8 Ways to Test Your Assumptions About Hospital Drug Purchasing Data
How hospitals view drug spend data drives many of their purchasing decisions. Yet all too often, assumptions around pricing and spend can become skewed, from how you source your data to how you approach calculations to the amount of context surrounding the data’s use. Add in the often-manual nature of pricing calculations and the speed and complexities involved in decision-making, and it’s natural for some purchasing insights to be more accurate and reliable than others.
The next time you’re examining drug purchasing options, ask yourself whether your organization’s assumptions are vulnerable to any of the following factors.
8 Areas That Can Affect the Quality of Your Drug Purchasing Decisions
Age of Your Data
Relying on historical data is never as useful as a current view of the purchasing landscape. Price shifts, drug shortages, and the emergence of generic alternatives are just some data points where access to real-time information can significantly change a hospital pharmacy’s perspective of cost. An analytics tool that provides real-time views of pricing, availability, discounts, and utilization data will provide greater accuracy around your purchasing.
That said, historical data is needed to view and understand trends. Coupling historical with real-time data allows for more thorough decision-making and avoids making commitments or choices based on snap-shots.
Objective, unfiltered data that avoids bias should be the aim when making purchasing decisions. Vendors such as QuicksortRx that do not endorse the use of a particular data source on their platform and are designed to support multi-data sources can aid decision-making based on unbiased, complete data.
Actual Versus Estimated Data
Relying on estimates to base calculations rather than actual data can also skew assumptions. Given the large volumes of data pharmacy teams must constantly stay on top of –– price changes, new products, and vendor offers for switching to an alternate in the same therapeutic class –– it’s not surprising that some teams will rely on estimated averages instead of actual data when making purchasing decisions.
However, this approach to data can result in failure to adequately represent drug utilization and purchasing. There are several examples of medications that are considered low-use but are absolutely required to be maintained in stock. As a result, estimating the use and purchase data, if viewed only on a quarterly basis, could provide an expectation of high use or inventory turns when, in fact, the medication is only used heavily once a year or replaced annually. Such a use of estimated data could then result in establishing pars that are too high, leading to a bloated inventory with a high likelihood of expiry.
The opposite is also true. If there has been little use or purchases in a fixed period, one could infer there is no need to maintain inventory, which could adversely affect patient care.
Limited Sampling Versus a Complete Data Set
Narrowing data focus can also lead to mistaken assumptions. For example, many organizations that lack sufficient pharmacy purchasing analytics may limit where they will search for drug spend savings opportunities given the size and scope of frequently changing data. As a result, the pharmacy team may look only at high-cost and/or high-use drugs with frequency while completely missing better opportunities across the entire drug catalog.
Degree of Context
Also important is visibility into the many factors that can influence an assumption. For example, a typical hospital will have a 340B, GPO, and WAC price for each class of trade.
If you look at only one aspect, such as a low WAC price, while ignoring contract implications elsewhere when making a purchase decision, you’ll potentially end up paying more than if you made a decision where all factors are considered.
Another note to consider is drill-down capability. When assumptions are based on aggregated data — for example, collective spend for a drug category rather than individual drugs — you may end up with inaccurate assumptions. Always keep an eye out for false conclusions based on data compilations instead of using actual data.
Inconsistent Processes for Managing Data
The more manual processes are, the greater the likelihood of mistakes being introduced through handoffs. All too often only one or two pharmacy staff manage pricing comparison calculations and purchasing insights. If a staff member leaves or needs to delegate some of these tasks, it increases the likelihood that the next individual performing calculations might not manage the data in an identical way.
Automation can be invaluable for performing proper calculations and eliminating assumptions. With automation, hospitals can calculate drug price consistently and correctly while better ensuring that data is always pulled in the same manner.
A consistent and automated approach to data management is a superior method but not without its own risks. Ensuring the flow of data is an integral part of data management. Whether this responsibility lies with the organization or a third party, there must be a mechanism in place to ensure the data is uninterrupted and valid. This is especially true in organizations experiencing growth.
Lack of Data Handling Oversight
Hospital pharmacy staffing resources are severely constrained, causing health systems to become more reliant on outsourced assistance. However, teams should not fully abdicate handover oversight of purchasing decisions. It’s important to work with vendors to understand how they will be using data and any assumptions they will be making while also curating an informed team to maintain and monitor tools. Routinely examining how data is being managed and creating systems for quality-checking a vendor’s data assumptions is important for accountability and determining the true impact on spend and performance.
Creating Systems to Minimize Data Bias and Errors
Introducing technology is vital to improve the accuracy of drug spend assumptions. Are you able to access pricing and contract analysis in real time? Do your opportunity alerts consider your entire drug catalog, contractual or regulatory compliance, and impacts to workflows?
Creating policies around data use also can be beneficial. For example, how are opportunities recognized? Is everyone across the team recognizing them in the same way? How are you alerted to changes and are there vulnerabilities in those processes?
Lastly, consider implementing systems for checking quality. How does your organization identify purchase or price discrepancies? How is this information fed back to avoid inaccuracies in decision-making in the future?
By accessing real-time pricing and contract information and examining opportunities across the organization’s entire drug catalog, pharmacy teams can remove errors and misconceptions associated with using old or limited data and make better purchasing decisions.
View Complete and Unbiased Drug Spend Analytics in Action
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