England , RNOH will work with UCLH to implement an extension of their Epic Electronic Health Record ( EHR ).

The collaboration will start later this month and RNOH will be able to utilise UCLH ’ s expertise in designing , implementing , and using Epic since March 2019 .

It is expected that Epic will be rolled out at RNOH in November 2025 , replacing paper records and many of the current clinical systems at RNOH with a single , fully integrated clinical record . It means that staff can always access the complete , accurate and upto-date information needed to ensure the best care for patients .

RNOH Chief Executive Professor Paul Fish , said : “ We ’ re very excited to be implementing an EHR at RNOH and to leverage its capabilities to improve care for patients . As more hospitals move in this direction , digital innovation and transformation such as Epic ’ s EHR is taking patient care forward to exciting places .

“ This new system will help maintain RNOH ’ s position as the UK ’ s leading provider of acute neuromusculoskeletal medicine to our complex patient group . Working in partnership with UCLH and being able to draw on their knowledge and experience of using Epic will set us up for success long into the future .”

University of Oxford researchers describes a first-of-its-kind method capable of distinguishing authentic and falsified vaccines by applying Machine Learning to mass spectral data . The method proved effective in differentiating between a range of authentic and ‘ faked ’ vaccines previously found to have entered supply chains .

This latest research will bring the world community one step closer to being able to tell apart falsified , ineffective vaccines from the real thing , making us all safer .

It has been a tremendous collaborative effort , with everyone having this same important goal in mind .

The results of the study provide a proof-of-concept method that could be scaled to address the urgent need for more effective global vaccine supply chain screening . A key benefit is that it uses clinical mass spectrometers already distributed globally for medical diagnostics .

In this new study , researchers developed and validated a method that is able to distinguish authentic and falsified vaccines using instruments developed for identifying bacteria in hospital microbiology laboratories . The method is based on matrix-assisted laser desorption / ionisation-mass spectrometry ( MALDI-MS ), a technique used to identify the components of a sample by giving the constituent molecules a charge and then separating them . The MALDI- MS analysis is then combined with opensource machine learning . This provides a reliable multi-component model which can differentiate authentic and falsified vaccines , and is not reliant on a single marker or chemical constituent .

Professor James McCullagh , study co-leader and Professor of Biological Chemistry in the Department of Chemistry , University of Oxford , said : “ We are thrilled to see the method ’ s effectiveness and its potential for deployment into real-world vaccine authenticity screening . This is an important milestone for the Vaccine Identity Evaluation ( VIE ) consortium which focusses on the development and evaluation of innovative devices for detecting falsified and substandard vaccines , supported by multiple research partners including the World Health Organization ( WHO ), medicine regulatory authorities and vaccine manufacturers .”

6 www . intelligenthealth . tech