Understanding how patients move through our hospitals

Understanding Patient Flow in Hospitals. Nuffield Trust; Sasha Karakusevic

“Round and round and round she goes; where she stops, nobody knows.” – The Original Amateur Hour.


This journal club discussion was based on a briefing paper published in October 2016 by the Nuffield Trust entitled “Understanding Patient Flow in Hospitals”. Whilst hospital management are the intended audience for the paper, we agreed that understanding the challenges and proposed solutions to patient flow would also be valuable from a clinical perspective. The aim of the paper was to explore the challenge of meeting the A&E target of admitting or transferring 95% of people within four hours. This metric was chosen due to its value in reflecting the patient flow through the hospital.


The paper undertook a mixed methods approach, utilising both theory and quantitative data. Firstly, it drew on principles understood from simpler systems (e.g. transportation research) to illustrate the dynamics of flow through a complex adaptive system (a hospital). Secondly, it used aggregated Hospital Episode Statistics (HES) data to perform high-level capacity and demand modelling across England.

Key Points

The author used the analogy of cars on a motorway to illustrate patients moving through a hospital. In this way they argued that as cars travelling faster require more space as a safety margin, so patients who move more quickly through the system require more bed space and resource relative to the time they spend in a bed. As wards fill up, providing this resource becomes less feasible and therefore there is congestion and movement through the system slows down.

An analysis of the HES data showed that in England 58% of admissions over the course of a year were for 12 hours or less, accounting for only 10% of total bed days. Conversely, 10% of all admissions had a length of stay greater than 7 days but that these occupied 65% bed days. The revenue generated per bed day was £454 827 for beds with short length of stay (less than 24 hours) and £79 411 for long length of stay (more than 7 days).

On comparing the twelve non-specialist trusts which managed to achieve the four hour standard with the twelve who did not, they found that hospitals with the least free space struggled most (as measured by bed occupancy, 94.5% versus 105.8%, and time between patients at midnight, 4.5 hours versus -1.1 hours).

An analysis of bed occupancy at midnight across England revealed that hospitals were frequently at capacity over the winter months, highlighting minimal system resilience. In order for hospitals to function at 85% capacity (as a management golden rule for efficiency) the total number of beds would need to be increased, the number of discharges increased or the number of admissions reduced.  Daily bed occupancy revealed that peak occupancy occurred at 8am (following overnight admissions and prior to patient discharge) and peak flow occurred in late afternoon (as most patients were being moved in and out of beds).

Suggested Solutions

  1. Reduce short stay admissions: reducing the volume of patients by redesigning assessment, diagnosis and short stay care. This would be unlikely to free up enough bed space to improve flow
  2. Achieving earlier discharge: reducing the time spent in hospital by redesigning rehabilitation and discharge processes. By reducing the length of stay of a small number of long term patients could have large gains in terms of bed days and patient flow.
  3. Improving control systems: by improving control systems to provide real-time workflow information to improve both individual patient care, system management and support process improvement. IT systems that reduce non-value-adding time and provide real-time operational data to support real-time decision making and service planning.



The paper highlighted the differences in how concepts and hypotheses are explored by managers compared to clinicians. It utilised analogies and stories to illustrate concepts, however, on discussion, it was felt that the analogies used were too simple to aid with the understanding of a complex adaptive system. Though the authors had access to a huge quantity of HES data, the analysis was very high level and did not take into account the nuances of local level hospital systems. In comparing trusts that were achieving and those which were failing against the target, there was no analysis of potential confounding factors or any analysis of sources of bias, and very little was written about the detail of the analysis. There was no appreciation of the workforce and senior decision-making as a barrier to effective patient flow, focusing instead on bed-space, which accounts for nursing but not necessarily medical staffing. There was an agreement that this is a complicated problem to tackle and that in order to do so there needs to be a collaboration between clinicians and managers at a local level in order to optimise conditions for hospitals to succeed.

Summary by Dr Alex Monkhouse.

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