Background: Over-utilization of telemetry can lead to reduced bed availability in telemetry units, which is a crucial bottleneck in hospital throughput. To address this, in our hospital a new orderset embedded in the EMR (Electronic Medical Record) was created. The orderset brought several changes to the workflow. Before, providers can admit patients to the telemetry unit by placing an indication via free text. Now, they must choose an indication from a predefined list adapted from the American Heart Association (AHA). The other change is the automatic BPA (Best Practice Alert) that occurs every 2 days that reminds providers that the telemetry order is active and that it must be addressed by either discontinuation of the order, or continuation but with documentation of an indication. This study aims to determine if these changes will regulate telemetry usage and have an effect on hospital throughput measures.
Methods: The study consisted of two phases relative to the start date of the new orderset (July 1, 2021), pre-intervention (April to June 2021) and post-intervention (July to September 2021). The population of study were patients admitted to the 9E Medical Telemetry Unit of Mount Sinai Morningside. The following pre- and post-intervention values were compared with the following tests: 1) number of admissions from the Emergency Department to 9E (chi-square test), 2) length of telemetry usage in days (unpaired t-test), 3) length of stay in hospital days (unpaired t-test) and 4) length of stay in the ED in hours from placement of the bed request order to admission to the floor (unpaired t-test).
Results: There was an absolute decline in the number and percentage of ED to 9E admissions out of all ED admissions from 593 (22.3%) to 478 (17.3%), that was statistically significant (p = 0.000138). For the length of telemetry usage, there was a decrease from a mean of 5.7 days to 4.1 days, with a difference of 1.6 days which was statistically significant (p < 0.0001). Average length of stay in hospital days decreased from 9 days to 8 days. The 1 day difference was not statistically significant (p value 0.2457). Lastly, the average waiting time for an available telemetry bed increased from 14.88 hours to 18.58 hours. The difference of 3.7 hours was statistically significant (p < 0.0001).
Conclusions: It is promising to see that the new order set seemed to have made an impact in regulating telemetry usage in terms of 1) the number of days that the order is active and 2) the number and percentage of total admissions going to the telemetry unit.With these changes, it was hoped that other important metrics would be positively impacted as well. That with regulating telemetry usage, it would lead to greater bed availability and quicker throughput of patients into the telemetry unit, as well as improve the quality of patients being ordered for telemetry in terms of appropriateness of indication, leading to shorter hospital stay. However, that was not seen, with length of hospital stay being unchanged and waiting time in the ED even increasing. This reinforces the fact that these hospital metrics are affected by many factors, not just telemetry utilization alone, which is just one variable, albeit a very important one. It is notable though that during the post-intervention phase, the emergency department saw a higher volume of patients (18907 patients from 16997), coinciding with a surge in COVID-19 cases in New York City. This could possibly explain why the waiting time for ED boarders became significantly longer.