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P006 How can electronic order sets reduce time taken to prescribe medications on admission to PICU?
  1. Jenny Gray,
  2. Jane Hutchinson
  1. Bristol Royal Hospital for Children


Aim Our paediatric intensive care unit (PICU) has been using the Phillips ICCA electronic prescribing system since 2016. This system has an ‘order set’ function that allows a pre- populated list of medications to be created for use in certain situations. Potential benefits include reduced time to prescribe medications, reduced medication error rate and improved prescribing efficiency. The PICU quality improvement group and Pharmacy Informatics team created an order set for patients under 1 year of age admitted from theatre following cardiac surgery, which was implemented in June 2017. Our theatres do not use the ICCA system so as the patients are transferred with infusions running, there is a time gap where the patient has infusions running on PICU without a live prescription on ICCA. The aim of this project was to establish a reduction in the time taken for all 13 medications to be prescribed. In turn this would reduce the risk of running infusions without a live prescription.

Methods Data was collected retrospectively from the ICCA system on 15 patients pre and 15 patients post the introduction of the order set. Time of admission was set when the patient was allocated a bed on ICCA. The times at which each medication was prescribed were taken directly from ICCA. A user satisfaction survey was also sent out to during the order set implementation phase.

Results The time taken to prescribe all 13 medications was reduced on average by 9.4 hours per patient. The average time saved per medication was 43 minutes. Pre implementation, the average time to prescribe the medications was 11.4 hours (95% CI [5.5, 17.3]). Post implementation, the time taken to prescribe the same medications was 2 hours (95% CI [0.5, 3.5]). Pre implementation, prescriptions were started at least 30 minutes (average) after the patient arrived on PICU. Post implementation, prescriptions were started 30 minutes before patient admission and completed within 30 minutes of arrival. 20 staff members completed the user satisfaction survey. The survey had a 13% return rate. 70% of users agreed or strongly agreed that using the order set function improved prescribing efficiency and 55% of users agreed or strongly agreed that the order set helped ensure appropriate doses.

Conclusion Implementation of an order set for this patient group removed the risk of running infusions without a live prescription. This project is an example of how prescribing support functions within electronic prescribing packages can reduce time taken to write up medications within our unit, allowing prescribers to spend more time on other duties. Following the success of this intervention, further order sets will be created for use on our unit. A high level of clinical knowledge from the pharmacy support team and strong engagement with the clinical team was essential in creating a product that was fit for purpose. Limitations of this project are that we did not have the capability to assess a reduction in medication error. We now have increased support within the Pharmacy Informatics team to enable this for future projects.

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