OBJECTIVES: To determine the effect of an automatic prescription refill program on the prescription pickup lag in community pharmacy. DESIGN: A post-only quasi-experimental design comparing automatic and manual refill prescription cohorts for each of the 3 Centers for Medicare and Medicaid medication adherence metrics. SETTING: A 29-store community pharmacy chain in the Midwest. PARTICIPANTS: Community-dwelling patients over the age of 65 years receiving prescription medications included in the statin, renin-angiotensin-aldosterone system antagonist, or non-insulin diabetes adherence metrics. INTERVENTION: An automatic prescription refill program that initiated prescription refills on a standardized, recurrent basis, eliminating the need for patients to phone in or drop off prescription refills. MAIN OUTCOME MEASURES: The prescription pickup lag, defined as the number of days between a prescription being adjudicated in the pharmacy and the prescription being picked up by the patient. RESULTS: A total of 37,207 prescription fills were examined. There were 20.5%, 22.4%, and 23.3% of patients enrolled in the automatic prescription refill program for the statin, renin-angiotensin-aldosterone system antagonist, and diabetes adherence metrics, respectively. Prescriptions in the automatic prescription refill cohorts experienced a median pickup lag of 7 days compared with 1 day for the manual refill prescriptions. 35.2% of all manual refill prescriptions had a pickup lag of 0 days compared with 13% for automatic refills. However, 15.4% of automatic prescription refills had a pickup lag of greater than 14 days, compared with 4.8% of manual refills. CONCLUSION: Prescriptions in the automatic prescription refill programs were associated with a significantly longer amount of time in the pharmacy before being picked up by the patient. This increased pickup lag may contribute positively by smoothing out workload demands of pharmacy staff, but may contribute negatively owing to an increased amount of rework and greater inventory requirements.
OBJECTIVES: To determine the effect of an automatic prescription refill program on the prescription pickup lag in community pharmacy. DESIGN: A post-only quasi-experimental design comparing automatic and manual refill prescription cohorts for each of the 3 Centers for Medicare and Medicaid medication adherence metrics. SETTING: A 29-store community pharmacy chain in the Midwest. PARTICIPANTS: Community-dwelling patients over the age of 65 years receiving prescription medications included in the statin, renin-angiotensin-aldosterone system antagonist, or non-insulin diabetes adherence metrics. INTERVENTION: An automatic prescription refill program that initiated prescription refills on a standardized, recurrent basis, eliminating the need for patients to phone in or drop off prescription refills. MAIN OUTCOME MEASURES: The prescription pickup lag, defined as the number of days between a prescription being adjudicated in the pharmacy and the prescription being picked up by the patient. RESULTS: A total of 37,207 prescription fills were examined. There were 20.5%, 22.4%, and 23.3% of patients enrolled in the automatic prescription refill program for the statin, renin-angiotensin-aldosterone system antagonist, and diabetes adherence metrics, respectively. Prescriptions in the automatic prescription refill cohorts experienced a median pickup lag of 7 days compared with 1 day for the manual refill prescriptions. 35.2% of all manual refill prescriptions had a pickup lag of 0 days compared with 13% for automatic refills. However, 15.4% of automatic prescription refills had a pickup lag of greater than 14 days, compared with 4.8% of manual refills. CONCLUSION: Prescriptions in the automatic prescription refill programs were associated with a significantly longer amount of time in the pharmacy before being picked up by the patient. This increased pickup lag may contribute positively by smoothing out workload demands of pharmacy staff, but may contribute negatively owing to an increased amount of rework and greater inventory requirements.
Authors: Daniel C Malone; Jacob Abarca; Grant H Skrepnek; John E Murphy; Edward P Armstrong; Amy J Grizzle; Rick A Rehfeld; Raymond L Woosley Journal: Med Care Date: 2007-05 Impact factor: 2.983