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  • 24th May 2023

How can behavioural insights reduce future short term demand in the NHS?

Part 3 of a 5 part series on how the role of behavioural insights in reducing backlog in the UK's National Health Service

Yesterday, we outlined thoughts on how behavioural insights might tackle current demand as efficiently as possible.

Today, we’re highlighting some ways in which we might reduce future short term demand, including:

  1. Reducing necessary referrals, follow-ups, and investigations
  2. Improving patient self-care and management, and
  3. Supporting patients while they wait.

A behavioural insights approach has successfully reduced healthcare ‘overuse’ in the past

1. Reduce unnecessary referrals, follow-ups, and investigations

Introducing interventions to reduce the number of referrals, follow-ups, and investigations must be done with great care to ensure that only unnecessary referrals, investigations and so on are reduced.

It is very important that any intervention should not reduce necessary referrals, appointments or investigations (in fact in early 2020, amidst concern that people were not seeking treatment when needed during the pandemic, BIT worked with NHS England to test posters encouraging people to go to hospital for urgent health issues).

To focus only on unnecessary demand, it is important to be very specific about the exact behaviour which the intervention aims to change, and the specific context or set of circumstances in which it is appropriate for that behaviour to be changed.

Equally, it is important to ensure that any intervention leaves the decision to refer, follow-up or investigate at the clinician’s discretion, to be decided based on the clinical presentation of the patient. 

Despite these challenges, a behavioural insights approach has successfully reduced healthcare ‘overuse’ in the past. In a trial with DHSC, PHE and the Chief Medical Officer (CMO) for England at the time, BIT found that a ‘social norm’ feedback letter sent to GPs from the CMO, highlighting that 80% of practices in their local area prescribed fewer antibiotics per head than theirs, reduced antibiotic prescribing by 3.3%.

BIT’s New Zealand team built on this work and found a 9.2% reduction in prescriptions. Importantly, the letter also suggested three alternative actions that the recipient could take to reduce unnecessary prescriptions, but left the decision about whether or not to prescribe to the recipient GP. 

It should also be noted that in some instances, increasing short-term demand will be a way of reducing longer-term demand and use of resources in the NHS.

For example, in a trial conducted in partnership with the Greater Manchester Health and Social Care Partnership, BIT found that letters sent to practices with urgent cancer referral rates below the average in England, comparing the recipient’s urgent referral rate to other practices in their local area, led to a 9.6% increase in urgent referral rates.

While this effect means increased demand on urgent cancer referral pathways in the short-term, it equates to 2,500 potential additional earlier cancer diagnoses for patients over 6 months, which should in turn lead to better patient outcomes and reduced care costs.

Based on this evidence, and with the above cautions in mind, it would be worth testing a behavioural insights approach to reduce healthcare overuse in other clinical contexts.

Health technology and artificial intelligence likely have a large role to play

Health technology and artificial intelligence likely have a large role to play in this space and could help reduce healthcare overuse in a number of ways: by detecting health problems as early as possible (eg using at-home testing for chronic kidney disease in those most at risk), supporting clinicians to decide whether or not to refer a patient (eg by using technology to screen potentially cancerous moles), and helping clinicians to determine when follow-up is required (eg through remote monitoring).

This is discussed in another blog in this series, along with ways in which behavioural insights can encourage uptake of such innovations.

2. Improve patient self-care and management

Better equipping patients to manage their own conditions could help alleviate avoidable demand in the short (and long) term. A key aspect of this is to support patients to assess conditions themselves, and know which healthcare service would be most appropriate for them to attend if required.

A behavioural insights approach has in the past supported patients with this. In a trial with Connecting Care for Children and the Health Foundation, BIT found that a printed information package given to parents attending paediatric emergency services reduced non-urgent re-attendance by 16% in those attending with a male child (although no significant improvement was seen for families attending with a female child).

The information booklet contained:

  1. an information booklet called “How to help your unwell child”
  2. a fact-sheet helping parents diagnose the severity of their child’s illness
  3. a fridge magnet prompting use of the NHS 111 helpline, and
  4. a letter describing these materials. The principles on which this intervention was based could be applied to other (non-emergency) clinical contexts to help patients care for themselves where appropriate, or decide which action to take if not.

3. Support patients while they wait

Ensuring patients are well supported while they wait for their care could also help alleviate future avoidable demand (as noted in NHS England’s plan to tackle the backlog).

In the past, BIT has successfully used text messages to support patients on waiting lists for Improving Access to Psychological Therapies (IAPT) services.

In this trial, which was conducted with the Cabinet Office and Mayden (a provider of IAPT patient management software), BIT found that a series of text messages increased the number of patients completing a course of treatment (defined by NHS England as attending two or more treatment appointments) by 3.5% and the average number of appointments attended by 4.6%.

The messages were based on the concept of ‘operational transparency’ (showing people the effort which goes into a service) and aimed to make the wait for IAPT programmes more transparent, for example by reassuring recipients that they had not been forgotten and that work was being done to find them an appointment as soon as possible. This work could be built on to support patients waiting for care in other clinical contexts. 

The Behavioural Insights Team provides organisations with support applying behavioural insights to their local challenges. In the past, support provided by BIT has ranged from light-touch advisory support to leading full-scale randomised controlled trials. If you would like to discuss anything in this series of blogs in more detail or if you would like to explore how BIT might be able to support you to apply behavioural insights to start reducing your healthcare backlog, please get in touch: