What are we trying to do?

We are working with Manchester University NHS Foundation Trust as they implement an integrated and innovative Electronic Patient Record solution called Hive. The Hive Programme is bringing about wide-spread change, and improvement, in every part and process in the organisation, transforming how they work for the continued benefit of their patients.  

 

This project aims to describe how a predictive model within Hive that supports the identification of patients who are likely to not attend their outpatient appointment can be used to help staff manage outpatient no-shows in the trust.

 

 

Why is this important?

Outpatient no-shows (i.e., missed appointments) are a substantial problem for healthcare systems. The no-show rate across the NHS is 8%, or 5,656,365 no-show outpatient appointments per year, at a cost of £225 million.

 

One of the solutions that have been developed to manage outpatient no-shows is based on the use of predictive modelling. Predictive models could be used to identify patients at high no-show risk, allowing health care professionals to take action to prevent or manage no-shows. Many such predictive models have been developed, but very few have been evaluated in clinical settings – and none of these studies took place in the UK. There is also very little information about how hospital staff and patients view the use of these predictive models.
 

How are we doing it?

Throughout this process, we are collaborating with our Public and Community Involvement & Engagement (PCIE) panel. In our discussion sessions with the panel, we will explore the panel’s views of outpatient no-shows and predictive modelling, we will co-interpret the findings of the pre-implementation study, and we will co-design the post-implementation study.

 

Before undertaking primary research, we completed a rapid systematic review of interventions aiming to reduce outpatient no-shows by using predictive models, examaning the effectiveness of these interventions, as well as the associated costs, acceptability to staff and patients, and effect on health inequities. This review found that several promising interventions can be used in combination with predictive models. Specifically, predictive model-based reminders and predictive model-based patient navigator phone calls are probably effective at reducing no-shows.  However, it is uncertain whether predictive model-based overbooking is effective. Additionally, the researchers concluded that more evidence is needed regarding the cost-effectiveness, acceptability, and equity of all identified intervention.

 

The full rapid systematic review is available from the Journal of the American Medical Informatics Association here.

 

Following on from this review we are conducting primary research both before and after the predictive model is implemented. We are currently interviewing staff, to help us understand how staff are currently managing outpatient no-shows, how they are intending to use the predictive model once it becomes available, and what barriers, facilitators, benefits, and consequences they foresee in the implementation of the model.

 

After the predictive model goes live, we will conduct both quantitative and qualitative research to identify how the predictive model is used within the trust and describe its impact.

 

 

Who are we working with?

• Manchester University NHS Foundation Trust 

 

 

Downloadable resources

 

• Publications
  • Oikonomidi, T, Norman, G, Mcgarrigle, L, Stokes, J, van der Veer, N, Dowding, D. (2022) Predictive model-based interventions to reduce outpatient no-shows: a rapid systematic review. Journal of the American Medical Informatics Association.
• News stories
  • A new rapid review finds that a number of interventions may reduce hospital outpatient no-shows (published January 2023)

 

 

More information

 

 

 

 

Head of ARC-GM

 

Sue Wood