Kandaswamy, Swaminathan

Context.— Complexity of ordering and transfusing blood is particularly evident in the pediatric population. Simplification, clarification, and standardization of blood orders can decrease complexity and improve patient safety. Objective.— To improve patient safety by optimizing electronic ordering of blood components in pediatrics through a collaborative process improvement initiative. Design.— A multidisciplinary working group, formed as part of a value stream analysis to improve transfusion safety at Children’s Healthcare of Atlanta (Atlanta, Georgia), focused on decreasing variability and providing clarity when ordering, preparing, and transfusing blood using the electronic health record. Through benchmarking with other pediatric institutions and a collaborative design process with multiple local stakeholders, an extensive redesign in the existing orders and order sets occurred. Metrics were collected to determine if a change was an improvement. Results.— Nurse and laboratory informaticists, a pathology informaticist, and a transfusion medicine specialist built the new orders based on the design. The new orders focused on the following changes: standardization, introduction of logic, naming conventions, clarifying definitions, adding calculations, improving transparency of history and laboratory data, removing aliquots, clarifying communication, and implementing additional modules to inform the provider of necessary information about the patient. Metrics included a decrease in the number of orders changed within an hour, decreased calls from the blood bank to the provider to clarify the order, and an absence of overtransfusions and transfusion-related serious safety events for a year following implementation. Conclusions.— This collaborative initiative, using standard process improvement tools, resulted in standardized blood orders improving transfusion safety.

Abstract Background Easy identification of immunocompromised hosts (ICHs) would allow for stratification of culture results based on host type. Methods We utilized antimicrobial stewardship program (ASP) team notes written during handshake stewardship rounds in the pediatric intensive care unit (PICU) as the gold standard for host status; clinical notes from the primary team, medication orders during the encounter, problem list, and billing diagnoses documented prior to the ASP documentation were extracted to develop models that predict host status. We calculated performance for three models based on diagnoses/medications, with and without natural language processing from clinical notes. The susceptibility of pathogens causing bacteremia to commonly used empiric antibiotic regimens was then stratified by host status. Results We identified 844 antimicrobial episodes from 666 unique patients; 160 (18.9%) were identified as ICHs. We randomly selected 675 initiations (80%) for model training and 169 initiations (20%) for testing. A rule-based model using diagnoses and medications alone yielded a sensitivity of 0.87 (08.6–0.88), specificity of 0.93 (0.92–0.93), and positive predictive value (PPV) of 0.74 (0.73–0.75). Adding clinical notes into XGBoost model led to improved specificity of 0.98 (0.98–0.98) and PPV of 0.9 (0.88–0.91), but with decreased sensitivity 0.77 (0.76–0.79). There were 77 bacteremia episodes during the study period identified and a host-specific visualization was created. Conclusions An electronic health record–based phenotype based on notes, diagnoses, and medications identifies ICH in the PICU with high specificity.