Fractal Patient
Analytics
Streamline pharma decisions with AI-powered insights
What is Patient Analytics?
Patient Analytics is an AI accelerator for pharmaceutical companies. It standardizes claims data into a single format, thereby streamlining data processing and making it a one-time exercise.
By integrating this standardized data, Patient Analytics can build a 360-degree view of patients and physicians. This empowers pharmaceutical companies to make better decisions by analyzing patients’ journeys and physician behavior.
Patient Analytics also provides insights into the company’s market share and enables pharmaceutical companies to respond to market shifts faster by tweaking the target list of physicians.
Patient Analytics further enhances the experience by offering a text-to-SQL bot. This allows non-technical users to build custom reports and gain insights, ultimately leading to better decision-making.
Why Patient Analytics?
Standardized claims data
Patient Analytics automatically standardizes all pharmaceutical companies’ claims into a single, unified format. This one-time setup saves countless hours that can then be reallocated for more value-adding analytical deep dives.
Patients and market insights
Patient Analytics provides easy-to-understand dashboards with key performance indicators. The dashboards cover end-to-end patient journeys, enabling better decision-making with granular tracking of major KPIs.
Text to SQL bot
Patient Analytics enhances the experience by offering a text-to-SQL bot. This allows non-technical users to build custom reports and gain insights, ultimately leading to better decision-making.
Delivering value with Patient Analytics
Streamlined Collaboration
Reduce internal meetings by 20%
Focused Analytics
Reduce redundant KPIs by 30%
Increased Productivity
Expand bandwidth by 60%
Success Story: Large pharma speeds up physician targeting with Patient Jarvis
Challenge
A major pharmaceutical manufacturer needed to analyze physician prescribing habits across three separate claims datasets. This siloed approach was time-consuming and prone to inconsistencies due to different coding formats.
Solution
Patient Jarvis’ data standardization feature unified all three datasets into a single, consistent format. This eliminated the need to analyze each dataset separately, saving the team a significant amount of time.