By Valerie Dimond

Standardizing products and services, improving efficiencies, and reducing costs are common goals among healthcare organizations, yet getting there appears somewhat slow, with unnecessary hospital supply chain spending accounting for almost $26 billion a year, according to a 2019 Guidehouse analysis of more than 2,100 hospitals — a near 12% increase from 2017.

To reverse the trend, high-performing supply chains rely on advanced data analytics to generate the variety of robust, accurate data needed to succeed. Even the C-suite is invested, with 73% of those surveyed earlier this year by Sage Growth Partners naming data/analytics as their No. 1 health IT investment over the next one to two years.

Beyond the item master
Advanced data analytics enables supply chain to transform the item master—that critical composite of factors impacting cost, quality and outcomes—which used to be little more than a perplexing tangle of misinformation, instead of the essential purchasing source it was meant to be.

“Having an item master that has incorrect units of measure can wreak havoc on a team trying to run utilization reports or spend analysis,” said Ian O’Malley, director of strategic sourcing at University of Chicago Medicine (UChicago Medicine), whose department eventually implemented an advanced analytics tool from Vizient. “If we look back prior to this item master cleanse, it’s almost disingenuous to call what we were able to do true analytics. In most cases we were able to do just spend analysis by manufacturer and cost center. Without [analytics], a suture used in a case can come across as box costs and skew the data, making it untrustworthy. The issues continue downstream and also affect revenue cycle management; so this quickly became an organizational imperative to correct these issues. Once corrected, we could begin to build confidence in the analytics and reporting we desperately needed.”

Another step needed to gain greater visibility was to link the item master to UChicago Medicine’s EMR so that clinical staff could document everything they used during patient care — not just the usual implants and chargeable supplies. “We absolutely had areas that needed more attention than others due to the amount of SKUs being used, which required targeted contracting efforts, but also a reinforcement of existing policies around products being used that aren’t contracted for and loaded in our item master,” said O’Malley, noting that this gave supply chain leverage to take meaningful action.

“Our team also set goals on contract accuracy to ensure the price in our item master reflected what we were being charged, so the true costs were reliable in the data,” he added. “The value analysis structure in place was already encompassing every clinical area in reviewing new technology requests, but also looking at standardization opportunities and waste reduction efforts intraoperatively. This structure was key, and the right venue to begin discussing and reviewing the analytics platform and targeting projects based on the direction the data was giving us.”

Parsing physician preference items
While standardizing requires identifying who is using which device and when, the process can be tough to accomplish since data sets are often stored separately. Basic spend data may live in the ERP and supplies-by-procedure in the EMR, for example. Also common — and unduly expensive — is when physician preference cards auto populate usage. Merging this data is essential.

“In many cases this basic data of supply cost/case by physician is enough to establish projects and gain clinical support,” said O’Malley. Also, “Sometimes sourcing-value analysis hits the ‘outcome claim’ barrier in which a physician is saying their device or implant has better outcomes. This is where a third data table, outcome metrics, needs to be integrated.”

Being able to generate a variety of advanced analytics to share with clinicians has a way of putting everyone on the same page, something not easily achieved before. “We have seen interesting behavioral changes by just reporting out on cost/case over a period of time,” said O’Malley. “While we may have targeted the primary implant for the project, we actually see other costs improve as well — things like dressings, sutures and other single-use items seem to drop.”

With advanced analytics, UChicago Medicine was able to reduce costs with its primary spine implant supplier by 15%. Within six months, the surgeons optimized their preference cards or eliminated waste, which led to a net total cost/case reduction of nearly 22%, according to the most recent three-month average cost.

“We used an analytics platform that aggregated our cost/case by clinician and was able to take the total supply cost for that DRG and benchmark it across cohort organizations,” explained O’Malley. “This was a completely new data point for us and unveiled some interesting information about the opportunity. Typical line item benchmarking certainly revealed an opportunity on the implants, but this aggregate supply cost compared across the country really engaged the clinicians to look at their procedure as a whole and see where they could align with national averages. We entered negotiations with the implant suppliers as a united front, with confidence in the data we had and shared goals to drive our cost-per-case down.”