Creating a Process to Trigger the Obstructive Sleep Apnea Order Set From the Stop Bang Evaluation in Total Joint Arthroplasty Patients

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Publication Date

11-17-2016

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Advisor:

MR Cooper, Jefferson College of Population Health, Thomas Jefferson University

Abstract

Obstructive sleep apnea (OSA) is a known cause of increased morbidity and mortality in the postop period. It frequently goes undiagnosed. STOP BANG (SB) is an inexpensive, valid screening tool to predict previously undiagnosed OSA. OSA order sets have been designed to mitigate the risk of OSA in the postop period. A process had been designed whereby the nurses in the preadmission testing area would perform an SB on each patient for elective surgery to screen for OSA. An SB > 4 would induce anesthesia to initiate the OSA order set. It became clear that this process had not taken root when data was collected that revealed that abnormal SB scores never triggered OSA orders. The department of orthopedics sought to find a consistent, sustainable process whereby an SB > 4 would trigger the OSA order set in elective total joint arthroplasty patients. Root cause analysis was used to uncover the reasons why the old process failed. Lean techniques were used to closely observe and analyze the process in preadmission testing. Many misconceptions, poor communication, and group fragmentation were discovered. There had been no data collection to show improvement, and no surveillance to make sure the change was sustained. Two phases of interventions were introduced to correct the process and streamline the workflow. Change management strategies were employed to get input on the issues, to communicate out the plan, and to get buy-in on the new process from key stakeholders. Data was collected after the interventions to show the percentage of total joint arthroplasty patients with SB > 4 who had OSA orders initiated. This percentage improved from 16.13% in May to 78.6% in August. The two phases of interventions resulted in marked improvement in rate of OSA orders in the patient population.

Presentation: 37:53

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