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Smarter Health Screening

25 Mar 2021

Digital can target those at highest risk and ensure timely appointments and safe reporting.

Most cancers have a better prognosis if detected and treated early. In the majority there is a range of risk factors invariably including age, lifestyle, hereditary and environmental factors. Present NHS screening programmes are mostly dependent on age criteria for entry. This means that younger people at considerable risk may not be included, equally older people at lower risk of cancer may be exposed to risks of investigations (e.g. radiation). The present uptake level for breast cancer screening is only 70% of those eligible. Cancer Research UK has calculated that by September 2020 the pandemic has created a backlog of around 3 million individuals

The present system is mostly impersonal. The individual receives a letter asking them to attend, if they do not attend then they are sent a reminder, and if they do not respond it is often presumed they do not wish to be screened. Digital advertisers are aware that the more the request addresses your personal needs and desires the more likely you are to make a purchase. Health Psychologists recognise the range of beliefs and attitudes that influence intention and follow through behaviour. If the individual risk of developing a cancer and the benefits of attending screening were made more salient, then those at risk may be more likely to attend and have their cancer detected earlier. Compare these two letters:

Dear Ms Jones, Welcome to NHS Breast screening. We would like to invite you for your free mammograms (breast x-rays). Screening aims to find breast cancer early when successful treatment is more likely. We have made you an appointment… (2 weeks time). (NHS standard letter template)

Dear Ms Jones, we believe that you may have a higher-than-average chance of developing breast cancer due to your age and medical history. You can see how your risk has been calculated by going to the NHS app and opening the page labelled health risks. You can also add more information to increase the accuracy of this calculation. Detecting breast cancer early means that successful treatment is more likely. For people with a high or medium risk we offer free mammography every 5 years, and you can book an appointment at a local clinic via the app or by contacting us at XXXXXX. If you would like to discuss your choices then please contact your GP but if you do not wish to have screening, then please let us know so we do not contact you again.

Which letter would make you most likely to respond? How many people actually open official looking letters now? Should we be able to choose our preferred route of communication?

Which approach reduces health inequalities? Using multi-channel communication and customer response management systems will reduce the risks of non-response, using risk stratification will focus on those with greatest need,

The second letter is supported by a range of digital solutions including the following:

  • Using psychologically informed user-based design, the wording is designed to increase the response rate. Multi-channel communication, repeated nudges and a responsive booking system remove barriers between intention and behaviour, increases uptake rates and reduce health inequalities, as is the visibility of non-response at the time of other NHS contacts. Machine learning is already being used to adjust the content of patient letters based on response.
  • Using more accurate risk stratification, by pulling in multiple datapoints from various NHS records and using weighted elements to create a personalised risk score, enables the screening resources to be focussed where they have most benefit.
  • Using digital customer services management approaches empowers patients, this is important where self-belief is consistently demonstrated to support adaptation to ill health and recovery in cancer patients. This allows more efficient use of resources. Using this approach from first invitation to attend through to informing the patient of the results and defining their next steps should increase uptake, and decrease the risks such as failed invitations and lost results.

But do we need more radical transformation?

How can we use artificial intelligence systems to improve the system? Machine learning could help improve the risk stratification and also improve response rates by understanding what encourages people to come for screening. Image recognition is now well established and could be used to support more rapid and safer diagnosis, particularly in areas where there are shortages of clinicians.

Can the existing screening programmes become a part of a wider prevention and early detection programme? Will providing individuals with visibility on their risk levels for a range of diseases be more likely to undertake more risk reducing actions (e.g. exercise, diet)? Can we improve earlier detection through education and guidance, e.g. symptom awareness and teaching self-examination?

We would love to hear your views on smarter screening.