Which technologies offer the greatest opportunities to save time in the NHS?

Artificial intelligence will also play an important role in the two other technologies ranked highest by respondents as likely to save them time in the next 5 years: clinical documentation tools and software for image analysis and test results An example of the former is AI-powered Ambient Voice Technology (AVT), which uses speech-to-text software to automatically transcribe patient queries and then natural language processing to turn those transcriptions into notes and summary letters. Asif Bachlani (consultant psychiatrist, Priory Group and associate non-executive director, Kent and Medway NHS and Social Care Partnership Trust) said speech recognition technology could offer a massive reduction in administrative burden for frontline clinical staff. Ben Jeeves (associate director of clinical information, clinical safety officer and advanced practice physiotherapist, North Integrated Musculoskeletal Service, Midlands Partnership University NHS Foundation Trust) similarly highlighted the heavy burden of administration and envisioned a future where AVT could lead to the complete elimination of the administration processes associated with all the clinical interactions that we have now to give us a much better quality in terms of the consultations that we had, how we interact with our patients.

When it comes to software for image analysis and test results, some of the most ambitious aspirations about its time-saving potential may take some time to be realized. Stephen Harden (Consultant Radiologist, University Hospital Southampton and Vice Chair of Clinical Radiology, Royal College of Radiologists) told us about his experiences with AI lung cancer screening, where current technology effectively identifies possible lung nodules and measure them It doesn’t speed me up a lot, but it does to some extent, he noted, but he also stressed that the technology offers a real benefit in helping to maintain the quality of image interpretation. However, in the longer term, there is considerable interest in the potential of AI to act as an image pre-reader, which could provide a more transformative way to save time and support force capability of work As Harden explained, the hope is that when reliable and accurate AI is fully developed and implemented, there would be a truly useful assistance for radiologists and imaging departments.

In the rapid evidence review, the sample of 500 studies found a potentially promising evidence base for the time-saving possibilities of automated screening and interpretation of test results, X-rays, patient records, voicemails, etc. Of the 34 studies in this space included in the sample, 85% reported a positive impact on staff time. However, as the review noted, newer technologies tend to have less evidence available about their impacts, and studies with positive results are more likely to be published than those in which the new technology did not work well. In contrast, more established technologies such as EHRs have generated a broader evidence base, which is more likely to contain studies with mixed or negative findings.

Imagining what technologies might free up time beyond the immediate future requires a significant degree of speculation. The exploratory nature of the interview format helped us address the nuances of this question. We asked experts to discuss the potential opportunities over the next 10 years and then 20 years and beyond, although many interviewees noted the inherent difficulty in forecasting these long-term time horizons.

Several interviewees discussed the potential of AI and other innovations to improve data analytics to support patient care and the health of the broader population. Experts spoke of a more proactive model of care, whereby AI could generate intelligence about a patient’s health or identify individuals or groups at risk of developing certain conditions or in vulnerable circumstances with the aim of ‘guide previous interventions and support the patient’s self-management. Interviewees described how this could not only improve patient health and address health inequalities, but could also reduce demand and create more capacity in the system:

We already have the data in front of us. It’s how we use it. And I think if we start learning how to use the data that’s already available, then we could really make a difference and actually save a lot of time if we automate some of that information gathering and that analysis… If you’re identifying the things early, whether it’s disruption, whether it’s risk, whatever it is, if you identify it early, it will save you time in the long run.
Faith Ndebele, Consultant Psychiatrist, Solent NHS Trust and Chair, Digital Psychiatry Special Interest Group, Royal College of Psychiatrists

This would be very powerful, if I could use my voice assistant within an EHR to ask: The patients blood pressure is a little low, what could I have done differently in the last 24 hours to prevent this from happening? And then [for the technology] to look at all blood results, medications, etc. and then come [up] with the relevant information. That would be the most helpful thing I could think of. It would save a lot of time.
Joseph Alderman, Anesthesia and Intensive Care Registrar, University Hospitals Birmingham NHS Foundation Trust and PhD Researcher, University of Birmingham

Interviewees also acknowledged barriers to achieving these ambitions, however, including variable data quality and the need to develop greater data analysis capacity among the NHS workforce.