Virtually every aspect of modern life has been, and is continuing to be, radically re-shaped by innovation and technology – and healthcare in the NHS is no exception.

In fact, the NHS is a hotbed of innovation and technological advancement that is revolutionising how our hospitals and clinicians work, and how patients are treated. Technology is continually opening up new possibilities for prevention, care and treatment.

The NHS Long Term Plan, also known as the NHS 10-Year Plan, is a document published by NHS England just over three years ago, which sets out its priorities for healthcare over the next 10 years as it looks towards its 80^th anniversary.

But here lies a conundrum. While the NHS might be a hotbed of innovations and technological advancement, the long term plan also acknowledges the way care is delivered remains locked into the service model largely created when the NHS was founded in 1948.

Part of the vision is for technology to play a central role in realising the NHS Long Term Plan, helping clinicians use the full range of their skills, reducing bureaucracy, stimulating research and enabling service transformation. People will have more control over the care they receive and more support to manage their health, to keep themselves well and better manage their conditions.

Digital transformation in action

• Ensure clinicians can access and interact with patient records and care plans wherever they are.

CHFT’s Medical Iinteroperability Gateway (MIG) makes vital GP, social care and community healthcare patient data available to hospital and other clinicians, increasing efficiency and improving care quality.

Designed to connect otherwise incompatible systems, the MIG plugs the gaps when patients admitted to hospital can’t provide medical and other historical details, providing patient data that ranges from demographics and diagnoses to social care plans, allergies, recent procedures, therapies and tests.

This enables Emergency Department (ED) and other acute clinicians can make informed treatment decisions faster, preventing unnecessary hospital admissions and duplicated tests.

• Use predictive techniques to support local health systems to plan care for populations.

THIS is using predictive analytics to gauge the survival chances of hospital patients, feeding into ‘real time’ standardised mortality ratios.

The pioneering work is being done at CHFT’s Huddersfield Royal Infirmary and Calderdale Royal Hospital in Halifax. It means the hospitals will have current mortality ratios instead of waiting for the national publication of the Hospital Standardised Mortality Ratio data (HSMR), which is delivered three months in arrears.

THIS’ Information Services (IS) team is collating data from the Trust’s Cerner Milliennium EPR and other far-reaching clinical systems to gather information that then populates predictive R analytics software, an open-source software used for all kinds of data science, statistics, and visualisation projects. R programming language is powerful, versatile, and able to be integrated into business information platforms, to help users get the most out of business-critical data.

The benefits of real time HSMR will enable to the trust to pinpoint trends and variations before the national figures are published, as well as improve its ability to improve its clinical review process.

Patients arriving at the hospitals are assessed on several criteria which create a HSMR metric – these include a patient’s age, gender, exposure to deprivation, how they were admitted, comorbidity, whether they have a paliative care code and the actual diagnosis.

Based on the information gathered, a patient is allocated an expected death calculation (EDC) ranging between 0 and 1. A score of 0 indicates certain death while a score of 1 indicates 100 per cent survival.

Taking into account all known factors, the scores are worked out incrementally, so for instance, someone might be given an EDC of 0.376 – which can be expressed as a percentage, ie they have a 37.6 per cent chance of dying.

The EDC figures create a ‘league table’ of patients - those at the ‘top’ with scores at the high end of the ratio scale and those at the ‘bottom’ being patients with the lower survival chances.

Oliver Hutchinson, who is part of THIS’ IS team and leads the mortality ratio analysis says:

“It will help us pinpoint potential clinical areas to improve. For example, if you’ve got a patient that had a low likelihood of death and they actually died, we can look into that case and hold a clinical review to see if there is anything we can learn to prevent a death in similar circumstances in the future.”

The system can also highlight errors in recording a patient’s medical history. For example, someone with a low EDC might have certain factors missing from their medical record, which would decrease their survival chances. With these issues signposted in advance, it allows for more accurate reporting.

Creating the real time HMSR is THIS’ latest predicitive analytics project. Other examples have focused on ICU admissions during the pandemic and, more recently, A&E admissions, to predict the percentage of patients arriving at A&E being admitted to hospital or sent home.

• Use intuitive tools to capture data as a by-product of care in ways that empower clinicians and reduce the administrative burden.

Voice recognition (VR) technology is being used at CHFT hospitals to reduce repetitive tasks surrounding the recording, checking and despatch of patients’ clinical notes and letters.

Working in partnership with Nuance Dragon Medical One, a cloud-based solution hosted on Microsoft Azur, THIS is pioneering VR in CHFT’s musculoskeletal and physiotherapy services with an ambition to extend its use further.

In one six-month period, staff inputted more than 40,000 minutes of speech text using the voice recognition technology. Because inputting speech with voice recognition is 3-5 times faster than typing, THIS estimates the new system has saved approximately 150,000 minutes (2,500 hours) that would previously been required to type up the information.

Using Nuance Dragon Medical One, each clinician reads through the relevant script displayed on-screen and uses their voice commands known as AutoTexts to add specific details, such as the individual patient’s name and medical details, at highlighted brackets tailored to the patient within the text.

Luke Stockdale:

“Traditionally, consultants spoke into dictating machines and secretaries typed up the patient notes which had to be checked before being filed or despatched as letters. The process could involve four handovers.

“Now with voice recognition, the clinician speaks and uses AutoTexts, the secretary checks the notes, and the notes are actioned. Clinicians can then focus more on patient care and secretaries can focus on other more valuable tasks relating to management of the practice or department. Voice recognition also removes variance and creates common quality with all notes.”