Get More With Physician-Level Data

Life science marketers rely on data-driven strategies to engage healthcare providers (HCPs) effectively, and Physician-Level Data (PLD) is one of the most powerful sources available (PLD). With more actionable insights into HCP behavior, PLD enables marketers to plan more effective channel strategies, better measure campaign impact, and optimize engagement in real time.   

Whether you’re already working with the data but it’s become overwhelming, or taking the first steps to integrate physician-level insights into campaign optimization, here’s your (brief) guide to all things PLD.  

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Physician-Level Data is subset of real-world data focused specifically on prescribing behavior at the individual physician (NPI) level. It tracks metrics like total prescriptions, brand switches, and rep engagement. PLD generally derives from EHR records or pharmacy claims which commonly tracks prescription fills and refills. These claims are compliantly sourced from retail pharmacy chains, mail-order and specialty pharmacies. 

For marketers, PLD often refers to impression counts by NPI, but that’s only a small piece of what’s available. Other categories include: 

• Prescribing Behavior & Brand Performance: Total prescriptions, new-to-brand prescriptions, switching behavior, refill rates, dosage/formulation/titration preferences, brand affinity scoring, and market share by specialty. 

• Patient Access & Payer Dynamics: Insurance mix, co-pay assistance utilization, claim status, prior authorization trends, formulary sensitivity.  

• HCP Engagement & Touchpoints: Rep access and detailing history, email and webinar engagement, programmatic and advertising data, EHR and ePrescribing behavior, prescription volume and trends, brand loyalty, and patient demographics. 

The Basics: Using PLD to Optimize HCP Marketing  

Physician-Level Data (PLD) is a game-changer for HCP marketing, giving you a clear, detailed look at what individual prescribers are doing and how they engage. This deep dive opens the door to all kinds of analyses, like: 

• Campaign Performance Measurement: Metrics such as new-to-brand prescriptions (NBRx), total prescriptions (TRx), and refill rates can indicate whether a physician’s behavior has changed after exposure to a campaign. 

• Audience Segmentation and Personalization: Categorizing physicians based on brand opportunities, such as high-volume prescribers who are already engaged with marketing, potential/interested adopters who have yet to switch, competitor-loyal prescribers who need targeted messaging to convert, potential referring physicians, and more. 

• Omnichannel Optimization: Analyzing response rates and script lift across channels allows brands to identify the most effective channels and refine their channel mix across point-of-care/EHR workflows, rep-driven engagement, programmatic media, and social media. 

• Competitive Benchmarking: By assessing market share changes at the physician level, companies can measure the effectiveness of messaging, identify potential barriers to adoption, and refine positioning strategies. 

Taking the Next Step: Predictive Analytics and Omnichannel Orchestration 

Traditionally, life sciences brands have looked at physician-level data post-campaign or mid-year to identify optimizations and support future planning. But these retrospective analyses don’t enable the real-time optimizations that are becoming increasingly important with competitive marketers, or where patients are few and far between.  

Instead, more brands are finding more value in predictive, AI-supported analyses of PLD that enable marketers to:  

• Anticipate treatment decision points   

• Identify HCPs showing signs of therapy switching, new patient diagnoses, or changes in access   

• Act before key prescribing moments—helping providers make the best choices for patients  

While AI is seemingly everywhere, it’s not always suited for every task. But in this instance, AI is the ideal tool to consolidate prescribing patterns, access dynamics, and behavioral signals across multiple sources, then use that analysis to make informed predictions about future care milestones and prescribing opportunities. With those predictions in hand, marketers can stop reacting to analyses of past data, and instead build campaigns that anticipate: 

• The channels each NPI is most likely to engage with 

• When each NPI is likely to have an eligible patient 

• What brand information is most valuable to that NPI 

It’s a proactive engagement strategy that reflects how each provider wants to engage with life science brands, so they can better diagnose and treat their patients. And that’s the real goal of pharma marketing—helping more individuals start and stay on life-changing therapy. 

Download the full white paper for more examples of PLD analysis that can shed new light on marketing performance and help your brand better engage provider audiences. 
 
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