Manage pharmacy spending with precision medicine via uplift modeling

An abstract of my recent white paper publication. You can find the link to the full version at the end of this article.

The escalating costs of the U.S. healthcare system have been a subject of concern for decades, with drug spending driving nearly half of the surge in healthcare costs recently. This white paper focuses on one approach for improving drug utilization management: precision medicine through a method known as uplift modeling.

Enhancing precision medicine with uplift modeling

Uplift modeling is a machine learning technique used to estimate the impact of an action or treatment at the individual level and could be used to help quantify the differences in medication effectiveness. This technique could be used to identify the patients that are most likely to benefit from any healthcare intervention such as a prescription drug or medical device, or even estimate the differences in effectiveness between multiple candidate treatments (e.g., dosages). Uplift modeling can be used to identify subpopulations of people with similar characteristics that responded to the treatment in similar ways. This process can offer data-driven insights as to why some groups might not have responded well—or even why some groups might have had an adverse reaction.

Uplift modeling on high-cost drugs

Ozempic, Mounjaro, Wegovy, and other glucagon-like peptide-1 (GLP-1) agonists have made major headlines in recent years for their ability to treat type 2 diabetes and, in some cases, promote weight loss. However, emerging data shows that not everyone can expect to see the same effects. Is there some way that we could reasonably predict which individuals might significantly benefit from GLP-1s? Figure 1 is an example of a potential output from uplifting modeling.

Figure 1: Cumulative gains chart for two GLP-1s (simulated data)

In this simulated example, we assume that approximately 5,000 people across the study lost weight. The blue line representing Drug X shows that targeting the specific individuals who are predicted to benefit most [from Drug X] is very effective for about 20% of the population, but less effective for the other 80%. Conversely, the yellow line representing Drug Y shows broader effectiveness when targeting a larger portion of the population (specifically between 50%-90%). However, in both cases, we see a large benefit over the baseline expectation (black dotted line), highlighting the usefulness of an uplift-based targeting strategy.

What to do with the uplift modeling output

Utilizing this tool in an ethical and sensitive way is vital. With this ethical context in mind, who could benefit from uplift modeling? In addition to patients who may benefit from getting placed on more effective treatment options sooner, the answer is everyone who is taking financial risk for healthcare spending.

Ultimately, the goal is to reduce the barriers to valuable treatments and protect patients from interventions that have a low likelihood of benefit (that come with financial cost and possible side effects), not to use uplift modeling to deny care—a crucial reminder of the importance of a grounded and well-respected code of ethics.

Conclusion

While precision medicine offers the potential of improved patient outcomes and cost savings, its implementation is not without challenges, including data privacy, regulatory hurdles, and potential exacerbation of existing healthcare disparities. Therefore, a careful, ethical approach is necessary to ensure that the benefits of precision medicine are accessible and equitable. By integrating precision medicine with evidence-based practice, healthcare providers can make treatment decisions that are both clinically sound and personalized to each patient's unique needs. While a multifaceted approach will be necessary to truly address this complex issue, precision medicine, particularly through an innovative approach like uplift modeling, is a great place to start and has significant potential to address escalating healthcare costs.

You can find the full white paper here.