Community pharmacy is at an interesting crossroads with respect to its core business model.
Community pharmacy is at an interesting crossroads with respect to its core business model. Ultimately, to remain viable, pharmacy will need to augment its current product-based gross margin with a new service-based value margin that focuses on patient-centered care. Many community pharmacy leaders and early adopters of patient-centered care are incorporating an appointment-based model (ABM) into their pharmacy workflow to efficiently begin this sea change in their core business practice.
Once the ABM is adopted, programs and services, such as medication synchronization, will become manageable and scalable to the dispensing operation. In addition to medication synchronization, many other pharmacy care opportunities can be folded into the ABM, including immunization services, medication therapy management (MTM), medication reconciliation and transitional care, and Medicare Star Rating and Healthcare Effectiveness Data and Information Set measure—focused activities.
Given the vast pool of untapped patient care opportunities, it is paramount that pharmacy resources be invested wisely using evidence-based approaches. Limited pharmacy resources, coupled with the immaturity of current payment models, necessitate that business decisions be driven by data so that new pharmacy programs yield the greatest benefit for the patient population, the community pharmacy, and payers. Analytics plays an integral role in this process; through patient-segmentation analyses, predictive modeling, and outcomes measurement, a pharmacy can monitor and improve the efficiency and cost effectiveness of the delivery of its patient care programs.
In this article, we provide an overview of the ABM and discuss nonadherence in specialized populations. In part 2, we will review a specialized patient population identified through data analytics and detail a use case that demonstrates the benefit of ABM service delivery to this group.
Traditionally, the pharmacy operations model is very reactive: pharmacy staff members accept new prescriptions or refill requests, contact prescribers if necessary, and fill prescription orders. Patients may arrive unexpectedly at the pharmacy to pick up their medications, often several times a month if they are on multiple medications. As a result, several repetitive activities are required to address a single patient’s medication needs.
In order to offer a more sophisticated patient care process, the ABM improves patient adherence while simultaneously increasing dispensing efficiency. The ABM is fully described in "Improving Quality Care: The Appointment-Based Model.” Here, we will provide an overview of the goals and concepts of the ABM. The core of the most common pharmacy application of ABM is medication synchronization. With all of a patient’s long-term medications being refilled on the same day of the month, the pharmacy team realizes several efficiencies in the dispensing process. For instance, multiple prescription renewal requests to prescribers can be performed simultaneously. As a result, a patient may make just 1 trip to the pharmacy each month, thereby reducing the transactional burden on the pharmacy and providing better convenience to that patient, thereby improving his or her satisfaction. Perhaps the single greatest benefit of synchronizing medications within the ABM model, however, is that the ongoing pharmacy and patient dialogue now centers on the complete medication regimen rather than on individual medications, each at different times. This holistic view dramatically shifts the discussion from the use of a specific medication to the overall impact of the medication regimen on the patient’s health.
Continuing the comprehensive discussions theme, the next component of the ABM is the pre-appointment call. Prior to the patient’s monthly appointment, the pharmacy staff contacts the patient to confirm the medications that need to be refilled. This communication affords opportunity for the pharmacy to learn of discontinued medications, recent prescriber visits, or any hospitalizations. Relevant changes to a patient’s medication list can be reviewed and assessed by pharmacy staff, and adherence issues can be easily identified.
The final piece of the ABM is the scheduled appointment day for patients to pick up their prescriptions. Because the pharmacy is aware of when the patient will arrive, the dispensing activities can be performed at any point prior to the patient’s arrival. This allows previously unpredictable workloads to become predictable and manageable. The operational efficiencies gained also allow the pharmacy to provide comprehensive medication reviews, vaccinations, or other MTM services that may benefit patients on their scheduled appointment day. Lastly, if a patient is not ready for their refills, the pharmacy has a perfect opportunity to identify and discuss nonadherence issues with the patient and agree on a plan to resolve them.
Medication adherence or, more appropriately, medication nonadherence, is a dominant underlying theme common to the majority of pharmacy care opportunities that are exposed by the ABM. Studies show that only 1 in 6 patients is able to adhere to prescribed dosage intervals and administration times and almost never miss a prescribed dose. Another 1 in 3 patients adheres satisfactorily, but occasionally omits 1 or more doses or takes an extra dose.1
There are several specialized populations in which medication nonadherence may arise due to unique factors specific to that patient group, including the following:
These are just some of the situations in which the efficiencies of the ABM allow a pharmacy to uncover problems related to a patient’s medication therapy and intervene appropriately. In part 2, we will take a closer look at patients who are experiencing their initial exposure to long-term therapy.
In summary, the ABM is able to enhance the interactions between pharmacies and their patients. The unique considerations that apply to certain specialized patient populations further enhance ABM benefits among these groups.
This article is published in collaboration with the Directions in Pharmacy CE Conference program.
Fei Yu, MS, is an advanced analyst and patient data modeler at Ateb. She is responsible for leading a team of statistical analysis system developers and business analysts in support of advanced study analyses and statistical design, modeling, and oversight. Mike Roberts, RPh, MBA, is director of analytics and pharmacy programs at Ateb. He is responsible for Ateb’s advanced analytics, business intelligence, and reporting strategies and methodologies. Eric Shen is a fourth-year PharmD candidate at the University of North Carolina Eshelman School of Pharmacy, completing an Advanced Pharmacy Practice Experience at Ateb. Rebecca W. Chater, RPh, MPH, FAPhA, is a career-long pioneer in innovative community-based clinical pharmacy practice and an executive health care strategist for Ateb.