The Walton Centre NHS FT and a consultancy services company have partnered to develop an artificial intelligence chatbot.
Tata Consultancy Services is working with the trust to develop an automated solution to help reduce waiting times for patients and create efficiencies.
Initially the chatbot will be developed as a prototype, be cloud native, and use conversational AI technologies.
The mission for the chatbot is to help reduce the three month wait time for patients, initially to focus on outpatient referrals to neurologists. According to The Walton Centre, patients with headaches make up the largest number of referrals, with a three-month average waiting period to be seen by a consultant. The consultancy company will now work with the trust with an aim to transform the way these patients are diagnosed.
Through a series of structured questions, the chatbot will collect details on a patient’s condition and symptoms, and along with their medical history, be available for a clinician to review. Depending on the clinician’s assessment, a patient may be put on a fast-track to be examined by a consultant or offered guidance on alleviating symptoms while they wait.
Dr Anita Krishnan, Divisional Clinical Director for Neurology, The Walton Centre, and a Consultant Neurologist specialising in headaches, said: “Technology is a huge part of medicine and it’s exciting to work with TCS to create a new artificial intelligence-based solution which will help our patients.
“The chatbot system also has potential to be extended into other areas of medicine, which could benefit even more patients. We are working closely with TCS and our other specialist partners to ensure the new solution is effective and safe and improves efficiency and patient outcomes.”
In June 2022, HTN reported on a pilot of chatbot technology used across Lancashire and South Cumbria NHS to understand the status of patients on their waiting lists. Initial results from the pilot with 2,282 patients, showed 15 percent indicated there were able to leave the waiting list, with the remaining indicating the need to stay on the waiting list. However, 10 percent of patients in the pilot highlighted the need for an appointment sooner than previously scheduled.
