The Safety Implications of Information Technology in Nursing: Japanese Incident Data Analysis.

This study delves into the impact of Information Technology (IT) on nursing practice in Japan, focusing on patient safety within the 2021-2022 Japanese Medical Accident Report Data. The research aims to understand how IT factors contribute to nursing-related medical incidents in a healthcare landscape rapidly integrating IT. The study identifies IT-related incidents through a retrospective analysis of medical incident reports, primarily in nursing, by analyzing categorized data and free-text descriptions for IT-related keywords. The findings indicate significant IT-related issues, with 'Other EHR Related' problems (36%) and 'EHR Reporting' errors (25%) being the most prevalent. These incidents often involve challenges in patient identification and medication management. The study suggests improvements like enhanced verification processes and automated systems to mitigate these risks. Conclusively, it underscores the dual nature of IT in nursing: while it holds the potential to enhance patient care, it also introduces challenges that necessitate specialized informatics expertise to ensure its beneficial integration into nursing practices.

ADscreen: A speech processing-based screening system for automatic identification of patients with Alzheimer's disease and related dementia.

Alzheimer's disease and related dementias (ADRD) present a looming public health crisis, affecting roughly 5 million people and 11 % of older adults in the United States. Despite nationwide efforts for timely diagnosis of patients with ADRD, >50 % of them are not diagnosed and unaware of their disease. To address this challenge, we developed ADscreen, an innovative speech-processing based ADRD screening algorithm for the protective identification of patients with ADRD. ADscreen consists of five major components: (i) noise reduction for reducing background noises from the audio-recorded patient speech, (ii) modeling the patient's ability in phonetic motor planning using acoustic parameters of the patient's voice, (iii) modeling the patient's ability in semantic and syntactic levels of language organization using linguistic parameters of the patient speech, (iv) extracting vocal and semantic psycholinguistic cues from the patient speech, and (v) building and evaluating the screening algorithm. To identify important speech parameters (features) associated with ADRD, we used the Joint Mutual Information Maximization (JMIM), an effective feature selection method for high dimensional, small sample size datasets. Modeling the relationship between speech parameters and the outcome variable (presence/absence of ADRD) was conducted using three different machine learning (ML) architectures with the capability of joining informative acoustic and linguistic with contextual word embedding vectors obtained from the DistilBERT (Bidirectional Encoder Representations from Transformers). We evaluated the performance of the ADscreen on an audio-recorded patients' speech (verbal description) for the Cookie-Theft picture description task, which is publicly available in the dementia databank. The joint fusion of acoustic and linguistic parameters with contextual word embedding vectors of DistilBERT achieved F1-score = 84.64 (standard deviation [std] = ±3.58) and AUC-ROC = 92.53 (std = ±3.34) for training dataset, and F1-score = 89.55 and AUC-ROC = 93.89 for the test dataset. In summary, ADscreen has a strong potential to be integrated with clinical workflow to address the need for an ADRD screening tool so that patients with cognitive impairment can receive appropriate and timely care.

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.

Factors associated with poor self-management documented in home health care narrative notes for patients with heart failure.

BACKGROUND: Patients with heart failure (HF) who actively engage in their own self-management have better outcomes. Extracting data through natural language processing (NLP) holds great promise for identifying patients with or at risk of poor self-management. OBJECTIVE: To identify home health care (HHC) patients with HF who have poor self-management using NLP of narrative notes, and to examine patient factors associated with poor self-management. METHODS: An NLP algorithm was applied to extract poor self-management documentation using 353,718 HHC narrative notes of 9,710 patients with HF. Sociodemographic and structured clinical data were incorporated into multivariate logistic regression models to identify factors associated with poor self-management. RESULTS: There were 758 (7.8%) patients in this sample identified as having notes with language describing poor HF self-management. Younger age (OR 0.982, 95% CI 0.976-0.987, p < .001), longer length of stay in HHC (OR 1.036, 95% CI 1.029- 1.043, p < .001), diagnosis of diabetes (OR 1.47, 95% CI 1.3-1.67, p < .001) and depression (OR 1.36, 95% CI 1.09-1.68, p < .01), impaired decision-making (OR 1.64, 95% CI 1.37-1.95, p < .001), smoking (OR 1.7, 95% CI 1.4-2.04, p < .001), and shortness of breath with exertion (OR 1.25, 95% CI 1.1-1.42, p < .01) were associated with poor self-management. CONCLUSIONS: Patients with HF who have poor self-management can be identified from the narrative notes in HHC using novel NLP methods. Meaningful information about the self-management of patients with HF can support HHC clinicians in developing individualized care plans to improve self-management and clinical outcomes.

Predicting Risk for Early Breastfeeding Cessation in Israel.

OBJECTIVES: This study aimed to 1) Examine factors associated with cessation of exclusive breastfeeding in Israel and 2) Develop predictive models to identify women at risk for early exclusive breastfeeding cessation. METHODS: The study used data from longitudinal national representative infant nutrition survey in Israel (n = 2119 participants). Logistic regression was used to identify risk factors and build predictive models. RESULTS: The rate of exclusive breastfeeding cessation increased from 45.4% at 2 months to 85.7% at 6 months. Five factors were significantly associated with higher odds of exclusive breastfeeding cessation at 2 months: being a primapara, low educational level, lack of previous breastfeeding experience, negative attitude towards birth, and lack of intention to breastfeed. Six factors were significantly associated with higher odds of exclusive breastfeeding cessation at 6 months: younger age, being in a relationship with a partner, lower religiosity level, cesarean delivery, not taking folic acid during pregnancy, and negative attitude towards birth. Both 2 and 6-months models had good predictive performance (C-statistic of .72 and .68, accordingly). CONCLUSIONS FOR PRACTICE: This nationwide study successfully identified several predictors of exclusive breastfeeding cessation and created breastfeeding cessation prediction tools for two time periods (2 and 6 months). The resulting tools can be applied to identify women at risk for stopping exclusive breastfeeding in hospitals or at community clinics. Further studies should examine practical aspects of applying these tools in practice and explore whether applying those tools can lead to higher exclusive breastfeeding rates.

Characterizing shared and distinct symptom clusters in common chronic conditions through natural language processing of nursing notes.

Data-driven characterization of symptom clusters in chronic conditions is essential for shared cluster detection and physiological mechanism discovery. This study aims to computationally describe symptom documentation from electronic nursing notes and compare symptom clusters among patients diagnosed with four chronic conditions-chronic obstructive pulmonary disease (COPD), heart failure, type 2 diabetes mellitus, and cancer. Nursing notes (N = 504,395; 133,977 patients) were obtained for the 2016 calendar year from a single medical center. We used NimbleMiner, a natural language processing application, to identify the presence of 56 symptoms. We calculated symptom documentation prevalence by note and patient for the corpus. Then, we visually compared documentation for a subset of patients (N = 22,657) diagnosed with COPD (n = 3339), heart failure (n = 6587), diabetes (n = 12,139), and cancer (n = 7269) and conducted multiple correspondence analysis and hierarchical clustering to discover underlying groups of patients who have similar symptom profiles (i.e., symptom clusters) for each condition. As expected, pain was the most frequently documented symptom. All conditions had a group of patients characterized by no symptoms. Shared clusters included cardiovascular symptoms for heart failure and diabetes; pain and other symptoms for COPD, diabetes, and cancer; and a newly-identified cognitive and neurological symptom cluster for heart failure, diabetes, and cancer. Cancer (gastrointestinal symptoms and fatigue) and COPD (mental health symptoms) each contained a unique cluster. In summary, we report both shared and distinct, as well as established and novel, symptom clusters across chronic conditions. Findings support the use of electronic health record-derived notes and NLP methods to study symptoms and symptom clusters to advance symptom science.

Factors Affecting Patient Prioritization Decisions at Admission to Home Healthcare: A Predictive Study to Develop a Risk Screening Tool.

There is a lack of evidence on how to identify high-risk patients admitted to home healthcare. This study aimed (1) to identify which disease characteristics, medications, patient needs, social support characteristics, and other factors are associated with patient priority for the first home health nursing visit; and (2) to construct and validate a predictive model of patient priority for the first home health nursing visit. This was a predictive study of home health visit priority decisions made by 20 nurses for 519 older adults. The study found that nurses were more likely to prioritize patients who had wounds (odds ratio = 1.88), comorbid condition of depression (odds ratio = 1.73), limitation in current toileting status (odds ratio = 2.02), higher number of medications (increase in odds ratio for each medication = 1.04), and comorbid conditions (increase in odds ratio for each condition = 1.04). This study developed one of the first clinical decision support tools for home healthcare called "PREVENT". (PRiority home health Visit Tool). Further work is needed to increase the specificity and generalizability of the tool and to test its effects on patient outcomes.

NimbleMiner: An Open-Source Nursing-Sensitive Natural Language Processing System Based on Word Embedding.

This study develops and evaluates an open-source software (called NimbleMiner) that allows clinicians to interact with word embedding models with a goal of creating lexicons of similar terms. As a case study, the system was used to identify similar terms for patient fall history from homecare visit notes (N = 1 149 586) extracted from a large US homecare agency. Several experiments with parameters of word embedding models were conducted to identify the most time-effective and high-quality model. Models with larger word window width sizes (n = 10) that present users with about 50 top potentially similar terms for each (true) term validated by the user were most effective. NimbleMiner can assist in building a thorough vocabulary of fall history terms in about 2 hours. For domains like nursing, this approach could offer a valuable tool for rapid lexicon enrichment and discovery.

Nurses "Seeing Forest for the Trees" in the Age of Machine Learning: Using Nursing Knowledge to Improve Relevance and Performance.

Although machine learning is increasingly being applied to support clinical decision making, there is a significant gap in understanding what it is and how nurses should adopt it in practice. The purpose of this case study is to show how one application of machine learning may support nursing work and to discuss how nurses can contribute to improving its relevance and performance. Using data from 130 specialized hospitals with 101 766 patients with diabetes, we applied various advanced statistical methods (known as machine learning algorithms) to predict early readmission. The best-performing machine learning algorithm showed modest predictive ability with opportunities for improvement. Nurses can contribute to machine learning algorithms by (1) filling data gaps with nursing-relevant data that provide personalized context about the patient, (2) improving data preprocessing techniques, and (3) evaluating potential value in practice. These findings suggest that nurses need to further process the information provided by machine learning and apply "Wisdom-in-Action" to make appropriate clinical decisions. Nurses play a pivotal role in ensuring that machine learning algorithms are shaped by their unique knowledge of each patient's personalized context. By combining machine learning with unique nursing knowledge, nurses can provide more visibility to nursing work, advance nursing science, and better individualize patient care. Therefore, to successfully integrate and maximize the benefits of machine learning, nurses must fully participate in its development, implementation, and evaluation.

Drug-Induced Anaphylaxis Documented in Electronic Health Records.

BACKGROUND: Although drugs represent a common cause of anaphylaxis, few large studies of drug-induced anaphylaxis have been performed. OBJECTIVE: To describe the epidemiology and validity of reported drug-induced anaphylaxis in the electronic health records (EHRs) of a large United States health care system. METHODS: Using EHR drug allergy data from 1995 to 2013, we determined the population prevalence of anaphylaxis including anaphylaxis prevalence over time, and the most commonly implicated drugs/drug classes reported to cause anaphylaxis. Patient risk factors for drug-induced anaphylaxis were assessed using a logistic regression model. Serum tryptase and allergist visits were used to assess the validity and follow-up of EHR-reported anaphylaxis. RESULTS: Among 1,756,481 patients, 19,836 (1.1%) reported drug-induced anaphylaxis; penicillins (45.9 per 10,000), sulfonamide antibiotics (15.1 per 10,000), and nonsteroidal anti-inflammatory drugs (NSAIDs) (13.0 per 10,000) were most commonly implicated. Patients with white race (odds ratio [OR] 2.38, 95% CI 2.27-2.49), female sex (OR 2.20, 95% CI 2.13-2.28), systemic mastocytosis (OR 4.60, 95% CI 2.66-7.94), Sjögren's syndrome (OR 1.94, 95% CI 1.47-2.56), and asthma (OR 1.50, 95% CI 1.43-1.59) had an increased odds of drug-induced anaphylaxis. Serum tryptase was performed in 135 (<1%) anaphylaxis cases and 1,587 patients (8.0%) saw an allergist for follow-up. CONCLUSIONS: EHR-reported anaphylaxis occurred in approximately 1% of patients, most commonly from penicillins, sulfonamide antibiotics, and NSAIDs. Females, whites, and patients with mastocytosis, Sjögren's syndrome, and asthma had increased odds of reporting drug-induced anaphylaxis. The low observed frequency of tryptase testing and specialist evaluation emphasize the importance of educating providers on anaphylaxis management.

Cardiovascular risk in patients with alopecia areata (AA): A propensity-matched retrospective analysis.

BACKGROUND: The cardiovascular risk of patients with alopecia areata (AA) is not well characterized, with limited studies evaluating the risk of acute myocardial infarction (AMI) and ischemic stroke. OBJECTIVE: We sought to determine the risk for patients with AA to develop subsequent stroke and AMI. METHODS: We conducted propensity-matched retrospective analysis between January 1, 2000, and January 1, 2010, from Brigham and Women's Hospital and Massachusetts General Hospital in Boston, MA. A comprehensive research patient data repository search was done for International Classification of Diseases, Ninth Revision code 704.01 and cases were verified using a natural language processing program. Propensity score matching was used to identify controls for AA cases based on age, race, gender, smoking status, and history of hypertension, diabetes, and hyperlipidemia. RESULTS: We identified 1377 cases of AA matched with 4131 controls. Patients with AA had decreased odds for developing stroke (odds ratio 0.39, 95% CI 0.18-0.87) and a trend toward decreased risk of AMI (odds ratio 0.91, 95% CI 0.59-1.39). LIMITATIONS: This was a retrospective study using a clinical database. CONCLUSION: Patients with AA had decreased risk for stroke and AMI, although not statistically significant. Further studies are needed to confirm these findings in other AA cohorts and to elucidate a potential mechanism.

A retrospective study on patient characteristics and telehealth alerts indicative of key medical events for heart failure patients at a home health agency.

OBJECTIVE: To explore association of patient characteristics and telehealth alert data with all-cause key medical events (KMEs) of emergency department (ED) visits and hospitalizations as well as cardiac-related KMEs of ED visits, hospitalizations, and medication changes. MATERIALS AND METHODS: A 6-month retrospective study was conducted of electronic patient records of heart failure (HF) patients using telehealth services at a Massachusetts home health agency. Data collected included patient demographic, psychosocial, disease severity factors and telehealth vital signs alerts. Association between patient characteristics and KMEs was analyzed by Generalized Estimating Equations. RESULTS: The sample comprised 168 patients with a mean age of 83 years, 56% females, and 96% white. Ninety-nine cardiac-related KMEs and 87 all-cause KMEs were recorded for the subjects. Odds of a cardiac-related KME increased by 161% with the presence of valvular co-morbidity (p=0.001) and 106% with increased number of telehealth alerts (adjusted p<0.0001). Odds of an all-cause KME increased by 124% (p=0.02), 127% (p=0.01), and 70% (adjusted p<0.0001) with the presence of cancer co-morbidity, anxiety, and increased number of telehealth alerts, respectively. Overall, only 3% of all telehealth alerts were associated with KMEs. CONCLUSIONS: The very low proportion of telehealth vital sign alerts associated with KMEs indicates that telehealth alerts alone cannot inform the need for intervention within the larger context of HF care delivery in the homecare setting. Patient-relevant data such as psychosocial and symptom status, involvement with HF self-management, and presence of co-morbidities could further inform the need for interventions for HF patients in the homecare setting.

Educating clinicians on new elements incorporated into the electronic health record: theories, evidence, and one educational project.

With the widespread use of health information technologies, there is a growing need to educate healthcare providers on the use of technological innovations. Appropriate health information technology education is critical to ensure quality documentation, patient privacy, and safe healthcare. One promising strategy for educating clinicians is the use of participatory e-learning based on the principles of Web 2.0. However, there is a lack of literature on the practical applications of this training strategy in clinical settings. In this article, we briefly review the theoretical background and published literature on distance education, or e-learning, of health information technology, focusing on electronic health records. Next, we describe one example of a theoretically grounded interactive educational intervention that was implemented to educate nurses on new elements incorporated into the existing electronic health record system. We discuss organizational factors facilitating nurses' in-service education and provide an example of software designed to create interactive e-learning presentations. We also evaluate the results of our educational project and make suggestions for future applications. In conclusion, we suggest four core principles that should guide the construction and implementation of distant education for healthcare practitioners.