Natural Language Processing of Nursing Notes: An Integrative Review.

Natural language processing includes a variety of techniques that help to extract meaning from narrative data. In healthcare, medical natural language processing has been a growing field of study; however, little is known about its use in nursing. We searched PubMed, EMBASE, and CINAHL and found 689 studies, narrowed to 43 eligible studies using natural language processing in nursing notes. Data related to the study purpose, patient population, methodology, performance evaluation metrics, and quality indicators were extracted for each study. The majority (86%) of the studies were conducted from 2015 to 2021. Most of the studies (58%) used inpatient data. One of four studies used data from open-source databases. The most common standard terminologies used were the Unified Medical Language System and Systematized Nomenclature of Medicine, whereas nursing-specific standard terminologies were used only in eight studies. Full system performance metrics (eg, F score) were reported for 61% of applicable studies. The overall number of nursing natural language processing publications remains relatively small compared with the other medical literature. Future studies should evaluate and report appropriate performance metrics and use existing standard nursing terminologies to enable future scalability of the methods and findings.

Cardiac symptom burden and arrhythmia recurrence drives digital health use: results from the iHEART randomized controlled trial.

AIMS: Digital health can transform the management of atrial fibrillation (AF) and enable patients to take a central role in detecting symptoms and self-managing AF. There is a gap in understanding factors that support sustained use of digital health tools for patients with AF. This study identified predictors of Alivecor® KardiaMobile ECG monitor usage among patients with AF enrolled in the iPhone®Helping Evaluate Atrial fibrillation Rhythm through Technology (iHEART) randomized controlled trial. METHODS AND RESULTS: We analysed data from 105 English and Spanish-speaking adults with AF enrolled in the intervention arm of the iHEART trial. The iHEART intervention included smartphone-based electrocardiogram self-monitoring with Alivecor® KardiaMobile and triweekly text messages for 6 months. The primary outcome was use of Alivecor® categorized as: infrequent (≤5 times/week), moderate (>5 times and ≤11 times/week), and frequent (>11 times/week). We applied multinomial logistic regression modelling to characterize frequency and predictors of use. Of the 105 participants, 25% were female, 75% were White, and 45% were ≥65 years of age. Premature atrial contractions (PACs) [adjusted odds ratio (OR): 1.23, 1.08-1.40, P = 0.002] predicted frequent as compared to infrequent use. PACs (adjusted OR: 1.17, 95% confidence interval 1.06-1.30, P = 0.003), lower symptom burden (adjusted OR: 1.06, 1.01-1.11, P = 0.02), and less treatment concern (adjusted OR: 0.96, 0.93-0.99, P = 0.02) predicted moderate as compared to infrequent use. CONCLUSIONS: Frequent use of AliveCor® is associated with AF symptoms and potentially symptomatic cardiac events. Symptom burden and frequency should be measured and incorporated into analyses of future digital health trials for AF management.

Exploring Depressive Symptoms and Anxiety Among Patients With Atrial Fibrillation and/or Flutter at the Time of Cardioversion or Ablation.

BACKGROUND: Depression and anxiety in patients with atrial fibrillation (AF) and/or atrial flutter may influence the effectiveness of cardioversion and ablation. There is a lack of knowledge related to depressive symptoms and anxiety at the time of these procedures. OBJECTIVE: We aimed to describe the prevalence and explore potential covariates of depressive symptoms and anxiety in patients with AF at the time of cardioversion or ablation. We further explored the influence of depressive symptoms and anxiety on quality of life at the time of procedure and 6-month AF recurrence. METHODS: Depressive symptoms, anxiety, and quality of life were collected at the time of cardioversion or ablation using the Patient Health Questionnaire-9, State-Trait Anxiety Inventory, and Atrial Fibrillation Effect on Quality of Life questionnaire. Presence of AF recurrence within 6 months post procedure was evaluated. RESULTS: Participants (N = 171) had a mean (SD) age of 61.20 (11.23) years and were primarily male (80.1%) and white, non-Hispanic (81.4%). Moderate to severe depressive symptoms (17.2%) and clinically significant state (30.2%) and trait (23.6%) anxiety were reported. Mood/anxiety disorder diagnosis was associated with all 3 symptoms. Atrial fibrillation symptom severity was associated with both depressive symptoms and trait anxiety. Heart failure diagnosis and digoxin use were also associated with depressive symptoms. Trends toward significance between state and trait anxiety and participant race/ethnicity as well as depressive symptoms and body mass index were observed. Study findings support associations between symptoms and quality of life, but not 6-month AF recurrence. CONCLUSION: Depressive symptoms and anxiety are common in patients with AF. Healthcare providers should monitor patients with AF for depressive symptoms and anxiety at the time of procedures and intervene when indicated. Additional investigations on assessment, prediction, treatment, and outcome of depressive symptoms and anxiety in patients with AF are warranted.

Identifying Symptom Information in Clinical Notes Using Natural Language Processing.

BACKGROUND: Symptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used in clinical notes is complex. A need exists for methods designed specifically to identify and study symptom information from EHR notes. OBJECTIVES: We aim to describe a method that combines standardized vocabularies, clinical expertise, and natural language processing to generate comprehensive symptom vocabularies and identify symptom information in EHR notes. We piloted this method with five diverse symptom concepts: constipation, depressed mood, disturbed sleep, fatigue, and palpitations. METHODS: First, we obtained synonym lists for each pilot symptom concept from the Unified Medical Language System. Then, we used two large bodies of text (clinical notes from Columbia University Irving Medical Center and PubMed abstracts containing Medical Subject Headings or key words related to the pilot symptoms) to further expand our initial vocabulary of synonyms for each pilot symptom concept. We used NimbleMiner, an open-source natural language processing tool, to accomplish these tasks and evaluated NimbleMiner symptom identification performance by comparison to a manually annotated set of nurse- and physician-authored common EHR note types. RESULTS: Compared to the baseline Unified Medical Language System synonym lists, we identified up to 11 times more additional synonym words or expressions, including abbreviations, misspellings, and unique multiword combinations, for each symptom concept. Natural language processing system symptom identification performance was excellent. DISCUSSION: Using our comprehensive symptom vocabularies and NimbleMiner to label symptoms in clinical notes produced excellent performance metrics. The ability to extract symptom information from EHR notes in an accurate and scalable manner has the potential to greatly facilitate symptom science research.

Detection and management of atrial fibrillation using remote monitoring.

Atrial fibrillation (AF) is the most common dysrhythmia encountered in the United States. Symptoms may be similar to those of other cardiac conditions, which can delay the timely detection, diagnosis, and management of AF. This article provides an overview of AF and modalities used in remote monitoring.

Multiple intravenous administrations of human umbilical cord blood cells benefit in a mouse model of ALS.

BACKGROUND: A promising therapeutic strategy for amyotrophic lateral sclerosis (ALS) is the use of cell-based therapies that can protect motor neurons and thereby retard disease progression. We recently showed that a single large dose (25 × 106 cells) of mononuclear cells from human umbilical cord blood (MNC hUCB) administered intravenously to pre-symptomatic G93A SOD1 mice is optimal in delaying disease progression and increasing lifespan. However, this single high cell dose is impractical for clinical use. The aim of the present pre-clinical translation study was therefore to evaluate the effects of multiple low dose systemic injections of MNC hUCB cell into G93A SOD1 mice at different disease stages. METHODOLOGY/PRINCIPAL FINDINGS: Mice received weekly intravenous injections of MNC hUCB or media. Symptomatic mice received 106 or 2.5 × 106 cells from 13 weeks of age. A third, pre-symptomatic, group received 106 cells from 9 weeks of age. Control groups were media-injected G93A and mice carrying the normal hSOD1 gene. Motor function tests and various assays determined cell effects. Administered cell distribution, motor neuron counts, and glial cell densities were analyzed in mouse spinal cords. Results showed that mice receiving 106 cells pre-symptomatically or 2.5 × 106 cells symptomatically significantly delayed functional deterioration, increased lifespan and had higher motor neuron counts than media mice. Astrocytes and microglia were significantly reduced in all cell-treated groups. CONCLUSIONS/SIGNIFICANCE: These results demonstrate that multiple injections of MNC hUCB cells, even beginning at the symptomatic disease stage, could benefit disease outcomes by protecting motor neurons from inflammatory effectors. This multiple cell infusion approach may promote future clinical studies.