"I worry we'll blow right by it:" Barriers to Uptake of the STRATIFY-CDS for Acute Heart Failure.

Clinical decision support (CDS) tools in electronic health records (EHRs) often face low uptake due to limited usability, workflow integration, and other implementation issues. We recently designed and implemented the STRATIFY-CDS tool, which calculates a validated risk-prediction model and recommends disposition for emergency department (ED) patients with acute heart failure. Despite applying human-centered design and implementation science strategies, initial utilization in the first 3 months of the STRATIFY-CDS tool was just 3%.

A Multi-Institutional Evaluation of Billing for Tobacco-Cessation Services: Opportunities to Improve Quality and Enhance Revenue Capture.

A recent paper identified significant gaps in billing for tobacco-cessation services for a multihospital health system. Underbilling can reflect poor billing practices, low rates of tobacco-cessation counseling, or a combination of both. The study design was applied to 2 academic medical centers.

Benchmarks after bowel resection in Crohn's disease: implementing clinically meaningful health-related quality of life targets.

Background: Patient-reported outcome measure scores must be interpretable to be effective in a clinical setting. In this retrospective cohort study, we sought to establish the minimum clinically important difference (MCID) and patient acceptable symptom state (PASS) for health-related quality of life in Crohn's disease and to apply these thresholds to patients undergoing Crohn's-related bowel resection.

Methods: Eligible participants were adults with Crohn's disease completing a patient-reported outcome measure and an additional anchor question about digestive satisfaction or change from a prior clinical visit.

The Pragmatic Removal of Penicillin Allergy Electronic Health Record Labels (PROPEL) Trial: A Randomized Clinical Trial.

Background: Inaccurate penicillin allergy labels (PALs) affect antimicrobial stewardship, health outcomes, and costs. More than 95% of PALs can be de-labeled when tested, but this rarely happens.

Objective: We sought to determine whether education and an electronic health record (EHR) tool kit to identify low-risk PALs would facilitate inpatient penicillin allergy de-labeling by pharmacists.

Predicting Heart Failure Outcomes Using Patient-Reported Health Status: Real-World Validation of the KCCQ-12.

Background: The Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12), a patient-reported outcome measure for adults with heart failure, is associated with hospitalizations and mortality in clinical trials. Curated data sets from controlled trials differ substantially from pragmatic data collected from real-world settings, however, and few data exist on the KCCQ-12's predictive utility in clinical practice.

Objectives: This study sought to evaluate the predictive utility of the KCCQ-12 for hospitalizations and mortality when administered during outpatient heart failure care.

Identifying Stakeholder Requirements for the Development of an Electronic Care Transitions Tool to Improve Health Outcomes for Patients with Multiple Chronic Conditions.

The transition from hospital to home can be a vulnerable and challenging period for patients, especially those living with multiple chronic conditions (MCC), as evidenced by their disproportionately high rates of readmission. Low health literacy, complexity of a new medication schedule, and "post-hospital syndrome" can all contribute to suboptimal adherence to discharge instructions. Timely and adequate support during transitional care has the potential to prevent adverse events and avoidable hospital readmissions.

Impact of a Pharmacist Driven Penicillin Allergy De-labeling Protocol on Rates of Reported Allergy in the Intensive Care Unit.

Inaccurate penicillin allergy labels (PALs) results in use of broader, less optimized antibiotics. Studies have shown challenging low-risk PALs is safe and effective. We assessed the proportion of PALs among critically ill patients after a pharmacist driven allergy de-labeling program was implemented in the medical intensive care unit (MICU) between November 2017 and March 2023.

High-Frequency Utilization of the Outpatient Messaging System in a Specialized Outpatient Catatonia Clinic for Individuals with Autism Spectrum Disorder.

Catatonia is a highly morbid psychomotor disorder that impacts autistic adults and children. There is very little literature that describes outpatient catatonia management practices, none of which discusses the use of the electronic health record (EHR). Thus, we conducted this study to analyze patient messages in a specialized catatonia clinic.

Detecting emergencies in patient portal messages using large language models and knowledge graph-based retrieval-augmented generation.

Objectives: This study aims to develop and evaluate an approach using large language models (LLMs) and a knowledge graph to triage patient messages that need emergency care. The goal is to notify patients when their messages indicate an emergency, guiding them to seek immediate help rather than using the patient portal, to improve patient safety.

Materials And Methods: We selected 1020 messages sent to Vanderbilt University Medical Center providers between January 1, 2022 and March 7, 2023.

The Use of Precision Medicine to Support the Precision of Clinical Decisions in care delivery.

Objectives: Objective: Precision medicine uses individualized patient data, including genomic and social determinants of health SDoH), to provide optimized personalized patient treatment. In this scoping review, we summarize studies published in the last two years that reported on implementation of precision medicine in clinical decision support (CDS) related to precision medicine.

Methods: We searched PubMed for manuscripts published in 2022 and 2023 to retrieve publications that included CDS and precision medicine keywords and Mesh terms.

User Actions within a Clinical Decision Support Alert for the Management of Hypertension in Chronic Kidney Disease.

This study aimed to examine user actions within a clinical decision support (CDS) alert addressing hypertension (HTN) in chronic kidney disease (CKD).A pragmatic randomized controlled trial of a CDS alert for primary care patients with CKD and uncontrolled blood pressure included prechecked default orders for medication initiation or titration, basic metabolic panel (BMP), and nephrology electronic consult (e-consult). We examined each type of action and calculated percentages of placed and signed orders for subgroups of firings.

Alert design in the real world: a cross-sectional analysis of interruptive alerting at 9 academic pediatric health systems.

Objective: To assess the prevalence of recommended design elements in implemented electronic health record (EHR) interruptive alerts across pediatric care settings.

Materials And Methods: We conducted a 3-phase mixed-methods cross-sectional study. Phase 1 involved developing a codebook for alert content classification.

Pediatric Predictive Artificial Intelligence Implemented in Clinical Practice from 2010 to 2021: A Systematic Review.

To review pediatric artificial intelligence (AI) implementation studies from 2010 to 2021 and analyze reported performance measures.We searched PubMed/Medline, Embase CINHAL, Cochrane Library CENTRAL, IEEE, and Web of Science with controlled vocabulary. Inclusion criteria: AI intervention in a pediatric clinical setting that learns from data (i.

Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines.

Objective: The objectives of this study are to synthesize findings from recent research of retrieval-augmented generation (RAG) and large language models (LLMs) in biomedicine and provide clinical development guidelines to improve effectiveness.

Materials And Methods: We conducted a systematic literature review and a meta-analysis. The report was created in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 analysis.

Fellows of the American Medical Informatics Association (FAMIA): Looking Back and Looking Ahead.

Background:  Over the past 30 years, the American Medical Informatics Association (AMIA) has played a pivotal role in fostering a collaborative community for professionals in biomedical and health informatics. As an interdisciplinary association, AMIA brings together individuals with clinical, research, and computer expertise and emphasizes the use of data to enhance biomedical research and clinical work. The need for a recognition program within AMIA, acknowledging applied informatics skills by members, led to the establishment of the Fellows of AMIA (FAMIA) Recognition Program in 2018.

Using large language model to guide patients to create efficient and comprehensive clinical care message.

Objective: This study aims to investigate the feasibility of using Large Language Models (LLMs) to engage with patients at the time they are drafting a question to their healthcare providers, and generate pertinent follow-up questions that the patient can answer before sending their message, with the goal of ensuring that their healthcare provider receives all the information they need to safely and accurately answer the patient's question, eliminating back-and-forth messaging, and the associated delays and frustrations.

Methods: We collected a dataset of patient messages sent between January 1, 2022 to March 7, 2023 at Vanderbilt University Medical Center. Two internal medicine physicians identified 7 common scenarios.

Surgically-relevant quality of life thresholds for the Short Inflammatory Bowel Disease Questionnaire in Crohn's disease.

Background: Despite growing interest in patient-reported outcome measures to track the progression of Crohn's disease, frameworks to apply these questionnaires in the preoperative setting are lacking. Using the Short Inflammatory Bowel Disease Questionnaire (sIBDQ), this study aimed to describe the interpretable quality of life thresholds and examine potential associations with future bowel resection in Crohn's disease.

Methods: Adult patients with Crohn's disease completing an sIBDQ at a clinic visit between 2020 and 2022 were eligible.

Development and Validation of an Automated, Real-Time Predictive Model for Postpartum Hemorrhage.

Objective: To develop and validate a predictive model for postpartum hemorrhage that can be deployed in clinical care using automated, real-time electronic health record (EHR) data and to compare performance of the model with a nationally published risk prediction tool.

Methods: A multivariable logistic regression model was developed from retrospective EHR data from 21,108 patients delivering at a quaternary medical center between January 1, 2018, and April 30, 2022. Deliveries were divided into derivation and validation sets based on an 80/20 split by date of delivery.

Implementable Prediction of Pressure Injuries in Hospitalized Adults: Model Development and Validation.

Background: Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care.

Objective: To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale).

Methods: We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features.

Identifying antinuclear antibody positive individuals at risk for developing systemic autoimmune disease: development and validation of a real-time risk model.

Objective: Positive antinuclear antibodies (ANAs) cause diagnostic dilemmas for clinicians. Currently, no tools exist to help clinicians interpret the significance of a positive ANA in individuals without diagnosed autoimmune diseases. We developed and validated a risk model to predict risk of developing autoimmune disease in positive ANA individuals.

Leveraging large language models for generating responses to patient messages-a subjective analysis.

Objective: This study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal.

Materials And Methods: Utilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large language model (LLaMA-65B). In addition, we used the OpenAI API to update physician responses from an open-source dataset into a format with informative paragraphs that offered patient education while emphasizing empathy and professionalism.

Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support.

Objectives: To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts.

Materials And Methods: We extracted user comments to alerts generated from September 1, 2022 to September 1, 2023 at Vanderbilt University Medical Center. For a subset of 8 alerts, comment summaries were generated independently by 2 physicians and then separately by GPT-4.

Leveraging explainable artificial intelligence to optimize clinical decision support.

Objective: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.

Methods: We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts.