Impact of Real-Time Clinical Decision Support on Blood Utilization and Outcomes in Hospitalized Patients with Solid Tumor Cancer Kelley N. Wachsberg,

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Impact of Real-Time Clinical Decision Support on Blood Utilization and Outcomes in Hospitalized Patients with Solid Tumor Cancer Kelley N. Wachsberg, MD, MS, Kevin J. O'Leary, MD, MS, Ryan Buck, MD, Lyndsey S. O'Hara, RN, CCRC, Jungwha Lee, PhD, MPH, Gerald W. Rouleau, MS, Kimberly Koloms, MS, Charlotta Weaver, MD, MS Joint Commission Journal on Quality and Patient Safety Volume 45, Issue 1, Pages 57-62 (January 2019) DOI: 10.1016/j.jcjq.2018.05.004 Copyright © 2018 Terms and Conditions

Figure 1 Any transfusion ordered for a cancer patient with a hemoglobin (Hb) value of > 7 g/dL triggered an alert containing an evidence-based recommendation for transfusion, a link to institutional policies (Northwestern Medicine [NM] Indications for Transfusion), the three most recent Hb values, and an “override” option to continue with the transfusion. Joint Commission Journal on Quality and Patient Safety 2019 45, 57-62DOI: (10.1016/j.jcjq.2018.05.004) Copyright © 2018 Terms and Conditions

Figure 2 This loess plot—a non-parametric scatterplot-smoothing technique—shows the mean number of red blood cell (RBC) units transfused per 100 inpatient-days over time. Shaded red areas show the 95% confidence limits. The beginning and the end of the training period are demarcated by the two vertical segments, while the implementation of clinical decision support in the electronic medical record is marked by the dotted blue line. This loess plot was generated using a smoothing parameter of 0.6. Joint Commission Journal on Quality and Patient Safety 2019 45, 57-62DOI: (10.1016/j.jcjq.2018.05.004) Copyright © 2018 Terms and Conditions