A point of care medication delivery system improves medication adherence to cardiovascular medications without increasing costs.
Ana Palacio, MD, MPH; Jessica Chen, MD; Leonardo Tamariz, MD, MPH; Sylvia D. Garay, MD; Hua Li, PhD; and Olveen Carrasquillo, MD, MPH
Objectives: To evaluate the impact of a physician-led point of care medication delivery system (POCMDS) on adherence to glucose, blood pressure, and cholesterol-lowering medications.
Study Design: Prepost intervention observational study.
Methods: From December 15, 2010, to December 14, 2012, we conducted a prepost analyses of 800 Medicare Advantage members receiving care in a network of capitated clinics in south Florida serving a predominantly minority population. Eligibility criteria included a diagnosis of diabetes for at least 1 year, taking at least 1 of the 3 classes of medications, and having received care in the clinic network for at least for 12 months before and after the POCMDS implementation. Our primary outcome is the medication possession ratio (proportion of days covered [PDC]) for each class of medication.
Results: We found an absolute increase of 17 percentage points (95% CI, 13-20) in the PDC for oral antidiabetic agents, 29 (95% CI, 26-32) for cholesterol medications, and 21 (95% CI, 19-23) for blood pressure medications. Most of the subjects (80%) reported POCMDS was more convenient than using retail pharmacies. By having the POCMDS prioritize generic medications, the increases in adherence were not associated with increased pharmacy costs. At baseline, over half the patients were at goal for low-density lipoprotein cholesterol, glycated hemoglobin, and systolic blood pressure; thus, we did not detect any additional improvements in these intermediate clinical outcomes.
Conclusions: Among elderly minority patients with diabetes, a POCMDS improved adherence to various medications classes without increasing pharmacy costs. Thus, POCMDS may be of interest to policy makers, particularly in our current era of healthcare reform.
Am J Manag Care. 2016;22(7):e264-e269
Medication adherence is an important barrier to the delivery of quality health services, particularly among minority populations and the elderly.1
Lack of medication adherence increases morbidity and mortality, resulting in up to $170 billion in additional health expenditures every year.2,3
Twenty percent to 50% of patients with a complex illness, such as heart failure or type 2 diabetes, have inadequate adherence to medications known to improve clinical outcomes.4-7
In spite of extensive documentation on the impact of adherence on outcomes and costs, we have not been able to identify sustainable solutions.8
Successful interventions have been multi-modal, as well as difficult to implement and to sustain in real-world clinical practice settings dependent on clinical revenue streams for most of their funding.9
The fact remains that medication adherence affects outcomes that will be used to measure physician performance, such as low-density lipoprotein cholesterol (LDL-C), glycated hemoglobin (A1C), or blood pressure.10,11
We examined the impact of allowing patients to receive medications directly from their healthcare provider in the context of a feasible financial model. The study intervention—an automated point of care medication delivery system (POCMDS)—was implemented as a pharmacy replacement service in a network of capitated clinics. We present a prepost evaluation of the impact of a POCMDS on medication adherence among a cohort of elderly patients with diabetes.
The intervention was implemented and evaluated in 5 capitated clinics located in underserved areas across south Florida. The clinics belong to Chen Neighborhood Medical Centers (CNMC) network. Approximately 75% of the CNMC patient population is black and 25% white; 20% of the population is of Hispanic descent. Patients are insured through Medicare Advantage plans. CNMC has a preventive model of care that mandates scheduling patients for primary care provider visits every 1 to 2 months.Intervention
From 2008 to 2009, CNMC deployed the POCMDS in 5 practices. The intervention was developed through a process of stakeholder engagement and consists of delivering medications at the time of clinical encounters to improve medication adherence. The system is an electronic controlled-access medication storage cabinet that automatically dispenses medications when ordered. During the encounter, providers place prescription orders through the electronic health record (EHR), which interfaces with the POCMDS. The system then releases a pre-sealed labeled medication bottle, and staff takes the medications to the ordering provider, who gives at least a 90-day supply of medications to the patient along with printed instructions. An outsourced company assists in completing regulatory issues, setting up the required hardware and software, establishing processes for adjudication of claims, and stocking and restocking inventory. The POCMDS formulary consists mostly of generic medications that require no co-payments. In the pre-POCMDS period, physicians could prescribe any brand or generic medication and co-payments would vary accordingly.Study Design and Study Population
In 2010, with support from the Robert Wood Johnson Foundation, investigators from the University of Miami conducted an independent evaluation of the POCMDS. As the system had already been implemented in all sites, we performed a prepost intervention observational study.
For our analysis, we focused on subjects with diabetes (International Classification of Diseases, Ninth Revision, Clinical Modification
code 250.xx). We included subjects who had a diagnosis of diabetes in the preintervention period who used medications for at least 1 of 3 indications (oral antidiabetic, blood pressure, or LDL-C–lowering medications) for 12 months before and after enrolling in the POCMDS. The purpose of this strategy was to compare adherence to medications for the same indication before and after the intervention. Primary Outcome
Our primary outcome was the proportion of days covered (PDC), a validated objective measure particularly useful for calculating adherence to multiple concurrent chronic medications.12-14
PDC is defined as the sum of the days each patient has availability to at least 1 medication for each class or indication during the pre- and post periods divided by the duration of each follow-up period. Data on medication supply were obtained using pharmacy claims files—which include National Drug Codes (NDCs)—as well as the date a medication was obtained, and number of pills dispensed.15
The NDCs were manually reviewed and each relevant medication was classified as an oral antidiabetic (OAD), blood pressure, or cholesterol-lowering agent. We will refer to these 3 categories as medication classes. For the few medications with multiple possibilities of daily dosing, such as metformin, we manually retrieved the dose and frequency from the EHR. We calculated the PDC for each medication class—hence, each subject could have up to 3 PDCs.
For each PDC, the index date was the first date in which a subject used the POCMDS to refill a medication in that class. For subjects who were taking more than 1 medication within the same class, we calculated the overall PDC across each class without accounting for terminal gaps in use.16
For example, if a subject used a beta-blocker for 7 months and a calcium channel blocker for 5 months with 2 months of overlap, the numerator included the days the subject was covered by either medication. Our PDC was interval-based and used 365 days as the denominator to avoid overestimation of adherence. In addition to PDC measured as a continuous outcome, we also reported PDC as a dichotomous variable with adequate adherence defined as a PDC of 0.80 or above.7,12,14,17
The strategy was nondifferential, as it was similarly used for the pre- and post periods.Secondary OutcomesIntermediate clinical outcomes.
Laboratory data on A1C and LDL-C were abstracted from the EHR. For the pre-POCMDS analyses, we used the laboratory values measured immediately before enrolling in the POCMDS for that particular class. For the post-POCMDS analyses, we used all the values within a year after the implementation to be able to conduct a repeated measures analysis. We used the EHR to obtain blood pressure data for all subjects who used blood pressure medications in the pre- and post periods. We collected all blood pressure values present in the EHR for an average of 10 blood pressure recordings per patient in each of the 2 study periods. We calculated the mean and median LDL-C, A1C, and blood pressure before and after using the POCMDS.Pharmacy costs.
We used CNMC claim data to collect the per-patient cost of prescription medications for each medication class. These values represent the payments made per prescription to retail pharmacies in the preintervention period or to the outsourced POCMDS company in the postintervention period (this cost includes a per-prescription dispensing fee). We added all the values in the cost field for each class. In our analysis, we report the mean difference in cost for the 3 classes of medications the year before and after enrolling in the POCMDS. Patient satisfaction.
As part of the evaluation, we also conducted a patient satisfaction survey in a sample of 270 patients in 4 of the implementation sites. In this report, we present data on the proportion of patients who agree or strongly agree that the implementation of the POCMDS had a positive impact on: 1) convenience, 2) patient–physician communication, 3) medication adherence, and 4) the quality of care they are receiving.Statistical Analyses
To test prepost intervention differences in PDC, A1C, LDL-C, and blood pressure, we used a paired t
test. To account for the within-subject correlation of our measures and the effect of time, we used linear mixed regression models, which also included categorical classification variables to index time intervals pre- and post intervention. The regression parameters from this model were examined to determine the statistical significance of our continuous outcomes pre- and post intervention at P
<.05. As this was a prepost comparison on the same subjects, we did not adjust for traditional covariates, such as gender or race. Models that adjusted for small changes in age and comorbidity during the time period had nearly identical results as our reported findings. Lastly, to examine PDC as a dichotomous variable, we used the McNemar test. Because of the distribution of the costs data, we used the Wilcoxon Signed Rank test to compare pharmacy costs in the pre- and post periods.
The fitness of the data was assessed using the deviance ratio. Analyses were performed using SAS version 9.0 (SAS Institute, Cary, North Carolina), and all significance tests were 2-tailed. The University of Miami Institutional Review Board approved the study.
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