Do you know how often the clinicians in your hospital visit the patients under their care? Do individual nurses visit patients about the same number of times per shift? Does the day of the week matter? Or time of day?
Until recently, it has been impossible to know when specific clinicians were visiting a particular patient room. But now, through our technology, clinical intervention data is easily captured and analyzed, making what was previously invisible…visible.
One example is a hospital customer that was able to watch how frequently patients were visited during weekday hours, but they thought nights and weekends might be the “Wild West.” We were able to pull data showing that this wasn’t the case. In the graph below, green dots are aggregated data for the day shift, and red dots represent the night shift.
Yes, there was a drop in clinical interventions (patient visits) during nights and weekends, compared to weekdays. This is to be expected to a certain extent, especially overnight as patients are sleeping. But the numbers were reasonable and there was no cause for concern.
For another customer, we looked at patient visits on a daily basis. Again below, green represents day shift and red night shift. Each vertical column is one day. Note that there are clinicians that are in and out of patient rooms more than 50 times in a shift, and others check on patients fewer than 10 times per shift.
The extremes at either end are a concern. The busiest clinicians are subject to stress, fatigue and burnout – not to mention they may be more likely to cause a medical error in their haste. The clinicians who rarely visit patients may cause patients to fall more often or suffer from pressure ulcers more, and to have lower patient satisfaction scores.
Clinical intervention data can help hospitals hyper-focus on where their outliers are. Our IoT sensors and data analytics platform can identify concerning problems in real-time. Once hospitals have identified individuals at either end of the “busyness” spectrum, they can observe their behavior and/or have discussions. There may be operational or workflow issues that can be solved, which often helps other staff members become more efficient and effective as well.
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This is part of a series about Making the Invisible Visible: Clinical Intervention Data.