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Pharma Tech Outlook | Tuesday, February 21, 2023
The healthcare industry is becoming more specialized, competitive, and cost-sensitive daily. Data and analytics are critical to an organization's success and growth of its product portfolio and its ability to leverage them efficiently and effectively.
FREMONT, CA: One medium-sized pharmaceutical company's marketing department sought a novel approach to fast identifying and geographically target the correct cohort of patients for their highly specialized therapy so that they could launch their new product. With the help of real-world data and cutting-edge analytics, they achieved a 40 percent improvement in their match rate between key targets, payers, providers, and claims, as well as a 20 percent improvement in uptake compared to previous launches.
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Understanding market dynamics and the patient experience is critical for the successful launch and management of brand performance, as demonstrated by this case. Studying patient-level real-world data—claims explicitly, provider, and payer data—is beneficial not just for patient adoption of new pharmaceuticals but also for examining the patient journey, market share, and other treatment patterns, such as treatment sequences, persistence, adherence, and switches.
As pharmaceutical companies transition from producing treatments for vast populations to developing specialized drugs, patient targeting becomes a crucial success factor. Due to market access challenges, fifty percent of pharmaceutical companies that released new drugs did not meet their initial sales projections in the first year. Two-thirds of patients with complex diseases cannot handle payer constraints such as prior authorization, step edits, pharmacy network restrictions, and National Drug Code (NDC) restrictions.
A more sophisticated data and analytics strategy is required to solve the most pressing business concerns of the present day. To achieve launch success, companies must utilize claims and patient-level data to identify the relevant patient group more precisely. Although most pharma companies acknowledge the value of using these data types, many need help with what to do once they have acquired the data sets. Frequently, the appropriate data exists but needs to be more compartmentalized among departments. Many firms need more personnel, skills, and procedures to combine and purify their data for productive use.
Although large pharmaceutical businesses spend up to $400 million on people, platforms, data, and analytics, many need to observe a significant return on investment.
Why do businesses purchase data yet fail to maximize its potential? Below are some reasons:
Lack of data integration: Businesses have been utilizing analytics for many years, but bringing together diverse data sources to inform decision-making may take time and effort. In many drug businesses, analytics divisions spend up to 80 percent of their effort connecting data, validating it, and coding the suitable model, leaving only 20 percent for basic analytics. Finding a method to reduce this front-end strain will help you exploit your data more effectively.
Preliminary analysis: Most analytical models are designed to address specific queries. The issue is that every inquiry leads to another, and the initial questions you ask are different from the ones you need to ask as you progress through the analysis. Businesses must be able to respond to inquiries as they occur without having to recode and model data for the new study.
Lack of experienced analysts: According to respondents of a recent survey of healthcare analytics experts, the lack of experienced analysts who not only have experience with large data analytics and statistical modeling but also have a working knowledge of market access and the clinical drug development process, continues to be one of the most significant concerns. Most respondents cited needing more experienced analysts as a significant investment hurdle. Discovering techniques to ease the examination of complicated data sets for less-experienced researchers is essential for extracting more excellent value from data.
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