Pharmacist-Led Medication Optimization Shows Growing Cost Savings in Accountable Care Organizations
In this interview, Travis Morgan, CPA, MBA, discusses how a pharmacist-led medication optimization program targeting polypharmacy among Medicare accountable care organization beneficiaries was associated with increasing reductions in total medical costs over time and highlights the importance of comprehensive medication risk assessment, aligned incentives, and longer evaluation periods for measuring program value.
Travis Morgan, CPA, MBA: My name is Travis Morgan. I’m the President and Co-Founder of DecisionRx. I’m a certified public accountant (CPA) and have a Master of Business Administration (MBA). I’ve spent almost 30 years in the precision diagnostics and health technology space.
Could you share a brief overview of the study and some of its key findings?
Morgan: There are many studies that quantify the impact of using a pharmacist to guide prescribing and show that doing so saves money. However, previous studies have calculated savings at the pooled program level.
This study is really the first to drill down to the per-member-per-month (PMPM) level, which allows us to quantify not only how much was saved but also how long it takes for those savings to accrue and how they accumulate over time. That’s really what this study adds.
It’s also the first study, I think, specific to an accountable care organization (ACO) population. There have been studies in Medicare Advantage and employer-sponsored plans, but this study was drawn entirely from ACO beneficiaries, which makes it a little different.
The headline finding is that it absolutely saves significant money. You don’t see statistically significant savings until 3 to 6 months after the intervention. For obvious reasons, the laws of physics preclude instant gratification. It takes time for recommendations to be considered by prescribers and then implemented.
Often, the recommendations aren’t going to be implemented all at once. They may be phased in over some period of time. In our data, during the first 3 months, there was a directional savings, but it was not statistically significant. After about 6 months, it started to become significant. By 9 months after the intervention, it was very significant, with a more than 30% reduction in total cost of care.
I’m not aware of any other clinical intervention available to a health plan that can move the needle by 30%. It’s a big deal.
What factors or program interventions appeared to drive the significant reduction in total medical costs?
Morgan: I would say it comes down to 2 things: having the right data and having the right alignment of incentives.
In terms of the data, in our case, that means looking at claims and targeting the intervention toward patients with the highest polypharmacy risk. It also means looking at polypharmacy as the condition to be targeted, rather than focusing on individual disease silos, which is the more conventional way of thinking about care management.
We’re targeting high-risk patients with multiple chronic conditions who have very complex care needs. First, we’re looking at a comprehensive measure of medication failure risk across 9 different vectors of risk, including genetics, therapeutic duplication, disease contraindications, lifestyle factors, and age-related factors.
Having an actual clinical pharmacist interpret the information and make recommendations is also key. It’s very different from simply giving a prescriber a laboratory result or an electronic health record (EHR) flag that they then have to interpret and determine how to act on. That approach is identifying problems. This system uses a clinical pharmacist to recommend solutions rather than simply identify problems, and I think that makes all the difference from a data perspective.
It also means looking at the patient holistically. These patients often have multiple diseases and multiple prescriptions managed by multiple prescribers who may or may not be coordinating care effectively. The pharmacist is often the first person to have that 360-degree view of the patient and can evaluate drug-drug interactions and polypharmacy rather than focusing on a specific disease silo.
That’s the data side. On the alignment side, because all of these are ACO patients, this is arguably where there is the greatest alignment of economic and other incentives among the patient, payer, and provider. It really is a situation where doing the right thing clinically is also the optimal thing economically and financially.
I think that alignment probably contributes to the higher savings rate observed in this study compared with some other studies.
Why did savings increase with longer participation, and how could this factor into payer evaluations of these programs?
Morgan: A patient doesn’t immediately generate savings when you get them off the wrong medications. It takes time for patients to see their prescribers, for prescribers to assess the recommendations, and for them to decide what they’re going to implement and in what sequence.
I think the average in this study was a little more than 3 actionable recommendations per patient. You may not want to make all of those changes at once. You may want a patient to finish a 90-day supply of a medication before switching to a new medication.
It takes time to implement those recommendations. Then, after implementation, it takes time for high-cost utilization events not to occur. That’s why the savings accrue over time.
The obvious implication for any plan considering something like this is that you can’t expect to see significant savings within a 3- to 6-month time frame. You have to take a longer-term perspective, ideally over multiple years, because that’s really where the benefit accrues. This study included 2.5 years of data.
If you can’t take a time horizon beyond 1 year, it’s going to be difficult to accurately understand the impact of this type of intervention. It may be the right thing clinically, but you may not be able to fully measure the economic impact during the first year.
What operational, financial, and provider engagement considerations should Medicare plans and ACOs evaluate before implementing this type of program across a broader beneficiary population?
Morgan: First, you need to have a long-term perspective. Think of this as an investment in multiple years of improved outcomes and savings.
Second, think of polypharmacy as a condition that you’re treating rather than focusing on traditional disease silos. Polypharmacy spans all of these diseases, which is why it can have such a substantial impact.
To put it in perspective, we spend 7% or 8% of total health care dollars on heart disease and about the same amount on cancer every year. We spend about 16% on medications that don’t work.
The biggest message is not to focus on pharmacy spending at the expense of medication efficacy and safety, because that’s where the costs and savings opportunities really lie. The opportunity is improving efficacy and safety, not saving a few percentage points on the cost of the drugs.
We say that the most expensive drugs are the ones that don’t work. That’s absolutely true, and this study highlights the degree to which that’s true.
ACOs, for the most part, aren’t directly responsible for Medicare Part D spending, but they are absolutely exposed to the costs of medication failure even if they aren’t paying for the medications themselves. It is probably one of the largest avoidable costs for their populations, and most aren’t spending sufficient energy mitigating those costs.
Is there anything else you think is important for the audience to take away from this study?
Morgan: I think you asked the right questions. It’s surprising how many people really don’t understand managed care and value-based care. It gets talked about a lot more than it gets implemented.
