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.

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.

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.

Retrospective study of the effect of disease progression on patient reported outcomes in HER-2 negative metastatic breast cancer patients.

BACKGROUND: This retrospective study evaluated the impact of disease progression and of specific sites of metastasis on patient reported outcomes (PROs) that assess symptom burden and health related quality of life (HRQoL) in women with metastatic breast cancer (mBC). METHODS: HER-2 negative mBC patients (n = 102) were enrolled from 7 U.S. community oncology practices. Demographic, disease and treatment characteristics were abstracted from electronic medical records and linked to archived Patient Care Monitor (PCM) assessments. The PCM is a self-report measure of symptom burden and HRQoL administered as part of routine care in participating practices. Linear mixed models were used to examine change in PCM scores over time. RESULTS: Mean age was 57 years, with 72% of patients Caucasian, and 25% African American. Median time from mBC diagnosis to first disease progression was 8.8 months. Metastasis to bone (60%), lung (28%) and liver (26%) predominated at initial metastatic diagnosis. Results showed that PCM items assessing fatigue, physical pain and trouble sleeping were sensitive to either general effects of disease progression or to effects associated with specific sites of metastasis. Progression of disease was also associated with modest but significant worsening of General Physical Symptoms, Treatment Side Effects, Acute Distress and Impaired Performance index scores. In addition, there were marked detrimental effects of liver metastasis on Treatment Side Effects, and of brain metastasis on Acute Distress. CONCLUSIONS: Disease progression has a detrimental impact on cancer-related symptoms. Delaying disease progression may have a positive impact on patients' HRQoL.

Use of the Patient Care Monitor to screen for depression in adult cancer patients interviewed with the structured clinical interview for DSM-IV.

OBJECTIVE: To evaluate the Patient Care Monitor (PCM1.0) Acute Distress and Despair normalized T scores as indicators of a diagnosis of Major Depression according to the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID). METHODS: Subjects were 21 adult cancer patients identified by treating community oncologists as having significant emotional distress matched on age, cancer type, treatment history, and sex to 21 patients not having significant distress. All completed e/tablet PCM 1.0 and SCID administered by trained interviewers. Unweighted kappa and receiver operating characteristics (ROC) analyses were used to assess scale properties. RESULTS: Agreement between SCID Major Depression and Acute Distress and Despair (T> or =65) were kappa=0.751 and 0.755, respectively. ROC area under the curve values for these two scales were 0.967 (SE+/-0.03) and 0.942 (SE+/-0.03), respectively, with optimal cut points of T=61 and 63, respectively. CONCLUSIONS: Under conditions of preselected extreme groups, PCM 1.0 Acute Distress and Despair T scores are reasonable screening indicators of clinical depression in cancer patients. PCM 1.0 provides an efficient method for point-of-care screening of depression in community oncology clinics.