Lin Shen1,2, Adam Wright2,3,4, Linda S Lee1,2, Kunal Jajoo1,2, Jennifer Nayor1,2,5, Adam Landman2,6. 1. Division of Gastroenterology, Hepatology, and Endoscopy, Brigham and Women's Hospital, Boston, Massachusetts, USA. 2. Harvard Medical School, Boston, Massachusetts, USA. 3. Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA. 4. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA. 5. Department of Gastroenterology, Emerson Hospital, Concord, Massachusetts, USA. 6. Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Abstract
OBJECTIVE: Determination of appropriate endoscopy sedation strategy is an important preprocedural consideration. To address manual workflow gaps that lead to sedation-type order errors at our institution, we designed and implemented a clinical decision support system (CDSS) to review orders for patients undergoing outpatient endoscopy. MATERIALS AND METHODS: The CDSS was developed and implemented by an expert panel using an agile approach. The CDSS queried patient-specific historical endoscopy records and applied expert consensus-derived logic and natural language processing to identify possible sedation order errors for human review. A retrospective analysis was conducted to evaluate impact, comparing 4-month pre-pilot and 12-month pilot periods. RESULTS: 22 755 endoscopy cases were included (pre-pilot 6434 cases, pilot 16 321 cases). The CDSS decreased the sedation-type order error rate on day of endoscopy (pre-pilot 0.39%, pilot 0.037%, Odds Ratio = 0.094, P-value < 1e-8). There was no difference in background prevalence of erroneous orders (pre-pilot 0.39%, pilot 0.34%, P = .54). DISCUSSION: At our institution, low prevalence and high volume of cases prevented routine manual review to verify sedation order appropriateness. Using a cohort-enrichment strategy, a CDSS was able to reduce number of chart reviews needed per sedation-order error from 296.7 to 3.5, allowing for integration into the existing workflow to intercept rare but important ordering errors. CONCLUSION: A workflow-integrated CDSS with expert consensus-derived logic rules and natural language processing significantly reduced endoscopy sedation-type order errors on day of endoscopy at our institution.
OBJECTIVE: Determination of appropriate endoscopy sedation strategy is an important preprocedural consideration. To address manual workflow gaps that lead to sedation-type order errors at our institution, we designed and implemented a clinical decision support system (CDSS) to review orders for patients undergoing outpatient endoscopy. MATERIALS AND METHODS: The CDSS was developed and implemented by an expert panel using an agile approach. The CDSS queried patient-specific historical endoscopy records and applied expert consensus-derived logic and natural language processing to identify possible sedation order errors for human review. A retrospective analysis was conducted to evaluate impact, comparing 4-month pre-pilot and 12-month pilot periods. RESULTS: 22 755 endoscopy cases were included (pre-pilot 6434 cases, pilot 16 321 cases). The CDSS decreased the sedation-type order error rate on day of endoscopy (pre-pilot 0.39%, pilot 0.037%, Odds Ratio = 0.094, P-value < 1e-8). There was no difference in background prevalence of erroneous orders (pre-pilot 0.39%, pilot 0.34%, P = .54). DISCUSSION: At our institution, low prevalence and high volume of cases prevented routine manual review to verify sedation order appropriateness. Using a cohort-enrichment strategy, a CDSS was able to reduce number of chart reviews needed per sedation-order error from 296.7 to 3.5, allowing for integration into the existing workflow to intercept rare but important ordering errors. CONCLUSION: A workflow-integrated CDSS with expert consensus-derived logic rules and natural language processing significantly reduced endoscopy sedation-type order errors on day of endoscopy at our institution.
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