A common question we get asked by data sellers is: what types of healthcare data are interesting to buyers? To answer this, we highlight common types of healthcare data and those most relevant for data buyers.

At Neudata, we typically categorise healthcare into three distinct areas – clinical development data, real-world data and real-world non-healthcare data. The following sections define these categories and provide some common use cases for data buyers.

1. CLINICAL DEVELOPMENT DATA

Clinical development data generally refers to data generated inside a controlled clinical setting, such as clinical trials and drug development pipelines. It is commonly associated with data collected during the process of testing and evaluating new drugs and treatments, but it can also include insights into the safety and effectiveness of treatments.

Investors typically seek predictive analytics, such as the likelihood of approval (LOA) metrics, phase transition success rates (PTSRs), sales forecasts and approval rates, to understanding adverse drug effects.

While this piece focuses on paid sources, investors also commonly use free sources such as company registries (Bayer trial finder, Lily trials), academic journals (The Lancet, Nature Medicine, PubMed search tools) or FDA emergency use authorisation information.

Common use cases:

• Tracking clinical development, including trial status, methodology and results.
• Estimating the likelihood of clinical trial progress/success.
• Social media analytics on pipeline drugs.

2. REAL-WORLD DATA

Note: This type of data doesn’t meet the healthcare sector’s definition of real-world healthcare but is included as such within the alternative data industry.

Real-world healthcare data typically refers to data generated outside a controlled clinical setting and within a real-world medical environment that investors can use. This type of data doesn’t suffer from the limitations associated with clinical data generated in tightly controlled research settings. For example, restrictive inclusion and exclusion criteria for trial participants (based on characteristics such as age and medical history) can result in data that is not representative of outcomes in a real-world patient population.

Given the difficulty in acquiring data from research settings, more common types of data investors use include clinical data generated during patient encounters with healthcare providers, claims, other administration data or patient self-reported/generated data.

Common use cases:

• Track drug use based on medical and pharmacy claims volumes.
• Track drug use based on physician prescribing volumes.
• Track ex-manufacturing drug sales.
• Anticipate future drug and medical device sales.

If you are a vendor who supplies real-world data, we recommend adding either a risk check or a DDQ to your profile to show that you are compliant when sourcing from patient-related data, as compliance risks are higher with this type of data.

3. REAL-WORLD NON-HEALTHCARE DATA

Real-world non-healthcare data commonly refers to non-healthcare data types that can be particularly relevant to certain healthcare-related use cases (e.g. trade data or subdomain activity data).

Common use cases:

• Using trade data on seaborne imports used to monitor pharmaceutical supply chains.
• Tracking the value and volume of particular goods being exported (e.g. surgical and medical gloves).
• Tracking companies winning public sector contracts.
• Tracking OTC drug sales at the product level using retail transactions with SKU-level granularity.

QUICK RECAP

In summary, we typically group healthcare data into three categories. The use cases in this report show several ways investors can utilise these types of data in their investment strategies. Given these use cases, we have seen a noticeable increase in healthcare data providers listing with Neudata. If you have a healthcare product that you want to add please reach out to requests@neudata.co