Friday 19 August 2016
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With numerous operational systems and data input procedures that vary depending on the individual and their department, uncovering causal links between patients and operating theatre efficiency in the NHS is not a straightforward task.
Theatre time is the most costly activity that a hospital delivers. If cancellations can be reduced, huge cost savings can be achieved. However, the latest NHS statistics reveal over 2700 more last-minute theatre cancellations for non-clinical reasons than in the same period for 2014/15, pointing to an increasing problem that must be urgently addressed.
One way to reduce theatre cancellations is to manage other areas of care that act as influencing factors. Unscheduled care, such as the largely unpredictable nature arrivals in the Accident and Emergency (A&E) department, has an immediate knock-on effect on a number of important factors such as bed availability, waiting lists and theatre utilisation.
Such irregularities are notoriously difficult to plan for, especially given the national rise in A&E admissions since the start of the year. One solution is to use predictive analytics, resulting in far more effective warnings given to decision makers in advance. For example, certain distinct variables may, when occurring simultaneously, result in an increased number of admissions. This trend would be noted by the analytics system and subsequently flagged as a time where more proactive planning of resources can be achieved. By using quantitative data from previous real-life scenarios in the same hospital to predict increase in demand, analytics can be used to support effective planning with an aim to better manage the often problematic demands of unscheduled care.
Why is this important?
Without effective planning, unscheduled care can have a profound effect on theatre utilisation. Reduced availability of beds and longer waiting lists results in two distinct outcomes: an extended wait for the patient and missed targets for the Trust.
For the patient, the inconvenience of reorganising all of their travel, work and child-care arrangements is added to the increased emotional stress for a patient who may further deteriorate in the time it takes for the operation to take place.
For the trust, there are immediate financial implications. The average tariff per case is £1,500, with Orthopaedic operations costing between £4,500 and £9,000. Furthermore, should the patient not be re-booked with 28 days, the patient can choose any other hospital for the operation to be performed while the original hospital pays – a double financial blow for a Trust’s finances as they pay for operations they are not carrying out. With the NHS under increased pressure to remain financially viable, these costs represent massive inefficiencies that, with the right planning, have the potential to be vastly reduced.
The impact of predictive analytics cannot be understated. Even applied to only one area of a Trust, such as aiding the management of unscheduled care, bed management is subsequently improved – a factor that often leads to last minute non-clinical cancellations.
The amount of data available to Trusts is as vast as it is varied. Used effectively alongside analytics, the resulting insight has the potential to inspire operational efficiencies that can make an enormous difference to both patients’ lives and Trust’s bank balances.