Evaluating a Large Language Model's Ability to Answer Clinicians' Requests for Evidence Summaries.

Objective: This study investigated the performance of a generative artificial intelligence (AI) tool using GPT-4 in answering clinical questions in comparison with medical librarians' gold-standard evidence syntheses.

Methods: Questions were extracted from an in-house database of clinical evidence requests previously answered by medical librarians. Questions with multiple parts were subdivided into individual topics.

Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition.

Implementing artificial intelligence to extract insights from large, real-world clinical data sets can supplement and enhance knowledge management efforts for health sciences research and clinical care. At Vanderbilt University Medical Center (VUMC), the in-house developed Word Cloud natural language processing system extracts coded concepts from patient records in VUMC's electronic health record repository using the Unified Medical Language System terminology. Through this process, the Word Cloud extracts the most prominent concepts found in the clinical documentation of a specific patient or population.

Systematic review of international studies evaluating MDRD and CKD-EPI estimated glomerular filtration rate (eGFR) equations in Black adults.

Use of race adjustment in estimating glomerular filtration rate (eGFR) has been challenged given concerns that it may negatively impact the clinical care of Black patients, as it results in Black patients being systematically assigned higher eGFR values than non-Black patients. We conducted a systematic review to assess how well eGFR, with and without race adjustment, estimates measured GFR (mGFR) in Black adults globally. A search across multiple databases for articles published from 1999 to May 2021 that compared eGFR to mGFR and reported outcomes by Black race was performed.

Real-time clinical note monitoring to detect conditions for rapid follow-up: A case study of clinical trial enrollment in drug-induced torsades de pointes and Stevens-Johnson syndrome.

Identifying acute events as they occur is challenging in large hospital systems. Here, we describe an automated method to detect 2 rare adverse drug events (ADEs), drug-induced torsades de pointes and Stevens-Johnson syndrome and toxic epidermal necrolysis, in near real time for participant recruitment into prospective clinical studies. A text processing system searched clinical notes from the electronic health record (EHR) for relevant keywords and alerted study personnel via email of potential patients for chart review or in-person evaluation.

SPI-Hub™: a gateway to scholarly publishing information.

Background: Advances in the health sciences rely on sharing research and data through publication. As information professionals are often asked to contribute their knowledge to assist clinicians and researchers in selecting journals for publication, the authors recognized an opportunity to build a decision support tool, SPI-Hub: Scholarly Publishing Information Hub™, to capture the team's collective publishing industry knowledge, while carefully retaining the quality of service.

Case Presentation: SPI-Hub's decision support functionality relies on a data framework that describes journal publication policies and practices through a newly designed metadata structure, the Knowledge Management Journal Record™.

Using Best Practices to Extract, Organize, and Reuse Embedded Decision Support Content Knowledge Rules from Mature Clinical Systems.

Clinical decision support (CDS) knowledge, embedded over time in mature medical systems, presents an interesting and complex opportunity for information organization, maintenance, and reuse. To have a holistic view of all decision support requires an in-depth understanding of each clinical system as well as expert knowledge of the latest evidence. This approach to clinical decision support presents an opportunity to unify and externalize the knowledge within rules-based decision support.

A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD).

Objective: The goal of this study was to develop a practical framework for recognizing and disambiguating clinical abbreviations, thereby improving current clinical natural language processing (NLP) systems' capability to handle abbreviations in clinical narratives.

Methods: We developed an open-source framework for clinical abbreviation recognition and disambiguation (CARD) that leverages our previously developed methods, including: (1) machine learning based approaches to recognize abbreviations from a clinical corpus, (2) clustering-based semiautomated methods to generate possible senses of abbreviations, and (3) profile-based word sense disambiguation methods for clinical abbreviations. We applied CARD to clinical corpora from Vanderbilt University Medical Center (VUMC) and generated 2 comprehensive sense inventories for abbreviations in discharge summaries and clinic visit notes.

Code status and resuscitation options in the electronic health record.

Aim: The advance discussion and documentation of code-status is important in preventing undesired cardiopulmonary resuscitation and related end of life interventions. Code-status documentation remains infrequent and paper-based, which limits its usefulness. This study evaluates a tool to document code-status in the electronic health records at a large teaching hospital, and analyzes the corresponding data.

Assisted annotation of medical free text using RapTAT.

Objective: To determine whether assisted annotation using interactive training can reduce the time required to annotate a clinical document corpus without introducing bias.

Materials And Methods: A tool, RapTAT, was designed to assist annotation by iteratively pre-annotating probable phrases of interest within a document, presenting the annotations to a reviewer for correction, and then using the corrected annotations for further machine learning-based training before pre-annotating subsequent documents. Annotators reviewed 404 clinical notes either manually or using RapTAT assistance for concepts related to quality of care during heart failure treatment.

Development and evaluation of RapTAT: a machine learning system for concept mapping of phrases from medical narratives.

Rapid, automated determination of the mapping of free text phrases to pre-defined concepts could assist in the annotation of clinical notes and increase the speed of natural language processing systems. The aim of this study was to design and evaluate a token-order-specific naïve Bayes-based machine learning system (RapTAT) to predict associations between phrases and concepts. Performance was assessed using a reference standard generated from 2860 VA discharge summaries containing 567,520 phrases that had been mapped to 12,056 distinct Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) concepts by the MCVS natural language processing system.

Analyzing differences between chinese and english clinical text: a cross-institution comparison of discharge summaries in two languages.

Worldwide adoption of Electronic Medical Records (EMRs) databases in health care have generated an unprecedented amount of clinical data available electronically. There has been an increasing trend in US and western institutions towards collaborating with China on medical research using EMR data. However, few studies have investigated characteristics of EMR data in China and their differences with the data in US hospitals.

Recommendations for the design, implementation and evaluation of social support in online communities, networks, and groups.

A new model of health care is emerging in which individuals can take charge of their health by connecting to online communities and social networks for personalized support and collective knowledge. Web 2.0 technologies expand the traditional notion of online support groups into a broad and evolving range of informational, emotional, as well as community-based concepts of support.

A comparative study of current Clinical Natural Language Processing systems on handling abbreviations in discharge summaries.

Clinical Natural Language Processing (NLP) systems extract clinical information from narrative clinical texts in many settings. Previous research mentions the challenges of handling abbreviations in clinical texts, but provides little insight into how well current NLP systems correctly recognize and interpret abbreviations. In this paper, we compared performance of three existing clinical NLP systems in handling abbreviations: MetaMap, MedLEE, and cTAKES.

Detecting abbreviations in discharge summaries using machine learning methods.

Recognition and identification of abbreviations is an important, challenging task in clinical natural language processing (NLP). A comprehensive lexical resource comprised of all common, useful clinical abbreviations would have great applicability. The authors present a corpus-based method to create a lexical resource of clinical abbreviations using machine-learning (ML) methods, and tested its ability to automatically detect abbreviations from hospital discharge summaries.

Integrated information systems for electronic chemotherapy medication administration.

Introduction: Chemotherapy administration is a highly complex and distributed task in both the inpatient and outpatient infusion center settings. The American Society of Clinical Oncology and the Oncology Nursing Society (ASCO/ONS) have developed standards that specify procedures and documentation requirements for safe chemotherapy administration. Yet paper-based approaches to medication administration have several disadvantages and do not provide any decision support for patient safety checks.

Generating Clinical Notes for Electronic Health Record Systems.

Clinical notes summarize interactions that occur between patients and healthcare providers. With adoption of electronic health record (EHR) and computer-based documentation (CBD) systems, there is a growing emphasis on structuring clinical notes to support reusing data for subsequent tasks. However, clinical documentation remains one of the most challenging areas for EHR system development and adoption.

Integrating best evidence into patient care: a process facilitated by a seamless integration with informatics tools.

The Vanderbilt University paper discusses how the Eskind Biomedical Library at Vanderbilt University Medical Center transitioned from a simplistic approach that linked resources to the institutional electronic medical record system, StarPanel, to a value-added service that is designed to deliver highly relevant information. Clinical teams formulate complex patient-specific questions via an evidence-based medicine literature request basket linked to individual patient records. The paper transitions into discussing how the StarPanel approach acted as a springboard for two additional projects that use highly trained knowledge management librarians with informatics expertise to integrate evidence into both order sets and a patient portal, MyHealth@Vanderbilt.

A real-time ventilator management dashboard: toward hardwiring compliance with evidence-based guidelines.

Care of mechanically ventilated patients requires coordination between multiple caregivers, necessitating the availability of accurate and timely information on patient status. Researchers have documented positive effects of several interventions on the rates of developing ventilator associated complications, such as providing regular oral care and elevating the head of the bed. Informatics tools, such as electronic whiteboards, reminders, and alerts have been shown to aid in clinician compliance with guidelines or protocols.

Implementing pneumonia quality care measures with an informatics-driven intervention.

Many institutions find it difficult to achieve compliance with quality measures associated with community acquired pneumonia. The goal of this study was to design and implement an automated pneumonia detection and management system in the Emergency Department. The system utilizes multiple information systems to improve communication among health care providers and support more timely decision making for ED patients suggestive of having pneumonia.

A framework for clinical communication supporting healthcare delivery.

Interpersonal communications related to healthcare delivery, called clinical communications, take up a considerable amount of time. Nonetheless, there exist few tools in electronic health record systems that support clinical communications. We describe a framework for clinical communications and describe our experiences implementing tools to manage and document them within an electronic health record system.

Implementing pediatric growth charts into an electronic health record system.

Electronic health record (EHR) systems are increasingly being adopted in pediatric practices; however, requirements for integrated growth charts are poorly described and are not standardized in current systems. The authors integrated growth chart functionality into an EHR system being developed and installed in a multispecialty pediatric clinic in an academic medical center. During a three-year observation period, rates of electronically documented values for weight, stature, and head circumference increased from fewer than ten total per weekday, up to 488 weight values, 293 stature values, and 74 head circumference values (p<0.

Effect of CPOE user interface design on user-initiated access to educational and patient information during clinical care.

Objective: Authors evaluated whether displaying context sensitive links to infrequently accessed educational materials and patient information via the user interface of an inpatient computerized care provider order entry (CPOE) system would affect access rates to the materials.

Design: The CPOE of Vanderbilt University Hospital (VUH) included "baseline" clinical decision support advice for safety and quality. Authors augmented this with seven new primarily educational decision support features.