability underscores the framework’s potential not only to serve as an initial tool within health care systems, but also to foster a more comprehensive, patient-centered approach to achieving pharmacoequity across diverse settings and conditions.
Conclusions
In this article, we introduce a novel pharmaco-equity measurement framework anchored in the patient medication-use journey. The framework includes the following domains: (1) access to health care services, (2) prescription generation, (3) primary medication nonadherence, (4) secondary medication nonadherence, and (5) medication monitoring. For each domain, we provide examples of outcome measures and potential data sources. It is crucial to emphasize that this framework is not intended to limit the scope of how to measure pharmacoequity. Instead, we designed this framework to act as a catalyst, encouraging further exploration and refinement for systematic measurement of pharmaco-inequities. Our goal is for health systems, health plans, and other population health stakeholders to employ this resource as they pursue, and ultimately achieve, pharmacoequity.
ACKNOWLEDGMENTS
The authors acknowledge Alex Snell from Snell Medical Communication Inc for her assistance with the design of the implementation workflow.
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Published online: 20 December 2024
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