Clinician adherence to pharmacogenomics prescribing recommendations in clinical decision support alerts.

Thoughtful integration of interruptive clinical decision support (CDS) alerts within the electronic health record is essential to guide clinicians on the application of pharmacogenomic results at point of care. St. Jude Children's Research Hospital implemented a preemptive pharmacogenomic testing program in 2011 in a multidisciplinary effort involving extensive education to clinicians about pharmacogenomic implications. We conducted a retrospective analysis of clinicians' adherence to 4783 pharmacogenomically guided CDS alerts that triggered for 12 genes and 60 drugs. Clinicians adhered to the therapeutic recommendations provided in 4392 alerts (92%). In our population of pediatric patients with catastrophic illnesses, the most frequently presented gene/drug CDS alerts were TPMT/NUDT15 and thiopurines (n = 3850), CYP2D6 and ondansetron (n = 667), CYP2D6 and oxycodone (n = 99), G6PD and G6PD high-risk medications (n = 51), and CYP2C19 and proton pump inhibitors (omeprazole and pantoprazole; n = 50). The high adherence rate was facilitated by our team approach to prescribing and our collaborative CDS design and delivery.

Incorporating G6PD genotyping to identify patients with G6PD deficiency.

Glucose-6-phosphate-dehydrogenase (G6PD) deficiency is a common X-linked enzyme disorder associated with hemolytic anemia after exposure to fava beans or certain medications. Activity testing is the gold standard for detecting G6PD deficiency; however, this test is affected by various hematologic parameters. Clinical G6PD genotyping is now included in pharmacogenetic arrays and clinical sequencing efforts and may be reconciled with activity results. Patients (n = 1391) enrolled on an institutional pharmacogenetic testing protocol underwent clinical G6PD genotyping for 164 G6PD variants. An algorithm accounting for known interferences with the activity assay is proposed. We developed clinical decision support alerts to inform prescribers when high-risk medications were prescribed, warning of gene-drug interactions and recommending therapy alteration. Of 1391 patients with genotype results, 1334 (95.9%) patients were predicted to have normal G6PD activity, 30 (2.1%) were predicted to have variable G6PD activity and 27 (2%) were predicted to have deficient G6PD activity. Of the 417 patients with a normal genotype and an activity result, 415 (99.5%) had a concordant normal G6PD phenotype. Of the 21 patients with a deficient genotype and an activity result, 18 (85.7%) had a concordant deficient activity result. Genotyping reassigned phenotype in five patients with discordant genotype and activity results: three switched from normal to deficient, and two switched from deficient to normal. G6PD activity and genotyping are two independent testing methods that can be used in conjunction to assign a more informed G6PD phenotype than either method alone.

Optimizing Drug-Drug Interaction Alerts Using a Multidimensional Approach.

OBJECTIVES: Excessive alerts are a common concern associated with clinical decision support systems that monitor drug-drug interactions (DDIs). To reduce the number of low-value interruptive DDI alerts at our hospital, we implemented an iterative, multidimensional quality improvement effort, which included an interdisciplinary advisory group, alert metrics, and measurement of perceived clinical value. METHODS: Alert data analysis indicated that DDIs were the most common interruptive medication alert. An interdisciplinary alert advisory group was formed to provide expert advice and oversight for alert refinement and ongoing review of alert data. Alert data were categorized into drug classes and analyzed to identify DDI alerts for refinement. Refinement strategies included alert suppression and modification of alerts to be contextually aware. RESULTS: On the basis of historical analysis of classified DDI alerts, 26 alert refinements were implemented, representing 47% of all alerts. Alert refinement efforts resulted in the following substantial decreases in the number of interruptive DDI alerts: 40% for all clinicians (22.9-14 per 100 orders) and as high as 82% for attending physicians (6.5-1.2 per 100 orders). Two patient safety events related to alert refinements were reported during the project period. CONCLUSIONS: Our quality improvement effort refined 47% of all DDI alerts that were firing during historical analysis, significantly reduced the number of DDI alerts in a 54-week period, and established a model for sustained alert refinements.

Using EHR Data to Detect Prescribing Errors in Rapidly Discontinued Medication Orders.

BACKGROUND: Previous research developed a new method for locating prescribing errors in rapidly discontinued electronic medication orders. Although effective, the prospective design of that research hinders its feasibility for regular use. OBJECTIVES: Our objectives were to assess a method to retrospectively detect prescribing errors, to characterize the identified errors, and to identify potential improvement opportunities. METHODS: Electronically submitted medication orders from 28 randomly selected days that were discontinued within 120 minutes of submission were reviewed and categorized as most likely errors, nonerrors, or not enough information to determine status. Identified errors were evaluated by amount of time elapsed from original submission to discontinuation, error type, staff position, and potential clinical significance. Pearson's chi-square test was used to compare rates of errors across prescriber types. RESULTS: In all, 147 errors were identified in 305 medication orders. The method was most effective for orders that were discontinued within 90 minutes. Duplicate orders were most common; physicians in training had the highest error rate (p < 0.001), and 24 errors were potentially clinically significant. None of the errors were voluntarily reported. CONCLUSION: It is possible to identify prescribing errors in rapidly discontinued medication orders by using retrospective methods that do not require interrupting prescribers to discuss order details. Future research could validate our methods in different clinical settings. Regular use of this measure could help determine the causes of prescribing errors, track performance, and identify and evaluate interventions to improve prescribing systems and processes.

Pharmacogenetics for Safe Codeine Use in Sickle Cell Disease.

After postoperative deaths in children who were prescribed codeine, several pediatric hospitals have removed it from their formularies. These deaths were attributed to atypical cytochrome P450 2D6 (CYP2D6) pharmacogenetics, which is also implicated in poor analgesic response. Because codeine is often prescribed to patients with sickle cell disease and is now the only Schedule III opioid analgesic in the United States, we implemented a precision medicine approach to safely maintain codeine as an option for pain control. Here we describe the implementation of pharmacogenetics-based codeine prescribing that accounts for CYP2D6 metabolizer status. Clinical decision support was implemented within the electronic health record to guide prescribing of codeine with the goal of preventing its use after tonsillectomy or adenoidectomy and in CYP2D6 ultra-rapid and poor metabolizer (high-risk) genotypes. As of June 2015, CYP2D6 genotype results had been reported for 2468 unique patients. Of the 830 patients with sickle cell disease, 621 (75%) had a CYP2D6 genotype result; 7.1% were ultra-rapid or possible ultra-rapid metabolizers, and 1.4% were poor metabolizers. Interruptive alerts recommended against codeine for patients with high-risk CYP2D6 status. None of the patients with an ultra-rapid or poor metabolizer genotype were prescribed codeine. Using genetics to tailor analgesic prescribing retained an important therapeutic option by limiting codeine use to patients who could safely receive and benefit from it. Our efforts represent an evidence-based, innovative medication safety strategy to prevent adverse drug events, which is a model for the use of pharmacogenetics to optimize drug therapy in specialized pediatric populations.

Alert dwell time: introduction of a measure to evaluate interruptive clinical decision support alerts.

Metrics for evaluating interruptive prescribing alerts have many limitations. Additional methods are needed to identify opportunities to improve alerting systems and prevent alert fatigue. In this study, the authors determined whether alert dwell time-the time elapsed from when an interruptive alert is generated to when it is dismissed-could be calculated by using historical alert data from log files. Drug-drug interaction (DDI) alerts from 3 years of electronic health record data were queried. Alert dwell time was calculated for 25,965 alerts, including 777 unique DDIs. The median alert dwell time was 8 s (range, 1-4913 s). Resident physicians had longer median alert dwell times than other prescribers (P < 001). The 10 most frequent DDI alerts (n = 8759 alerts) had shorter median dwell times than alerts that only occurred once (P < 001). This metric can be used in future research to evaluate the effectiveness and efficiency of interruptive prescribing alerts.

Adverse drug event detection in pediatric oncology and hematology patients: using medication triggers to identify patient harm in a specialized pediatric patient population.

OBJECTIVE: To investigate the use of a trigger tool for the detection of adverse drug events (ADE) in a pediatric hospital specializing in oncology, hematology, and other catastrophic diseases. STUDY DESIGN: A medication-based trigger tool package analyzed electronic health records from February 2009 to February 2013. Chart review determined whether an ADE precipitated the trigger. Severity was assigned to ADEs, and preventability was assessed. Preventable ADEs were compared with the hospital's electronic voluntary event reporting system to identify whether these ADEs had been previously identified. The positive predictive values (PPVs) of the entire trigger tool and individual triggers were calculated to assess their accuracy to detect ADEs. RESULTS: Trigger occurrences (n = 706) were detected in 390 patients from 6 medication triggers, 33 of which were ADEs (overall PPV = 16%). Hyaluronidase had the greatest PPV (60%). Most ADEs were category E harm (temporary harm) per the National Coordinating Council for Medication Error Reporting and Prevention index. One event was category H harm (intervention to sustain life). Naloxone was associated with the most grade 4 ADEs per the Common Terminology Criteria for Adverse Events v4.03. Twenty-one (64%) ADEs were preventable, 3 of which were submitted via the voluntary reporting system. CONCLUSION: Most of the medication-based triggers yielded low PPVs. Refining the triggers based on patients' characteristics and medication usage patterns could increase the PPVs and make them more useful for quality improvement. To efficiently detect ADEs, triggers must be revised to reflect specialized pediatric patient populations such as hematology and oncology patients.

PG4KDS: a model for the clinical implementation of pre-emptive pharmacogenetics.

Pharmacogenetics is frequently cited as an area for initial focus of the clinical implementation of genomics. Through the PG4KDS protocol, St. Jude Children's Research Hospital pre-emptively genotypes patients for 230 genes using the Affymetrix Drug Metabolizing Enzymes and Transporters (DMET) Plus array supplemented with a CYP2D6 copy number assay. The PG4KDS protocol provides a rational, stepwise process for implementing gene/drug pairs, organizing data, and obtaining consent from patients and families. Through August 2013, 1,559 patients have been enrolled, and four gene tests have been released into the electronic health record (EHR) for clinical implementation: TPMT, CYP2D6, SLCO1B1, and CYP2C19. These genes are coupled to 12 high-risk drugs. Of the 1,016 patients with genotype test results available, 78% of them had at least one high-risk (i.e., actionable) genotype result placed in their EHR. Each diplotype result released to the EHR is coupled with an interpretive consult that is created in a concise, standardized format. To support-gene based prescribing at the point of care, 55 interruptive clinical decision support (CDS) alerts were developed. Patients are informed of their genotyping result and its relevance to their medication use through a letter. Key elements necessary for our successful implementation have included strong institutional support, a knowledgeable clinical laboratory, a process to manage any incidental findings, a strategy to educate clinicians and patients, a process to return results, and extensive use of informatics, especially CDS. Our approach to pre-emptive clinical pharmacogenetics has proven feasible, clinically useful, and scalable.

Development and use of active clinical decision support for preemptive pharmacogenomics.

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.

Safe and successful implementation of CPOE for chemotherapy at a children's cancer center.

Computerized prescriber order entry (CPOE) for medications has been implemented in only approximately 1 in 6 United States hospitals, with CPOE for chemotherapy lagging behind that for nonchemotherapy medications. The high risks associated with chemotherapy combined with other aspects of cancer care present unique challenges for the safe and appropriate use of CPOE. This article describes the process for safe and successful implementation of CPOE for chemotherapy at a children's cancer center. A core principle throughout the development and implementation of this system was that it must be as safe (and eventually safer) as existing paper systems and processes. The history of requiring standardized, regimen-specific, preprinted paper order forms served as the foundation for safe implementation of CPOE for chemotherapy. Extensive use of electronic order sets with advanced functionality; formal process redesign and system analysis; automated clinical decision support; and a phased implementation approach were essential strategies for safe implementation of CPOE. With careful planning and adequate resources, CPOE for chemotherapy can be safely implemented.

Development and implementation of a pharmacist-managed clinical pharmacogenetics service.

PURPOSE: The development and implementation of a pharmacist-managed clinical pharmacogenetics service are described. SUMMARY: A pharmacist-managed clinical pharmacogenetics service was designed and implemented at an academic specialty hospital to provide clinical pharmacogenetic testing for gene products important to the pharmacodynamics of medications used in the hospital's patients. A series of accredited educational seminars were conducted for our pharmacists to establish competencies in providing pharmacogenetic consults for the genes to be tested by the clinical pharmacogenetics service. The service was modeled after and integrated with an already-established clinical pharmacokinetics service. A steering committee was formed to evaluate the use of available tests, new evidence for implementation of additional tests, and other service quality metrics. All clinical pharmacogenetic test results are first reported to one of the pharmacists, who reviews the result and provides a written consultation. The consultation includes an interpretation of the result and recommendations for any indicated changes to therapy. In 2009, 136 clinical pharmacogenetic tests were performed. The service has been met with positive clinician feedback. The successful implementation of this service highlights the leadership role that pharmacists can take in moving pharmacogenetics from research to patient care. CONCLUSION: The development of and experience with a pharmacist-managed clinical pharmacogenetics service are described. The program's success has depended on collaboration between the clinical laboratory and pharmacists, and pharmacists' pharmacogenetic recommendations have been well accepted by prescribers.