It was once a promise by the Labour government
that by 2004 nobody seeking care in A&E departments
in the English National Health Service
should be waiting more than four hours for a decision to admit, transfer or discharge.
Even though there is evidence to suggest that
the introduction of a target reduced A&E waiting times
and improved patient satisfaction initially,
NHS performance against the target has been declining ever since.
So there are several potential reasons why NHS performance has been deteriorating,
including a rise in the number of people who are unable to access care in the community
so those are the so-called potentially avoidable hospital attendances,
or avoidable hospital admissions,
but also that the patients coming to hospital today
are in fact much more clinically complex
than those arriving 10 or 20 years ago.
We also know from the data and experiences of NHS staff
that A&E performance is particularly affected during the winter period,
which is likely because of people being sicker
and more likely to require a hospital admission during these times
putting pressure on the limited capacity available within the NHS.
There were about 600,000 so-called "trolley waits" in 2019.
So trolley waits are indicative of sub-standard care
that results from hospitals' insufficient capacity
to deal with patient demands.
Over the last 30 years hospital beds have been halved in the NHS
leaving England with one of the lowest bed-to-population ratios
across the developed world.
As a consequence of a mismatch between reductions in bed stock on the one hand
and changes in demand on the other hand,
we have seen a significant increase in average bed occupancy rates
to over 92% in recent years.
Previous research from the late 1990s
suggested that any bed occupancy above 90%
will adversely impact on the ability to deliver high-quality healthcare.
At that time, bed occupancy was somewhere in the low-80%.
So hard to imagine that one day bed occupancy
might actually exceed 90%
and sometimes even get to 100% for certain hospitals
on certain days.
Together with my co-author Rosa Juarez,
we investigated the spill over effects of high bed occupancy rates
on A&E performance in England.
Our research is also very timely
and important because the current government has pledged
to invest an additional £20.5 billion
until 2023-24, which provides a window of opportunity for hospitals
to regain the strength to improve their A&E performance.
We obtained daily information on A&E performance
so the proportion of patients missing the four-hour waiting time target,
and the number of trolley waits.
Compared with previous studies we were able to use a time varying denominator
for a more accurate estimate of daily bed occupancy rates.
We then constructed a panel data set
with information on A&E performance, bed occupancy rates
and other time varying information such as the number of hospital admissions,
or the number of attendances for every day
and every hospital trust in the country
to perform our statistical analysis.
So the data covered about 72 million patients
that attended A&E departments across the study period.
The average bed occupancy rate was around 93%
which remained relatively stable over time.
About 71 patients are waiting on average longer than 4 hours per day
in the average hospital,
which saw a significant increase from around 42 patients at the beginning of the study period.
So we found that more than 1.6 million patients
had been waiting on trolleys
from June 2016 to October 2019
with over 9000 patients waiting longer than 12 hours.
Most interestingly, we were able to model the non-linear relationship
between various levels of bed occupancy and A&E performance
and saw clear evidence to suggest that hospitals
should aim to reduce their bed occupancy rates to below 90%.
This is because A&E performance appears unaffected
by bed occupancy below this threshold.
Based on this finding, we were then able to predict
that the NHS would require about 4000 extra hospital beds
based on current demands for its health services.
There have been concerns about potentially negative effects
of high bed occupancy on hospital performance
and patient care, but so far the evidence
has often relied on cross-sectional data,
bed occupancy data from one particular unit of a hospital,
or flawed estimates of bed occupancy data.
We were able to fill this gap with our study
by examining the spill over effects
of bed occupancy on A&E performance
with evidence supporting the government's move
to invest in additional infrastructure.
Substantial capital investments could reduce bed occupancy
to more manageable levels
and translate into improved working conditions for staff
and enhanced quality of care for patients.