In a rapidly converging healthcare ecosystem firms trust Tessella to work with them to identify and transform data potential into better clinical outcomes and greater business value. Our clients are embracing digitalization, behaving more collaboratively and becoming increasingly connected; the result is a greater demand for data analytics, AI & industrialization support.
We focus on fast-moving life science and healthcare organizations, from Biopharma through to Insurers/Payers, seeking to transform their business digitally beyond what their core capabilities can support. Our long term client relationships are a testimony to our ability to reduce risk and uncertainty on complex critical projects, our customer-centric approach, and the maturity of our data science and data engineering capability.
We work with clients to put patients at the heart of their products and services. Translating research, clinical and commercial questions from across your value chain into improved health outcomes and better business decisions, through pragmatic analytics roadmap planning, data science, and engineering services.
Whether you are interested in precision medicine and automated image analysis in R&D, digital therapeutics & improved adherence through behavior change, or bringing a new device to market combined with continuous evidence development, we are able to help you plan and realize your data journey.
The pressure to reduce the costs of bringing new products and services to the market has never been greater. Participants across the healthcare industry are responding by adopting industry 4.0, more sophisticated automation, and ever more integrated and collaborative supply chains.
Tessella is well placed to advise and deliver these complex digital journeys for our clients. We have a deep understanding of how AI and advanced analytics is applied in this context: from improving the performance and quality of technology assets by better use of data in condition monitoring, through to employing data science for improved precision and greater operational throughput in diagnostic image analysis or indeed using Machine Learning to automate care pathways, such as patient admittance, to improve operational efficiency.