Following the successful conclusion of a pilot study, Clinithink’s CLiX accelerate is now being used by the Newcastle upon Tyne Hospitals NHS Foundation Trust - one of the UK’s leading clinical research centres - to improve patient recruitment in their clinical trials.
A strong, collaborative relationship has been established between Clinithink and the Newcastle Hospitals and, as CLiX accelerate has made Newcastle a more attractive clinical research site thanks to its improved patient recruitment capabilities, Newcastle is now looking to expand its use of Clinithink’s CLiX accelerate into its non-commercial trial space too.
Professor Julia Newton, Deputy Medical Director of Newcastle upon Tyne Hospitals said: “Working with Clinithink has changed how we deliver commercial work. We have topped the NIHR league tables for research for seven consecutive years now, we are proud of how we deliver to time and target, and we are always looking for ways to work better and more efficiently. Clinithink represents such innovation for us.”
Challenge & Solution
Just like the rest of the clinical trials industry Newcastle had, in the past, suffered from recruitment challenges. The institution knew all too well that more than three-quarters of all clinical trials experience delay, with the daily bill for this delay being somewhere between $600,000 and $8m. Awareness of this, coupled with a desire to increase its commercial research activity, drove Newcastle to seek out a solution that would identify the appropriate patients for clinical trial recruitment more effectively.
And Clinithink’s CLiX accelerate is that solution. It uses market-leading artificial intelligence (AI) technology to accurately ‘read’ and interpret up to two million clinical documents an hour, automating the review of a hospital’s clinical notes and connecting physicians and patients with suitable clinical trials.
CLiX accelerate has been proven to dramatically reduce the time and investment currently required to find and enrol patients into clinical trials. By quickly and efficiently processing existing unstructured patient data, sites are able to identify eligible subjects against trial-specific inclusion and exclusion criteria up to 20 times faster than by reviewing patient data manually.
Prior to the use of CLiX accelerate, the Newcastle research team had spent three years manually reviewing over 2000 patients, identifying 30 potentially eligible candidates during that time. But with CLiX accelerate on board the team was able to process the records of 270,000 patients in less than 24 hours providing a shortlist of 100 potentially eligible patients in a fraction of the previous time.
What’s more, this success has led not only to Clinithink’s software being embedded into Newcastle’s data warehouse but also into Newcastle’s recently completed Phase III Trial Unit.
Professor Yan Yiannakou, Director of the Phase III Unit for the Newcastle Hospitals, said: “This is a genuinely intelligent system which is not dependent on coding - it actually reads the health record, producing eligibility lists which are always accurate. Our innovative P3 Clinical Research Unit is open for business and using Clinithink software because we know it works.”
Next Steps
So what's next for Clinithink and Newcastle? The partners are looking to work with more NHS Trusts in and around Newcastle to expand the amount of patient data available and to further increase the number of potentially suitable patients identified for clinical trials. On an even wider scale, the NHS has announced its intention to replicate the model of Newcastle’s Phase 3 unit (with CLiX as an inherent part of that model) across five other regional trusts across the UK.
Sarah Beeby, EVP, GM Life Sciences, Clinithink said: “We are extremely proud to be working with such a dynamic Trust and the collaboration between Clinithink and Newcastle continues to inspire and excite us. We very much look forward to the next steps in our mission to deliver patient insights for everyone.”
For further information email marketing@clinithink.com