Glossary

The percentage of patients discharged from a hospital who are readmitted within 30 days. Used as a quality and efficiency metric by CMS and payers.

No single data source tells the whole story. Claims data captures diagnoses and procedures but may miss clinical nuance. EHRs capture lab values and physician notes but are limited to a single health system. Linking these sources at the patient level – using privacy-preserving methods such as tokenization – enables analysts to build more accurate and complete datasets for research, targeting and measurement. The integrity of any linked dataset depends on the quality of the matching methodology and the comprehensiveness of the underlying sources.

DTC advertising in pharma and healthcare allows brands to raise awareness of treatments, conditions and services among patients themselves. In the US, pharmaceutical DTC advertising is heavily regulated by the FDA. Healthcare data platforms are increasingly playing a role in enabling privacy-compliant audience targeting for DTC campaigns.

In data contexts, Dx typically refers to ICD-10 (or ICD-9) diagnosis codes that appear on medical claims. These codes are the foundation for identifying patient populations and understanding disease prevalence and comorbidity patterns.

HCPs are a central unit of analysis in healthcare data. Prescribing behavior, specialty, geographic location and patient characteristics are all captured and used to target and measure engagement. HCP-level data is often linked via NPI (national provider identifier).

HCOs are key targets for life sciences and payer engagement strategies. Understanding a HCO's patient population, prescribing behavior and network affiliations helps life sciences companies and payers identify the right organizations to engage and measure impact at the institutional level.

Medical claims data is one of the richest sources of real-world evidence (RWE) available. Each claim captures diagnosis codes, procedure codes, provider identifiers, dates of service and cost information. Analysts use Mx data to understand disease burden, treatment patterns, care pathways and patient journeys at scale.

The NPI is the universal key for linking provider data across disparate datasets such as claims, prescribing records, directories and credentialing files. It allows analysts to track individual HCP behavior across payers, geographies and time periods. NPI-level data is fundamental to building accurate provider profiles and segmentation models.

NBRx is a more precise signal of true brand adoption than NRx. It captures net new patients starting a brand, making it a critical metric for launch performance and patient acquisition strategies. Comparing NBRx to TRx (total prescriptions) gives a picture of how much growth is driven by new patients versus repeat fills.

NRx is an important measure of ongoing prescribing activity, but it should not be conflated with new patient starts. Because NRx captures prescription renewal events rather than first-time use, it reflects continuity of care as much as it does new demand. Each NRx event may also involve a clinical reassessment – i.e., the prescriber may run updated labs and adjust dosage or therapy accordingly, which can generate additional claims activity worth tracking.

PRx data forms the backbone of most prescription analytics, covering the high-volume widely distributed drugs that make up the majority of dispensing activity across therapeutic areas. Because retail pharmacy data is collected at scale and consistently, PRx is reliable for tracking market dynamics, patient adherence and prescriber behavior for mainstream therapies.

RWD encompasses a wide range of source types, including medical and pharmacy claims, EHRs, patient registries, wearables and mortality data. The quality, completeness and representativeness of RWD directly determines the strength and credibility of any evidence derived from it. Not all RWD is equal – factors like patient coverage and data curation standards vary significantly across vendors and datasets.

Whereas RCTs test what a treatment can do under ideal conditions, RWE reveals what it actually does across diverse patient populations in routine clinical practice. RWE is generated by analyzing sources such as medical claims (Mx), pharmacy claims (Rx), electronic health records (EHR) and patient registries. Regulatory agencies including the FDA increasingly accept RWE to support expanded indications, post-market surveillance and safety monitoring, making it a strategic priority for life sciences teams.

In healthcare analytics, Rx data provides visibility into which drugs patients are actually filling and taking, as opposed to what was simply prescribed. It includes drug name, dosage, quantity dispensed, prescriber NPI (national provider identifier), dispensing pharmacy and payer information. When combined with Mx (medical claims) data, Rx data enables a comprehensive view of the treatment landscape.

Specialty pharmacy data is distinct from traditional PRx data because it covers a narrower but clinically significant patient population. Specialty drugs, including biologics, oncology therapies and rare disease treatments, are often only available through limited distribution networks or exclusive specialty pharmacy hubs. SP data captures dispensing, patient support program enrollment, adherence and persistence patterns for these therapies, making it essential for launch tracking and patient journey analysis in complex disease areas.

TRx is one of the most widely used metrics for measuring overall drug utilization and market performance. It reflects the full volume of dispensing activity for a product, making it essential for market share calculations, sales forecasting and competitive benchmarking. Tracking TRx alongside NRx and NBRx reveals whether volume is being driven by new patient starts or ongoing adherence among existing patients.