Education, Research, and Quality Improvement: Education and Quality Improvement |

Use of a Data Mining Clinical Decision Support Tool to Optimize Pharmaceutical Care FREE TO VIEW

George Udeani, PharmD; Nana Akuffo, PharmD; John Evans, PharmD; Salim Surani, MD; Joseph High, PharmD
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Corpus Christi Medical Center, Corpus Christi, TX

Copyright 2016, American College of Chest Physicians. All Rights Reserved.

Chest. 2016;149(4_S):A237. doi:10.1016/j.chest.2016.02.246
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SESSION TITLE: Education and Quality Improvement

SESSION TYPE: Original Investigation Poster

PRESENTED ON: Saturday, April 16, 2016 at 11:45 AM - 12:45 PM

PURPOSE: Medication errors have been postulated as important preventable factors in patient treatment. Clinical Decision Support System (CDSS), an electronic clinical surveillance system is a data mining tools which feed real-time ADT (Admission, Discharge, and Transfer) laboratory, pharmacy, radiology, and surgery data. The system processes these data, based on pre-defined rules to detect changes in patients’ conditions in real-time, then flags clinical alerts. Real-time critical alerts can be utilized by physicians and pharmacists in optimizing care and minimizing adverse events.

METHODS: Clinical pharmacy team acknowledged alerts, consulted with the medical team or used exiting protocols, to modify drug therapies, based on various clinical parameters. Alerts flagged by CDSS, one-year post implementation - July 2015 were evaluated. Approval for the study was obtained from the IRB.

RESULTS: In July 2015, CDSS fired 5970 alerts and 5961 (99.85%) were acknowledged. 19.81% of the alerts were in the critical care units. Acknowledgment categories were as follows: drug therapy modified (69%), no action necessary post review (11%), expired alerts/discharged patients (10%), consults (2%) associated activations (5%), rejections (1%), follow-up (1%), non-drug interventions (0.4%), and follow-up urgent (0.1%). Numbers of alerts for top 10 drugs were: enoxaparin (634), famotidine (465), levofloxacin (224), pantoprazole (209), meropenem (201), vancomycin (155), ciprofloxacin (142), metronidazole (139), warfarin (126), and ketorolac (124). Numbers of alerts for top ten intervention categories were: venous thromboembolic prophylaxis (975), renal dosage adjustment (894), intravenous to oral conversion (659), intravenous to oral conversion of antibiotics (478), renal dosage adjustment of antibiotics (894), inappropriate for condition/dose (323), vancomycin monitoring (280), glycemic control (271), anticoagulation (160), and de-escalation of therapy, based on culture and sensitivity data (158).

CONCLUSIONS: CDSS is being effective in helping to optimize treatment, reduce costs and prevent medical errors.

CLINICAL IMPLICATIONS: CDSS software in conjunction with pharmacists and clinicians can be helpful in optimizing therapy in real-time.

DISCLOSURE: The following authors have nothing to disclose: George Udeani, Nana Akuffo, John Evans, Salim Surani, Joseph High

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