In Japan, the excessive length of time required for nursing records has become a social problem. A shift to concise \"bulleted\" records is needed to apply speech recognition and to work with foreign caregivers. Therefore, using 96,000 descriptively described anonymized nursing records, we identified typical situations for each information source and attempted to convert them to \"bulleted\" records using ChatGPT-3.5(For return from the operating room, Status on return, Temperature control, Blood drainage, Stoma care, Monitoring, Respiration and Oxygen, Sensation and pain, etc.). The results showed that ChatGPT-3.5 has some usable functionality as a tool for extracting keywords in \"bulleted\" records. Furthermore, through the process of converting to a \"bulleted\" record, it became clear that the transition to a standardized nursing record utilizing the \"Standard Terminology for Nursing Observation and Action (STerNOA)\" would be facilitated.

OBJECTIVE: To determine the prevalence and clustering of NANDA-International nursing diagnoses in patients assisted by pre-hospital emergency teams.
METHODS: Retrospective descriptive study of electronic record review.
METHODS: Episodes recorded during 2019, including at least a nursing diagnosis, were recovered from the electronic health records of a Spanish public emergency agency (N = 28,847). Descriptive statistics were used to characterize the sample and determine prevalence. A two-step cluster analysis was used to group nursing diagnoses. A comparison between clusters in sociodemographic and medical problems was performed. Data were accessed in November 2020.
RESULTS: Risk for falls (00155) (27.3%), Anxiety (00146) (23.2%), Acute pain (00132), Fear (00148) and Ineffective breathing pattern (00032) represented 96.1% of all recorded diagnoses. A six-cluster solution (n = 26.788) was found. Five clusters had a single high-prevalence diagnosis predominance: Risk for falls (00155) in cluster 1, Anxiety (00146) in cluster 2, Fear (00148) in cluster 3, Acute pain (00132) in cluster 4 and Ineffective breathing pattern (00032) in cluster 6. Cluster 5 had several high prevalence diagnoses which co-occurred: Risk for unstable blood glucose level (00179), Ineffective coping (00069), Ineffective health management (00078), Impaired comfort (00214) and Impaired verbal communication (00051).
CONCLUSIONS: Five nursing diagnoses accounted for almost the entire prevalence. The identified clusters showed that pre-hospital patients present six patterns of nursing diagnoses. Five clusters were predominated by a predominant nursing diagnosis related to patient safety, coping, comfort, and activity/rest, respectively. The sixth cluster grouped several nursing diagnoses applicable to exacerbations of chronic diseases.
UNASSIGNED: Knowing the prevalence and clustering of nursing diagnoses allows a better understanding of the human responses of patients attended by pre-hospital emergency teams and increases the evidence of individualized/standardized care plans in the pre-hospital clinical setting.
CONCLUSIONS: What problem did the study address? There are different models of pre-hospital emergency care services. The use of standardized nursing languages in the pre-hospital setting is not homogeneous. Studies on NANDA-I nursing diagnoses in the pre-hospital context are scarce, and those available are conducted on small samples. What were the main findings? This paper reports the study with the largest sample among the few published on NANDA-I nursing diagnoses in the pre-hospital care setting. Five nursing diagnoses represented 96.1% of all recorded. These diagnoses were related to patients\' safety/protection and coping/stress tolerance. Patients attended by pre-hospital care teams are grouped into six clusters based on the nursing diagnoses, and this classification is independent of the medical conditions the patient suffers. Where and on whom will the research have an impact? Knowing the prevalence of nursing diagnoses allows a better understanding of the human responses of patients treated in the pre-hospital setting, increasing the evidence of individualized and standardized care plans for pre-hospital care.
UNASSIGNED: STROBE checklist has been used as a reporting method.
UNASSIGNED: Only patients\' records were reviewed without further involvement.

OBJECTIVE: The aim of this study is to generate empirical evidence, drawing from clinical records, with the goal of elevating the level of evidence supporting the nursing diagnosis (ND) of \'chronic pain\'.
BACKGROUND: Chronic pain is a prevalent condition that affects all age groups. Patients often feel disbelieved about their pain perception, leading to adverse psychological effects, difficulty accessing healthcare and poor rehabilitation outcomes.
METHODS: Retrospective descriptive study. Standards for Reporting Diagnostic Accuracy Studies guidelines were followed in this study.
METHODS: Data were extracted from Electronic Health Records (EHR) of patients admitted to the University Hospital of Perugia, Italy, between March 2016 and December 2022. The study sample comprised individuals without a specific medical diagnosis or high-risk population. Out of 1,048,565 EHR, 43,341 clinical-nursing diaries with the keyword \'pain\' were identified, from which 283 clinical-nursing notes were selected based on a keyword-based retrieval technique and diagnostic definition for further analysis.
RESULTS: Our study findings support the diagnostic descriptors of the \'chronic pain\' ND in clinical-nursing diaries. We observed the presence of 9 out of 11 defining characteristics, 7 out of 10 related factors, 4 out of 8 at-risk populations and 11 out of 17 associated conditions.
CONCLUSIONS: The study validated diagnostic criteria for chronic pain and proposed \'haematological pathology\' as a new associated condition. The findings were presented to the Diagnosis Development Committee of NANDA-International for further review. However, limitations of the study prompted the need for further analysis using natural language processing and artificial neural network techniques. As a result, a new research direction using artificial intelligence (AI) tools was initiated.
CONCLUSIONS: The study validates diagnostic descriptors for chronic pain and proposes future directions in semantic analysis and AI tools, aiming to enhance clinical practice and decision-making in nursing care.
UNASSIGNED: No patient or public contribution.

BACKGROUND: Psychomotor agitation is increased psychomotor activity, restlessness and irritability. People with psychomotor agitation respond by overreacting to intrinsic and extrinsic stimuli, experiencing stress and/or cognitive impairment. the aim was to analyse the association of nursing diagnoses with the disinhibition dimension, the aggressiveness dimension and the lability dimension of the Corrigan Agitated Behaviour Scale.
METHODS: This study was conducted in Spain using a multicentre cross-sectional convenience sample of 140 patients who had been admitted to psychiatric hospital units and had presented an episode of psychomotor agitation between 2018 and 2021.
RESULTS: The Corrigan Agitated Behaviour Scale was used to assess psychomotor agitation. Associated nursing diagnoses, violence directed at professionals and the environment are shown to be predictive values for the severity of the agitation episode. Moderate-severe psychomotor agitation episodes are shown as predictors of violence directed mainly at professionals and the environment.
CONCLUSIONS: There is an urgent need for mental health nurses to have knowledge of the extended clinic in order to care for users and improve their health conditions in dealing with people, with their social, subjective and biological dimension.

Standardized nursing terminologies (SNTs) are necessary to ensure consistent knowledge expression and compare the effectiveness of nursing practice across settings. This study investigated whether SNTs can support semantic interoperability and outcoming tracking over time by implementing an AI-powered CDS tool for fall prevention across multiple EMR systems.
The study involved 3 tertiary academic hospitals and 1 public hospital with different EMR systems and nursing terms, and employed an AI-powered CDS tool that determines the fall risk within the next hour (prediction model) and recommends tailored care plans (CDS functions; represented by SNTs). The prediction model was mapped to local data elements and optimized using local data sets. The local nursing statements in CDS functions were mapped using an ICNP-based inpatient fall-prevention catalog. Four implementation models were compared, and patient outcomes and nursing activities were observed longitudinally at one site.
The postimplementation approach was practical for disseminating the AI-powered CDS tool for nursing. The 4 hospitals successfully implemented prediction models with little performance variation; the AUROCs were 0.8051-0.9581. The nursing process data contributed markedly to fall-risk predictions. The local nursing statements on preventing falls covered 48.0%-86.7% of statements. There was no significant longitudinal decrease in the fall rate (P = .160, 95% CI = -1.21 to 0.21 per 1000 hospital days), but rates of interventions provided by nurses were notably increased.
SNTs contributed to achieving semantic interoperability among multiple EMR systems to disseminate AI-powered CDS tools and automatically track nursing and patient outcomes.

Meaningful data to determine safe and efficient nursing workload are needed. Reasoning a nurse can accomplish a finite number of interventions and location changes per hour, examination of time pressure using time motion study (TMS) methods will provide a comparable indication of safe and efficient workload for an individual nurse.
An observer shadowed 11 nurses at a 250-bed nursing home in the Southeastern United States and recorded 160 h of observations using TimeCaT, web-based TMS data recording software. Predefined Omaha System nursing interventions (N = 57) and locations (N = 8) were embedded within TimeCaT. The time-stamped data were downloaded from TimeCaT and analyzed using descriptive and inferential statistics. Five time pressure metrics were derived from previous TMS findings in acute care settings.
Overall, nurses spent 66 s for each intervention, performed 65 interventions per hour, stayed 130 s at each location, changed locations 28 times per hour, and multitasked for 29% of working time. Computed hourly time pressure metrics enabled visualization of variability in time pressure metrics over time, with differences in multitasking by licensure, unit/role, and observation session time.
Nursing home nurses consistently experienced a high degree of time pressure, especially multitasking for one-third of their working time. To inform staffing decision making and improve the quality of care, resident outcomes, and nurse satisfaction, it is critical to identify ways to mitigate time pressure. Additional research is needed to refine and extend the use of the time pressure metrics.

OBJECTIVE: This study aims to identify NANDA-I nursing diagnoses that midwives working in obstetrics and gynecologic service use while managing the electronic nursing care process.
METHODS: This retrospective study was conducted in a descriptive way to evaluate electronic care plan records of 3025 patients staying in obstetrics and gynecologic service between April 1, 2020. and April 1, 2021. Diagnoses in the records of the electronic care process were digitalized by two faculty members. Then, NANDA-I nursing diagnoses used by midwives were identified. NANDA-I nursing diagnoses used by midwives were identified.
RESULTS: It was determined that diagnoses in care plans documented from the system within the 1-year period fell into eight domains and 10 classes, and 5819 diagnoses were given in total. The most frequent diagnoses given in obstetrics and gynecologic service were \"acute pain\" and \"risk for bleeding.\"
CONCLUSIONS: Findings of this study revealed that nursing care records in obstetrics and gynecologic service did not have a large number of diagnoses and interventions.
CONCLUSIONS: Care plans directly reflect the contribution of the care to the patient. Consequently, midwives being aware of and recording nursing diagnoses while giving care will ensure a standardized language and visibility in care. More coverage of midwifery-related diagnoses in the midwifery curriculum will make NANDA-I nursing diagnoses more visible in midwifery as well.

OBJECTIVE: To develop a care plan focused on assisting a puerperal woman who decides to feed their child through breastfeeding on the contralateral breast.
METHODS: A case study conducted with a 36-year-old primiparous woman who underwent tumorectomy and left axillary emptying, chemotherapy and radiotherapy 8 years ago. The data were collected in the Nursing process, with initial assessment using the Marjory Gordon functional health patterns. The care plan used the NANDA-I, NIC and NOC taxonomies.
RESULTS: Three diagnoses were identified: [00208] Readiness for Enhanced Childbearing Process, [00106] Readiness for Enhanced Breastfeeding and [00167] Readiness for Enhanced Self-Concept. The indicators were evaluated in the initial and later phase, with gains in the first two diagnoses. The third diagnosis proved to be partially sufficient, as it did not allow assessing the developmental and non-pathological self-image in the puerperium. In the puerperal phase, maternal roles are challenging and demanding. Nursing care contributed to the adaptation to breastfeeding in the contralateral breast and to the guarantee of adequate nutrition for the newborn. The childbearing process was strengthened. Breastfeeding is carried out with maternal satisfaction, good latch and normal weight evolution in the newborn.
CONCLUSIONS: The case study strengthened knowledge by addressing a little investigated theme. The NANDA-I taxonomy may need further study in the Self-perception domain, more specifically in the self-image reported during the pregnancy-puerperal phase.

In the last few decades, the impact of chronic health conditions on health systems, as well as on the quality of life, frailty, and dependence of those affected, has been brought to light. The objective of this study was to describe the population care needs of highly complex chronic patients (HCCPs).
METHODS: An epidemiological observational study was conducted.
RESULTS: A total of 13,262 patients were identified, 51% of which were elderly women. Among all patients, 84.4% had received a nursing assessment related to health patterns. Three diagnoses were established in 25% of the sample: readiness for enhanced health management, impaired skin integrity, and risk for falls. There were significant differences according to age, most importantly in terms of impaired skin integrity (39% of patients under 80 years old). Risk for falls, social isolation, situational low self-esteem, chronic low self-esteem, impaired home maintenance, anxiety, ineffective health management, ineffective coping, impaired memory, insomnia, and self-care deficits were more common in those living alone. A total of 37 diagnoses featured differences according to frailty/dependence. Approximately 23% of HCCPs suffered from frail elderly syndrome.
CONCLUSIONS: This study presents the most common care needs of HCCPs, describing the sociodemographic profile of this part of the population. The planning of HCCP care varies in nature. Factors such as the dependence level and frailty of these people should be taken into consideration.

To investigate the effect of discharge training on surgical recovery in oncology patients.
A two-arm parallel-group randomized controlled trial (RCT) registered at clinicaltrials.gov (NCT04862104) and reporting according to the CONSORT checklist.
The study was conducted with 78 patients who had undergone cancer surgery in a university hospital. The intervention group took discharge training; the control group received routine care. The surgical recovery was measured before discharge and 2, 4 and 8 weeks after the discharge.
There was a higher surgical recovery score in the intervention group compared with the usual care group at the second, fourth and eighth week after discharge. This study is expected to support discharge training as enhancing recovery in oncology surgical patients.
This pilot study shows that discharge training developed based on the Nursing Intervention Classification can be used in clinics to enhance the surgical recovery of patients.