by Frank Zadravecz, MPH
Fortunately, my research world isn’t rife with inter-colleague conflict. Data, on the other hand continues to pick fights. As researchers, we have banded together to make sense of the patient measurements, physiologic alarms, and adverse outcomes we see in our hospital. Often where we are led by the data is perplexing. Pieces that we assume would be straightforward often aren’t clear-cut, making our complex task even more convoluted.
We see this complexity when we need to reference a single hospital bed across databases, chronologically determine which patients generated vital signs from that bed, and then assign alarms from a bedside monitor to both their respective patient and nurse responder. One data feed may list our single occupancy rooms in two different ways; one feed labels the bed with a number and the other with a letter. Sometimes our single beds are given conflicting labels, or not even labeled at all. Looking to determine which patient was occupying a bed at a given time, sometimes we see that a patient was assigned an invisible, or “Ghost Bed” when a transfer order was placed but census availability was prohibitive. Next, making the assignment of which bedside device monitored a given patient or generated a specific alarm is difficult. Although we know with relative certainty where a patient is at a particular time and we have serial or machine numbers for our bedside devices, sometimes these devices change rooms. Multiply our triangulation by nearly 15,000 unique ward encounters over a six-month period and this complexity balloons.
To answer the question about how bedside alarm load impacts adverse events in our population, our success depends on the effectiveness of our interdisciplinary team. I have been fortunate to have access to our institution’s Clinical Research Data Warehouse programmers to explain why our data appear the way they do, as well as access to the NIH funded Informatics for Integrating Biology & the Bedside interface to explore relationships between data sources. The inpatient data we collect have caveats and intricacies. Working with the combined skillsets of our in-house information technology, clinical engineering, and medical professionals we are providing context to the data we collect over a patient’s hospital stay.
Are we cheating if we have multiple fighters in the ring? Our tag team fight is not yet a knockout or a unanimous decision win, but we’d like to think that we’re slowly gaining the upper hand.
Frank Zadravecz, MPH, is an alumnus of the Columbia University Mailman School of Public Health, a rising second year medical student at the University of Illinois at Chicago College of Medicine, and a Society for Hospital Medicine Student Hospitalist Scholar at the University of Chicago Medical Center. You can follow Frank on Twitter @frankzadravecz.
Leave A Comment