In the competitive pharma marketplace, physician engagement plays a critical role in a drug manufacturer’s commercialization strategy. However, before manufacturers can promote a new therapy directly to providers, they must first determine which physicians treat the relevant patient population. By investing in timely lab data, manufacturers can identify specific providers to target—before a diagnosis or prescribing decision is made.
Broadly speaking, lab data refers to any type of diagnostic test that passes through a laboratory, from blood tests to biopsies and genetic testing. According to the Centers for Disease Control and Prevention, 14 billion lab tests are ordered each year in the United States, the results of which influence approximately 70% of medical decisions.
Unfortunately, many manufacturers fail to incorporate lab data into their commercial strategies, instead relying on retrospective claims data to identify appropriate physicians and patients. This can be a costly mistake, as aggregated claims data can only identify target providers after an initial treatment decision has been made.
Comprehensive lab data, on the other hand, can help manufacturers proactively pinpoint patient count, test sequencing and unique ordering providers for all tests associated with a chosen condition. Certain lab datasets can also reveal patients who have tested positive for a particular condition as recently as the day before.
By enabling manufacturers to proactively promote their therapies to the right physicians well before the patient’s diagnosis or treatment plan is finalized, lab data plays a critical role in a successful physician engagement strategy. Specifically, lab data allows manufacturers to:
1. Establish and track a given patient population
While some manufacturers choose to purchase data directly from commercial labs, this data is not immediately available for analysis, as different labs rely on different data structures. By contrast, normalized lab datasets aggregate tests and results from an array of sources—including commercial labs, hospital labs and integrated health delivery networks (IDNs)—and reformat them to provide manufacturers with accurate, uniform, ready-to-use data.
The inclusion of inpatient testing in this data flow is critical, as it allows manufacturers to see the complete picture for transitional patients who may be tested both within the hospital and reference labs. With normalized lab data, manufacturers can view daily, weekly, or monthly counts of unique patients diagnosed with a certain condition using indication-specific tests.
2. Reach physicians before the point of decision
Comprehensive, normalized lab data gives manufacturers valuable information about which providers are ordering relevant diagnostic tests for a specific condition. This data reveals not only which physicians are ordering tests that might result in the use of a specific drug, but also which physicians are not ordering such tests.
For example, determining which patients are prime candidates for select oncology therapies requires specific biomarker tests, but not all physicians test for all biomarkers. Identifying key providers who test for specific biomarkers in certain geographies allows a manufacturer to deploy its field sales teams more efficiently, which is critical both for smaller, resource-strapped pharma companies and for larger companies. Identifying testing physicians also allows manufacturers to make more effective use of their digital programmatic advertisements.
3. Educate physicians and payers
Understanding physician testing patterns, especially within therapeutic areas impacted by the emergence of new biomarkers, can help manufacturers develop strong education programs. Given the rapid advances in genomic testing, many physicians may not know to test for a specific biomarker, or may not order pharmacogenetic testing to narrow treatment options.
By identifying physicians who aren’t ordering the appropriate tests for potential target patients, manufacturers can launch educational campaigns to help correct for these omissions. Once manufacturers identify specific regions with a higher percentage of testing physicians or positive patients, they can work with the top payers within those regions to ensure better coverage or hub services for their drug and the relevant test.
4. Identify target patients and testing trends
Manufacturers can also use comprehensive lab data to identify patients with a specific disease, which is especially useful in the rare disease space. In the absence of relevant ICD-10 codes or existing treatments, longitudinal testing history can help manufacturers locate hard-to-find patients who might potentially be diagnosed with an uncommon disease. Testing trends can also help manufacturers identify net new NPIs beyond their traditional target list that are diagnosing a particular patient population.
5. Determine gaps in patient access
Longitudinal patient-level lab data can also equip manufacturers with a better understanding of a patient’s testing and treatment journey. Once patients complete a particular series of tests, how long does it take for them to begin therapy—and how long until they see improvements? By linking lab data to claims data, manufacturers can trace the patient’s disease and treatment progression. If the patient has received treatment but their lab results show a steady worsening of their condition, the patient might be a prime target for a manufacturer’s second- or third-line therapy.
For manufacturers, successful physician engagement hinges on the ability to prioritize physicians with a high volume of potentially eligible patients—and to interact with those physicians at the ideal moment in time, when they are most receptive to brand messaging. Lab data enables a more efficient and proactive approach for physician and patient identification. Beyond commercial uses, lab data can also play a beneficial role in early drug development by optimizing site selection and trial enrollment.
Want to bolster your physician engagement strategy with accurate, comprehensive lab data? Learn more about MMIT’s Lab Data for Commercial Targeting solution.