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This article examines the potential of generative artificial intelligence (AI), such as ChatGPT (Chat Generative Pre-trained Transformer), in nursing education and the associated challenges and recommendations for their use. Generative AI offers potential benefits such as aiding students with assignments, providing realistic patient scenarios for practice, and enabling personalized, interactive learning experiences. However, integrating generative AI in nursing education also presents challenges, including academic integrity issues, the potential for plagiarism and copyright infringements, ethical implications, and the risk of producing misinformation. Clear institutional guidelines, comprehensive student education on generative AI, and tools to detect AI-generated content are recommended to navigate these challenges. The article concludes by urging nurse educators to harness generative AI's potential responsibly, highlighting the rewards of enhanced learning and increased efficiency. The careful navigation of these challenges and strategic implementation of AI is key to realizing the promise of AI in nursing education. [J Nurs Educ. 2025;64(6):e40–e43.]

Artificial intelligence (AI) scribes have been rapidly adopted across health systems, driven by their promise to ease the documentation burden and reduce clinician burnout. While early evidence shows efficiency gains, this commentary cautions that adoption is outpacing validation and oversight. Without greater scrutiny, the rush to deploy AI scribes may compromise patient safety, clinical integrity, and provider autonomy.

ObjectivesThis 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.MethodsThe 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.ResultsThe 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 PracticeThis 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.

The prevalence of patients who are Incapacitated with No Evident Advance Directives or Surrogates (INEADS) remains unknown because such data are not routinely captured in structured electronic health records. This study sought to develop and validate a natural language processing (NLP) algorithm to identify information related to being INEADS from clinical notes. We used a publicly available dataset of critical care patients from 2001 through 2012 at a United States academic medical center, which contained 418,393 relevant clinical notes for 23,904 adult admissions. We developed 17 subcategories indicating reduced or elevated potential for being INEADS, and created a vocabulary of terms and expressions within each. We used an NLP application to create a language model and expand these vocabularies. The NLP algorithm was validated against gold standard manual review of 300 notes and showed good performance overall (F-score = 0.83). More than 80% of admissions had notes containing information in at least one subcategory. Thirty percent (n = 7,134) contained at least one of five social subcategories indicating elevated potential for being INEADS, and <1% (n = 81) contained at least four, which we classified as high likelihood of being INEADS. Among these, n = 8 admissions had no subcategory indicating reduced likelihood of being INEADS, and appeared to meet the definition of INEADS following manual review. Among the remaining n = 73 who had at least one subcategory indicating reduced likelihood of being INEADS, manual review of a 10% sample showed that most did not appear to be INEADS. Compared with the full cohort, the high likelihood group was significantly more likely to die during hospitalization and within four years, to have Medicaid, to have an emergency admission, and to be male. This investigation demonstrates potential for NLP to identify INEADS patients, and may inform interventions to enhance advance care planning for patients who lack social support.

Background: Little is known about symptom documentation related to Alzheimer’s disease and related dementias (ADRD) by home healthcare (HHC) clinicians. Objective: This study: (1) developed a natural language processing (NLP) algorithm that identifies common neuropsychiatric symptoms of ADRD in HHC free-text clinical notes; (2) described symptom clusters and hospitalization or emergency department (ED) visit rates for patients with and without these symptoms. Method: We examined a corpus of −2.6 million free-text notes for 112,237 HHC episodes among 89,459 patients admitted to a non-profit HHC agency for post-acute care with any diagnosis. We used NLP software (NimbleMiner) to construct indicators of six neuropsychiatric symptoms. Structured HHC assessment data were used to identify known ADRD diagnoses and construct measures of hospitalization/ED use during HHC. Results: Neuropsychiatric symptoms were documented for 40% of episodes. Common clusters included impaired memory, anxiety and/or depressed mood. One in three episodes without an ADRD diagnosis had documented symptoms. Hospitalization/ED rates increased with one or more symptoms present. Conclusion: HHC providers should examine episodes with neuropsychiatric symptoms but no ADRD diagnoses to determine whether ADRD diagnosis was missed or to recommend ADRD evaluation. NLP-generated symptom indicators can help to identify high-risk patients for targeted interventions.

Asthma is a common chronic illness affecting 19 million US adults. Inhaled corticosteroids are a safe and effective treatment for asthma, yet, medication adherence among patients remains poor. Shared decision-making, a patient activation strategy, can improve patient adherence to inhaled corticosteroids. This study aimed to explore whether audio-recorded patient-primary care provider encounters can be used to: 1. Evaluate the level of patient-perceived shared decision-making during the encounter, and 2. Predict levels of patient's inhaled corticosteroid adherence. Shared decision-making and inhaled corticosteroid adherence were assessed using the SDM Questionnaire-9 and the Medication Adherence Report Scale for Asthma (MARS-A). Speech-to-text algorithms were used to automatically transcribe 80 audio-recorded encounters between primary care providers and asthmatic patients. Machine learning algorithms (Naive Bayes, Support Vector Machines, Decision Tree) were applied to achieve the study's predictive goals. The accuracy of automated speech-to-text transcription was relatively high (ROUGE F-score = .9). Machine learning algorithms achieved good predictive performance for shared decision-making (the highest F-score = .88 for the Naive Bayes) and inhaled corticosteroid adherence (the highest F-score = .87 for the Support Vector Machines). This was the first study that trained machine learning algorithms on a dataset of audio-recorded patient-primary care provider encounters to successfully evaluate the quality of SDM and predict patient inhaled corticosteroid adherence. Machine learning approaches can help primary care providers identify patients at risk for poor medication adherence and evaluate the quality of care by measuring levels of shared decision-making. Further work should explore the replicability of our results in larger samples and additional health domains.

Objective To identify gaps and delays in the detection of early onset cancer. Methods We examined firsthand experiences shared on an online discussion board hosted by the Young Survival Coalition—an advocacy group for young adults diagnosed with breast cancer—spanning the years 2009 to 2019. We used natural language processing to detect codes: “first signs and symptoms,” “steps to diagnosis,” “healthcare interactions,” “patient-provider-system feelings,” and “staging/type.” In the training dataset, we used qualitative content analysis to code text from 750 of the forum’s 571,914 posts. We developed and evaluated automated approaches to quantify the proportion of codes in all posts. Lastly, we qualitatively reviewed the classified posts to identify areas for improvement along the clinical pathway. Results The vast majority (81%) of young adults self-detected their breast cancer rather than the cancer being detected through a clinical breast exam. Young adults (70%) were dissatisfied with their care because they encountered delays at three crossroads along the clinical pathway: 1) whether the clinician ordered tests or dismissed the individual as too young; 2) whether imaging modalities were sensitive or not; 3) whether a biopsy confirmed or missed the cancer. Mental health challenges and parenting pressures compounded these delays . True positive cases who experienced these delays strongly encouraged their peers to self-advocate, persist and insist on further testing until diagnosed accurately. Conclusion Dismissal and delays in diagnosis of early onset breast cancer mean potentially worse prognosis since later stage cancers are more aggressive with fewer treatment options. The perspectives from survivors highlight the need for more research informing early detection in young adults by considering breast awareness, use of MRI and ultrasound, biopsy referrals for exhibited breast symptoms in the absence of positive imaging, and sociomedical support for individuals in their role as current or future parent.

Daily preferences are a reflection of how adults wish to have their needs and values addressed, contributing to joy and satisfaction in their daily lives. Clinical settings often regard older adults as a uniform group, neglecting the diversity within this population, which results in a shortfall of person-centered care that overlooks their distinct daily care preferences. At the heart of person-centered care lies the imperative to comprehend and integrate these preferences into the care process. Recognizing and addressing gender differences in older adults is critical to customizing care plans, thereby optimizing quality of life and well-being for individuals. This study addresses the need to understand the diverse daily care preferences of adults, particularly among older populations, who represent a growing demographic with unique needs and interests. This study aims to identify and analyze the key themes and daily care preferences from unstructured adult text narratives with a focus on uncovering gender-specific variations. This study used 4350 deidentified, unstructured textual data from MyDirectives (MyDirectives, Inc), an interactive online platform. Advanced topic modeling techniques were used to extract meaningful themes, and gender-specific term frequency and distribution were examined to identify gender differences in these elements. The study sample included 2883 women (mean age 63.02, SD 13.69 years) and 1467 men (mean age 67.07, SD 11.73 years). Our analysis identified six major themes: (1) \"entertainment\" (12.14%, 528/4350), (2) \"music\" (10.39%, 452/4350), (3) \"personal interests and memories\" (38.18%, 1661/4350), (4) \"intimate relationships\" (14.92%, 649/4350), (5) \"natural comforts\" (16.18%, 704/4350), and (6) \"emotional, cultural, and spiritual foundations\" (8.18%, 356/4350). Gender differences were evident: women were more likely to express preferences for \"personal interests and memories\" (40.7% vs 33.3%), \"natural comforts\" (18.4% vs 11.9%), and \"emotional and spiritual foundations\" (9.3% vs 6.1%) than men. Men expressed stronger preferences for \"entertainment\" (18.1% vs 9.1%) and \"music\" (16.8% vs 7.2%). Common terms across all participants included \"dog,\" \"love,\" \"friends,\" and \"book.\" Notably, the study revealed significant gender differences in daily care preferences, especially regarding familial relationships and entertainment choices. The findings underscore the importance of recognizing individual daily care preferences in person-centered care, particularly regarding gender. Understanding these preferences is crucial for improving care quality and patient satisfaction, thereby enhancing the overall quality of life for adults receiving care across our health care system.

Ambient artificial intelligence listening tools promise faster nursing documentation and improved patient engagement, yet they introduce risks of hallucinations, omission, and bias when nurses are excluded from the design and oversight process. Empowering nurses through continuing education and leadership in model development, deployment, and auditing is crucial for ensuring safe and equitable integration across care settings.