Drug pricing data is not scarce. CMS publishes ASP quarterly. State Medicaid agencies report NADAC weekly. WAC is commercially available from multiple aggregators. What has been scarce—genuinely, consequentially scarce—is a platform that normalizes these datasets across time, aligns them on common drug identifiers, and makes the relational signals between them analytically accessible. MedPricer.org is attempting to fill that gap. Understanding why the infrastructure is the product requires understanding how fragmented the existing data landscape actually is.
The Normalization Problem
NDC codes—the eleven-digit identifiers attached to specific drug products—are not consistent across data sources. Manufacturers repackage drugs, change labelers, and issue new NDCs for reformulations. CMS’s ASP file uses one identifier structure. The NADAC file uses another. WAC data from commercial sources uses yet another. A research analyst attempting to trend WAC against ASP for a specific molecule over five years will encounter NDC discontinuities, labeler changes, and product reformulations that require careful, drug-specific normalization work before any analysis is possible.
This is not a trivial technical problem. It is the reason that most academic and policy research using these datasets either restricts itself to very narrow drug samples or relies on manually curated lists of NDC crosswalks. At scale, across thousands of drugs and multiple years, the normalization challenge has historically required either custom technical infrastructure or vendor relationships with IQVIA or Truven, at costs that place the data beyond reach of most research organizations.
Time-Series Alignment and Its Implications
Even after normalization, the three major pricing benchmarks operate on different temporal rhythms. WAC changes can occur at any time and are typically effective immediately upon manufacturer submission to CMS. ASP is a quarterly calculation reflecting sales from two quarters prior. NADAC updates weekly but is subject to survey methodology changes that can introduce discontinuities in the trend data.
A platform that simply presents these three numbers side-by-side without addressing temporal alignment will produce misleading comparisons. A WAC change effective in February of a given year will not appear in ASP data until late in that same year. An analyst comparing the two without accounting for this lag will systematically misread the spread. MedPricer’s value is that this temporal alignment has been addressed in the data architecture rather than left to the end user.
The API Question and Downstream Use Cases
For hedge funds, equity research desks, and health policy research organizations, the most valuable form of this data is not a dashboard but an API. The ability to query normalized, time-series drug pricing data programmatically—to pull WAC-ASP spreads for a specific therapeutic class, trend them against earnings announcement dates, or compare NADAC trajectories across competing generics—enables a class of quantitative analysis that a visual interface cannot support at scale.
MedPricer’s architecture positions it for this downstream use case. Whether the platform pursues an API licensing model, a dashboard subscription model, or a data licensing arrangement for institutional use will determine the addressable market. The dataset, properly maintained and normalized, is valuable to multiple constituencies: financial analysts, policy researchers, hospital formulary teams, state Medicaid programs. Each constituency requires a different interface but the same underlying infrastructure.
The Competitive Moat Is Maintenance, Not Creation
The hardest part of building this infrastructure is not the initial normalization—it is keeping it current as NDCs change, as NADAC methodology evolves, as CMS revises its ASP file format, as manufacturers submit corrections to historical quarters. The competitive advantage of a well-maintained drug pricing dataset is not its initial construction but its ongoing curation quality. A dataset that was accurate eighteen months ago and has accumulated methodology drift is worse than no dataset at all for an analyst relying on trend data.
This is why Bloomberg’s dominance in financial data is not primarily about its original data collection—it is about the operational infrastructure required to maintain, correct, and continuously improve a vast historical dataset. MedPricer’s analogous challenge is to build not just the initial normalized archive but the operational discipline to maintain it. That is both the barrier to entry for competitors and the primary ongoing cost of the business.
