Clinithink’s patented CNLP technology has been proven to deliver meaningful clinical and financial impact across a wide range of use cases. Explore the supporting evidence and insights below regarding the various applications of CLiX® technology.
Research study presented at the ASCO conference (2024) demonstrated how Clinithink’s AI technology can predict risk of lung cancer at an earlier stage and revolutionise UK cancer care - accelerating detection to help find patients at an earlier, more treatable stage of disease.
Research presented at the ASCO conference (2023) detailed a large scale evaluation of pulmonary nodule workup: A real-world study of over 150,000 patients in New York State. Clinithink's CLiX unlock was used to analyze 58 million patient documents from a New York State health information exchange and identify suitable candidates for further investigation or surgery.
In this study, Rady Children’s Hospital in San Diego and the University of Utah used CLiX unlock enterprise to automate the prioritision of sick newborns for whole genome sequencing. The approach combined a clinical natural language processing (CNLP) workflow with a machine learning-based prioritisation tool named Mendelian Phenotype Search Engine (MPSE). The results of this study indicated that an automated pipeline for selecting acutely ill infants in neonatal intensive care units (NICU) for WGS can meet or exceed diagnostic yields obtained through current selection procedures.
Clinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. This study demonstrated that expediting genome interpretation enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.
A study published in the International Journal of Medical Informatics documents the role played by Clinithink’s patented clinical natural language processing platform in identifying patients with non-alcoholic fatty liver disease at risk for disease progression. The study demonstrated that NLP-based approaches have superior accuracy in identifying NAFLD within the EHR compared to ICD/text search-based approaches.
A study published in Science Translational Medicine documents the role played by Clinithink’s patented clinical natural language processing platform in delivering genetic diagnoses to neonatal and pediatric intensive care physicians in record breaking time.
Genetic disorders are a leading cause of morbidity and mortality in infants. Rapid whole-genome sequencing (rWGS) can diagnose genetic disorders in time to change acute medical or surgical management (clinical utility) and improve outcomes in acutely ill infants. By employing a novel AI-based approach to the diagnosis of rare genetic disorder in the NICU, the study demonstrated, using retrospective data, the potential for significant cost savings.
An evaluation found that by using CLiX® unlock enterprise it would result in savings amounting to around £2.3 million pa for the North East London Integrated Care System (NEL ICS). In addition to annual cost savings, it identified improvements in capacity planning and service utilisation. Compliance to NICE guidelines for the care of patients with DFD also increased.
When using CLiX unlock enterprise alongside the Access Rio Patient Insight software, this evaluation demonstrated East London NHS Foundation Trust would make operational savings in excess of £840,000 pa. This included a 1% reduction in admissions and a 25% reduction in follow-up appointments - resulting in much shorter waiting times for patients.
CLiX has been proven to dramatically reduce the time required to find eligible patients for clinical trials at Newcastle upon Tyne Hospitals NHS Foundation Trust. The evaluation demonstrated that automation of pre-screening can save clinical research time, reduce elapsed time for recruitment and improve access to clinical trials.
CLiX was used to identify patients with the specific clinical and social characteristics associated with potential early-stage dementia much faster than previously possible - freeing up valuable clinician time in the process. CLiX® was used to accurately ‘read’ and ‘interpret’ up to one million clinical documents an hour, automating the review of clinical notes and providing clinicians with invaluable insights in a fraction of the time - which would otherwise take weeks, months, or even years.
The evaluation demonstrated by using CLiX unlock enterprise, enrolment numbers can be significantly increased in a markedly shorter timeline. CLiX unlock represents a breakthrough in recruitment efficiency where sponsors and research centres can achieve enrolment targets not only on time, but ahead of schedule with much less manual effort compared to common practice.
This evaluation demonstrates how CLiX unlock enterprise can transform patient recruitment in clinical trials. Not only did it show that recruitment can be made exponentially more efficient by the use of tailored CLiX queries, but that the pre-screening yield can be dramatically increased.