The requests received for data became more complex, requiring some to move away from just patient use data and incorporate information from multiple sources.
This article was originally published July 20, 2021 on PSQH by Matt Phillion
Hospital pharmacies have their work cut out for them. They need to perform mission-critical responsibilities that enable both quality patient care and reliable operational continuity.
There’s value to be found in the data related to hospital pharmacies, as well, but unlocking actionable analytics and insights from metrics reporting requires the right viewpoints and skill sets. Global medical technology company BD recently hosted a panel discussion inviting key pharmacy leaders and experts to talk about how analytics have impacted high-performing hospital pharmacy programs.
“The financial performance of a hospital pharmacy department is a huge contributor to the overall bottom line of an organization,” said David Kvancz, MS, RPh, FASHP, senior vice president of Visante and the panel’s moderator. “In some cases it’s the equivalent of a mid- to large-sized company. It’s critical that we have the necessary data and analytics to move forward from a one-dimensional area” into something more expansive.
Analytics: Now versus then
Chad Hatfield, PharmD, MHA, BCPS, chief pharmacy officer with University of California Davis Medical Center, said that even as recently as three years ago, “it was stone and chisel with how we did analytics” compared to now.
“We couldn’t get to our data, and what data we could get to we didn’t really trust,” he said. “We went from caveman days to rocket ships trying to put someone on Mars—we now have a total of four FTEs, a pharmacist, and three analysts.” IT was the backbone of this development, Hatfield notes, using Tableau and other tools to build dashboards and data sets.
Jeffrey Chalmers, PharmD, system director of pharmacy informatics and technology with the Cleveland Clinic, described a similar experience. “Our journey started in earnest when we implemented a more modern EMR platform,” said Chalmers.
The requests they received for data became more complex, requiring them to move away from just patient use data and incorporate information from multiple sources. “We needed the ability to compare hospitals with regards to drug costs and tie clinical outcomes to drug usage, and to tie pharmacists’ work to clinical outcomes,” said Chalmers. “We needed to get data from multiple sources and be able to manipulate data in multiple ways—that’s when we realized we needed things like a data warehouse.”
Gilbert Romero, BS Pharm, RPh, Six Sigma MBB, senior consultant with Visante, noted that as part of his experience with a community teaching hospital, resources were scarce. “We started not to measure for the sake of measurement, but measuring to improve,” he said.
“Pharmacy has always been data-hungry and data-driven, but we’ve been flying blind for the past 10 to 20 years,” noted Joe Lassiter, MS, PharmD, president of The Robertson Group.
So what was the tipping point to change the approach to data collection and analysis?
“I wouldn’t say that there was one day when we said we should hire 10 people to get us data,” said Chalmers. “We’ve always wanted to be making data-driven decisions. We had some success with individual projects and were able to get momentum for additional resources—leadership teams were able to say those resources were well-spent.”
“For us, it was more about change management,” said Hatfield. “We’ve always had a thirst for data, but we didn’t do a great job telling the story” until they began properly data mining and understanding the information collected.
Lassiter noted that there arose a need to pull a lot of information together in a short amount of time and “questions came up that weren’t easily answered, and that leads to resources to grow the program.”
Working with stakeholders
How did each organization win over its key stakeholders in justifying the need for the resources to move their pharmacy reporting programs to analytics programs?
“I’ve never had a hard time justifying resources for the pharmacy or informatics teams—it doesn’t take a huge impact for the cost to be more than offset by more efficient medication use,” said Chalmers.
Outside of the financial component, though, the pandemic enabled the analytics team to build an inventory dashboard, allowing them to monitor medications they were having trouble acquiring and then compare usage data, days on hand, and more—all in near-real time. “It was a huge win, but also something we can use to justify our team in the future,” said Chalmers.
For Hatfield, it began with an internal discussion within the department. “We had a lot of questions in terms of the data sets we wanted and had to pare that down. Once we had those identified, we spent time with IT to understand our needs,” he said.
From there, they worked with the CFO and the business side to build a case. “We had quick wins on both sides,” he said. “We got some projects off the ground, started showing positive returns, and every day we had requests coming to the one [staff member], which became too much for one person to handle, which led to more FTEs.”
“It may be unique that we heavily engaged our enterprise business [team],” said Chalmers. “They had the programmers and data scientists who did the kind of work we wanted to do. We met with those groups to understand the kinds of skills we’d need, and they helped us with the interviews to get the right people hired for the right positions.”
Romero echoed his fellow panelists about connecting with the right stakeholders. “Medication use is in every unit, in every area,” he said. “We started listening to the hospital. Pain management was an issue in our hospital, and a lot of groups had tried to improve it without a lot of success.”
They were able to get pain management out of the pharmacy, and “we went after a topic that was important to the hospital, measured it, and reported back. If you show some success, stakeholders are more likely to open their hand.”
How ROI factors in
When ROI enters the conversation, each team has different tactics for addressing it.
Inpatient and ambulatory are essentially opposites, said Chalmers. Inpatient is trying to control spend, while his team mostly works to support clinical teams where it’s “fairly easy to show savings or cost avoidance when following up on situations where meds aren’t being used in the right criteria,” he says. On the ambulatory side, they want volume, and they want to spend because that equals revenue, Chalmers said.
“We’ve spent a lot of time in the last year trying to increase our capture rates of our own prescriptions to our pharmacy,” explained Chalmers. “We currently only capture about 100% of prescriptions by Cleveland Clinic physicians. It’s about getting in front of different providers and explaining some of the services the retail pharmacy has.”
“We used a lot of cost containment, particularly with acute care, with data to provide cost savings,” said Hatfield. “A lot of it comes back to trust—it’s really about showing the data on the back end.”
The panel also discussed where in the organizational structure pharmacy analytics lives. Depending on the organization, this might be pharmacy itself, IT, or a data group.
“The most important aspect is not who you report to but the relationship with … the pharmacy—if IT never sets foot in the pharmacy, never sits in on meetings, you may be dissatisfied with the setup,” said Chalmers. “My pharmacy IT team sits in on meetings and very much has skin in the game. They work hard with the pharmacy to get problems solved.”
Kvancz put the question to the panel: Where do they see the need for operational pharmacy analytics in the future? “What is life beyond dashboards?” he asked. “How do you see them in one or two years?”
“I hear death by dashboard, but I don’t think we’re there yet,” responded Lassiter. “For me it’s a centralizing idea: What are the problems we’re trying to solve, how do we measure it, and how do we visualize it? That will help mitigate the risk of too many dashboards.”
Hatfield sees the industry moving toward more predictive analytics. “Can I look at what’s coming in the fall and predict it based on the past two or three years? How do we do that on a grander scale?” he said.
“It’s not going away,” said Chalmers. “We’ll see better optimization and efficiency.”
Dashboards will continue to help analytics focus where it needs to, observed Romero. “When you’re driving a car, there’s a million things going on, but the dashboard shows you what you need to know,” he says. “It makes sure you’ve got what’s important and leaves the rest somewhere else.”
Matt Phillion is a freelance writer covering healthcare, cybersecurity, and more. He can be reached at email@example.com.
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