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      Development and use of active clinical decision support for preemptive pharmacogenomics

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          Abstract

          Background

          Active clinical decision support (CDS) delivered through an electronic health record (EHR) facilitates gene-based drug prescribing and other applications of genomics to patient care.

          Objective

          We describe the development, implementation, and evaluation of active CDS for multiple pharmacogenetic test results reported preemptively.

          Materials and methods

          Clinical pharmacogenetic test results accompanied by clinical interpretations are placed into the patient's EHR, typically before a relevant drug is prescribed. Problem list entries created for high-risk phenotypes provide an unambiguous trigger for delivery of post-test alerts to clinicians when high-risk drugs are prescribed. In addition, pre-test alerts are issued if a very-high risk medication is prescribed (eg, a thiopurine), prior to the appropriate pharmacogenetic test result being entered into the EHR. Our CDS can be readily modified to incorporate new genes or high-risk drugs as they emerge.

          Results

          Through November 2012, 35 customized pharmacogenetic rules have been implemented, including rules for TPMT with azathioprine, thioguanine, and mercaptopurine, and for CYP2D6 with codeine, tramadol, amitriptyline, fluoxetine, and paroxetine. Between May 2011 and November 2012, the pre-test alerts were electronically issued 1106 times (76 for thiopurines and 1030 for drugs metabolized by CYP2D6), and the post-test alerts were issued 1552 times (1521 for TPMT and 31 for CYP2D6). Analysis of alert outcomes revealed that the interruptive CDS appropriately guided prescribing in 95% of patients for whom they were issued.

          Conclusions

          Our experience illustrates the feasibility of developing computational systems that provide clinicians with actionable alerts for gene-based drug prescribing at the point of care.

          Related collections

          Most cited references33

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          Effect of clinical decision-support systems: a systematic review.

          Despite increasing emphasis on the role of clinical decision-support systems (CDSSs) for improving care and reducing costs, evidence to support widespread use is lacking. To evaluate the effect of CDSSs on clinical outcomes, health care processes, workload and efficiency, patient satisfaction, cost, and provider use and implementation. MEDLINE, CINAHL, PsycINFO, and Web of Science through January 2011. Investigators independently screened reports to identify randomized trials published in English of electronic CDSSs that were implemented in clinical settings; used by providers to aid decision making at the point of care; and reported clinical, health care process, workload, relationship-centered, economic, or provider use outcomes. Investigators extracted data about study design, participant characteristics, interventions, outcomes, and quality. 148 randomized, controlled trials were included. A total of 128 (86%) assessed health care process measures, 29 (20%) assessed clinical outcomes, and 22 (15%) measured costs. Both commercially and locally developed CDSSs improved health care process measures related to performing preventive services (n= 25; odds ratio [OR], 1.42 [95% CI, 1.27 to 1.58]), ordering clinical studies (n= 20; OR, 1.72 [CI, 1.47 to 2.00]), and prescribing therapies (n= 46; OR, 1.57 [CI, 1.35 to 1.82]). Few studies measured potential unintended consequences or adverse effects. Studies were heterogeneous in interventions, populations, settings, and outcomes. Publication bias and selective reporting cannot be excluded. Both commercially and locally developed CDSSs are effective at improving health care process measures across diverse settings, but evidence for clinical, economic, workload, and efficiency outcomes remains sparse. This review expands knowledge in the field by demonstrating the benefits of CDSSs outside of experienced academic centers. Agency for Healthcare Research and Quality.
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            CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network.

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              Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review.

              Iatrogenic injuries related to medications are common, costly, and clinically significant. Computerized physician order entry (CPOE) and clinical decision support systems (CDSSs) may reduce medication error rates. We identified trials that evaluated the effects of CPOE and CDSSs on medication safety by electronically searching MEDLINE and the Cochrane Library and by manually searching the bibliographies of retrieved articles. Studies were included for systematic review if the design was a randomized controlled trial, a nonrandomized controlled trial, or an observational study with controls and if the measured outcomes were clinical (eg, adverse drug events) or surrogate (eg, medication errors) markers. Two reviewers extracted all the data. Discussion resolved any disagreements. Five trials assessing CPOE and 7 assessing isolated CDSSs met the criteria. Of the CPOE studies, 2 demonstrated a marked decrease in the serious medication error rate, 1 an improvement in corollary orders, 1 an improvement in 5 prescribing behaviors, and 1 an improvement in nephrotoxic drug dose and frequency. Of the 7 studies evaluating isolated CDSSs, 3 demonstrated statistically significant improvements in antibiotic-associated medication errors or adverse drug events and 1 an improvement in theophylline-associated medication errors. The remaining 3 studies had nonsignificant results. Use of CPOE and isolated CDSSs can substantially reduce medication error rates, but most studies have not been powered to detect differences in adverse drug events and have evaluated a small number of "homegrown" systems. Research is needed to evaluate commercial systems, to compare the various applications, to identify key components of applications, and to identify factors related to successful implementation of these systems.
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                Author and article information

                Journal
                J Am Med Inform Assoc
                J Am Med Inform Assoc
                amiajnl
                jamia
                Journal of the American Medical Informatics Association : JAMIA
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                1067-5027
                1527-974X
                February 2014
                26 August 2013
                26 August 2013
                : 21
                : e1
                : e93-e99
                Affiliations
                [1 ]Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital , Memphis, Tennessee, USA
                [2 ]Department of Information Sciences, St. Jude Children's Research Hospital , Memphis, Tennessee, USA
                [3 ]Department of Oncology, St. Jude Children's Research Hospital , Memphis, Tennessee, USA
                [4 ]Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic , Rochester, Minnesota, USA
                [5 ]Department of Pediatrics, Medical College of Wisconsin , Milwaukee, Wisconsin, USA
                Author notes
                [Correspondence to ] Dr James M Hoffman, Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Mail Stop 313, 262 Danny Thomas Place, Memphis, TN 38105-3678, USA; james.hoffman@ 123456stjude.org
                Article
                amiajnl-2013-001993
                10.1136/amiajnl-2013-001993
                3957400
                23978487
                e398f0c2-e83b-436d-80d8-ed176d3d351d
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/

                History
                : 7 May 2013
                : 3 July 2013
                : 4 August 2013
                Categories
                1507
                1506
                Research and Applications
                Custom metadata
                editors-choice
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                Bioinformatics & Computational biology
                pharmacogenetics,electronic health record,clinical decision support,personalized medicine

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