Conversions sit at the heart of value analysis work. They are visible, measurable, and often accompanied by strong clinical and contracting rationale. When executed well, they reflect alignment between supply chain, clinicians, and leadership. And yet, many organizations share a familiar frustration: a conversion that looked solid on paper delivers underwhelming, or even negative, financial results.
This outcome is usually not the result of poor execution or bad intent. In many cases, the sourcing work is sound, the pricing is improved, and the clinical case is defensible. The disappointment comes later, when realized savings does not reflect projected savings, and the value analysis team is left explaining why “doing the right thing” did not produce the expected financial outcome.
In my experience, this is often driven by a common root cause: our limited ability to predict future spend patterns. That limitation is unavoidable as no one can forecast utilization, compliance, and clinical behavior with perfect accuracy. But there are ways to mitigate the risk. When we fail to do so, conversions become vulnerable to two recurring (though non-exhaustive) and often overlooked drivers of financial erosion.
Driver One: Modeling Conversions at 100% Compliance
The first driver is most visible when a conversion is recommended or supported by a third party, be it a consultant, GPO, or external advisor. The proposed savings model often assumes full conversion and full compliance, sometimes implicitly and sometimes explicitly. On the surface, this seems reasonable. After all, the conversion is being approved with the intent to standardize.
In practice, however, many categories (particularly PPI categories) rarely achieve 100% compliance. Contracts themselves often acknowledge this reality by including pricing effective with compliance thresholds below 100%, or rebates tied to these thresholds. The problem is not that the model is “wrong,” but that it is incomplete.
Whether validating a third-party benchmarking opportunity, or doing analytics in-house, this needs to be parsed out carefully. Assuming full compliance can be mitigated by looking at historical product mix, as well as scenario modeling informed by expected shifts in discussing with VA teams and physicians
When doing these scenario models, it is important to not only focus on the portion of spend that successfully converts. In a sole source agreement with an 80% compliance threshold, for instance, the remaining 20% of spend must be assumed to be at list price (or GPO access, whatever is best available). Without examining what the remaining 20% of spend will look like, it can fully erode savings. A common shortcut is to assume that non-compliant spend continues at the prior rate; an assumption which is rarely safe. The prior rate was often dependent on aggregate volume or contractual leverage that no longer exists once volume shifts away.
As a quick aside, this is also a great time to examine actual feasibility of contracts with decision makers. It is not enough to assume that 20% of units can be off the sole source agreement, since they will likely be much higher cost. If the off contract unit price is double the on contract unit price, assuming the contract language is based on spend, that means only 10% of units can be off contract since those 10% of units will equate to 20% of spend. Unit based contracting language can help in appropriate categories if desired as well. Often the messaging to decision makers and physicians is that we can purchase 20% off contract, but in their minds that may mean 1 in 5 cases can use the off contract vendor.
The result is a quiet but significant offset. Savings generated on the compliant portion of the conversion are partially or fully eroded by cost increases on the residual spend. In extreme cases, the erosion outweighs the savings entirely, turning a “successful” conversion into a net financial loss.
A more resilient approach is to model one or (preferably) multiple scenarios which consider:
- The target compliance level, informed by historical behavior or rigorous and thoughtful consideration by physicians and decision makers.
- The realistic post-conversion pricing for the remaining non-compliant volume.
In addition, proper modeling of a minimum acceptable compliance threshold can be determined for a breakeven. In other words, what is the worst possible compliance we could have on this conversion and still come out ahead assuming non-compliant spend is at list price? If that minimum acceptable compliance is quite high, it might be too ambitious of a commitment. Even a simple sensitivity analysis and stress testing can dramatically change how a conversion is perceived and how risk is communicated. The goal is not to discourage conversions, but to ensure that leadership understands where the financial exposure lives so focus and priority can be given to those with the most bang for their buck.
Driver Two: Baseline Distortion in Multi-Vendor Conversions
The second driver is subtler and, in many ways, more dangerous because it often masquerades as success. Multi-vendor product shifts, where volume is intentionally shifted between clinically equivalent previously purchased products from different manufacturers regardless of contracting status, require particularly careful baseline construction. Both products may be on contract before and after the conversion, which even further creates the illusion that pricing improvements alone determine value.
Consider a simplified example with only two contracted manufacturers in a category. Last year, your organization purchased:
- 1,000 units of Product A at contracted price of $100
- 50 units of Product B at contracted price of $200
Both products are clinically equivalent and on contract. New agreements introduce a $10 discount on Product A and a $50 discount on Product B. At the same time, the organization intentionally shifts volume toward Product B.
Post-conversion, purchases look like this:
- 500 units of Product B at $150
- 500 units of Product A at $90
On a unit basis, both prior products are cheaper than before. Each purchase is recorded as a “savings” relative to its prior price. Every time Product B is purchased, $50 of savings is reported. Every time Product A is purchased, $10 of savings is reported. Spend went up, units purchased stayed the same, all while savings were reported.
The baseline treated each product independently, without accounting for the intentional shift in mix. Savings were calculated relative to prior prices, not relative to prior utilization patterns. The act of moving volume toward the higher-cost item despite its improved price was never isolated as a cost driver.
So how do we fix this? For any conversion of clinically equivalent products, a Weighted Average of all like items should be used as the old price for all items purchased. In scenario modeling for the future, this would resonate if we expected to see the shift. The WA price last year was (1000 x 100 + 50 x 200) / 1050 = 104.7. This should be the baseline price for all future expected products, not their individual prior prices. This would have shown that moving primarily to product B would drive costs upward even with a lower price than the prior year. Every time product A was purchased, we saved $14.70 (not just $10). Every time product B is purchased, we are actually losing $45.24. Assuming equal distribution, it is obvious moving to product B will increase overall costs.
If the conversion was driven due to expected savings, then this scenario would counteract the intention. However, it should be noted if the conversion was for a clinical reason out of Value Analysis/Sourcing control (better patient safety, fewer critical shortages, etc.) this analysis could be used as a discussion point. Not to discourage the conversion, but simply to show the impact so that finance is prepared for the impact rather than expecting savings. The sourcing work to receive the product discounts, in this case, could still be viewed as cost avoidance, as work was still completed in securing lower prices.
When volume shifts are intentional, they must be evaluated along with the price effects. Otherwise, organizations risk declaring savings while simultaneously increasing spend which erodes credibility with finance and leadership.
Article by:
Brett J. Webster, Healthcare Supply Chain Analytics Leader, MemorialCare
Brett is a healthcare supply chain analytics leader at MemorialCare with a background in finance and quantitative analysis. His work involves translating complex sourcing activity into clear, defensible financial outcomes that strengthen alignment between supply chain and finance teams.
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