Reinterpreting the Nursing Record for an Electronic Context: Development Principles.

The full potential for electronic health record systems in facilitating a positive transformation in care, with improvements in quality and safety, has yet to be realised. There remains a need to reconceptualise the structure, content and use of the nursing component of electronic health record systems. The aim of this study was to engage and involve a diverse group of stakeholders, including nurses and electronic health record system developers, in exploring together both issues and possible new approaches to documentation that better fit with practice, and that facilitate the optimal use of recorded data. Three focus groups were held in the UK and USA, using a semi-structured interview guide, and a common reflexive approach to analysis. The findings were synthesised into themes that were further developed into a set of development principles that might be used to inform a novel electronic health record system specification to support nursing practice.

Design and Development of a Fully Intelligent Nursing Information System Based on International Standard Nursing Terminology.

OBJECTIVE: Design and develop a Clinical Care Classification (CCC) nursing information system aligned with nursing terminology CCC, emphasizing standard procedures and a responsibility-based nursing model to enhance efficiency and quality of care. METHODS: Conduct thorough investigation into clinical nursing informatics needs, analyze existing system shortcomings, utilize Microsoft.net for development, integrate standard nursing procedures and clinical operating protocols into system functions. Structure database based on bed characteristics, implant CCC Nursing Terminology and clinical nursing knowledge base. RESULTS: Successfully design and develop CCC Nursing Information System featuring patient list, nurse assignment, nursing evaluation, diagnosis, goals, plan, interventions, special care, shift handover, record query, workload statistics, and intelligent guidance based on patient assessment and nursing elements. CONCLUSION: The CCC Nursing Information System advances standard nursing procedures in clinical practice, promoting standardization and responsibility-based holistic care. It harnesses big data to enhance system intelligence.

Digital Documentation in Healthcare: Empowering Nurses and Patients for Optimal Care.

In response to challenges associated with extensive documentation practices within the NHS, this paper presents the outcomes of a structured brainstorming session as part of the Chief Nurse Fellows project titled 'Digital Documentation in Healthcare: Empowering Nurses and Patients for Optimal Care." Grounded in Dr. Rozzano Locsin's theory of "Technological Competency as Caring in Nursing," this project leverages a Venn diagram framework to integrate Digital Maturity Assessment (DMA) results with the "What Good Looks Like" (WGLL) Framework, the ANCC Pathway to Excellence, and the eHospital EPR program vision of University Hospitals of Leicester NHS Trust. Participants, including Clinical IT facilitators and nursing leaders, engaged in identifying synergies and gaps across digital proficiency, nursing excellence, and patient-centric care, contributing actionable insights towards an optimized digital patient care model. The findings emphasize the need for holistic digital solutions that enhance documentation efficiency, support staff excellence, and improve patient outcomes.

Cross-Mapping the Portuguese Nursing Ontology with ICNP, SNOMED CT and NANDA-I.

EHR Interoperability is crucial to obtain a set of benefits. This can be achieved by using data standards, like ontologies. The Portuguese Nursing Ontology (NursingOntos) is a reference model describing a set of nursing concepts and their relationships, to represent nursing knowledge in the Electronic Health Records (EHR). The purpose of this work was to define a set of correspondences between Nursing Ontology concepts of NursingOntos and other terminologies, which have the same or similar meaning. In this project, we are using the ISO/TR12300:2016 standard on the principles of mapping between terminological systems. Regarding the domain of "airway clearance", we can say that Portuguese Nursing Ontology has a good level of mapping with other terminologies. In conclusion, we can say that Portuguese Nursing Ontology can be used in EHR with the purpose of a global digitalization of health.

Trial of Converting Nursing Records into Simplified and Structured Information Utilizing ChatGPT-3.5.

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.

Representing Nursing Practice in EMR/EHRs.

OBJECTIVE: Review of the ISO 18104 technical standard for a Nursing Categorial structure to best represent nursing practice in EMR/EHRs and digital health ecosystems. METHODS: Application of ISO standard review guidelines in consultation with ISO member stakeholders. RESULTS: Comprehensive views of the nursing practice knowledge domain are presented as mindmaps. Groups of patients can now be identified using the 'type of subject of care' category. The collaborative role of nurses is now recognized. This high level structured information model recognises nursing diagnosis, nursing actions and nurse sensitive outcomes relative to other categories and sub-categories known to influence nursing actions and nurse sensitive outcomes. DISCUSSION: This nursing practice framework reflects the nursing process. It supports conceptual and logical analysis of patient journey related nursing practice. CONCLUSION: This updated categorial structure is a good fit with today's information technologies. Its adoption enables the value of nursing services provided to be demonstrated.

Voices Unheard: Exploring New Data Sources in Nursing Through Speech Processing.

The complex nature of verbal patient-nurse communication holds valuable insights for nursing research, but traditional documentation methods often miss these crucial details. This study explores the emerging role of speech processing technology in nursing research, emphasizing patient-nurse verbal communication. We conducted case studies across various healthcare settings, revealing a substantial gap in electronic health records for capturing vital patient-nurse encounters. Our research demonstrates that speech processing technology can effectively bridge this gap, enhancing documentation accuracy and enriching data for quality care assessment and risk prediction. The technology's application in home healthcare, outpatient settings, and specialized areas like dementia care illustrates its versatility. It offers the potential for real-time decision support, improved communication training, and enhanced telehealth practices. This paper provides insights into the promises and challenges of integrating speech processing into nursing practice, paving the way for future patient care and healthcare data management advancements.

Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing.

Hospital-acquired falls are a continuing clinical concern. The emergence of advanced analytical methods, including NLP, has created opportunities to leverage nurse-generated data, such as clinical notes, to better address the problem of falls. In this nurse-driven study, we employed an iterative process for expert manual annotation of RNs clinical notes to enable the training and testing of an NLP pipeline to extract factors related to falls. The resulting annotated data corpus had moderately high interrater reliability (F-score=0.74) and captured a breadth of clinical concepts for extraction with potential utility beyond patient falls. Further research is needed to determine which annotation tasks most benefit from nursing expert annotators, to optimize efficiency when tapping into the invaluable resource represented by the nursing workforce.

The National Transformation of Nursing Documentation in Wales.

In response to findings indicating poor standardisation and quality in nursing documentation, NHS Wales embarked on a transformative journey with the National Electronic Nursing Documentation Programme in 2014. Evolving into the Welsh Nursing Care Record (WNCR) by 2019, this initiative sought to streamline practices and enhance accuracy, compliance, and efficiency through a standardised digital platform. The case study explores the comprehensive project design, emphasising evidence-based practices, governance processes and the introduction of National Nursing Informatics roles. Accomplished from April 2021 to September 2023, the WNCR implementation achieved a 79% roll out across eligible wards, marked by strategic investments, robust training, and phased deployment. Evaluations to date show positive impacts on key performance indicators, exemplified by a 9-minute time saving per patient. Lessons learned noted the significance of user engagement, cultural transformation, and ongoing staff development, paving the way for future strategies including nationwide implementation, benefits realisation, and data visualisation initiatives.

Project RENEW: A Nursing Informatics Led Initiative to Reduce Documentation Burden.

Documentation burden within the United States has grown to unprecedented proportions. Poor usability of the electronic health record has been identified as one of the root causes of documentation burden. By using a structured review process, the nursing informatics team at Northwestern Medicine Healthcare identified opportunities for improved usability. Solutions were generated and proposed to a team of nurse experts for review and approval. The approved solutions were implemented into the electronic health record through this efficient and agile process. Project RENEW impacted documentation burden by reducing unnecessary nursing tasks, improving usability of flowsheets, and reducing clicks to complete documentation.

Machine Learning-Based Prediction Models of Mortality for Intensive Care Unit Patients Using Nursing Records.

This study aimed to develop ICU mortality prediction models using a conceptual framework, focusing on nurses' concerns reflected in nursing records from the MIMIC IV database. We included 46,693 first-time ICU admissions of adults over 18 years with a minimum 24-hour stay, excluding those receiving hospice or palliative care. Predictors included demographics, clinical characteristics, and nursing documentation frequencies related to nurses' concerns. Four models were trained with 10-fold cross-validation after adjusting class imbalance. The random forest (RF) model was identified as the best-performing, with key predictors of mortality in this model being the frequency of vital signs, the frequency of nursing note documentation, and the frequency of monitoring-related nursing notes. This suggests that predictive models using nursing records, which reflect nurses' concerns as represented by the frequency of nursing documentation, may be integrated into clinical decision support tools, potentially enhancing patient outcomes in ICUs.

Effectiveness of Using Clinical Decision Support System in Nursing Records.

This study evaluates the impact of Clinical Decision Support System (CDSS) integration into the Nursing Information System 2.0 on nursing records and nurse satisfaction. Longitudinal data collection employs questionnaires and nursing records audits. Findings show improved electronic signature integration and nursing problem identification, benefiting real-time patient information access and record completeness. Younger, less experienced, highly educated nurses exhibit higher CDSS usage and acceptance. Overall, 80.2% agreement rate confirms CDSS's positive impact, highlighting the importance of user effectiveness evaluation in system implementation for nursing innovation.

Development of a Nursing Diagnosis/Record Generative AI System Based on Virtual Patient Data.

This study investigates how to reduce nurses' repetitive electronic nursing record tasks. We applied generative AI by learning nursing record data practiced with virtual patient data. We aim to evaluate generative AI's usefulness, usability, and availability when applied to nursing record creation tasks. The nursing record data collected through the electronic nursing record system for nursing students without privacy issues is in the form of NANDA, FocusDAR, SOAPIE, and narrative records. We trained 50,000 nursing record data and upgraded the performance through generative AI and fine-tuning. A separate API was used to connect with the practice electronic nursing record system, and 40 experienced nurses from a university hospital conducted tests. The electronic nursing record, through generative AI, is expected to contribute to easing the workload of nurses.

Initial Findings of a Mixed-Methods Study to Reduce Nursing Documentation-Related Burden.

Amidst the current healthcare workforce crisis, nurses continue to experience burnout, with one contributor being the growing amount of required documentation and other electronic health record (EHR) tasks that must be completed. This study aims to identify ways in which nurses can be better supported through consideration for the burden that EHR systems may present due to increasing documentation requirements and areas of inefficient use. This study leverages nursing engagement to ensure the needs of nurses are identified and EHR efficiency is improved. Practical strategies and EHR system improvements will be proposed based on the recommendations and guidance of nursing staff.

Speech Recognition Technology for Nursing Charting: A Literature Review.

This literature review explores the impact of Speech Recognition Technology (SRT) on nursing documentation within electronic health records (EHR). A search across PubMed, CINAHL, and Google Scholar identified 156 studies, with seven meeting the inclusion criteria. These studies investigated the impact of SRT on documentation time, accuracy, and user satisfaction. Findings suggest SRT, particularly when integrated with artificial intelligence can speed up documentation by up to 15%. However, challenges remain in its implementation in real-world clinical settings and existing EHR workflows. Future studies should focus on developing SRT systems that process conversational nursing assessments and integrate into current EHRs.

Development of a Mapping Table for Nursing Notes Based on Nurses' Concerns in ICU Patients.

This study aimed to develop a mapping table that connects nursing notes with standard terminology, focusing on nurses' concerns for ICU patients. After extracting nursing notes from a publicly accessible database, a research team, including a nursing informatics professor and researchers with ICU experience, developed a mapping table through a four-step process: initially reviewing literature on nurses' concerns, then extracting nursing notes from MIMIC IV and filtering the duplicate notes, subsequently defining and coding these concerns, and finally mapping them according to the CCC. Of 11,430,637 unstructured nursing notes from MIMIC IV, 265 unique notes remained after deduplication, with 208 notes reflecting nurses' concerns and categorized into 15 groups aligned with CCC. This mapping table will be a fundamental tool for predicting clinical deterioration in ICU patients by identifying important lexicons through natural language processing.

Use of Electronic Health Records and Documentation Burden Among Nurses in Acute and Critical Care.

Nurses who provide the majority of hands-on care for hospitalized patients are disproportionately affected by the current state of electronic health records (EHRs), and little is known about their lived perception of EHR use. Using a mixed-methods research design, we conducted an in-depth analysis and synthesis of data from EHR usage log files, interviews, and surveys and assessed factors contributing to the nurse documentation burden in acute and critical at a large academic medical center. There remain substantial spaces where we can develop viable solutions for enhancing the usability of multi-component EHR systems.

Right-sizing Documentation: What the Pandemic Taught Us about Clinical Documentation and Quality of Care.

Nursing documentation is essential to communicate patient care delivery. This review explores available evidence on the contribution of nursing documentation toward quality care delivery during the COVID-19 pandemic. Nine articles were evaluated for at least one of the 6 factors of quality (eg, safe, timely, equitable, patient-centered, effective, and efficient). Analysis suggests that right-sizing documentation for optimal care quality requires continued efforts to reinforce the value and need of nursing documentation as a primary data source. Continued practice and research efforts are needed to reframe nursing documentation's essential role in benefiting a patient's current and future health care needs.

A nursing note-aware deep neural network for predicting mortality risk after hospital discharge.

BACKGROUND: ICU readmissions and post-discharge mortality pose significant challenges. Previous studies used EHRs and machine learning models, but mostly focused on structured data. Nursing records contain crucial unstructured information, but their utilization is challenging. Natural language processing (NLP) can extract structured features from clinical text. This study proposes the Crucial Nursing Description Extractor (CNDE) to predict post-ICU discharge mortality rates and identify high-risk patients for unplanned readmission by analyzing electronic nursing records. OBJECTIVE: Developed a deep neural network (NurnaNet) with the ability to perceive nursing records, combined with a bio-clinical medicine pre-trained language model (BioClinicalBERT) to analyze the electronic health records (EHRs) in the MIMIC III dataset to predict the death of patients within six month and two year risk. DESIGN: A cohort and system development design was used. SETTING(S): Based on data extracted from MIMIC-III, a database of critically ill in the US between 2001 and 2012, the results were analyzed. PARTICIPANTS: We calculated patients' age using admission time and date of birth information from the MIMIC dataset. Patients under 18 or over 89 years old, or who died in the hospital, were excluded. We analyzed 16,973 nursing records from patients' ICU stays. METHODS: We have developed a technology called the Crucial Nursing Description Extractor (CNDE), which extracts key content from text. We use the logarithmic likelihood ratio to extract keywords and combine BioClinicalBERT. We predict the survival of discharged patients after six months and two years and evaluate the performance of the model using precision, recall, the F1-score, the receiver operating characteristic curve (ROC curve), the area under the curve (AUC), and the precision-recall curve (PR curve). RESULTS: The research findings indicate that NurnaNet achieved good F1-scores (0.67030, 0.70874) within six months and two years. Compared to using BioClinicalBERT alone, there was an improvement in performance of 2.05 % and 1.08 % for predictions within six months and two years, respectively. CONCLUSIONS: CNDE can effectively reduce long-form records and extract key content. NurnaNet has a good F1-score in analyzing the data of nursing records, which helps to identify the risk of death of patients after leaving the hospital and adjust the regular follow-up and treatment plan of relevant medical care as soon as possible.

Utilizing nursing standards in electronic health records: A descriptive qualitative study.

BACKGROUND: The electronic health record (EHR), including standardized structures and languages, represents an important data source for nurses, to continually update their individual and shared perceptual understanding of clinical situations. Registered nurses' utilization of nursing standards, such as standardized nursing care plans and language in EHRs, has received little attention in the literature. Further research is needed to understand nurses' care planning and documentation practice. AIMS: This study aimed to describe the experiences and perceptions of nurses' EHR documentation practices utilizing standardized nursing care plans including standardized nursing language, in the daily documentation of nursing care for patients living in special dementia-care units in nursing homes in Norway. METHODS: A descriptive qualitative study was conducted between April and November 2021 among registered nurses working in special dementia care units in Norwegian nursing homes. In-depth interviews were conducted, and data was analyzed utilizing reflexive thematic analysis with a deductive orientation. Findings Four themes were generated from the analysis. First, the knowledge, skills, and attitude of system users were perceived to influence daily documentation practice. Second, management and organization of documentation work, internally and externally, influenced motivation and engagement in daily documentation processes. Third, usability issues of the EHR were perceived to limit the daily workflow and the nurses' information-needs. Last, nursing standards in the EHR were perceived to contribute to the development of documentation practices, supporting and stimulating ethical awareness, cognitive processes, and knowledge development. CONCLUSION: Nurses and nursing leaders need to be continuously involved and engaged in EHR documentation to safeguard development and implementation of relevant nursing standards.