Ebook: Innovation in Applied Nursing Informatics

Message from the NI2024 Scientific Programme Committee

On behalf of the Scientific Program Committee, welcome to the 16th International Congress on Nursing Informatics (NI2024). We would like to welcome all IMIA NI members, students, practitioners, industry partners and others interested in nursing and health informatics attending NI2024. NI2024 is the preeminent conference for IMIA NI and the leading scientific meeting for health and nursing informatics research and practice. This meeting is particularly special, as we celebrate the 40th anniversary of the establishment of IMIA NI, the Nursing Specialist Working Group for the International Medical Informatics Association. The theme for NI2024 is Innovation in Applied Nursing Informatics, highlighting the importance of ensuring that any innovations in the field are applied into health care settings. Given our focus on application of nursing informatics we have introduced a new type of presentation, the case study, for this year’s conference. Case studies highlight how we can take evidence-based digital interventions into practice settings. The conference has also provided the opportunity for individuals to meet and learn from some of our pioneers in the field of informatics.

The Scientific Programme Committee was responsible for eliciting, evaluating and organising the NI2024 conference programme in line with the goals of IMIA NI. We received 408 submissions for papers, case studies, posters, panels, debates, workshops, technology demonstrations and pre-conference tutorials, from 27 countries. Each submission was reviewed by up to 3 reviewers, with feedback provided to authors. Every effort was made to ensure that we provided the opportunity for as many authors as possible to share their work given the constraints of the conference timetable. We have a total of 275 accepted submissions, the results of which are reflected in the Conference Programme and the Proceedings. The Proceedings contain open access full papers, case studies and posters indexed in MEDLINE. We have an exciting programme that includes 3 keynote speakers, a plenary panel, and a fireside chat with individuals early in their career in informatics.

Our keynote and plenary sessions cover topics such as artificial intelligence in nursing, leadership and the role of nursing informatics for sustainability. There are 22 paper/case study sessions with 88 papers and 24 case studies, 18 panel discussions, 16 workshops, 4 debates, 4 technology demonstration sessions with 16 technology demos, 6 tutorials and 2 poster sessions with 103 poster presentations to attend. We hope that attendees have the opportunity to learn from and network with others and find new ideas and ways innovate in their areas of practice and research. We are grateful to the members of the Scientific Programme Committee - Emma Collins, Karen Courtney, Angel Lu, Judy Murphy, Mustafa Ozkaynak, Laura-Maria Peltonen and Ann-Kristin Rotegård - for their support in organising the scientific programme. We are also indebted to members of the Local Organising Committee - Paula Anderson, Fran Beadle, Dorothy Bean, Kelly Calvert, Richard Cox, Claire Ford, Kumbi Kariwo, Fiona Mills, Sam Neville, Melanie Ruston, Chunhu Shi, Yimin Tang and Cristina Vasilica. The conference is being held at the University of Manchester, UK and we hope that you find the time to enjoy the vibrant culture of the city while you are here.

Dawn Dowding and Leanne M. Currie

Co-Chairs of the Scientific Programme Committee, NI2024

Our goal is to apply artificial intelligence (AI) and statistical analysis to understand the relationship between various factors and outcomes during pregnancy and labor and delivery, in order to personalize birth management and reduce complications for both mothers and newborns. We use a structured electronic health records database with data from approximately 130,000 births to train, test and validate our models. We apply machine learning (ML) methods to predict various obstetrical outcomes before and during labor, with the aim of improving patient care management in the delivery ward. Using a large cohort of data (∼180 million data points), we then demonstrated that ML models can predict successful vaginal delivery, in the general population as well as a sub-cohort of women attempting trial of labor after a cesarean delivery. The real-time dynamic model showed increasing rates of accuracy as the delivery process progressed and more data became available for analysis. Additionally, we developed a cross-facilities application of an AI model that predicts the need for an unplanned cesarean delivery, illuminating the challenges associated with inter-facility variation in reporting practices. Overall, these studies combine novel technologies with currently available data to predict and assist safe deliveries for mothers and babies, both locally and globally.

This study aimed to validate and refine an information model on pain management in a Brazilian hospital, considering the institutional culture, using an expert consensus approach. The first stage took place through a computerized questionnaire and Content Validity Index calculation. Pain management attributes were considered validated with 75% consensus among 19 experts. The second stage validated and refined the information model by three experts via an online meeting. Results showed that out of 11 evaluated attributes, five were validated. In the second stage, the inclusion of new attributes was suggested to address institutional culture. The final information model resulted from 23 sets of revised attributes: 12 validated, seven suggested and four not validated. The resulting Brazilian model has the potential to support the implementation of interventions and propose improvements to the institution’s electronic system, which can be reused in other institutions.

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.

Smart glasses allow care providers to connect to remote experts for consultation and have the potential to improve care. The purpose of this study was to evaluate the user experience with smart glasses in a simulated nursing care environment. We collected data via post-simulation semi-structured interviews and System Usability Scale (SUS) surveys. The median SUS score was 74 (range 57.5 to 90). The qualitative and quantitative findings of our study highlighted the potential benefits of smart glasses for assisting novice nurses in patient care, as well as the technical and workflow challenges that need further investigation.

Heart failure (HF) is a prevalent global health issue projected to escalate, notably in aging populations. The study aimed to identify predictive markers for Heart Failure with preserved Ejection Fraction (HFpEF). We scrutinized vital parameters like age, BMI, eGFR, and comorbidities like atrial fibrillation, coronary artery disease (CAD), diabetes mellites (DM). Evaluating phonocardiogram indicators—third heart sound(S3) and Systolic Dysfunction Index (SDI)—our logistic regression revealed age (≥ 65years), BMI (≥ 25 kg/m²), eGFR (<60 mL/min/1.73m²), CAD, DM, S3 intensity ≥5, and SDI ≥5 as HFpEF predictors, with AUC = 0.816 (p < .001). ROC diagnosis curve showed that the sensitivity, specificity and Youden’s index J of the model were 0.755, 0.673 and 0.838, respectively. Nonetheless, further exploration is crucial to delineate the clinical applicability and constraints of these markers.

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.

The pilot study explores how data visualization influences patient comprehension and engagement in understanding hyperlipidemia test results across diverse patient groups. Employing Gestalt theory and the Relational Information Display (RID) framework, intuitive visual tools were developed using Google Sheets, QlikView^®, and Microsoft^® Excel^®. The survey conducted with patients used a Likert scale to evaluate six different line and bar graphs, each presenting the same LDL cholesterol data. The study emphasized the creation of graphs that were easily interpretable. The survey aimed to assess preferences for various data visualization formats. The survey results indicated that patients preferred stacked area charts, while healthcare providers favored line charts. The results highlight the importance of user-centric design and the effective application of theoretical frameworks in creating visualizations that enhance patient engagement and comprehension. The study highlights the role of tailored data visualizations in healthcare, emphasizing the need for such tools in user-centered health technology.

For the elderly, the risks associated with pulmonary aspiration are significant, and can severely impact their quality of life. Therefore, the management of pulmonary aspiration risk is crucial in promoting healthy aging, an aspect often overlooked in self-health management for the elderly. To enhance self-management of pulmonary aspiration risk among the elderly, we have organized a multidisciplinary team to develop an intelligent risk hierarchical management system on pulmonary aspiration for the elderly. This system tailors the assessment with different questionnaires based on the elderly individual’s environment. Additionally, it utilizes elderly-friendly formats such as pictures, videos and animations, making it easier for seniors to comprehend the occurrence and hazards of pulmonary aspiration. This enables them to proactively take preventive measures to manage the risk, thus achieving self-management of pulmonary aspiration risk.

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.

Despite widespread adoption and maturity, paper persistence endures in many Electronic Health Record (EHR) systems, particularly for complex workflows involving multiple steps from different stakeholders separated in time. In our health system, Latent Tuberculosis Infection (LTBI) testing was one such workflow where a Tuberculin Skin Test (TST) must be administered and then correctly read 48-72 hours later and documented. This paper discusses a low-resource workflow analysis and clinical decision support approach to replace a paper workflow and garner the benefits of the EHR for clearer documentation and retrieval of LTBI results. Our approach resulted in a significant increase in completed TST documentation, 57% (24/42) to 95% (18/19), P < 0.003. Human-centered design practices such as work system analysis and formative usability testing are feasible with limited resources and improve the likelihood of success of electronic workflows by designing solutions that fit existing clinical workflows and automating processes wherever possible.

Pediatric patients are at high risk of peripheral intravenous infiltration or extravasation (PIVIE) leading to injury and increased costs. Most of the work in addressing PIVIE has focused on the implementation of workflow bundles and evidenced based guidelines. This project showed that Clinical Decision Support can be used to help support identification and treatment of Severe PIVIE through use of an interruptive alert that increases placement of vascular access team consults.

The study investigated barriers and enablers of nurse’s adoption of digital health technology to facilitate the delivery of healthcare in resource-limited settings. Using a self-administered questionnaire, data were collected from ninety-three nurses. Descriptive statistics were conducted to analyse and summarise the data. The study found that barriers to digital technology use included workload, time constraints, limited access to computers and a lack of skills in searching for information, while positive attitudes and confidence were enabling factors. Providing access to technology and skills training will improve the adoption of technology in healthcare delivery by nurses.

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.

Rapid advances in artificial intelligence (AI) have reshaped healthcare, including psychiatric nursing, to address the limitations of traditional approaches and meet escalating mental health challenges. A scoping review analyzed 48 articles examining the application of AI in psychiatric nursing across different technologies and topics, noting trends in publications and countries involved. The articles covered different aspects of mental health using AI technologies such as machine learning and robotics, and primarily explored AI applications in mental health, specifically dementia, autism and schizophrenia. These studies highlighted the role of AI in personalized care plans, symptom monitoring and risk assessment. AI is promising, but faces challenges such as data bias and ethical concerns. Future research needs to focus on long-term studies, diverse populations, patient interaction and personalized treatments for practical integration into psychiatric nursing.

An intranet is a beneficial tool, most commonly utilised and researched in corporate settings, but can also be found within healthcare. An organisational intranet has many of the same functions as the internet while also having a security firewall associated with it, meaning that only those with security access to the site are able to gain access. An evaluation study, using a two-phase process, of a Nursing Intranet within a healthcare organisation in one urban hospital in New Zealand is presented. First a content audit was undertaken, before using a selected framework to evaluate the content, design and functionality of the Nursing Intranet. The results from this evaluation identified some strengths, but also areas to improve. Further research, including the development of tools to evaluate intranets in a healthcare setting are needed to ensure information is more readily accessible to health professional staff.

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.

High cholesterol levels significantly contribute to the risk of atherosclerotic cardiovascular disease (ACVD), with a notable portion of ischemic heart disease cases linked to elevated cholesterol levels. Effective graphical displays of lipid panel tests and other cardiac risk factors are crucial for quick and accurate data interpretation, enabling early intervention for individuals with hyperlipidemia. Applying design theories such as Gestalt and distributed cognitive theories is essential for creating user-centered graphical data displays in the context of cardiovascular (CV) risk factors. The proposed dashboard informed by these theories is expected to help healthcare providers better address cardiovascular disease (CVD), enhancing diagnosis, treatment, and prevention. Moreover, this approach may help alleviate clinical provider burnout, improve patient outcomes, and reduce provider stress, thus contributing to safer and more effective healthcare systems.

The advancement of technology and Artificial Intelligence applied health information systems demand high informatics competencies from nurses. To prepare nursing students to meet this demand, informatics courses are designed to increase informatics competencies. We offered an online informatics course to graduate students in a Nurse Educator program and assessed their informatics competency, including subdomains. Survey data were collected between Fall 2020 and Fall 2022 using an online Self-Assessment of Informatics Competency Scale for Health Professionals. We analyzed 109 responses and found that students were competent in overall informatics competency and the subdomains of “basic computer skills” and “applied computer skills (clinical informatics).” They were proficient in the ‘role’ subdomain. However, students reported less competency in managing data and incorporating standard terminology into practice. These findings provide detailed insights of the current nursing students’ informatics competencies and can guide informatics faculty in improving their courses.

Nurses must excel in using Artificial Intelligence (AI) - applied hospital systems, making their informatics competency crucial. ChatGPT has been trained with extensive amounts of informatics- and technology-related health data and has gained popularity. Nurses could have the opportunity to enhance their informatics competency through the knowledge generated by ChatGPT. However, its informatics competency has not been evaluated. We used the Self-Assessment of Informatics Competency Scale to measure the level of informatics competency of ChatGPT. ChatGPT fell within the range of ‘somewhat competent’ and ‘competent,’ lower than that of students in graduate programs. One subdomain, applied computer skills (clinical informatics), demonstrated competency levels close to that of students. Although the results presented certain limitations and concerns, we recognize the potential of ChatGPT to help researchers and healthcare practitioners. Nursing is advancing and continuously integrating AI technology; therefore, we should now embrace both the benefits and risks associated with ChatGPT.

The use of technology in nursing has increased, most notably since the Covid-19 pandemic which highlighted benefits of digital health in nursing practice. Understanding the enablers and barriers associated with nurses’ use of digital technology is important as these can impact adoption and engagement. To understand the factors that impacted New Zealand nurses’ use of technology a national online survey was undertaken in August 2022. Participants (n=191) came from varied clinical settings across the country. Their responses to the open-ended questions were thematically analyzed and are reported here. Four themes were identified: 1) Knowledge of digital technology, 2) connectivity, 3) devices and systems, and 4) training and education. Understanding the factors that impact nurses use of technology can support actions to build the digital competency of nurses, enhance the nursing workforce and therefore benefit patient care.

Worldwide, chronic kidney disease (CKD) is a public health problem due to its high morbidity and mortality rates. For CKD patients, mobile health applications have functioned as a strategy that promotes patient care through valid and reliable educational materials. This is a prospective and descriptive three-stage study using content experts. Results created three visual and three audiovisual materials with acceptable evaluations. The design and validation of educational materials are a valid and reliable method for patient health education through mobile health applications.