Thought Leadership

Initially developed to manage type 2 diabetes, glucagon-like peptide-1 (GLP-1) agonist medications have become exceptionally popular for weight loss. High-profile celebrity endorsements and widespread media coverage has led to an overwhelming spike in demand.

By any estimation, the American market for weight-loss GLP-1s is enormous. According to a Kaiser Family Foundation analysis, more than 40% of privately insured adults under 65 are clinically eligible for a GLP-1 drug to treat obesity. Although there are several GLP-1 agonists on the market, only three are currently approved for weight management: Novo Nordisk’s Saxenda (liraglutide), Novo Nordisk’s Wegovy (semaglutide), and Eli Lilly’s Zepbound (tirzepatide).

In recent years, pharma companies have begun to embrace lab data as a richly rewarding dataset for a whole host of purposes, including trial enrollment and site identification, patient monitoring and disease progression, and commercial targeting and market access insights. Along with test orders, values, and results, deidentified lab data includes a wealth of information, such as a patient’s demographic information, diagnosis codes, and ordering provider and facility details.

During the pandemic, the number of individuals in the U.S. enrolled in both fee-for-service and managed Medicaid programs increased dramatically. This increase was tied to a provision in the Families First Coronavirus Response Act, which boosted federal dollars to the states in exchange for keeping individuals covered through Medicaid, even if their eligibility changed since they enrolled.

In a competitive market, pharma companies are increasingly relying on various datasets to drive their decision-making. With so many disparate sources, however, data standardization presents a challenge, especially for companies eager to use predictive analytics tools. In a recent survey of 125 pharma executives, nearly half cited data integration and cleanliness as the primary roadblocks to adopting technologies like AI. 

As payers grapple with their ever-increasing spend on healthcare services, many are entering into various arrangements with pharma manufacturers to help mitigate their risk when paying for cell, gene and specialty therapies—which often have price tags ranging from several hundred thousand to millions of dollars. A warranty-based agreement is one approach that can benefit both stakeholders. These agreements can potentially boost the uptake of high-cost treatments by increasing the likelihood that payers will cover products, particularly those with unclear efficacy or durability.

To fully understand how payers impact patient access, pharma companies should be evaluating payers using multiple datasets, including both coverage/restriction data and claims data. While payer policies indicate how payers plan to manage a product or service, medical and pharmacy claims reveal how payers actually manage that product in reality.

For the past decade, biosimilar adoption in the U.S. has proceeded at a relatively slow pace, due in large part to limited financial rebates for payers and provider preferences for biologics. Recent research suggests that utilization is finally picking up steam as more health plans place both biosimilars and their reference biologics on preferred formulary tiers.

A Health Affairs study found that preferred coverage of a single biologic declined by 47% between 2017 and 2022, while preferred coverage of multiple drugs rose 46% in the same period. In September 2024, an analysis of coverage data from MMIT Analytics confirmed the progression of this trend, finding that most patients covered by the same health plans tracked in the Health Affairs study are covered for both biosimilars and their reference biologics.

When the FDA approved the first cell and gene therapy (CGT) for hemophilia almost two years ago, it represented a massive leap forward in treating the bleeding disorder. The one-and-done agents offer the promise of a cure, giving patients the prospect of freedom from the disease without regular treatments.

Although three hemophilia CGTs are now on the U.S. market, access has not yet met expectations, and only a handful of patients have received treatment to date. These therapies have been slow to pick up market share due to their costly price tags, despite manufacturer-provided warranties that offer full or partial reimbursement if the agents lose their efficacy.

Lab data has become an increasingly essential dataset for pharmaceutical companies at all stages of the product life cycle. A key use case for our MMIT clients is using lab data for commercial targeting. Since lab tests often represent one of the earliest data points in a patient’s diagnostic journey, pharma companies are using lab results to identify potential patients and their physicians in time to directly impact the patient’s treatment plan. By coupling lab data with clinical expertise, pharma companies can focus on physicians who recommend and prescribe treatments for applicable patients.

In the complex healthcare analytics landscape, understanding the nuances of open and closed claims is critical for researchers and analysts. Open and closed claims each offer unique insights into the patient journey.

Open claims, sourced from various clearinghouses and warehouses across the U.S., present a vast collection of data, while closed claims, adjudicated by insurers, provide a detailed, individualized perspective. Open claims are rather like a wide-angle view of the forest, whereas closed claims are more like a view of the individual trees.