Managing multiple medications is a challenge for many individuals, particularly the elderly. One study by Almodóvar et al. (2019) found that among Medicare beneficiaries eligible for medication therapy management (MTM), 51% had used 11 or more medications.
One approach to improving medication management is to use an appointment-based model (ABM) and other forms of medication synchronization.
What is medication synchronization? Luder et al. (2024) write:
One nonadherence intervention is to improve convenience by simplifying the steps to obtain and manage multiple medications. Medication synchronization coordinates refills so all medications can be picked up at the same time. A meta-analysis found that patients with synchronized medications had a 2.3 greater odds of adherence (95% CI = 1.99-2.64) compared with usual care. [Nsiah et al. 2021]4 Specifically, the appointment-based model (ABM) had the largest effect on adherence (odds ratio 3.1, 95% CI = 2.72-3.63) compared with telephone reminder calls, automated refills, and other non-ABM program types.
Even if ABM can improve medication adherence, another question is whether it saves money. Luder and co-authors found that it does.
During the follow-up period, the median PPPM [per patient per month] TCOC [total cost of care] for the ABM group was $656 and was $723 for the control group (P = 0.011). Median pharmacy costs were also significantly less in the ABM group ($161 vs $193, P < 0.001), whereas median medical costs were $328 in the ABM group and $358 among controls (P = 0.254). More patients in the ABM group were adherent during follow-up, with 84% achieving PDC of at least 80% vs 82% among controls (P = 0.009).
It is interesting that although adherence increased, pharmacy cost decreased. The authors hypothesize that this could have occured due to more frequently discontinuing unnecessary or redundant medications, reducing medication doses for supratherapeutic doses, or switching to less expensive therapeutic alternatives (e.g., generics, biosimilars). However, the authors do not have data to test empirically which specific pathways had the biggest impact on cost.
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