Public Funding for Evergreen Health Solutions Ltd

Collapsing morale among general practitioners (GPs), a shrinking GP workforce, relentless demands, and increasing workload have caused many to sound alarm on a general practice in "crisis" \[1\]. Online Consultation (OC) systems are part of the solution. They help GPs to manage patient requests and workload more effectively while allowing patients to contact their GP without waiting on the phone. Systems that use a free-text interface improve patient uptake\[2\] and experience by allowing patients to describe their needs in their own words. They, however, require clinical staff to manually sort and code requests introducing a time and resource burden of around £240Million/year in England\[3\]. This means clinical staff spend time on administration that could be better spent elsewhere, especially if requests are insufficiently descriptive due to education or language barriers. In addition, OC captures current patient symptoms meaning the GP must refer to the patient's longitudinal clinical history, for context, which can be time consuming in an already short consultation. Natural language processing (NLP) builds on artificial intelligence to enable text understanding and summarisation. Advances in NLP allow training computer models that process text at a speed and volume way beyond the capability of humans. In this project we will utilise our in-house academic expert in NLP to significantly improve free-text OC by developing a tool named ASPIRE, that can automatically categorise and code\[4\] free-text requests from patients and provide a clinical summary and recommendation to GPs. Importantly, the tool will be patient-led and personalised. Meaning ASPIRE will utilise data from both the patient's free-text request and their personal health record (PHR); this is the most comprehensive health record, including both medical history (GP record) data and patient-reported environmental data (e.g. over-the-counter medication, allergies, self-reported health outcomes). General practice will benefit from improved accuracy and reduced burden of clinical coding. GPs will benefit from support to make better informed decisions from the automatic coding and flagging of relevant data. Patients will benefit from more personalised care-decisions made within the context of their PHR. \[1\]\_General\_Practice\_In\_England\_[https://journals.lww.com/ambulatorycaremanagement/Abstract/2022/04000/General\_Practice\_in\_England\_\_The\_Current\_Crisis,.7.aspx][0] \[2\]\_ Access\_to\_and\_delivery\_of\_general\_practice\_services\_https://www.health.org.uk/publications/access-to-and-delivery-of-general-practice-services \[3\]\_Calculation\_based\_on\_~30seconds\_GP\_coding\_time\_required\_per\_consultation,\_at\_an\_equivalent\_cost\_of\_£80/hr.~367\_million\_GP\_appointments\_in\_2021\_equates\_to\_~£240million. \[4\]\_clinical 'coding' in this context describes the use of structured clinical vocabulary for use in an electronic health record to ensure care information is clearly recorded, consistent, and comprehensive. [0]: https://journals.lww.com/ambulatorycaremanagement/Abstract/2022/04000/General_Practice_in_England__The_Current_Crisis,.7.aspx

Chemotherapy is the treatment of disease using chemical substances, commonly used to treat cancer. Chemotherapy is often cytotoxic and two common risks relate to kidney and liver damage. Affected patients may require delays or changes to treatment and even treatment suspension. Whilst only <10% of patients will encounter kidney and or liver damage, current best practice requires monitoring for all patients with regular blood testing. There is currently no way to stratify risk in individual patients. **The vision for this project is to accurately predict the risk of kidney and liver damage in patients receiving chemotherapy**. The result of this prediction will mean low risk patients can be saved unnecessary trips to hospital for blood tests and monitoring whilst high risk patients can receive more appropriate management. To date, computer scientists at Durham University, working closely with pharmacists at University College London Hospital (UCLH) have developed a machine learning algorithm to accurately predict liver and kidney function. Results have been shared at a number of key conferences and stakeholder events and there is strong clinical interest in utilising this algorithm in clinical practice. **The main area of focus for this project will be a prospective diagnostic accuracy study**. This will produce a commercial proof-of-concept (POC) software application that will allow the validated algorithm, produced by Durham University academics, to be used in live clinical practice. Key objectives will include: * Develop POC software to allow algorithm to be tested in a live clinical setting * Rapid iterative development of POC to enable commercial launch * Data collection to validation algorithm * Identification of third-party software interface opportunities (particularly chemotherapy e-prescribing systems) * Development of commercialisation plan Currently ~375k patients are diagnosed with cancer every year in the UK, with ~28% of these expected to receive chemotherapy for their primary disease[[1]][0]. With similar rates of cancer incidence across the Western world, this innovation could personalise care for millions globally. https://www.cancerresearchuk.org/health-professional/cancer-statistics/incidence [0]: #_ftn1 [1]: #_ftnref1