Building the right central-fill solution
Feedback from pharmacies that have incorporated high-volume automation into their operation and an ongoing collaboration with Binghamton University's Watson Institute for Systems Excellence has given Innovation an unparalleled ability to ensure that the systems they install run smoothly and efficiently, speakers at the company's inaugural change management symposium noted earlier this month.
“We've collected feedback from tons of customers on how implementation went for them and what Innovation can learn from their experiences,” Joe Galati, general manager of Innovation's Advanced Systems division, told symposium attendees, many of whom had come to explore the possibility of adding high-volume automation as part of their prescription fulfillment and patient-facing strategy.
Combining that feedback with the ongoing simulation and modeling work being done at the university, he said, has given Innovation keen insights into what is required to best design the right system, get it up and running, and keep it that way.
“During the implementation/setup phase, there's a lot of work to be done,” Galati said. “People tend to underestimate the amount of work and sometimes think they can do it in a day or two. It often takes weeks.”
According to Galati, depending on the complexity of the central fill solution being installed, the prescription volume being handled and how many stores it needs to service, an installation can take as long as three months to complete. Testing can take an additional two to six weeks, and training a pharmacy staff to use the system can require another week or two.
Pharmacy companies that implement a new system usually do so in one of three manners, he said. Many of those whose system will handle a relatively low volume of prescriptions opt for what Galati termed as the “big bang” approach, getting their systems up and running quickly. Others prefer what he called the “slow crawl,” opting to take a calculated and methodical approach over several weeks or months. Most often, Galati said, users choose to follow a hybrid approach, where the system is quickly used to service the most important stores and then is gradually rolled out to service clusters of stores in the ensuing weeks and months.
After a new high-volume pharmacy automation system is in place, customers are likely to run into issues — whether it be glitches in performance or pharmacy staff unsure of how to use the technology. Innovation's director of customer service operations Chris Kearse said that is where the company's field service team and high volume call center comes in.
Pinpointing the exact cause of an issue, he said, can sometimes be difficult if users are unable to clearly explain the problem at hand.
“It is almost inevitable that users' training will not all be retained,” Kearse said. “Pharmacies should try to make as many people as familiar as possible with the system so that they can communicate with our field service engineers or call center, and clarify any issues that occur.”
While that can help get a system back up and running quickly, Kearse noted that Innovation's work with WISE also has been immensely helpful in getting to the root of problems. By analyzing dispenser status, counting statistics and a system’s mechanical attributes for thousands of users, researchers have been able to develop a model that is about 80% accurate in identifying the root cause of a system problem. In addition, the modeling can provide probabilities for a range of causes that may be behind an issue.
“By studying the history of an issue, we can know which aspects are different according to the data,” Husam Dauod, an engineering graduate research assistant who led the university’s work with Innovation, explained. “This allows us to identify what led to the variation in the performance, and helps get at the root cause faster and more accurately.”
“We've collected feedback from tons of customers on how implementation went for them and what Innovation can learn from their experiences,” Joe Galati, general manager of Innovation's Advanced Systems division, told symposium attendees, many of whom had come to explore the possibility of adding high-volume automation as part of their prescription fulfillment and patient-facing strategy.
Combining that feedback with the ongoing simulation and modeling work being done at the university, he said, has given Innovation keen insights into what is required to best design the right system, get it up and running, and keep it that way.
“During the implementation/setup phase, there's a lot of work to be done,” Galati said. “People tend to underestimate the amount of work and sometimes think they can do it in a day or two. It often takes weeks.”
According to Galati, depending on the complexity of the central fill solution being installed, the prescription volume being handled and how many stores it needs to service, an installation can take as long as three months to complete. Testing can take an additional two to six weeks, and training a pharmacy staff to use the system can require another week or two.
Pharmacy companies that implement a new system usually do so in one of three manners, he said. Many of those whose system will handle a relatively low volume of prescriptions opt for what Galati termed as the “big bang” approach, getting their systems up and running quickly. Others prefer what he called the “slow crawl,” opting to take a calculated and methodical approach over several weeks or months. Most often, Galati said, users choose to follow a hybrid approach, where the system is quickly used to service the most important stores and then is gradually rolled out to service clusters of stores in the ensuing weeks and months.
After a new high-volume pharmacy automation system is in place, customers are likely to run into issues — whether it be glitches in performance or pharmacy staff unsure of how to use the technology. Innovation's director of customer service operations Chris Kearse said that is where the company's field service team and high volume call center comes in.
Pinpointing the exact cause of an issue, he said, can sometimes be difficult if users are unable to clearly explain the problem at hand.
“It is almost inevitable that users' training will not all be retained,” Kearse said. “Pharmacies should try to make as many people as familiar as possible with the system so that they can communicate with our field service engineers or call center, and clarify any issues that occur.”
While that can help get a system back up and running quickly, Kearse noted that Innovation's work with WISE also has been immensely helpful in getting to the root of problems. By analyzing dispenser status, counting statistics and a system’s mechanical attributes for thousands of users, researchers have been able to develop a model that is about 80% accurate in identifying the root cause of a system problem. In addition, the modeling can provide probabilities for a range of causes that may be behind an issue.
“By studying the history of an issue, we can know which aspects are different according to the data,” Husam Dauod, an engineering graduate research assistant who led the university’s work with Innovation, explained. “This allows us to identify what led to the variation in the performance, and helps get at the root cause faster and more accurately.”