Objective We sought to develop and evaluate an electronic health record (EHR) genetic testing tracking system to address the barriers and limitations of existing spreadsheet-based workarounds. Materials and Methods We evaluated the spreadsheet-based system using mixed effects logistic regression to identify factors associated with delayed follow up. These factors informed the design of an EHR-integrated genetic testing tracking system. After deployment, we assessed the system in 2 ways. We analyzed EHR access logs and note data to assess patient outcomes and performed semistructured interviews with users to identify impact of the system on work. Results We found that patient-reported race was a significant predictor of documented genetic testing follow up, indicating a possible inequity in care. We implemented a CDS system including a patient data capture form and management dashboard to facilitate important care tasks. The system significantly sped review of results and significantly increased documentation of follow-up recommendations. Interviews with key system users identified a range of sociotechnical factors (ie, tools, tasks, collaboration) that contribute to safer and more efficient care. Discussion Our new tracking system ended decades of workarounds for identifying and communicating test results and improved clinical workflows. Interview participants related that the system decreased cognitive and time burden which allowed them to focus on direct patient interaction. Conclusion By assembling a multidisciplinary team, we designed a novel patient tracking system that improves genetic testing follow up. Similar approaches may be effective in other clinical settings.

Objectives: To create and evaluate the feasibility, acceptability, and usability of a clinical decision support (CDS) tool within the electronic health record (EHR) to help pediatricians provide smoking cessation counseling and treatment to parents of hospitalized children exposed to secondhand smoke (SHS).Methods: Mixed method study of first-year pediatric residents on one inpatient unit. Residents received training in smoking cessation counseling, nicotine replacement therapy (NRT) prescribing, and use of a CDS tool to aid in this process. The tool, which alerted when a patient was identified as exposed to SHS based on the history taken on admission or during a prior encounter, had the following capabilities: adding SHS exposure to the patient's problem list; referral to Free Quitline through discharge instructions; and linking to a printable NRT prescription form. We measured feasibility by EHR utilization data. We measured acceptability and usability of the tool by administering questionnaires to residents.Results: From June-August 2015, the alert triggered for 106 patients, and the tool was used for 52 (49%) patients. 41 (39%) patients had SHS exposure added to the problem list, 34 (32%) parents were referred to the Quitline through discharge instructions, and 15 (14%) parents were prescribed NRT. 10 out of 15 (67%) eligible pediatricians used the tool. All clinicians surveyed (9 out of 10) found the tool acceptable and rated its usability good to excellent (average System Usability Scale score was 85 out of 100, 95% CI, 76-93).Conclusions: A non-interruptive CDS tool to help residents provide smoking cessation counseling in the hospital was feasible, acceptable, and usable. Future work will investigate impacts on patient outcomes.

Following implementation of automatic end dates for antimicrobial orders to facilitate antimicrobial stewardship at a large, academic children’s hospital, no differences were observed in patient mortality, length of stay, or readmission rates, even among patients with documented bacteremia. Infect Control Hosp Epidemiol 2016;37:974–978

OBJECTIVE To develop and evaluate an electronic dashboard of hospital-wide electronic health record medication alerts for an alert fatigue reduction quality improvement project. METHODS We used visual analytics software to develop the dashboard. We collaborated with the hospital-wide Clinical Decision Support committee to perform three interventions successively deactivating clinically irrelevant drug-drug interaction (DDI) alert rules. We analyzed the impact of the interventions on care providers' and pharmacists' alert and override rates using an interrupted time series framework with piecewise regression. RESULTS We evaluated 2 391 880 medication alerts between January 31, 2011 and January 26, 2014. For pharmacists, the median alert rate prior to the first DDI deactivation was 58.74 alerts/100 orders (IQR 54.98-60.48) and 25.11 alerts/100 orders (IQR 23.45-26.57) following the three interventions (p<0.001). For providers, baseline median alert rate prior to the first round of DDI deactivation was 19.73 alerts/100 orders (IQR 18.66-20.24) and 15.11 alerts/100 orders (IQR 14.44-15.49) following the three interventions (p<0.001). In a subgroup analysis, we observed a decrease in pharmacists' override rates for DDI alerts that were not modified in the system from a median of 93.06 overrides/100 alerts (IQR 91.96-94.33) to 85.68 overrides/100 alerts (IQR 84.29-87.15, p<0.001). The medication serious safety event rate decreased during the study period, and there were no serious safety events reported in association with the deactivated alert rules. CONCLUSIONS An alert dashboard facilitated safe rapid-cycle reductions in alert burden that were temporally associated with lower pharmacist override rates in a subgroup of DDIs not directly affected by the interventions; meanwhile, the pharmacists' frequency of selecting the 'cancel' option increased. We hypothesize that reducing the alert burden enabled pharmacists to devote more attention to clinically relevant alerts.