Information Management in Hospital Unit Daily Operations: A Descriptive Study With Nurses and Physicians.

Operations management of a hospital unit is a shared activity involving nursing and medical professionals, characterized by suddenly changing situations, constant interruptions, and ad hoc decision-making. Previous studies have explored the informational needs affecting decision-making, but only limited information has been collected regarding factors affecting information management related to the daily operations of hospital units. The aim of this study was to describe the experiences of nursing and medical professionals of information management in the daily operations of hospital units. This qualitative study consists of interviews following the critical incidence technique. Twenty-six nurses and eight physicians working in operational leadership roles in hospital units were interviewed, and the data were subjected to thematic analysis. The data analysis showed that strengths of current systems were organizational operational procedures, general instruments supporting information management, and a digital operations dashboard, whereas opportunities for improvement included the information architecture, quality of information, and technology use. The study findings highlight that despite several decades of efforts to provide solutions to support information management in hospital daily operations, further measures need to be taken in developing and implementing information systems with user-centered strategies and systematic approaches to better support healthcare professionals.

Exploring the Documentation of Delirium in Patients After Cardiac Surgery: A Retrospective Patient Record Study.

Delirium is a common disorder for patients after cardiac surgery. Its manifestation and care can be examined through EHRs. The aim of this retrospective, comparative, and descriptive patient record study was to describe the documentation of delirium symptoms in the EHRs of patients who have undergone cardiac surgery and to explore how the documentation evolved between two periods (2005-2009 and 2015-2020). Randomly selected care episodes were annotated with a template, including delirium symptoms, treatment methods, and adverse events. The patients were then manually classified into two groups: nondelirious (n = 257) and possibly delirious (n = 172). The data were analyzed quantitatively and descriptively. According to the data, the documentation of symptoms such as disorientation, memory problems, motoric behavior, and disorganized thinking improved between periods. Yet, the key symptoms of delirium, inattention, and awareness were seldom documented. The professionals did not systematically document the possibility of delirium. Particularly, the way nurses recorded structural information did not facilitate an overall understanding of a patient's condition with respect to delirium. Information about delirium or proposed care was seldom documented in the discharge summaries. Advanced machine learning techniques can augment instruments that facilitate early detection, care planning, and transferring information to follow-up care.

User Needs and Factors Associated With the Acceptability of Audiovisual Feedback Devices for Chest Compression Monitoring in Cardiopulmonary Resuscitation.

The use of audiovisual feedback devices to guide the quality of chest compressions during cardiopulmonary resuscitation has increased in recent years. Audiovisual feedback devices can be classified as integrated (eg, Zoll AED Plus defibrillator) or standalone (eg, CPRmeter). This study aimed to explore users' needs and factors affecting the acceptability of audiovisual feedback devices. Semistructured interviews were conducted with healthcare professionals involved in lifesaving activities. The Unified Theory of Acceptance and Use of Technology was used as a theoretical framework for the study. The Unified Theory of Acceptance and Use of Technology model has four constructs: performance expectancy, effort expectancy, social influence, and facilitating factors. Ten themes were identified under the four constructs. The performance expectancy constructs include three themes: perceived usefulness, outcome expectation, and applicability in diverse situations. The effort expectancy construct encompasses two themes: user-friendliness and complexity. The social influence construct has two themes: social and organizational factors. Lastly, the facilitating factors construct includes three themes: staff competence, perceived cost, and compatibility of devices. Exploring the needs and factors influencing the acceptability of audiovisual feedback devices used during cardiopulmonary resuscitation will inform healthcare providers, managers, manufacturers, and procurers on how to improve the efficiency and use of these devices.

Exploring nurses' online perspectives and social networks during a global pandemic COVID-19.

OBJECTIVES: Examine the online interactions, social networks, and perspectives of nursing actors on COVID-19 from conversations on Twitter to understand how the profession responded to this global pandemic. DESIGN: Mixed methods. SAMPLE: Ten-thousand five-hundred and seventy-four tweets by 2790 individuals and organizations. MEASUREMENTS: NodeXL software was used for social network analysis to produce a network visualization. The betweenness centrality algorithm identified key users who were influential in COVID-19 related conversations on Twitter. Inductive content analysis enabled exploration of tweet content. A communicative figurations framework guided the study. RESULTS: Nursing actors formed different social groupings, and communicated with one another across groups. Tweets covered four themes; (1) outbreak and clinical management of the infectious disease, (2) education and information sharing, (3) social, economic, and political context, and (4) working together and supporting each other. CONCLUSION: In addition to spreading knowledge, nurses tried to reach out through social media to political and healthcare leaders to advocate for improvements needed to address COVID-19. However, they primarily conversed within their own professional community. Action is needed to better understand how social media is and can be used by nurses for health communication, and to improve their preparedness to be influential on social media beyond the nursing community.

Identifying nursing sensitive indicators from electronic health records in acute cardiac care-Towards intelligent automated assessment of care quality.

AIM: The aim of this study is to explore the potential of using electronic health records for assessment of nursing care quality through nursing-sensitive indicators in acute cardiac care. BACKGROUND: Nursing care quality is a multifaceted phenomenon, making a holistic assessment of it difficult. Quality assessment systems in acute cardiac care units could benefit from big data-based solutions that automatically extract and help interpret data from electronic health records. METHODS: This is a deductive descriptive study that followed the theory of value-added analysis. A random sample from electronic health records of 230 patients was analysed for selected indicators. The data included documentation in structured and free-text format. RESULTS: One thousand six hundred seventy-six expressions were extracted and divided into (1) established and (2) unestablished expressions, providing positive, neutral and negative descriptions related to care quality. CONCLUSIONS: Electronic health records provide a potential source of information for information systems to support assessment of care quality. More research is warranted to develop, test and evaluate the effectiveness of such tools in practice. IMPLICATIONS FOR NURSING MANAGEMENT: Knowledge-based health care management would benefit from the development and implementation of advanced information systems, which use continuously generated already available real-time big data for improved data access and interpretation to better support nursing management in quality assessment.

Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative.

AIM: To develop a consensus paper on the central points of an international invitational think-tank on nursing and artificial intelligence (AI). METHODS: We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical ethics, AI in primary care, AI legal aspects, philosophy of AI in health, nursing practice, implementation science, leaders in health informatics practice and international health informatics groups, a representative of patients and the public, and the Chair of the ITU/WHO Focus Group on Artificial Intelligence for Health. The NAIL Collaborative convened at a 3-day invitational think tank in autumn 2019. Activities included a pre-event survey, expert presentations and working sessions to identify priority areas for action, opportunities and recommendations to address these. In this paper, we summarize the key discussion points and notes from the aforementioned activities. IMPLICATIONS FOR NURSING: Nursing's limited current engagement with discourses on AI and health posts a risk that the profession is not part of the conversations that have potentially significant impacts on nursing practice. CONCLUSION: There are numerous gaps and a timely need for the nursing profession to be among the leaders and drivers of conversations around AI in health systems. IMPACT: We outline crucial gaps where focused effort is required for nursing to take a leadership role in shaping AI use in health systems. Three priorities were identified that need to be addressed in the near future: (a) Nurses must understand the relationship between the data they collect and AI technologies they use; (b) Nurses need to be meaningfully involved in all stages of AI: from development to implementation; and (c) There is a substantial untapped and an unexplored potential for nursing to contribute to the development of AI technologies for global health and humanitarian efforts.

Pain process of patients with cardiac surgery-Semantic annotation of electronic patient record data.

AIMS AND OBJECTIVES: To describe and compare the pain process of the patients' with cardiac surgery through nurses' and physicians' documentations in the electronic patient records. BACKGROUND: Postoperative pain assessment and management should be documented regularly, to ensure optimal pain care process for patients. Despite availability of evidence-based guidelines, pain assessment and documentation remain inadequate. DESIGN: A retrospective patients' record review. METHODS: The original data consisted of the electronic patient records of 26,922 patients with a diagnosed heart disease. A total of 1,818 care episodes of patients with cardiac surgery were selected from the data. We used random sampling to obtain 280 care episodes for annotation. These 280 care episodes contained 2,156 physician reports and 1,327 days of nursing notes. We developed an annotation manual and schema, and then, we manually conducted semantic annotation on care episodes, using the Brat annotation tool. We analysed the annotation units using thematic analysis. Consolidated criteria for reporting qualitative research guideline was followed in reporting where appropriate in this study design. RESULTS: We discovered expressions of six different aspects of pain process: (a) cause, (b) situation, (c) features, (d) consequences, (e) actions and (f) outcomes. We determined that five of the aspects existed chronologically. However, the features of pain were simultaneously existing. They indicated the location, quality, intensity, and temporality of the pain and they were present in every phase of the patient's pain process. Cardiac and postoperative pain documentations differed from each other in used expressions and in the quantity and quality of descriptions. CONCLUSION: We could construct a comprehensive pain process of the patients with cardiac surgery from several electronic patient records. The challenge remains how to support systematic documentation in each patient. RELEVANCE TO CLINICAL PRACTICE: The study provides knowledge and guidance of pain process aspects that can be used to achieve an effective pain assessment and more comprehensive documentation.

Information needs in day-to-day operations management in hospital units: A cross-sectional national survey.

AIM: To describe and compare shift leaders' important information needs by profession, unit, time of day and type of hospital. BACKGROUND: Professionals responsible for care provision in hospital units make ad hoc decisions about available resources to meet patient care needs but, currently, much effort is needed to obtain the necessary information to support decision making. METHODS: This survey was carried out in nine randomly chosen hospitals in Finland. Nurses and physicians responsible for day-to-day operations were eligible to participate (N = 873). The response rate was 65% (n = 570, including 453 nurses and 111 physicians). Data were collected in 2015-2016 using the Hospital Shift Leaders' Information Needs Questionnaire with 114 information need items. RESULTS: Shift leaders reported many real-time information needs. Nurses' important information needs concerned patients, personnel, and materials, and physicians' needs focused on patient care. Large mean differences existed in the needs between nurses and physicians, and imaging units when compared to other units. CONCLUSION: Real-time information systems for shift leaders should consider the needs of different users to support shared situational awareness and operational intelligence. IMPLICATIONS FOR NURSING MANAGEMENT: The important information-need items identified here may be used in designing and developing information systems that better support shift leaders' work in hospitals.

Assessing managerial information needs: Modification and evaluation of the Hospital Shift Leaders' Information Needs Questionnaire.

AIMS: The aims were (1) to evaluate the modified version of the Intensive Care Unit Information Need Questionnaire for the broader hospital setting, and (2) to describe the differences in respondents' managerial activities and information needs according to the position held by the respondent and the type of hospital unit. BACKGROUND: Information systems do not support managerial decision-making sufficiently and information needed in the day-to-day operations management in hospital units is unknown. METHODS: An existing questionnaire was modified and evaluated. Shift leaders, that is, the nurses and physicians responsible for the day-to-day operations management in hospital units were reached using purposive sampling (n = 258). RESULTS: The questionnaire ascertained the importance of information. Cronbach's α ranged from .85-.96 for the subscales. Item - total correlations showed good explanatory power. Managerial activities and information needs differed between respondents in different positions, although all shared about one-third of important information needs. The response rate was 26% (n = 67). CONCLUSIONS: The validity and reliability of the questionnaire were good. Attention should be paid to the positions of shift leaders when developing information systems. IMPLICATIONS FOR NURSING MANAGEMENT: The questionnaire can be used to determine important information when developing information systems to support day-to-day operations management in hospitals.

Nurse managers' decision-making in daily unit operation in peri-operative settings: a cross-sectional descriptive study.

AIM: To describe the tactical and the operational decisions made by nurse managers when managing the daily unit operation in peri-operative settings. BACKGROUND: Management is challenging as situations change rapidly and decisions are constantly made. Understanding decision-making in this complex environment helps to develop decision support systems to support nurse managers' operative and tactical decision-making. DESIGN: Descriptive cross-sectional design. METHOD: Data were collected from 20 nurse managers with the think-aloud method during the busiest working hours and analysed using thematic content analysis. RESULTS: Nurse managers made over 700 decisions; either ad hoc (n = 289), near future (n = 268) or long-term (n = 187) by nature. Decisions were often made simultaneously with many interruptions. Ad hoc decisions covered staff allocation, ensuring adequate staff, rescheduling surgical procedures, confirmation tangible resources and following-up the daily unit operation. Decisions in the near future were: planning of surgical procedures and tangible resources, and planning staff allocation. Long-term decisions were: human recourses, nursing development, supplies and equipment, and finances in the unit. CONCLUSIONS: Decision-making was vulnerable to interruptions, which sometimes complicated the managing tasks. IMPLICATIONS FOR NURSING MANAGEMENT: The results can be used when planning decision support systems and when defining the nurse managers' tasks in peri-operative settings.

Care episode retrieval: distributional semantic models for information retrieval in the clinical domain.

Patients' health related information is stored in electronic health records (EHRs) by health service providers. These records include sequential documentation of care episodes in the form of clinical notes. EHRs are used throughout the health care sector by professionals, administrators and patients, primarily for clinical purposes, but also for secondary purposes such as decision support and research. The vast amounts of information in EHR systems complicate information management and increase the risk of information overload. Therefore, clinicians and researchers need new tools to manage the information stored in the EHRs. A common use case is, given a--possibly unfinished--care episode, to retrieve the most similar care episodes among the records. This paper presents several methods for information retrieval, focusing on care episode retrieval, based on textual similarity, where similarity is measured through domain-specific modelling of the distributional semantics of words. Models include variants of random indexing and the semantic neural network model word2vec. Two novel methods are introduced that utilize the ICD-10 codes attached to care episodes to better induce domain-specificity in the semantic model. We report on experimental evaluation of care episode retrieval that circumvents the lack of human judgements regarding episode relevance. Results suggest that several of the methods proposed outperform a state-of-the art search engine (Lucene) on the retrieval task.

Building Bridges for Federated Learning in Healthcare: Review on Approaches for Common Data Model Development.

Common data models provide a standardized way to represent data used in federated learning tasks. The aim of this review was to explore the development and use of common data models to harmonize electronic health record data in health research. The data search yielded 724 records, of which 19 were included for this study. None of the research focused on nursing specific topics. All studies either utilized the Observational Medical Outcomes Partnership (OMOP) common data model, or developed a model partly based on the OMOP. A roadmap to guide research for the development of common data models for federated learning are warranted.

The effect of standalone audio-visual feedback devices on the quality of chest compressions during laypersons' cardiopulmonary resuscitation training: a systematic review and meta-analysis.

AIMS: Individual studies that investigated the effect of standalone audio-visual feedback (AVF) devices during laypersons' cardiopulmonary resuscitation (CPR) training have yielded conflicting results. This review aimed to evaluate the effect of standalone AVF devices on the quality of chest compressions during laypersons' CPR training. METHOD AND RESULT: Randomized controlled trials of simulation studies recruiting participants without actual patient CPR experience were included. The intervention evaluated was the quality of chest compressions with standalone AVF devices vs. without AVF devices. Databases, such as PubMed, Cochrane Central, Embase, Cumulative Index to Nursing & Allied Health Literature (CINAHL), Web of Science, and PsycINFO, were searched from January 2010 to January 2022. The risk of bias was assessed using the Cochrane risk of bias tool. A meta-analysis alongside a narrative synthesis was used for examining the effect of standalone AVF devices.Sixteen studies were selected for this systematic review. A meta-analysis revealed an increased compression depth of 2.22 mm [95% CI (Confidence Interval), 0.88-3.55, P = 0.001] when participants performed CPR using the feedback devices. Besides, AVF devices enabled laypersons to deliver compression rates closer to the recommended range of 100-120 per min. No improvement was noted in chest recoil and hand positioning when participants used standalone AVF devices. CONCLUSION: The quality of the included studies was variable, and different standalone AVF devices were used. Standalone AVF devices were instrumental in guiding laypersons to deliver deeper compressions without compromising the quality of compression rates. However, the devices did not improve the quality of chest recoil and placement of the hands. REGISTRATION: PROSPERO: CRD42020205754.

Exploring Delays in Cardiac Care Processes Through Electronic Health Records.

Cardiovascular diseases are the leading cause of death globally. Timely health services are fundamental to the appropriate prevention, identification, care and rehabilitation of these diseases. This study aimed to explore the potential of using electronic health records as a data source to help identify health system -related delays in care processes of cardiac patients. This retrospective registry study is based on a sample of electronic health records of 200 cardiac patients admitted to one out of twenty wellbeing services counties in Finland during the years 2021-2022. A total of 426 health system -related delays were identified. All expressions were found in unstructured format and most of these (58.7%) were generated by nurses. These results show that the electronic health records contained a variety of information on health system -related patient care delays, and that most delays were associated with difficulties in finding a bed for the patient in a post-acute care facility (49.8%), but also in-hospital process delays were common (27.7%). These findings show great potential for exploring electronic health record data with natural language processing methods in the future for the development of tools to better identify and monitor different types of delays in care processes. Such tools may support leadership to respond to organisational procedures in need of improvement.

Towards Automated Evaluation of Patient Centered Care-Assessing the Potential of Electronic Health Records.

Providing patient centered care is a crucial element of high quality care. It can be defined as a responsive way of caring for and empowering patients, embodying compassion, empathy, and responsiveness to the patient's needs. The aim of this study was to assess the potential of using EHRs as information source in the development of tools for assessing PCC. An annotation guide following the Person-centred Practice Framework proposed by McCance and McCormack was developed for the purpose of this study. Twenty patients' documents were manually annotated, resulting in 539 expressions. All dimensions of the framework were covered in the documents, with 61.3% of expressions describing the activity of engaging authentically with the patient. The results of this study indicate that electronic health records are one potential source of information in automated evaluation of patient centered care, however more information is still needed on how to interpret this information.

The ChatGPT Effect: Nursing Education and Generative Artificial Intelligence.

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. 2024;63(X):XXX-XXX.].

A rapid review on current and potential uses of large language models in nursing.

BACKGROUND: The application of large language models across commercial and consumer contexts has grown exponentially in recent years. However, a gap exists in the literature on how large language models can support nursing practice, education, and research. This study aimed to synthesize the existing literature on current and potential uses of large language models across the nursing profession. METHODS: A rapid review of the literature, guided by Cochrane rapid review methodology and PRISMA reporting standards, was conducted. An expert health librarian assisted in developing broad inclusion criteria to account for the emerging nature of literature related to large language models. Three electronic databases (i.e., PubMed, CINAHL, and Embase) were searched to identify relevant literature in August 2023. Articles that discussed the development, use, and application of large language models within nursing were included for analysis. RESULTS: The literature search identified a total of 2028 articles that met the inclusion criteria. After systematically reviewing abstracts, titles, and full texts, 30 articles were included in the final analysis. Nearly all (93 %; n = 28) of the included articles used ChatGPT as an example, and subsequently discussed the use and value of large language models in nursing education (47 %; n = 14), clinical practice (40 %; n = 12), and research (10 %; n = 3). While the most common assessment of large language models was conducted by human evaluation (26.7 %; n = 8), this analysis also identified common limitations of large language models in nursing, including lack of systematic evaluation, as well as other ethical and legal considerations. DISCUSSION: This is the first review to summarize contemporary literature on current and potential uses of large language models in nursing practice, education, and research. Although there are significant opportunities to apply large language models, the use and adoption of these models within nursing have elicited a series of challenges, such as ethical issues related to bias, misuse, and plagiarism. CONCLUSION: Given the relative novelty of large language models, ongoing efforts to develop and implement meaningful assessments, evaluations, standards, and guidelines for applying large language models in nursing are recommended to ensure appropriate, accurate, and safe use. Future research along with clinical and educational partnerships is needed to enhance understanding and application of large language models in nursing and healthcare.

Navigating the Global Landscape of Nursing Informatics Research Collaboration: A Blueprint for Success.

International collaboration is crucial in the field of nursing informatics research to enhance our ability to conduct globally relevant research that informs policy and practice. In this case study we describe how we have established an international research collaboration to evaluate nurses' experiences of technology use during the pandemic. We firstly describe how the collaboration was created and the successes associated with our work, before highlighting the facilitators to make an international collaboration work. We also discuss the challenges we have encountered during this collaborative enterprise, to enable other researchers who wish to establish international collaborations and learn from our experiences.