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Background: Pediatric patient blood management (PBM) programs require continuous surveillance of errors and near misses. However, most PBM programs rely on passive surveillance methods. Our objective was to develop and evaluate a set of automated trigger tools for active surveillance of pediatric PBM errors., Materials and Methods: We used the Rand-UCLA method with an expert panel of pediatric transfusion medicine specialists to identify and prioritize candidate trigger tools for all transfused blood products. We then iteratively developed automated queries of electronic health record (EHR) data for the highest priority triggers. Two physicians manually reviewed a subset of cases meeting trigger tool criteria and estimated each trigger tool's positive predictive value (PPV). We then estimated the rate of PBM errors, whether they reached the patient, and adverse events for each trigger tool across four years in a single pediatric health system., Results: We identified 28 potential triggers for pediatric PBM errors and developed 5 automated trigger tools (positive patient identification, missing irradiation, unwashed products despite prior anaphylaxis, transfusion lasting >4 hours, over-transfusion by volume). The PPV for ordering errors ranged from 38-100%. The most frequently detected near miss event reaching patients was first transfusions without positive patient identification (estimate 303, 95% CI: 288-318 per year). The only adverse events detected were from over-transfusions by volume, including 4 adverse events detected on manual review that had not been reported in passive surveillance systems., Discussion: It is feasible to automatically detect pediatric PBM errors using existing data captured in the EHR that enable active surveillance systems. Over-transfusions may be one of the most frequent causes of harm in the pediatric environment.

Background: Children are at increased risk from transfusion-related medical errors. Clinical decision support (CDS) can enhance pediatric providers' decision-making regarding transfusion practices including indications, volume, rate, and special processing instructions. Our objective was to use CDS in a pediatric health system to reduce:blood product-related safety events from ordering errors;special processing ordering errors for patients with T-cell dysfunction, sickle cell disease (SCD), or thalassemia;transfusions administered faster than 5 mL/kg/h., Materials and Methods: In this single-center before and after quality improvement study, we evaluated how user-centered design of pediatric blood product orders influenced pediatric transfusion practices and outcomes. Safety events were identified through active and passive surveillance. Other clinically relevant outcomes were identified through electronic health record queries., Results: Blood product-related safety events from ordering errors did not change significantly from the baseline period (6 events, 0.4 per month, from 1/1/2018-3/27/2019) to the intervention period (1 event, 0.1 per month, from 3/28/2019-12/31/2019; rate ratio: 0.27 [0.01-2.25]). Packed red blood cell (PRBC) and platelet orders for patients with T-cell dysfunction that did not specify irradiation decreased significantly from 488/12,359 (3.9%) to 204/6,711 (3.0%, risk ratio: 0.77 [0.66-0.90]). PRBC orders for patients with SCD or thalassemia that did not specify phenotypically similar units fell from 386/2,876 (13.4%) to 57/1,755 (3.2%, risk ratio: 0.24 [0.18-0.32]). Transfusions administered faster than 5 mL/kg/h decreased from 4,112/14,641 (28.1%) to 2,125/9,263 (22.9%, risk ratio: 0.82 [0.78-0.85])., Discussion: User-centered design of CDS for pediatric blood product orders significantly reduced special processing ordering errors and inappropriate transfusion rates. Larger studies are needed to evaluate the impact on safety events.

Objective: Excess physician work hours contribute to burnout and medical errors. Self-report of work hours is burdensome and often inaccurate. We aimed to validate a method that automatically determines provider shift duration based on electronic health record (EHR) timestamps across multiple inpatient settings within a single institution., Methods: We developed an algorithm to calculate shift start and end times for inpatient providers based on EHR timestamps. We validated the algorithm based on overlap between calculated shifts and scheduled shifts. We then demonstrated a use case by calculating shifts for pediatric residents on inpatient rotations from July 1, 2015 through June 30, 2016, comparing hours worked and number of shifts by rotation and role., Results: We collected 6.3 × 10 7 EHR timestamps for 144 residents on 771 inpatient rotations, yielding 14,678 EHR-calculated shifts. Validation on a subset of shifts demonstrated 100% shift match and 87.9 ± 0.3% overlap (mean ± standard error [SE]) with scheduled shifts. Senior residents functioning as front-line clinicians worked more hours per 4-week block (mean ± SE: 273.5 ± 1.7) than senior residents in supervisory roles (253 ± 2.3) and junior residents (241 ± 2.5). Junior residents worked more shifts per block (21 ± 0.1) than senior residents (18 ± 0.1)., Conclusion: Automatic calculation of inpatient provider work hours is feasible using EHR timestamps. An algorithm to assess provider work hours demonstrated criterion validity via comparison with scheduled shifts. Differences between junior and senior residents in calculated mean hours worked and number of shifts per 4-week block were also consistent with differences in scheduled shifts and duty-hour restrictions., Competing Interests: None declared., (Georg Thieme Verlag KG Stuttgart · New York.)

Opportunities for education in clinical informatics exist throughout the spectrum of formal education extending from high school to postgraduate training. However, physicians in residency represent an underdeveloped source of potential informaticians. Despite the rapid growth of accredited fellowship programs since clinical informatics became a board-eligible subspecialty in 2011, few resident physicians are aware of their role at the intersection of clinical medicine and health information technology or associated opportunities. In an effort to educate and engage residents in clinical informatics, Children's Hospital of Philadelphia has developed a three-pronged model: (1) an elective rotation with hands-on project experience; (2) a longitudinal experience that offers increased exposure and mentorship; and (3) a resident founded and led working group in clinical informatics. We describe resident participation in these initiatives and lessons learned, as well as resident perceptions of how these components have positively influenced informatics knowledge and career choices. Since inception of this model, five residents have pursued the clinical informatics fellowship. This educational model supports resident involvement in hospital-wide informatics efforts with tangible projects and promotes wider engagement through educational opportunities commensurate with the resident's level of interest., Competing Interests: None., (Schattauer GmbH Stuttgart.)