Why Managing NHS Waiting Lists is about Safety, Not Just Numbers

C2-Ai Opinion Article by Dr Mark Ratnarajah, practising paediatrician, UK Managing Director, C2-Ai.
NHS waiting lists have remained highly visible in the public eye, especially since the pandemic. With more than seven million people now waiting for elective procedures, media reports have focused on growing numbers month after month.

Addressing the elective recovery challenge has understandably become a national priority, with targets set to reduce long waiters, and commitments made to bring numbers down.

But in a time when healthcare resource is stretched, numbers form only part of the challenge facing NHS professionals.

Finding those in greatest need: the people sometimes hidden in the numbers

An urgency to manage waiting list safely has become imperative for NHS organisations and integrated care systems.

More and more are adopting innovative approaches, as they examine how they can bring the waiting list down as expediently as possible, whilst also considering patient safety, and managing the list in a way that minimises avoidable harm and suffering.

Strategies have developed far beyond waiting list validation techniques, such as a numerical cleaning of the list, where duplicates are removed, or where patients are contacted to see if they still want their operation.

Instead, there has been a shift to understanding changing clinical risks for individuals at scale, in order to identify and expedite support to vulnerable high-risk patients on waiting lists, who might otherwise be missed.

Those patients might not necessarily be cancer patients, or people who have been waiting for 74 or even 104 weeks - who might be relatively straightforward to find. Some of the most vulnerable might be waiting for a seemingly routine procedure, but still be at a high risk of decompensating, without appropriate intervention. They might be at risk of developing additional complexity, or even face an increased risk of mortality as they wait, due to the impact of comorbidities or progression of their underlying condition.

There is no average patient

Healthcare systems are introducing new methodologies and technology supported approaches, to help them manage lists based on a simple idea: that there is no average patient.

No two patients on a waiting list are the same. Much more than a number, each person waiting has a unique and potentially complex mix of clinical risks, as well as biological, social and psychological needs that might need to be considered.

These factors can be more difficult to measure than time spent on a list, but can be important in determining an individual’s ability to wait well, or highlight their risk of coming to harm if nothing is done to prioritise their needs.

Measuring the dynamic individual need of the patient, and doing so at scale, has consequently become a new requirement in the mission to safely stratify elective lists.

Healthcare providers and systems are, in response, using technology and combining data to gain an increasingly sophisticated understanding of where a culmination of events will take an individual patient in the future.

They are acquiring a means to plot a trajectory and to anticipate the needs of the patient in the near future, in order to mitigate serious consequences or harm for that individual, as well as avoiding additional cost and requirements placed on the health system.

Acting on clinical decision support, working as a system

Trusts and entire ICSs are now deploying clinical decision support to help clinical and operational teams make decisions. But in a constrained system where there is limited resource, including human capital, beds, ICU access, theatre capacity, how can this intelligence be best used?

Prioritisation of patients - moving them up the waiting list - might be possible to an extent. But balanced against capacity limitations, system-wide thinking is starting to deliver the biggest impact.

Configuring system wide services to help to de-risk patients, has been one successful approach. This has in part taken the form of targeted and tailored prehabilitation, based on individual patient needs. In other words - understanding what measures can be applied in the community to support a patient as they prepare for surgery, to improve the success of their operation, and to enhance and speed up their recovery. And thinking beyond a single operation - measures to help them manage their chronic condition and support their ongoing wellbeing into the future, to prevent further demand on the system.

Regions can use intelligence to better match demand and supply. This might mean creating surgical hubs in the right locations to manage low complexity, high volume activity. Or it might mean matching supply and demand across a region to individual patient risks - for example moving patients to sites with appropriate additional capacity, and not moving high-risk patients to sites without an emergency department or ICU. It could also mean more judiciously using private capacity - matching patients to the capabilities of those sites.

Genuine patient engagement

Intelligence being generated can also provide patients with more informed, and potentially safer choice.

Simply asking a patient if they still want a procedure without an evidence base can place a lot of pressure on an individual. Some might decline their operation because they don’t want to become a burden, or because of a sense of duty. Others might feel apprehension about going into hospital.

But a discussion with those most at risk, can allow informed decisions based on a trajectory of what is likely to happen if an operation does or does not happen, and potentially about the type of procedure they have, such as a lower risk anaesthetic where appropriate.

There is now an opportunity to measure the inflection point at which things go wrong, and to present the best options for patients to mitigate that problem, to monitor and engage patients all the way to the point at which they have their surgery, and to help ICSs deliver on their fundamental mandate of delivering integrated health and care for individuals across their population.

Most Popular Now

AI Tool Offers Deep Insight into the Imm…

Researchers explore the human immune system by looking at the active components, namely the various genes and cells involved. But there is a broad range of these, and observations necessarily...

Do Fitness Apps do More Harm than Good?

A study published in the British Journal of Health Psychology reveals the negative behavioral and psychological consequences of commercial fitness apps reported by users on social media. These impacts may...

AI Tool Beats Humans at Detecting Parasi…

Scientists at ARUP Laboratories have developed an artificial intelligence (AI) tool that detects intestinal parasites in stool samples more quickly and accurately than traditional methods, potentially transforming how labs diagnose...

Making Cancer Vaccines More Personal

In a new study, University of Arizona researchers created a model for cutaneous squamous cell carcinoma, a type of skin cancer, and identified two mutated tumor proteins, or neoantigens, that...

AI, Health, and Health Care Today and To…

Artificial intelligence (AI) carries promise and uncertainty for clinicians, patients, and health systems. This JAMA Summit Report presents expert perspectives on the opportunities, risks, and challenges of AI in health...

AI can Better Predict Future Risk for He…

A landmark study led by University' experts has shown that artificial intelligence can better predict how doctors should treat patients following a heart attack. The study, conducted by an international...

AI System Finds Crucial Clues for Diagno…

Doctors often must make critical decisions in minutes, relying on incomplete information. While electronic health records contain vast amounts of patient data, much of it remains difficult to interpret quickly...

Improved Cough-Detection Tech can Help w…

Researchers have improved the ability of wearable health devices to accurately detect when a patient is coughing, making it easier to monitor chronic health conditions and predict health risks such...

A New AI Model Improves the Prediction o…

Breast cancer is the most commonly diagnosed form of cancer in the world among women, with more than 2.3 million cases a year, and continues to be one of the...

Multimodal AI Poised to Revolutionize Ca…

Although artificial intelligence (AI) has already shown promise in cardiovascular medicine, most existing tools analyze only one type of data - such as electrocardiograms or cardiac images - limiting their...

New AI Tool Makes Medical Imaging Proces…

When doctors analyze a medical scan of an organ or area in the body, each part of the image has to be assigned an anatomical label. If the brain is...