Publications by authors named "Allison B Mccoy"
Background: The Vanderbilt Clinical Informatics Center (VCLIC) is based in the Department of Biomedical Informatics (DBMI) and operates across Vanderbilt University Medical Center (VUMC) and Vanderbilt University (VU) with a goal of enabling and supporting clinical informatics research and practice. VCLIC supports several types of applied clinical informatics teaching, including teaching of students in courses, professional education for staff and faculty throughout VUMC, and workshops and conferences that are open to the public.
Objectives: In this paper, we provide a detailed accounting of our center and institution's methods of educating and training faculty, staff, students, and trainees from across the academic institution and health system on clinical informatics topics, including formal training programs and informal applied learning sessions.
Article Synopsis
- Clinical prediction models are valuable tools intended to improve clinical decision making, but their use in practice is limited due to issues like incomplete data in electronic health records (EHR).
- The article proposes a new submodel approach that enhances prediction model application by refining the model coefficients to better handle real-time data inconsistencies.
- Simulation results indicate that this new method performs well across different scenarios and is better suited for practical implementation than existing methods, specifically in identifying emergency department patients with acute heart failure eligible for safe discharge.
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.
View Article and Find Full Text PDFObjective: 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.
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.
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.
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.
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.
View Article and Find Full Text PDFObjective: 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.
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.
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.
Article Synopsis
- Geocoding effectively converts addresses into geographic coordinates, which helps personalize healthcare interventions based on patients' environments.
- POINT is an offline, web-based application designed for secure address geocoding, usable by multiple users in an organization.
- Evaluation of POINT showed a high success rate in geocoding addresses (99.4% for one dataset and 99.8% for another) with accuracy comparable to existing solutions, while ensuring patient data confidentiality.
Background: Early detection of clinical deterioration among hospitalized patients is a clinical priority for patient safety and quality of care. Current automated approaches for identifying these patients perform poorly at identifying imminent events.
Objective: Develop a machine learning algorithm using pager messages sent between clinical team members to predict imminent clinical deterioration.
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.
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.
Objectives: This literature review summarizes relevant studies from the last three years (2020-2022) related to clinical decision support (CDS) and CDS impact on health disparities and the digital divide. This survey identifies current trends and synthesizes evidence-based recommendations and considerations for future development and implementation of CDS tools.
Methods: We conducted a search in PubMed for literature published between 2020 and 2022.
Objective: To develop and validate an approach that identifies patients eligible for lung cancer screening (LCS) by combining structured and unstructured smoking data from the electronic health record (EHR).
Methods: We identified patients aged 50-80 years who had at least one encounter in a primary care clinic at Vanderbilt University Medical Center (VUMC) between 2019 and 2022. We fine-tuned an existing natural language processing (NLP) tool to extract quantitative smoking information using clinical notes collected from VUMC.
Background: Clinical trials indicate continuous glucose monitor (CGM) use may benefit adults with type 2 diabetes, but CGM rates and correlates in real-world care settings are unknown.
Objective: We sought to ascertain prevalence and correlates of CGM use and to examine rates of new CGM prescriptions across clinic types and medication regimens.
Design: Retrospective cohort using electronic health records in a large academic medical center in the Southeastern US.
Objective: To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions.
Methods: We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy.
Objective: To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions.
Methods: We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy.
Background: Chest pain (CP) is the hallmark symptom for acute coronary syndrome (ACS) but is not reported in 20-30% of patients, especially women, elderly, non-white patients, presenting to the emergency department (ED) with an ST-segment elevation myocardial infarction (STEMI).
Methods: We used a retrospective 5-year adult ED sample of 279,132 patients to explore using CP alone to predict ACS, then we incrementally added other ACS chief complaints, age, and sex in a series of multivariable logistic regression models. We evaluated each model's identification of ACS and STEMI.
Objective: To develop and test an accurate deep learning model for predicting new onset delirium in hospitalized adult patients.
Methods: Using electronic health record (EHR) data extracted from a large academic medical center, we developed a model combining long short-term memory (LSTM) and machine learning to predict new onset delirium and compared its performance with machine-learning-only models (logistic regression, random forest, support vector machine, neural network, and LightGBM). The labels of models were confusion assessment method (CAM) assessments.
Objectives: To improve clinical decision support (CDS) by allowing users to provide real-time feedback when they interact with CDS tools and by creating processes for responding to and acting on this feedback.
Methods: Two organizations implemented similar real-time feedback tools and processes in their electronic health record and gathered data over a 30-month period. At both sites, users could provide feedback by using Likert feedback links embedded in all end-user facing alerts, with results stored outside the electronic health record, and provide feedback as a comment when they overrode an alert.
Article Synopsis
- Interruptive clinical decision support systems can provide valuable alerts in healthcare, but their use must be balanced to avoid alert fatigue among medical professionals.
- This review highlights effective strategies for managing these alerts, ensuring they remain useful rather than overwhelming.
- It emphasizes the need for a comprehensive understanding of the entire alerting ecosystem, not just those within electronic health records, to enhance decision-making and governance in medical practice.