Restoring Healthcare- smarter use of data

a case study of bed occupancy

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Understanding the data we utilise is key to operations management. Without a knowledge of how it is calculated, knowledge of how the system behaves, and observation of the processes in real life then we are liable to misinterpret the data. In this blog we will use the example of the bed occupancy data to illustrate this.

The press statement issued on 19th November 2020 by NHS England States that “the average occupancy rate for all beds open overnight was 77.0% in Quarter 2 2020/21 compared with 64.3% in Quarter 1 2020/21 and 88.0% Quarter 2 2019/20.”  On first reading it would be reasonable to assume that hospitals have many empty beds. Many newspapers, and even more on social media, picked up on these figures as a sign that there were plenty of beds available for the next wave of the pandemic. There are many reasons why these statistics alone could be taken to be misrepresentative use of data. In Q1 of 2020, the number of non-COVID emergencies was exceptionally low, electives were cancelled, and discharges were rapidly undertaken (with subsequent controversy and debate about appropriateness). Since the summer however, the NHS had been undertaking elective and routine emergencies as well as COVID work. Many individuals working in hospitals reported that they were working at full capacity similar to the severest winters. A more in-depth look into methods of counting and patient flow through hospitals reveals why these figures alone could be easily misinterpreted.

It is widely accepted that waiting times in emergency departments, and assessment units, will decrease if there is a bed available for an admission as soon as it is required. In 1999 demonstrated the association between bed occupancy and flow, with an 85% occupancy level being cited as the critical level. In healthcare management this has been misinterpreted as meaning you should aim for 85% occupancy. If you never exceed 85%, you are wasting 15% of hospital capacity. If you have extremely elevated levels of occupancy but never exceed 100% for the hospital overall, you will likely have good flow and few waits. If you have below 100% for each individual specialty, then you will have good flow and better care, as there is evidence that patients dealt with by the correct specialty and correct location have better outcomes and shorter length of stay.

The basic principle of bed management should be “is there a bed of the appropriate type available when it is needed?”

Points to consider when looking at bed occupancy figures include:

  • True occupancy is over 100% during the day when the patients in a discharge lounge who are not counted in the bed occupancy because they do not occupy beds
  • The numerator figure also does not include those waiting in the emergency department for a bed. For example, if a 100 bedded hospital has 85 beds with patients in but has 15 patients in the Emergency Department (ED) who an hour later will be moved to beds that will count as 85%
  • The denominator in the bed occupancy calculation is the total number of beds. On any day there will be some that are not available for use, e.g. not staffed due to sickness. Staff sickness rose to over 6% in wave one and it is recognised that staffing ratios also fell
  • If there is high bed occupancy across the whole hospital it is less likely that the specific speciality bed needed by an individual patient will be available. The evidence shows that if they go to the wrong ward for their needs, they have worse outcomes and longer stays. 100% can never be achieved as some beds will be for a specific purpose, e.g. stroke unit, coronary care, surgical vs medical beds
  • Bed occupancy changes throughout the day and measuring occupancy at midnight could be seen to be misleading. As can be seen from the graph below from Understanding Patient Flow in Hospitals, bed occupancy is highest during the day and evening, which is when the operational impact of poor patient flow is most likely to be seen
Figure 1: Daily and Weekly Cycle of Bed Occupancy (Health Foundation 2016)
Figure 1: Daily and Weekly Cycle of Bed Occupancy (Health Foundation 2016)
  • COVID has meant that some beds are not available. You cannot put COVID patients in a ward with non-COVID patients. COVID wards must operate at low occupancy to guarantee appropriate isolation of a surge of cases or as a wave increases. Similarly, you do not want chemotherapy or immuno-supressed patients in a bed next to someone with any transmissible infection. the more you have beds ring-fenced for single specialty use the lower will be the occupancy required to maintain flow
  • If hospitals had true spare capacity, they would have minimal wait from the decision to admit in the Emergency department to moving to the ward bed. This data is publicly available and shows there were “71,041 (of a total of 451,800) four-hour delays from decision to admit to admission this ’ with “2,141 were delayed over twelve hours (from decision to admit to admission)”. We know this is an underestimate as the DTA (decision to admit) is open to delayed decisions and gaming. It is very unlikely that hospitals are empty and have long waits for beds

Therefore, true occupancy could be more accurately calculated using the following formulae:

source: Capgemini (2021)
source: Capgemini (2021)

Using specialty specific occupancy as well as hospital figures would add clarity, as would a balancing quality measure of patients not in the best specialty bed for their needs.

We can now see that like with all data, no one metric should be used in isolation. Flow and capacity can only be understood by looking at measures along the whole pathway to understand true capacity. That data can then be utilised along with knowledge of the system and observation in real time to understand the system dynamics and the areas of improvement.

Data without knowledge and observation is ignorance.
We hope we can help you to make better use of your data by having a deeper understanding of its implications and presenting it in such a way as to support smart decision making.

We would love to hear from you if you are thinking about how you can use your data more effectively to help patient flow and think we might be able to help you develop your ideas.

 

Authors


Matthew Cooke

Matthew is Chief Clinical Officer at Capgemini. He spent most of his career working in the NHS as an emergency physician and was the National Clinical Director for Urgent and Emergency Care.

 

 

Mustafa Ghafouri

Mustafa is a Data Scientist and Healthcare Consultant at Capgemini and a medical doctor, who has worked with multiple healthcare organisations in the field of Demand and Capacity and Patient Flow.

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