What is Pharma Data? Examples, Providers & Datasets to Buy

Pharma Data Use Cases in Detail

Pharma data is important to users for several reasons. First, it fosters predictive modeling of biological processes hence enhancing the sophisticated nature of drugs. Pharma companies can enhance their predictive modeling capabilities by taking advantage of the diversity of existing molecular and clinical data to point out new molecular models that are target-specific and safe to use.

Secondly, pharma data also helps pharma companies accurately select patients and enroll them for clinical trials based on more sources. By the power vested in digital media and social media, reaching out to new volunteers to take part in clinical trials has become a great deal simpler, thanks to data-driven outreach.

Lastly, pharma data can also be used for real-time monitoring of clinical trials of new drugs while at the same time enhancing collaboration among industry stakeholders.

How is Pharma Data collected?

Pharma data collection and analytics are critical to the future of the pharma industry. Close to 40% of pharma professionals agree that pharma data holds the key to the future transformation of the industry. Modern digital technologies have fostered the ability to rapidly and efficiently collect, store, analyze, and transmit data on a large scale, resulting in the unlocking of huge potential in clinical development, customer engagement, and patient safety. Pharma data collection can be identified in terms of sources that include primary and secondary data sources.

Primary sources of data

• Clinical trials
• Direct patient testimonials

Secondary sources of pharma data

• Medical-related websites
• Medical journals
• FDA database

What Does Pharma Data Typically Include?

Pharma data often encompasses detailed information about pharmaceutical locations, prescription deliveries, drug store demographics, pricing data, pharmacist contact lists, and consumer purchasing behavior.

What Are the Main Attributes of Pharma Data?

With the collection of enormous amounts of data, pharmaceutical companies are breaking new grounds as far as saving on expenditure while managing patient safety, controlling risks, bolstering the efficiency of clinical trials, and enhancing collaboration with other industry members is concerned. Some of the key attributes of pharma data that have helped pharma companies realize this transformation includes:

• Clearly outlined strategies in research and development programs
• Research and development agenda
• Manufacturing data concerning product ranges and the manufacturing process for each range
• Distribution data on multiple channels of dissemination and storage in the supply chain
• Data on the pricing of developed products
• Pharma data on marketing and sales strategies.

How Can I Asses the Quality of Pharma Data?

Pharma data can contain sensitive information and PII. Therefore, when assessing the quality of this data, users should look to ascertain its accuracy and safety aspects. Because pharma data is usually collected to enhance clinical trials for new drugs that are under development, the data used must be accurate enough to guarantee the safety of patients involved in the trials.

Additionally, when assessing the quality of pharma data, the aspect of time is also important. Collecting medical data is a rigorous process that involves testing and trying out new methods and analysis to ensure quality. As such, this means that quality pharma data cannot be derived from unreliable sources using rushed methodologies.

High-quality datasets achieve accuracy rates of 95% or higher, with stringent validation processes. For example, pharmacist contact lists are often verified through email confirmation and de-duplication, ensuring maximum deliverability and relevance.