Every day, scientists in laboratories across the world are processing millions of samples, but with such huge amounts of data being generated, it is difficult to structure and share research findings to derive the insight needed to get effective drugs to market, fast.
Whitehat Analytics’ extensive range of services can help healthcare providers to cut costs and improve efficiencies across the board to contribute to better care for patients.
Life science organisations benefit from Whitehat Analytics’ expertise to optimise research processes, increase return on investment and uncover more information from their data assets by using advanced machine learning technologies.
Our founders have vast experience in the life science sector, with many years’ combined experience in some of the world’s top research laboratories. This experience, plus our deep understanding of data, analytics and business intelligence, means that we are able to deliver quantifiable business results for our clients.
We help clients in the healthcare sectors manage:
- Health outcome prediction
- Health risk assessment and management
- Increase customer service and satisfaction
- Statistical analysis
We help clients in life sciences, within academia, R&D and pharmaceutical manufacturing, manage:
- Bioinformatics analytics
- High performance and distributed computing
- Scientific Data Management
- Computational Biology
- GxP compliant analytics
Our connected company, Aigenpulse, offers a state-of-the-art data intelligence platform designed to help life science organisations expedite the drug discovery and development process. By harnessing the latest artificial intelligence and machine learning tools, it delivers advanced data analytics to underpin scientific decision making.
Case study: Characterising high cost patients
Challenge: Our client is a healthcare benefits consulting firm. We examined their data on healthcare claims and characterised high cost patient profiles. Identifying high cost patients with similar personality types can help create specific engagement strategies to improve their health.
Solution: We analysed claims data from more than 100,000 existing patients and enriched their records with socio-economic indicators and modelled personality profiles.
Technologies: Java, Hadoop/Spark, unsupervised learning
Result: We identified several substantial opportunities to deliver savings of up to $11 million annually in one condition category, as well as suggesting potential strategies for improving patient compliance with treatment regimens to reach better health outcomes.