Special Issue on Informatics Education: The need for expanded educational opportunities in clinical informatics for pediatric trainees.

Pediatric healthcare has nuances not encountered in the delivery of adult medicine. In order to support these unique pediatric workflows, pediatric healthcare providers need to be involved in the development of pediatric focused health information systems. Unfortunately, not enough pediatrics are training in clinical informatics with only 31 clinical informatics fellows listing pediatrics as their primary specialty between 2016-2024. This is leading to a void in practicing pediatric clinical informaticists which is supported by the fact that only 320 clinical informaticists in the United States are pediatricians. Increased focus on cultivating the next generation of pediatric clinical informaticists is required. Contributing factors to the low attrition of pediatricians into clinical informatics training include the lack of clinical informatics specific content on the American Board of Pediatrics exam, and the small number of pediatric specific clinical informatics fellowship programs. Collaborative approaches are needed to ensure that enough pediatricians are trained in clinical informatics by 1) increasing trainee awareness of and interest in clinical informatics, 2) increasing pediatric trainees' knowledge of core clinical informatics concepts and 3) supporting the careers of future pediatric informaticists.

An Algorithm to Assess Guideline Concordance of Antibiotic Choice in Community-Acquired Pneumonia.

BACKGROUND AND OBJECTIVE: This study aimed to develop and evaluate an algorithm to reduce the chart review burden of improvement efforts by automatically labeling antibiotic selection as either guideline-concordant or -discordant based on electronic health record data for patients with community-acquired pneumonia (CAP). METHODS: We developed a 3-part algorithm using structured and unstructured data to assess adherence to an institutional CAP clinical practice guideline. The algorithm was applied to retrospective data for patients seen with CAP from 2017 to 2019 at a tertiary children's hospital. Performance metrics included positive predictive value (precision), sensitivity (recall), and F1 score (harmonized mean), with macro-weighted averages. Two physician reviewers independently assigned "actual" labels based on manual chart review. RESULTS: Of 1345 patients with CAP, 893 were included in the training cohort and 452 in the validation cohort. Overall, the model correctly labeled 435 of 452 (96%) patients. Of the 286 patients who met guideline inclusion criteria, 193 (68%) were labeled as having received guideline-concordant antibiotics, 48 (17%) were labeled as likely in a scenario in which deviation from the clinical practice guideline was appropriate, and 45 (16%) were given the final label of "possibly discordant, needs review." The sensitivity was 0.96, the positive predictive value was 0.97, and the F1 was 0.96. CONCLUSIONS: An automated algorithm that uses structured and unstructured electronic health record data can accurately assess the guideline concordance of antibiotic selection for CAP. This tool has the potential to improve the efficiency of improvement efforts by reducing the manual chart review needed for quality measurement.

Digital Antimicrobial Stewardship Decision Support to Improve Antimicrobial Management.

OBJECTIVES: We sought to create a digital application to support clinicians in empiric and pathogen-directed antibiotic ordering based on local susceptibility patterns and evidence-based treatment durations, thereby promoting antimicrobial stewardship. METHODS: We formed a multidisciplinary team that met bimonthly from 2017 to 2018 to design and construct a web-based antimicrobial stewardship platform called Antibiogram + . We used an iterative and agile technical development process with frequent feedback from clinicians. RESULTS: Antibiogram+ is an online tool, accessible via the electronic health record and hospital intranet, which offers institutional antibiotic susceptibilities for major pathogens, recommendations for empiric antibiotic selection and treatment durations for common pediatric conditions, antimicrobial dosing and monitoring guidance, and links to other internal clinical decision support resources. The tool was accessed 11,823 times with 492 average monthly views during the first 2 years after release. Compared with use of a preexisting print antibiogram and dosing card, pediatric residents more frequently reported "often" being sure of antibiotic dosing with Antibiogram+ (58 vs. 15%, p < 0.01). Respondents also reported improved confidence in choice of antibiotic, but this finding did not reach statistical significance (55 vs. 35%, p = 0.26). CONCLUSION: We report the successful development of a digital antimicrobial stewardship platform with consistent rates of access during the first 2 years following release and improved provider comfort with antibiotic management.

Design, Usability, and Acceptability of a Needs-Based, Automated Dashboard to Provide Individualized Patient-Care Data to Pediatric Residents.

BACKGROUND AND OBJECTIVES: Pediatric residency programs are required by the Accreditation Council for Graduate Medical Education to provide residents with patient-care and quality metrics to facilitate self-identification of knowledge gaps to prioritize improvement efforts. Trainees are interested in receiving this data, but this is a largely unmet need. Our objectives were to (1) design and implement an automated dashboard providing individualized data to residents, and (2) examine the usability and acceptability of the dashboard among pediatric residents. METHODS: We developed a dashboard containing individualized patient-care data for pediatric residents with emphasis on needs identified by residents and residency leadership. To build the dashboard, we created a connection from a clinical data warehouse to data visualization software. We allocated patients to residents based on note authorship and created individualized reports with masked identities that preserved anonymity. After development, we conducted usability and acceptability testing with 11 resident users utilizing a mixed-methods approach. We conducted interviews and anonymous surveys which evaluated technical features of the application, ease of use, as well as users' attitudes toward using the dashboard. Categories and subcategories from usability interviews were identified using a content analysis approach. RESULTS: Our dashboard provides individualized metrics including diagnosis exposure counts, procedure counts, efficiency metrics, and quality metrics. In content analysis of the usability testing interviews, the most frequently mentioned use of the dashboard was to aid a resident's self-directed learning. Residents had few concerns about the dashboard overall. Surveyed residents found the dashboard easy to use and expressed intention to use the dashboard in the future. CONCLUSION: Automated dashboards may be a solution to the current challenge of providing trainees with individualized patient-care data. Our usability testing revealed that residents found our dashboard to be useful and that they intended to use this tool to facilitate development of self-directed learning plans.

Changes Made to Orders Placed by Overnight Admitting Residents on Teaching Rounds the Next Day.

OBJECTIVES: Increased focus on health care quality and safety has generally led to additional resident supervision by attending physicians. At our children's hospital, residents place orders overnight that are not explicitly reviewed by attending physicians until morning rounds. We aimed to categorize the types of orders that are added or discontinued on morning rounds the morning after admission to a resident team and to understand the rationale for these order additions and discontinuations. METHODS: We used our hospital's data warehouse to generate a report of orders placed by residents overnight that were discontinued the next morning and orders that were added on rounds the morning after admission to a resident team from July 1, 2017 to June 29, 2018. Retrospective chart review was performed on included orders to determine the reason for order changes. RESULTS: Our report identified 5927 orders; 538 were included for analysis after exclusion of duplicate orders, administrative orders, and orders for patients admitted to non-Pediatric Hospital Medicine services. The reason for order discontinuation or addition was medical decision-making (n = 357, 66.4%), change in patient trajectory (n = 151, 28.1%), and medical error (n = 30, 5.6%). Medical errors were most commonly related to medications (n = 24, 80%) and errors of omission (n = 19, 63%). CONCLUSIONS: New or discontinued orders commonly resulted from evolving patient management decisions or changes in patient trajectory; medical errors represented a small subset of identified orders. Medical errors were often errors of omission, suggesting an area to direct future safety initiatives.

COVID-19 Impact on Intern Exposure to Common Inpatient Diagnoses.

OBJECTIVE: We sought to understand the impact of the coronavirus disease 2019 (COVID-19) pandemic on the clinical exposure of pediatric interns to common pediatric inpatient diagnoses. METHODS: We analyzed electronic medical record data to compare intern clinical exposure during the COVID-19 pandemic from June 2020 through February 2021 with the same academic blocks from 2017 to 2020. We attributed patients to each pediatric intern on the basis of notes written during their pediatric hospital medicine rotation to compare intern exposures with common inpatient diagnoses before and during the pandemic. We compared the median number of notes written per intern per block overall, as well as for each common inpatient diagnosis. RESULTS: Median counts of notes written per intern per block were significantly reduced in the COVID-19 group compared with the pre-COVID-19 group (96 [interquartile range (IQR): 81-119)] vs 129 [IQR: 110-160]; P < .001). Median intern notes per block was lower in the COVID-19 group for all months except February 2021. Although the median number of notes for many common inpatient diagnoses was significantly reduced, they were higher for mental health (4 [IQR: 2-9] vs 2 [IQR: 1-6]; P < .001) and suicidality (4.5 [IQR: 2-8] vs 0 [IQR: 0-2]; P < .001). Median shifts worked per intern per block was also reduced in the COVID-19 group (22 [IQR: 21-23] vs 23 [IQR: 22-24]; P < .001). CONCLUSIONS: Our findings reveal a significant reduction in resident exposure to many common inpatient pediatric diagnoses during the COVID-19 pandemic. Residency programs and pediatric hospitalist educators should consider curricular interventions to ensure adequate clinical exposure for residents affected by the pandemic.

COVID-19 Impact on Intern Exposure to Common Inpatient Diagnoses.

OBJECTIVE: We sought to understand the impact of the coronavirus disease 2019 (COVID-19) pandemic on the clinical exposure of pediatric interns to common pediatric inpatient diagnoses. METHODS: We analyzed electronic medical record data to compare intern clinical exposure during the COVID-19 pandemic from June 2020 through February 2021 with the same academic blocks from 2017 to 2020. We attributed patients to each pediatric intern on the basis of notes written during their pediatric hospital medicine rotation to compare intern exposures with common inpatient diagnoses before and during the pandemic. We compared the median number of notes written per intern per block overall, as well as for each common inpatient diagnosis. RESULTS: Median counts of notes written per intern per block were significantly reduced in the COVID-19 group compared with the pre-COVID-19 group (96 [interquartile range (IQR): 81-119)] vs 129 [IQR: 110-160]; P < .001). Median intern notes per block was lower in the COVID-19 group for all months except February 2021. Although the median number of notes for many common inpatient diagnoses was significantly reduced, they were higher for mental health (4 [IQR: 2-9] vs 2 [IQR: 1-6]; P < .001) and suicidality (4.5 [IQR: 2-8] vs 0 [IQR: 0-2]; P < .001). Median shifts worked per intern per block was also reduced in the COVID-19 group (22 [IQR: 21-23] vs 23 [IQR: 22-24]; P < .001). CONCLUSIONS: Our findings reveal a significant reduction in resident exposure to many common inpatient pediatric diagnoses during the COVID-19 pandemic. Residency programs and pediatric hospitalist educators should consider curricular interventions to ensure adequate clinical exposure for residents affected by the pandemic.

A Quality Improvement Initiative to Decrease Platelet Ordering Errors and a Proposed Model for Evaluating Clinical Decision Support Effectiveness.

BACKGROUND AND OBJECTIVES: Clinical decision support (CDS) and computerized provider order entry have been shown to improve health care quality and safety, but may also generate previously unanticipated errors. We identified multiple CDS tools for platelet transfusion orders. In this study, we sought to evaluate and improve the effectiveness of those CDS tools while creating and testing a framework for future evaluation of other CDS tools. METHODS: Using a query of an enterprise data warehouse at a tertiary care pediatric hospital, we conducted a retrospective analysis to assess baseline use and performance of existing CDS for platelet transfusion orders. Our outcome measure was the percentage of platelet undertransfusion ordering errors. Errors were defined as platelet transfusion volumes ordered which were less than the amount recommended by the order set used. We then redesigned our CDS and measured the impact of our intervention prospectively using statistical process control methodology. RESULTS: We identified that 62% of all platelet transfusion orders were placed with one of two order sets (Inpatient Service 1 and Inpatient Service 2). The Inpatient Service 1 order set had a significantly higher occurrence of ordering errors (3.10% compared with 1.20%). After our interventions, platelet transfusion order error occurrence on Inpatient Service 1 decreased from 3.10 to 0.33%. CONCLUSION: We successfully reduced platelet transfusion ordering errors by redesigning our CDS tools. We suggest that the use of collections of clinical data may help identify patterns in erroneous ordering, which could otherwise go undetected. We have created a framework which can be used to evaluate the effectiveness of other similar CDS tools.