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J Am Pharm Assoc (2003). Author manuscript; available in PMC 2017 July 01. Published in final edited form as: J Am Pharm Assoc (2003). 2016 ; 56(4): 427–432. doi:10.1016/j.japh.2016.03.010.
The Prescription Pick-up Lag, an Automatic Prescription Refill Program, and Community Pharmacy Operations Corey A Lester, PharmD, MS and PhD Student, Social and Administrative Sciences Division, School of Pharmacy, University of Wisconsin-Madison
Author Manuscript
Michelle A Chui, PharmD, PhD Associate Professor, Social and Administrative Sciences Division, School of Pharmacy, University of Wisconsin-Madison
Background
Author Manuscript
The current reimbursement model under which pharmacists are only paid for a dispensed drug product encourages a focus on prescription volume and dispensing speed. As a result, in an effort to increase patient satisfaction and loyalty, a number of community pharmacies have offered prescription time guarantees.(1,2) However, a survey conducted by the Institute for Safe Medication Practices (ISMP) found that 49% of pharmacists felt that time guarantees were a significant factor contributing to medication errors in community pharmacies.(3) ISMP concluded that the unrushed pharmacist and unhurried patient will contribute to fewer medication errors. One way to accomplish these goals would be to decrease the frequency of patients that request ‘urgent’ prescription fills in the pharmacy.
Author Manuscript
Automatic prescription refill programs may help accomplish these goals. Automatic prescription refills have become commonplace in community pharmacy over the last several years.(4,5) These programs initiate prescription refills on a standardized, recurrent basis up to one week prior to a patient running out of medication. This removes the need for patients to drop medication refills off at the pharmacy or telephone prescriptions in as is required for manually refilled prescriptions. Depending on the pharmacy’s algorithm, prescriptions in the automatic prescription refill programs are queued in the dispensing software and filled up to 7 days in advance of the prior prescription running out. This means that an automatic prescription refill for a 30 day supply would be generated 23 days after that medication was last picked up. These programs are viewed anecdotally as a method for improving patient medication adherence and subsequently Center for Medicare and Medicaid (CMS) Star
Correspondence: Corey A Lester, 777 Highland Avenue, Madison, WI 53705, Fax: (608) 262-5262, [email protected]. Disclosure: Dr. Lester is employed as a part-time pharmacist in the participating pharmacy chain. The authors report no other relevant conflict of interest. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Lester and Chui
Page 2
Author Manuscript
Ratings Program.(6) However, a potentially important side effect is the changes to pharmacist work as a result of fewer ‘urgent’ prescriptions.
Author Manuscript
In an automatic refill program, prescriptions are likely initiated sooner for processing compared to manual refill prescriptions. However, an automatic prescription refill program only addresses initiation of the refilling and does not directly address the patient picking up the prescription. As a result, it is reasonable to hypothesize that the amount of time in the pharmacy would be longer with automatic prescription refills compared to manual prescription refills. This time period has been coined by the authors as the “prescription pickup lag.” After the prescription refill is initiated by either the automatic refill program or the patient, the prescription pickup lag includes the time in between when the refill is adjudicated and when the patient picks up of a prescription. During the prescription pickup lag, pharmacy staff processes the prescription including the counting and verification of the prescription. Figure 1 provides a graphical representation of the hypothesis. The purpose of this study was to measure the differences in the prescription pickup lag time for automatic prescription refill programs compared to manual refill prescriptions.
Methods A post-only quasi-experimental design was used for this analysis. This type of design is appropriate since patients were not randomized to enroll in the automatic prescription refill program at the pharmacy chain. Patients were separated into automatic and manual prescription refill cohorts and data was collected for the 2014 calendar year. This study was approved by the authors institution’s IRB.
Author Manuscript
Data Source Prescription claims data were obtained through a 29-store independently owned pharmacy chain in the Midwest. The majority of these pharmacies are located in small and medium sized towns. Variables that were included in the data file were patient age, gender, National Drug Code, prescription adjudication date, prescription pick up date, days’ supply, drug name, directions for use, quantity supplied, and automatic refill status. Patient Population
Author Manuscript
Inclusion criteria for this study were patients over the age of 65 taking at least one of the following medications: HMG-CoA reductase inhibitors (i.e., statins), angiotensin-converting enzyme inhibitors (ACE-I), angiotensin-II receptor antagonists (ARB), sulfonylureas, biguanides, dipeptidyl peptidase IV (DPP-4) inhibitors, thiazolidinediones, and subtype II sodium-glucose transport protein (SGLT2) inhibitors. These medication classes were selected based on their use in the CMS Five Star Rating Program. This was important for a separate analysis conducted by the authors. In the current analysis, the medication classes were analyzed separately to show consistency of the prescription pickup lag across the three groups. Patients taking one of these medications had to have at least 2 prescription fills of that medication during the 2014 calendar year. The first fill had to occur at least 91 days before the end of the calendar year. This ensures that patients receiving 90-day supplies of medication were able to obtain a second fill during the observation period. Patients that had J Am Pharm Assoc (2003). Author manuscript; available in PMC 2017 July 01.
Lester and Chui
Page 3
Author Manuscript
the same medication filled in the automatic refill program and the manual refill program were not included in the analysis. This was done to isolate the effect of being in only one of the two programs. Data Analysis A comparison between the automatic and manual prescription refills for the prescription pick up lag was calculated. The prescription pickup lag was determined by subtracting the prescription adjudication date from the prescription pickup date for each prescription refill during the observation period:
Author Manuscript
This calculation provided the number of days a prescription was in the pharmacy after being adjudicated by the pharmacy staff and before being picked up by the patient. Since the distribution of days was not normally distributed, the nonparametric Mann Whitney U test was used to determine if there were significant differences in the number of days an automatic prescription refill spent in the pharmacy queue compared to manual prescription refills. Non-parametric effect size calculations, using Cliff’s Delta and Vargha and Delaney A measure, were performed to determine the magnitude of those differences. Nonparametric tests are considered more robust compared to parametric tests since they do not make assumptions about the distribution of the data and are not violated when the data lack normality.(7)
Author Manuscript
Since this is a quasi-experimental design and sample groups were not randomized or matched, a Mann Whitney U or Chi-Square test was performed to see if significant differences existed between each comparator group. For any significant differences found from the Mann Whitney U test, an effect size calculation was performed using Cliff’s delta and Vargha and Delaney’s A. Romano et al suggests that thresholds of negligible (
J Am Pharm Assoc (2003). Author manuscript; available in PMC 2017 July 01. Published in final edited form as: J Am Pharm Assoc (2003). 2016 ; 56(4): 427–432. doi:10.1016/j.japh.2016.03.010.
The Prescription Pick-up Lag, an Automatic Prescription Refill Program, and Community Pharmacy Operations Corey A Lester, PharmD, MS and PhD Student, Social and Administrative Sciences Division, School of Pharmacy, University of Wisconsin-Madison
Author Manuscript
Michelle A Chui, PharmD, PhD Associate Professor, Social and Administrative Sciences Division, School of Pharmacy, University of Wisconsin-Madison
Background
Author Manuscript
The current reimbursement model under which pharmacists are only paid for a dispensed drug product encourages a focus on prescription volume and dispensing speed. As a result, in an effort to increase patient satisfaction and loyalty, a number of community pharmacies have offered prescription time guarantees.(1,2) However, a survey conducted by the Institute for Safe Medication Practices (ISMP) found that 49% of pharmacists felt that time guarantees were a significant factor contributing to medication errors in community pharmacies.(3) ISMP concluded that the unrushed pharmacist and unhurried patient will contribute to fewer medication errors. One way to accomplish these goals would be to decrease the frequency of patients that request ‘urgent’ prescription fills in the pharmacy.
Author Manuscript
Automatic prescription refill programs may help accomplish these goals. Automatic prescription refills have become commonplace in community pharmacy over the last several years.(4,5) These programs initiate prescription refills on a standardized, recurrent basis up to one week prior to a patient running out of medication. This removes the need for patients to drop medication refills off at the pharmacy or telephone prescriptions in as is required for manually refilled prescriptions. Depending on the pharmacy’s algorithm, prescriptions in the automatic prescription refill programs are queued in the dispensing software and filled up to 7 days in advance of the prior prescription running out. This means that an automatic prescription refill for a 30 day supply would be generated 23 days after that medication was last picked up. These programs are viewed anecdotally as a method for improving patient medication adherence and subsequently Center for Medicare and Medicaid (CMS) Star
Correspondence: Corey A Lester, 777 Highland Avenue, Madison, WI 53705, Fax: (608) 262-5262, [email protected]. Disclosure: Dr. Lester is employed as a part-time pharmacist in the participating pharmacy chain. The authors report no other relevant conflict of interest. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Lester and Chui
Page 2
Author Manuscript
Ratings Program.(6) However, a potentially important side effect is the changes to pharmacist work as a result of fewer ‘urgent’ prescriptions.
Author Manuscript
In an automatic refill program, prescriptions are likely initiated sooner for processing compared to manual refill prescriptions. However, an automatic prescription refill program only addresses initiation of the refilling and does not directly address the patient picking up the prescription. As a result, it is reasonable to hypothesize that the amount of time in the pharmacy would be longer with automatic prescription refills compared to manual prescription refills. This time period has been coined by the authors as the “prescription pickup lag.” After the prescription refill is initiated by either the automatic refill program or the patient, the prescription pickup lag includes the time in between when the refill is adjudicated and when the patient picks up of a prescription. During the prescription pickup lag, pharmacy staff processes the prescription including the counting and verification of the prescription. Figure 1 provides a graphical representation of the hypothesis. The purpose of this study was to measure the differences in the prescription pickup lag time for automatic prescription refill programs compared to manual refill prescriptions.
Methods A post-only quasi-experimental design was used for this analysis. This type of design is appropriate since patients were not randomized to enroll in the automatic prescription refill program at the pharmacy chain. Patients were separated into automatic and manual prescription refill cohorts and data was collected for the 2014 calendar year. This study was approved by the authors institution’s IRB.
Author Manuscript
Data Source Prescription claims data were obtained through a 29-store independently owned pharmacy chain in the Midwest. The majority of these pharmacies are located in small and medium sized towns. Variables that were included in the data file were patient age, gender, National Drug Code, prescription adjudication date, prescription pick up date, days’ supply, drug name, directions for use, quantity supplied, and automatic refill status. Patient Population
Author Manuscript
Inclusion criteria for this study were patients over the age of 65 taking at least one of the following medications: HMG-CoA reductase inhibitors (i.e., statins), angiotensin-converting enzyme inhibitors (ACE-I), angiotensin-II receptor antagonists (ARB), sulfonylureas, biguanides, dipeptidyl peptidase IV (DPP-4) inhibitors, thiazolidinediones, and subtype II sodium-glucose transport protein (SGLT2) inhibitors. These medication classes were selected based on their use in the CMS Five Star Rating Program. This was important for a separate analysis conducted by the authors. In the current analysis, the medication classes were analyzed separately to show consistency of the prescription pickup lag across the three groups. Patients taking one of these medications had to have at least 2 prescription fills of that medication during the 2014 calendar year. The first fill had to occur at least 91 days before the end of the calendar year. This ensures that patients receiving 90-day supplies of medication were able to obtain a second fill during the observation period. Patients that had J Am Pharm Assoc (2003). Author manuscript; available in PMC 2017 July 01.
Lester and Chui
Page 3
Author Manuscript
the same medication filled in the automatic refill program and the manual refill program were not included in the analysis. This was done to isolate the effect of being in only one of the two programs. Data Analysis A comparison between the automatic and manual prescription refills for the prescription pick up lag was calculated. The prescription pickup lag was determined by subtracting the prescription adjudication date from the prescription pickup date for each prescription refill during the observation period:
Author Manuscript
This calculation provided the number of days a prescription was in the pharmacy after being adjudicated by the pharmacy staff and before being picked up by the patient. Since the distribution of days was not normally distributed, the nonparametric Mann Whitney U test was used to determine if there were significant differences in the number of days an automatic prescription refill spent in the pharmacy queue compared to manual prescription refills. Non-parametric effect size calculations, using Cliff’s Delta and Vargha and Delaney A measure, were performed to determine the magnitude of those differences. Nonparametric tests are considered more robust compared to parametric tests since they do not make assumptions about the distribution of the data and are not violated when the data lack normality.(7)
Author Manuscript
Since this is a quasi-experimental design and sample groups were not randomized or matched, a Mann Whitney U or Chi-Square test was performed to see if significant differences existed between each comparator group. For any significant differences found from the Mann Whitney U test, an effect size calculation was performed using Cliff’s delta and Vargha and Delaney’s A. Romano et al suggests that thresholds of negligible (
