Assessing the impact of allocating waiting list resources in proportion to health need

Case study: Our work with the Northumbria Healthcare NHS Foundation Trust

What we did

We worked with the Northumbria Healthcare NHS Foundation Trust to explore this issue, using their Referral To Treatment (RTT) orthopaedic waiting list and Patient Reported Outcome Measures (PROMs) database.

The latter holds anonymised information on a patient’s pre- and post-op EQ5D score, a general measure of self-reported health across a number of domains (pain, anxiety, mobility, usual activity and self-care) to assess and compare quality of life. In this case study, references to measurable health refer to EQ5D.

Our specific aims were:

• To characterise the existing waiting list and explore if there were inequalities in waiting times or health status (EQ5D score) across different stratifications of the RTT population, including deprivation, sex, age and risk factors such as obesity and smoking.
• To identify if there was an opportunity to reallocate resources in a proportionate way that targeted patients with highest unmet need, and how this would impact overall population health and health inequalities across the RTT population – we refer to this as the ‘proportionate approach’.
• To build a model which would simulate the proportionate approach and quantify the impact this would have on both the individual patients and any health inequalities in the population compared to the baseline universal approach to RTT populations.

The proportionate approach: targeting resources to those with the greatest need

The results

As a result of our work on this project, we found that:

• Waiting times are broadly equal – we found no evidence to suggest that there are inequalities in waiting times within the Northumbria orthopaedic wait list across deprivation, age, sex, rurality, obesity status or smoking status.
• There are inequalities in the health of the orthopaedic RTT population - patients who lived in more deprived areas, were younger, living with obesity or were smokers were more likely to be living in poorer health.
• Patients in the poorest health stand to gain the most from receiving the operation.
• The proportionate approach reduces avoidable time spent in poor health across the RTT population.
• Taking a more proportionate approach according to unmet health need also reduces health inequalities.
• Targeting those with the highest unmet need also has a positive economic impact.
• The benefits of the proportionate approach to RTT populations would be greatest for areas with the biggest waiting list challenges.

Conclusions and next steps

There are substantial inequalities in illness and health across populations in England. Tackling the increasing waiting lists and levelling up health are two government priorities with clear synergies. Our work with Northumbria Healthcare NHS Foundation Trust demonstrates the potential value in leveraging analytics to inform approaches to the elective recovery programme and population health management more broadly to ensure resources are targeted proportionately to unmet health need. Furthermore, levelling up the health of populations will have clear economic and wider societal benefits too.

This analysis could be developed and extended further to:

• support the elective recovery programme in ensuring resources are allocated in proportion to unmet health need within and across regions;
• estimate national level total and specialty specific RTT need and unmet need, including those who are likely to have not joined the waiting list yet but would’ve been expected to in the absence of the pandemic; and
• identify areas and patient groups across England and within specific regions and integrated care systems with highest unmet need most likely to benefit from a proportionate approach.