The CDC calculated that given a 275-bed hospital if the average length of stay could be reduced by just 4 hours, that would be equivalent to adding 10 additional hospital beds to the facility. The Length of stay is the leading KPI, tied directly to healthcare costs and efficient bed management.
Discharging patients late at the root of the problem?
In UK, delays in discharging elderly patients from hospital cost the NHS £820 million per year. According to a report by the National Audit Office (NAO) this happens primarily when a patient (usually someone over 65) remains in hospital after their treatment has finished because there isn’t any suitable place to discharge them.
To put it in other words, NHS reports that:
More than a million hospital days were lost in UK, due to delayed discharges over a year (2015).
On top of that, delays in discharging patients from a hospital in UK have even risen 23% since June 2015.
In Ireland, Home and Community Care Ireland (HCCI) found out that 685 patients are in beds across the country despite acute care having ended. With the HSE estimating the cost of keeping a person in a hospital overnight as between €800 and €900, these beds would cost the taxpayer at least €540,000 a night in Ireland.
One-third of delays are avoidable?
A smaller study made on 888 inpatient days concluded that the majority of patients experienced delays, delays interfered with the discharge of half of the patients, while avoidable delays occurred one-third of the time. Below you can see a table with the causes of delays. Also, this study reports that delays were mainly related to processes that could be improved by interventions by care teams and managers.
According to this study, for ED patients, the root cause of discharging delays is the laboratory processing time and the time from the result being available. It represents the longest time periods on which to focus to minimize the total ED throughput time.
Reducing Admitting Delays
The data from Memorial Hermann Southwest Hospital in Houston, Texas revealed it was taking roughly 7.5 hours from the time a discharge order was written to the time a bed was available for the next patient.
In a 950-bed hospital, ED patients were assigned to an inpatient bed when the room was empty but not yet cleaned. ED staff had no way of knowing when the room was clean and ready for the patient without making multiple phone calls to several departments.
The working hours for most of the housekeeping staff did not match the peak workflow of patients discharged in the late afternoon. Consequently, the bulk of beds that needed to be cleaned occurred when the fewest housekeepers were on duty.
In another case mentioned on our blog, waiting time for beds was slowing down patient flow significantly, while a hospital improvement consultant found out housekeepers were simply playing cards in peak hours.
Turn up the heat when it comes to data
Hospitals clearly need to get more out of data. Let’s have a look at how accurate data can help in solving some of the Length of Stay and bed availability problems mentioned above.
Know when a bed is clean and available
These analytics tell how many beds are occupied, to be cleaned, in process, available and reserved at a specific time. Knowing this is possible thanks to tracking the location of patients and cleaning staff. Beds change colors on dashboards to blue: occupied, red: to be cleaned, blue: a doctor is with a patient and green when a bed is available. This gives staff a clear and simple tool to understand not only when a patient was discharged but when are the beds really available and clean.
Bed Turnover Time
Thanks to understanding the bed availability in a hospital more accurately (as we explained above), Bed Turnover Time becomes available accurately in a hospital as the next highly important KPI. This is because it adds another answer to the complexity of bed availability, quantifying how long it did take to housekeepers to prepare the bed from the time a patient has been discharged.
Laboratory Turnaround Time
Measuring the time it takes to get the test results back from the lab accurately is the first step in improving Laboratory TAT. Quantifying its relation to patient flow issues is important in identifying the problem.
Staffing of Housekeepers
With the right data, it’s now easy to plan the shifts of cleaning personnel in line with their actual need in peak hours, when the highest number of patients is discharged.
Number of patients in waiting and waiting times
Knowing how many patients were waiting and what were the waiting times in any given moment, it’s now easy to compare it to times when bed availability was low to find out if there was a strong relation between them in your hospital.
Download this short brochure to find out how can Locatible 3 feet accurate RTLS accurately monitor all this data and improve visibility, safety, and efficiency in any hospital.
How did you like this post? Thanks for rating!