Structure and Funding of Clinical Informatics Fellowships: A National Survey of Program Directors.

BACKGROUND: In 2011, the American Board of Medical Specialties established clinical informatics (CI) as a subspecialty in medicine, jointly administered by the American Board of Pathology and the American Board of Preventive Medicine. Subsequently, many institutions created CI fellowship training programs to meet the growing need for informaticists. Although many programs share similar features, there is considerable variation in program funding and administrative structures. OBJECTIVES: The aim of our study was to characterize CI fellowship program features, including governance structures, funding sources, and expenses. METHODS: We created a cross-sectional online REDCap survey with 44 items requesting information on program administration, fellows, administrative support, funding sources, and expenses. We surveyed program directors of programs accredited by the Accreditation Council for Graduate Medical Education between 2014 and 2021. RESULTS: We invited 54 program directors, of which 41 (76%) completed the survey. The average administrative support received was $27,732/year. Most programs (85.4%) were accredited to have two or more fellows per year. Programs were administratively housed under six departments: Internal Medicine (17; 41.5%), Pediatrics (7; 17.1%), Pathology (6; 14.6%), Family Medicine (6; 14.6%), Emergency Medicine (4; 9.8%), and Anesthesiology (1; 2.4%). Funding sources for CI fellowship program directors included: hospital or health systems (28.3%), clinical departments (28.3%), graduate medical education office (13.2%), biomedical informatics department (9.4%), hospital information technology (9.4%), research and grants (7.5%), and other sources (3.8%) that included philanthropy and external entities. CONCLUSION: CI fellowships have been established in leading academic and community health care systems across the country. Due to their unique training requirements, these programs require significant resources for education, administration, and recruitment. There continues to be considerable heterogeneity in funding models between programs. Our survey findings reinforce the need for reformed federal funding models for informatics practice and training.

Development and Validation of Inpatient Hypoglycemia Models Centered Around the Insulin Ordering Process.

BACKGROUND: The insulin ordering process is an opportunity to provide clinicians with hypoglycemia risk predictions, but few hypoglycemia models centered around the insulin ordering process exist. METHODS: We used data on adult patients, admitted in 2019 to non-ICU floors of a large teaching hospital, who had orders for subcutaneous insulin. Our outcome was hypoglycemia, defined as a blood glucose (BG) <70 mg/dL within 24 hours after ordering insulin. We trained and evaluated models to predict hypoglycemia at the time of placing an insulin order, using logistic regression, random forest, and extreme gradient boosting (XGBoost). We compared performance using area under the receiver operating characteristic curve (AUCs) and precision-recall curves. We determined recall at our goal precision of 0.30. RESULTS: Of 21 052 included insulin orders, 1839 (9%) were followed by a hypoglycemic event within 24 hours. Logistic regression, random forest, and XGBoost models had AUCs of 0.81, 0.80, and 0.79, and recall of 0.44, 0.49, and 0.32, respectively. The most significant predictor was the lowest BG value in the 24 hours preceding the order. Predictors related to the insulin order being placed at the time of the prediction were useful to the model but less important than the patient's history of BG values over time. CONCLUSIONS: Hypoglycemia within the next 24 hours can be predicted at the time an insulin order is placed, providing an opportunity to integrate decision support into the medication ordering process to make insulin therapy safer.

Clinician collaboration to improve clinical decision support: the Clickbusters initiative.

OBJECTIVE: We describe the Clickbusters initiative implemented at Vanderbilt University Medical Center (VUMC), which was designed to improve safety and quality and reduce burnout through the optimization of clinical decision support (CDS) alerts. MATERIALS AND METHODS: We developed a 10-step Clickbusting process and implemented a program that included a curriculum, CDS alert inventory, oversight process, and gamification. We carried out two 3-month rounds of the Clickbusters program at VUMC. We completed descriptive analyses of the changes made to alerts during the process, and of alert firing rates before and after the program. RESULTS: Prior to Clickbusters, VUMC had 419 CDS alerts in production, with 488 425 firings (42 982 interruptive) each week. After 2 rounds, the Clickbusters program resulted in detailed, comprehensive reviews of 84 CDS alerts and reduced the number of weekly alert firings by more than 70 000 (15.43%). In addition to the direct improvements in CDS, the initiative also increased user engagement and involvement in CDS. CONCLUSIONS: At VUMC, the Clickbusters program was successful in optimizing CDS alerts by reducing alert firings and resulting clicks. The program also involved more users in the process of evaluating and improving CDS and helped build a culture of continuous evaluation and improvement of clinical content in the electronic health record.

A Unified Approach to Clinical Informatics Education for Undergraduate and Graduate Medical Education.

Vanderbilt University Medical Center has adopted a unified approach to undergraduate and graduate clinical informatics education. Twenty-three learners have completed the course which is designed around four key activities: 1) didactic sessions 2) informatics history and physical where learners observe clinical areas, document workflows, identify a problem to solve and propose an informatics-informed solution 3) informatics clinic where learners are side-by-side with practicing clinical informaticians and 4) interactive learning activities where student groups work through case-based informatics problems with an informatics preceptor. These experiences are coupled with opportunities for asynchronous projects, reflections, and weekly assessments. The curriculum learning objectives are modeled after the clinical informatics fellowship curriculum. Feedback suggests the course is achieving the planned goals. It is a feasible model for other institutions and addresses knowledge gaps in clinical informatics for undergraduate and graduate medical education learners.

X Marks the Spot: Mapping Similarity Between Clinical Trial Cohorts and US Counties.

When patients and doctors collaborate to make healthcare decisions, they rely on clinical trial results to guide discussions. Trials are designed to recruit diverse participants. The question remains - how well do trial results apply to me or to people who live in our area? This study compared one complete clinical trial dataset (SPRINT) and one published study (ACCORD) to the Community Health Status Indicators dataset to assess the similarity of the trial populations to US county populations. Counties up to 495 miles to the closest SPRINT trial site and up to 712 miles to the closest ACCORD trial site had populations that were significantly more similar to the study cohort than counties farther away. The investigators detail a generalizable method for both assessing recruitment gaps in large multicenter trials and creating maps for clinicians to provide intuition on trial applicability in their area.