GoodRx Savings Helped Prevent Over 250,000 ER Visits and Hospitalizations for Major Cardiovascular Events

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High drug prices can be deadly.

When patients can't afford an essential medication, they may be forced to skip or delay filling their prescription. And not taking their prescription medication may lead to loss of productivity, unnecessary hospitalizations, and even premature death. These effects can be even more severe for people with chronic conditions, like high cholesterol and coronary artery disease.

In honor of heart health month, the GoodRx Research team set out to understand just how much medication savings can save lives. And the findings are striking.

By helping users afford their prescriptions, GoodRx has directly contributed to the prevention of at least 140,000 emergency room visits and 110,000 hospitalizations for major adverse cardiovascular events like heart attack and stroke.

How is this possible? By making medications more affordable, GoodRx helps people access and keep taking their lifesaving medications (also known as staying adherent).

Below we walk you through our findings, which are drawn from our recent report, The GoodRx Effect.

How GoodRx saves lives

Patients are having a harder time predicting and affording the price of prescription medications due to increasingly higher healthcare costs. One study found that 32% of adults have been denied insurance coverage for a medication prescribed by their provider, and over 46% of those patients ended up not filling the prescription.

Unaffordable medication costs can result in patients:

• Rationing doses

• Skipping or delaying a prescription fill

• Opting for a cheaper but less effective treatment

• Stopping the medication entirely

In a recent GoodRx survey, 39% of adults taking prescription drugs reported at least one change in their medication adherence over the last year due to cost. Another study found that 14% of adults age 65 and older with Medicare did not fill a prescription because it was too expensive.

In The GoodRx Effect, we found that GoodRx has helped patients fill 184 million prescriptions they otherwise may not have been able to afford. These prescriptions include medications that treat high cholesterol, high blood pressure, and atrial fibrillation - chronic conditions that could worsen without a proper treatment regimen. These nearly 200 million prescriptions also represent a sizable population that was able to remain adherent to their lifesaving medications.

Medication adherence due to more affordable medications can help prevent health problems down the road. In the report, we found average reductions in hospitalization rates in the last year for people who used GoodRx. These ranged from -3.1% for adults under 65 filling atrial fibrillation medications, to -6.8% for adults over 65 filling blood pressure medications.

Average decreases in the ER visit rate due to GoodRx ranged from -2.3% for adults under 65 filling atrial fibrillation medications, to -8.5% for adults over 65 filling blood pressure medications in the last year.

Based on this, we estimate that since 2012, GoodRx savings have contributed to at least 140,000 fewer emergency room visits and 110,000 fewer hospitalizations for major adverse cardiovascular events like heart attack and stroke.

This reduction in healthcare use has also led to savings for the healthcare system as a whole. Specifically, we estimated the average total spending and out-of-pocket costs associated with these healthcare visits. For example, hospitalizations for major adverse cardiovascular events like a heart attack averaged over $22,800 in 2021.

Applying the relevant average costs, we estimate that by preventing over 250,000 ER visits and hospitalizations for major cardiovascular events, GoodRx has helped save the healthcare system over $2 billion since 2012. This translates to at least $44 million in out-of-pocket savings for our users.

Methodology

GoodRx effect on medication adherence: Medication adherence with and without GoodRx was calculated based on the average amount saved per prescription for GoodRx users. Average amount saved per prescription was estimated based on median GoodRx prices, averaged across the 200 most prescribed medications and weighted by claim volume. The distribution of savings relative to insurance copay and retail price for each drug was estimated using a regression model of GoodRx relative fills on GoodRx discount off median retail price and the GoodRx discount off average commercial insurance copay, weighted by the proportion of claims where insured patients paid full price (either due to deductibles, restricted coverage, or no coverage for the medication, based on third-party data). The estimated relationship between average amount saved and medication adherence was based on a meta-analysis of 160 cost-related nonadherence studies in Eaddy et al. (2012) and assumed an 80% baseline adherence at $0 out-of-pocket cost. Based on the literature, for every $10 savings, medication adherence is estimated to increase by 3.8%. The total number of newly adherent GoodRx claims since 2012 was calculated based on the estimated share of newly adherent claims in each year.

GoodRx effect on health outcomes: To assess the effect of GoodRx savings on health outcomes, we performed a meta-analysis of peer-reviewed research articles published between 1990 and 2023 studying the effect of medication adherence on disease exacerbations, inpatient hospitalizations, ER visits, and productivity loss due to disease. We performed this systematic literature review in concordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework.

We reviewed studies where the intervention consisted of pharmaceuticals to treat either COPD, Type 2 diabetes, depression, hypertension, hyperlipidemia, or stroke risk reduction. Medications listed in the studies were mapped to the GoodRx drug database based on drug class and form. We performed a search of PubMed and Google Scholar, with search terms developed using the PICO framework. At least 35 search phrases were used in this study and at least 100 results per condition were viewed. Articles with keywords that met the PICO framework for this study were reviewed for inclusion by three reviewers. Where applicable, we extracted effect measures that adjusted for covariates (for example, adjusted odds ratios). The final set of studies included in this analysis are listed in the table below and references.

To create a standardized response variable, effect measures were converted to percent change in outcome due to treatment. The main explanatory variable was the change in adherence based on the study; when adherence was defined by a threshold (for example, above and below 80% adherence), the midpoint adherence for each threshold was used to calculate the change in adherence. Using this standardized dataset, we performed a linear meta-regression for each outcome and condition based on a final sample of 15 studies, weighed by study sample size. The coefficient estimates from the meta-regressions represent the expected percent change in outcome for a 100% change in adherence (measured by proportion of days covered).

We scaled these estimated effect sizes down according to GoodRx users' observed average medication adherence for the relevant medications and the share of fills estimated to be newly adherent due to GoodRx in each 1-year period. To calculate the level change in health outcomes, we applied the expected percent change in outcome due to GoodRx to a baseline outcome rate extracted from the included studies, and applied this change in baseline outcome rate to the number of unique GoodRx users who filled one of the medications of interest in a given year. The total change in health outcomes was calculated as the sum of the change in health outcomes for each year from 2012 through 2023.

GoodRx effect on avoidable healthcare costs: Average hospitalization and ER visit costs were estimated using healthcare expenditure data from the Medical Expenditure Panel Survey, 2012-2021. Nationally representative sampling weights were applied to all estimates. Relevant hospitalization and ER visits were identified using the following 3-digit ICD-10 codes:

• Major adverse cardiovascular events: I21, I48, I50, I63, G45

• Diabetes: E11

• COPD exacerbations: J43, J44, J45

• Depression/anxiety: F32, F41

• Hypertension: I10

We note that out-of-pocket expense estimates were based on observational data and thus reflect the insurance composition of the individuals observed in the dataset. Actual out-of-pocket expenses for avoided hospitalizations and ER visits may differ based on the individual's insurance status.

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