Gross to Net Metrics: Bridging Physician and Contracting Strategy

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The goal of a public company and the fiduciary responsibility of company executives is to maximize shareholder value. In simple terms, maximize profit, which drives long-term shareholder value. Pharmaceutical companies are no exceptions.

However, when commercial strategy is scrutinized, an interesting trend emerges. Let us assume that an executive goal is to maximize profit over the life of the firm. Typically, this strategic goal is implemented by the commercial strategy group and further subdivided into physician strategy and contracting strategy. It stands to reason that the sum of physician strategy and contracting strategy should help drive the organization objective. But is that really the case?

Although strategic plans may mandate alignment between physician and contracting strategy, in practice that is rarely achieved. Companies may be achieving sub-optimal performance by not fully aligning their physician and contracting strategies.

In our experience, most commercial sales and marketing activities are driven by maximizing volume. From a tactical perspective, analytical models used by commercial operations to answer critical business questions, such as physician targeting, sales force incentive compensation, sampling optimization, marketing mix optimization, and so on, are based on the use of the number of scripts (derived from using a third-party data provider or other plan level data) as the dependent variable.

The use of these models to drive commercial decisions invariably drives manufacturers to spend resources to maximize the number of scripts. Driving script volume is important, but driving scripts through the most profitable channels is even more important. Although capturing market share may be necessary for short-term strategic reasons, manufacturers should not lose sight of the long-term profitability at the expense of volume gains.  At the very least, resource allocation decisions should be aligned with commercial strategy and both volume and profitability metrics should be consciously weighted — according to overall strategy (100% volume weight for maximizing volume and 100% profitability weight for maximizing profit). The question is how to achieve this balance in practice.

Integrating data points available from the Gross to Net team is one potential way to close the gap. To illustrate, consider the following scenario: there are two physicians who have the potential to write one incremental script. In typical commercial models, both physicians will be weighted equally and will be randomly assigned a sales call, especially if resource constraints exist. However, if from data we know that Physician A serves exclusively Medicaid patients with an average gross to net percent of 50% due to downstream rebates and Physician B exclusively serves patients from a commercial plan with an average gross to net of 75% and we had one sales call left, would we convert Physician A or Physician B to a prescriber? Given the script-based incentive goal, the sales team is probably neutral as, either way, they make the same money.

But from a leadership perspective, which physician is more important? In pure economic terms, Physician B is more important. And to maximize the benefit, given equal probability of customer conversion, resources should be diverted to Physician B as the incremental script generated is more profitable. From a call planning perspective, extrapolating the concept over the entire physician universe, a call should be diverted to physicians with the highest profit potential, irrespective of underlying volume.

By not incorporating a profitability metric, manufacturers could make other sub-optimal decisions. Brand managers typically use behavioral segmentation and cluster analysis to classify physicians into segments. A common technique used for clustering is to classify physicians based on brand share, without considering physician profitability based on channel mix. These physician clusters are then used to evaluate the size of an opportunity and in conjugation with attitudinal surveys, define the marketing tactics. Returning to our example, assuming a total of 10 scripts for each physician, both A and B have a brand share of 10%. With a 20% cutoff for high-share physicians, A and B will be classified as medium-share physicians. However, when physician profitability is accounted for, Physician A will be classified as a low-share physician and Physician B will be classified as a high-share physician.

Granted, these scenarios are very simplistic. But these individual sub-optimal decisions create long-term value loss. In the figure below, the area between the curves indicates lost profit. Companies are likely leaving a lot of money on the table.

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Given analytics advances, widespread availability of physician plan level prescribing data, enterprise revenue management systems, and internally tracked historical gross to net percent for each plan, it is possible to derive the average gross to net for an incremental script written by the physician. Use of this weighted Rx in analytical models can drive manufacturers to make commercial decisions that help them capture incremental margins.

If the concept is so intuitive, then why is adoption so slow? In my opinion, there is general reluctance to align any commercial decision to profitability metrics. Both marketing and sales organizations are typically comfortable with volume-based goals and are skeptical of profitability metrics. With executive pressure, volume objectives are sometimes achieved by masking lower profitability. An initial challenge is to convert skeptics by demonstrating the value in moving toward a profit-based approach, proving that it is unbiased, and getting the support from an executive-level change champion.

Other challenges include cultivating better communication between the gross to net and commercial analytics teams so they both understand what metrics are available and how they can be utilized for the benefit of themselves and the company as a whole. Involvement of contracting and data teams is also mandatory. This is necessary in order to extract plan profitability information from revenue management systems and map internal plan ids to third-party data to generate a plan level gross to net file on a quarterly basis.

Given the potential impact on long-term profitability, at minimum manufacturers should conduct exploratory analysis and undertake a pilot program to attain an awareness of feasibility, operational challenges, and long-term ROI regarding the use of gross to net metrics as a bridge to guide physician and managed markets strategies. Organizational acceptance will likely be gradual; however, it’s important to start the conversation now, frame decisions from a profit perspective, and clarify how short-term decisions fit into long-term objectives. The weights assigned to integrated profitability metrics in commercial models may be immaterial. But the long-term impact of the mindset shift will be consequential. 

Gross to Net Benchmarking Survey

Tags: Life Sciences, pcma