How the healthcare system is using AI

Mid and South Essex (MSE) NHS Foundation Trust supports a population of 1.2 million people. It had an average did-not-attend (DNA) and short-notice cancellation rate of 8 per cent (the average in England is approximately 7.6 per cent). 

DNAs are more prominent in patients grappling with work and caring responsibilities, who find it difficult travelling to the hospital at a specific time. This disproportionately affects patients from minority and marginalised communities and can thereby lead to a widening of health inequalities. MSE found that its DNA rate for the most deprived was consistently higher than that for the least deprived across all age groups. 

Previously

Many practices and hospitals send letters, emails, text and calls to remind patients of their appointments. These blanket approaches do not account for individual patient behaviours. 

Healthcare staff may manually review appointment histories and contact patients who had missed appointments in the past, but this is time and resource intensive. Some organisations may overbook certain clinics to compensate for anticipated no shows.

Solution

MSE rolled out Deep Medical, which uses a model that integrates machine learning to predict patient no-shows and short-notice cancellations (<48 hours). Using AI, the system processes both structured data (like patient demographics and appointment history) and unstructured data (like electronic health record notes) to identify relevant patterns. 

The predictive analytics model is developed through a deep learning model which is a subset of supervised machine learning. This model is trained using training data, fine-tuned with validation data and evaluated with the test data to ensure the model is accurate and precise and has high sensitivity and specificity. The neural network format of this model allows it to recognise complex patterns in real-world data. 

It uses AI to understand patient engagement and develop personalised reminder schedules for patients. It then uses automation to send these reminders and manage patient appointments. It also identifies frail patients where low compliance could potentially be of clinical concern and highlights them to the relevant clinical teams. 

Outcomes

Based on a six-month pilot scheme at MSE, the model:  

• created an additional 1,910 patient visits into clinic 
• prevented 377 DNAs 
• filled 217 last-minute cancellation spots ️ 
• reduced DNA rate by up to 50 per cent when patients are proactively contacted two to three weeks ahead of their appointments
• when used at full scale, it is predicted it will allow an additional 80,000 patients to be seen each year at the trust, increasing productivity significantly

Top tips

Make sure you are clear on:

• what the specificity and sensitivity of the data is and what the impact of the mis-classified data is
• where the training data has come from and how valid it is in your local population or whether revalidation is required
• how to audit the machine learning to ensure it doesn’t drift from the primary use or introduce new biases.

Transforming wound care

Background

In North Cumbria approximately 50 per cent of the community nurses' workload involves managing both acute and chronic wounds, which amounts to the cost of around £41.7 million a year. 

Previously

Prior to using the AI model, community nurses would assess wounds with a manual tape measure. Band 3 and 4 staff are sometimes unsure how to dress and treat more complex wounds, therefore, this would require taking a photo and sending it back to base where a band 6 or 7 would tell them how to treat the wound by looking at photos and advising how to change and apply the dressing remotely. In more severe cases the band 6/7 would need to go out to treat the wounds themselves, reducing time to care.

This was a very subjective way to treat and dress the wounds with a lot of room for interpretation and no consistency across community teams.  There was no way to keep a record of the wounds and the changes. 

Solution

North Cumbria Integrated Care (NCIC) Foundation Trust, as part of North East and North Cumbria (NENC) ICB, transformed its approach to wound care by introducing a digital tool, Minuteful for Wound by Healthy.io, that uses a model which uses an AI algorithm to support clinicians at various experience levels to assess wounds confidently, consistently and safely. 

This model offers a higher degree of accuracy and precision than the current standard care. The digital tool enables consistent wound imagery, measurements and assessments to be captured easily at the point of care. The AI-powered colour recognition technology automatically detects wound area and tissue types within the wound bed from a three-second video scan of the wound by a smartphone. The AI also ensures image quality by recognising lighting conditions, distance and scan technique. Assessment prompts within the app guide allow clinicians of all experience levels to record assessments aligned to best practice guidance according to wound type.

The approach offers a live caseload review portal to provide central access to wound data. With this easily accessible data, clinical teams are able to optimise care plans.

The data gathered from AI measurements and assessments enables wounds that may be deteriorating or static over time to be identified and flagged for review that might otherwise not be identified. The new approach includes assigning clinicians to perform regular virtual caseload reviews within the tool's portal. This earlier detection and ability to review the caseload virtually, leads to earlier intervention and faster healing for patients.

Outcomes

Since using the model, NCIC has made the following positive impact for both staff and patients. 

For the workforce, it has: 

• opened access to data to help them understand wound care admission and case loads 
• upskilled and retrained the workforce to dress and treat wounds with confidence, which has led to greater empowerment of junior clinical staff to undertake assessments
• provided assurance for staff as they are creating a log of the wound which provides detail on how the wound was treated if the patient is admitted into hospital
• released time to care for band 6/7 staff
• reduced admin burden for staff.

For patients, it has: 

• reduced healing time and improved patient outcomes 
• reduced hospital admissions 
• supported self-care 
• minimised the likelihood of future infections, admissions to hospital and amputations. 

By working in partnership with the company, NENC ICB is hoping to: 

• reduce incidents by early identification of deteriorating wounds and clinical risks
• standardise clinical data collection and reporting
• improve consistency of adherence to formulary and best-practice guidelines
• increase system-wide collaboration with ICS partners to expand digital wound management beyond the trust, to primary care, acute services and nursing/care home settings
• enable access to services, including emergency departments/vascular specialists both via app and the web page portal for greater information sharing. 

Top tips

Make sure you are clear on:

• what IT infrastructure and resources are required for roll out and whether you have these already
• what training, standard operating procedures and ways of working are required for an effective roll out.

Impact on the workforce

To find out more about the benefits of this project for the workforce as well as patients, please read this case study.

Improving patient triage and optimising staff time

Background

Chapelford Medical Centre wanted to update its systems to triage patients in a smarter way, helping them get the care they need while also optimising use of staff time. 

Previously

Chapelford Medical Centre had a triage tool and questionnaire to collate information that supported staff with triaging patients. This used automation and operated like a decision tree. The centre wanted to move to an approach that allowed evidenced-based and AI-supported decisions about the best course of action for a patient’s care. The ultimate goal was to make informed decisions and spot opportunities that would not be possible with a human-only approach, based on combining the information presented by the patient with information within patient records. 

Solution

Anima, an integrated care platform, has both automation and AI modules. In the automation module, patients complete a questionnaire to support with triage and based on their answers, they will be directed to an appropriate care pathway. 

In the AI module, Anima uses analysis of the structured and unstructured data input into the questionnaire by patients, and machine learning based on the outcomes of patients who go through the pathway to predict the best course of action for future patients. It uses a model and algorithm to follow the pathway of patients through triage and their outcomes, and refine the course of action for future patients with similar characteristics who fill in the questionnaire. 

Outcomes

Anima went live within the GP practice in August 2023 and the ambition is to expand across the primary care network from August 2024. 

The Chapelford Medical Centre team plans to be in contact with Anima regularly and provide mutual training support. The team will review and work out whether improvements can be made to the AI suggestion, and also improve their own experiential learning for next time by using a trusted source of information.

They are aiming to create an enhanced access system based on need, and not ‘first in the queue’. This includes better management of patients, ultimately less demand, an improved workload profile for clinicians, and a fully-functional multi-disciplinary team. AI will support the team at each stage, enabling staff to signpost effectively, and employ more junior team members to make effective and supported decisions. Access increases, patient need decreases, clinician workload levels out, and work-life balance improves.

Chapelford hopes to achieve a position where all patients get the care they need every time, first time. This is a challenge, but one that will significantly improve patient journey, and also maximise the potential of appointments by managing patients within one consultation. They aim to maximise individual clinician’s skills and achieve a 0 per cent need for referral between teams for the presenting condition. 

Numbers of staff are unlikely to increase, so Chapelford hopes to increase effective capacity through more efficient use of the team and getting it right first time. By feeding back through an AI system, the system will continually improve and have a real-time feedback loop.

Top tips

Make sure you are clear on: 

• which elements of the solution use AI and that it really is AI
• what the AI is improving or making better, and how it is measured
• what the clinical safety case is for the solution you are buying
• what your North Star is and whether the AI product feeds into a bigger and wider solution.

Transforming the cataract care pathway

Background

The cataract care pathway at Chelsea and Westminster Hospital was experiencing long waiting times and limited capacity,

Previously

The pathway required patients to attend a mandatory in-person pre-operative appointment before being placed on the surgery waiting list for their first eye. The did-not-attend (DNA) rate for these pre-operative clinics ranged from 30 per cent to 50 per cent, resulting in wasted clinical capacity.

Post surgery, although there are very low risks of complications and adverse outcomes from cataract surgery (less than 5 per cent), all post-operative patients were requested to attend an in-person appointment four weeks later. After consultation, patients were listed for their second eye surgery or discharged to the community.

Solution

Chelsea and Westminster Foundation Trust leveraged Ufonia’s Dora, an AI clinical assistant that can telephone patients and have a routine clinical conversation using AI-enabled automation, to transform their cataract care pathway. 

Dora has a telephone-based voice conversation with patients at multiple timepoints in their cataract journey. This includes pre-operative assessment health screening, surgery and appointment reminders, post-operative checks and patient-reported outcome measures. The technology uses AI natural language processing to be able to interpret a patient's responses. It focuses on making AI accessible, meaning patients do not require any technology understanding, user accounts, hardware devices or training – they simply have a conversation. 

Outcomes

Dora was initially deployed as a pilot, with pre-defined technical and operational success criteria. These were met during the initial phase meaning the trust now plans to adopt Dora to support their cataract patient pathways, advocate for regional implementation across North West London ICB, and is considering expanding the solution to other clinical pathways. 

Successes include:

• 65 per cent call completion rate at pre-operative assessment stage, surpassing the original target of 60 per cent
• 91 per cent agreement rate between Dora and Chelsea and Westminster Hospital clinical staff, exceeding the goal of 90 per cent
• on-the-day cancellation rate has dropped significantly to just one patient in six months, compared to four patients per month before implementing Dora, surpassing the target of two patients per month.
• fewer than 2.5 per cent of patients at the post-operative check experienced an unexpected management change, well below our target of 10 per cent
• 63 per cent of completed calls passed the Dora assessment, exceeding the target of 50 per cent 
• honoured as finalists in the HSJ Digital Awards 2024 for the Driving Change through AI and Automation Award.

Top tips

Make sure you are clear on: 

• what implementation support exists from the tech side to ensure key milestones can be met
• what resources might be required and what lessons could be learned from previous implementations, to support internal resource allocation
• which metrics are being collected and when, to ensure robust evaluation following implementation.