Ebook: Nurses and Midwives in the Digital Age

On behalf of the Scientific Program Committee, I extend a warm welcome to the IMIA-NI members, students, practitioners, informatics researchers, industry partners, and others interested in health and nursing informatics who are attending the NI 2021, 15th International Congress on Nursing Informatics.

NI 2021has been a long time in the planning. Originally planned to be held in 2020, the international year of the nurse and midwife, the conference with the theme of Nursing and Midwifery in the Digital Age aims to showcase achievements of nurses and midwives in the area of digital health. Unfortunately, as with many other aspects of our lives, the advent of the SARS-CoV-2 pandemic resulted in the postponement of the NI 2020 conference. But we are now in the position to bring you NI 2021.

The mission of the Scientific Program Committee is to solicit for, evaluate and schedule NI 2021 conference program to be consistent with the goal of the IMIA-NI. The conference delay enabled us to hold two calls for submissions and because of the virtual nature of the conference we have been able to extend the conference to occur over nine days. Each day has a different theme including: Data analytics and the use of nursing data; System implementations and digital workplaces; Nursing information systems; Innovation and AI in nursing; Evidence based practice; Digital health nursing workforce development; International updates; Health policy, service delivery and ethics; and Patient participation and citizen involvement.

We received 437 submissions for papers, posters, case studies, panels, and workshops from more than 40 countries. Each submission was reviewed by three reviewers selected from a panel of experts. Reviewers’ feedback was provided to the authors and every effort was made to ensure the best submissions given the constraints of the conference timetable. In the end, a total of 212 submissions were selected for inclusion. The result of the Scientific Program Committee’s activity is reflected in the Conference Program and Proceedings. The proceedings contain 67 full papers, indexed in MEDLINE, and also workshop, panel, case study, and poster abstracts.

The Scientific Program Committee has prepared a wonderful program. We have 13 keynote speakers addressing the state-of-the-art for health and nursing informatics ranging from data, through healthcare delivery and policy, culminating in patient and citizen involvement in digital health. There are 198 pre-recorded paper, case study, and poster presentations, 8 panel discussions, 6 workshops, 1 fireside chat, available to attend. All content will be available online through the Global Showcase is a collection of papers, case study abstracts and posters curated into 10 channels that you can watch anytime.

I hope that all participants have the opportunity to learn from and network with others through keynotes, paper sessions, poster sessions, panel discussions, case study presentations and workshops and find new and exciting ideas to inspire them.

I am very grateful to the contributors for their contributions and the reviewers for providing expert reviews. I am especially grateful to the Scientific Program Committee, for their leadership and strong support, and to the AIDH Conference Team.

Elizabeth Cummings, PhD, RN, RM, FAIDH, Co-Chair Scientific Program Committee,

Adjunct Associate Professor, School of Nursing, University of Tasmania, Australia;

Adjunct Associate Professor, School of Health Information Science, University of Victoria, Victoria, British Columbia, Canada

The scientific community focused on nursing informatics can be described as a graph with the authors as vertices and the author-coauthor relationship as the connecting edges. Methods to describe and analyze networks like average path length, diameter, centrality measures, or partitioning into subcommunities are applied to the nursing informatics community. It is shown that the community consists of one large connected subnet with many small disjoint subnets, each representing one or several authors. The interconnectivity of the large subnet is quite high indicating an information flow along several different paths. Using different centrality measures important authors for e.g. the information flow can be identified. While each small disjoint subnet represents a small sub-community, the large central subnet can also be partitioned into subcommunities connected with each other. Some seem to be focused on specific aspects of nursing informatics.

Diseases have no borders, and global health operates from both within and beyond. Global health informatics can adopt an assets-oriented approach to mitigate concerns by maximizing global health data, principles, and resources combined with geographic information systems’ use case mapping. This exploratory study utilizes an assets-oriented approach to analyze four global social determinants of health indicators, including Skilled Birth Attendance, Measles Immunization Coverage, Education (Female), and the Healthcare Access and Quality Index in relation to countries’ income and geographical region. Data were extracted and analyzed from two publicly available datasets. Positive trends and variations were detected among all variables aggregated by countries’ income category and geographical region. These findings pinpoint potential health assets that the discipline of nursing can leverage to build healthier global health communities.

The goal of this natural language processing (NLP) study was to identify patients in home healthcare with heart failure symptoms and poor self-management (SM). The preliminary lists of symptoms and poor SM status were identified, NLP algorithms were used to refine the lists, and NLP performance was evaluated using 2.3 million home healthcare clinical notes. The overall precision to identify patients with heart failure symptoms and poor SM status was 0.86. The feasibility of methods was demonstrated to identify patients with heart failure symptoms and poor SM documented in home healthcare notes. This study facilitates utilizing key symptom information and patients’ SM status from unstructured data in electronic health records. The results of this study can be applied to better individualize symptom management to support heart failure patients’ quality-of-life.

The clinical nursing and midwifery dashboard (CNMD) was built to provide a near real-time information and data visualisations for nurse unit managers (NUMs) and maternity unit managers (MUMs) within only a 5-15 minutes delay from when they enter data to the integrated electronic medical records (ieMR) system. The dashboard displays metrics and information about current adult inpatients in overnight wards. The aim is to support NUMs and MUMs to manage their daily workload and have continuous visibility of patients nursing risk and safety assessment documentation. A quantitative evaluation approach was conducted to measure the impact of the dashboard on key performance indicators. Statistical analysis was completed to compare risk assessment average completion times prior to and post CNMD implementation. The results of the evaluation were positive, and the statistical analysis shows significant reduction in the average time to complete different risk assessments with p-value<0.01.

Diabetes is a chronic disease that can be effectively managed and controlled using strategies such as self-management education and ongoing support. Virtual environments offer innovative and realistic settings where patients can achieve self-management education and obtain ongoing self-management support from peers and healthcare professionals. Transcribed real-time conversations in an innovative virtual community were analyzed using qualitative and linguistic analysis. These virtual interactions were manually coded to identify embedded behavior change techniques and linguistic features. Results showed 13 behavior change techniques were manifested. Further, language differences were observed between behavior change techniques and social support types. Our research can provide valuable insights into the design of effective digital health interventions that maximize sustained use of virtual environments, subsequently impacting self-management of chronic conditions such as diabetes.

In this study, we drew on methods originating in complex adaptive systems and social network analysis to develop a novel way to quantify fundamental care. Data were obtained from a public statement from the Australian Royal Commission into Aged Care Quality and Safety. Results support the importance of using a systemic approach to assess the multiple dimensions of the fundamentals of care. Our method allows measurement of the problem within its system, providing a detailed quantification of care events and identifying excellence and improvement opportunities. We illustrate the strengths of this approach using principal component analysis and heat mapping. The application of the proposed methodology in healthcare decision-making, planning, and quality improvement is discussed.

Diabetes can be diagnosed by either Fasting Plasma Glucose or Hemoglobin A1c. The aim of our study was to explore the differences between the two criteria through the development of a machine learning based diabetes diagnostic algorithm and analysing the predictive contribution of each input biomarker. Our study concludes that fasting insulin is predictive of diabetes defined by FPG, but not by HbA1c. Besides, 28 other fasting blood biomarkers were not significant predictors of diabetes.

This study used topic modeling to analyse key topics of nursing handoff research. Six topics were identified. The findings indicate that future studies should implement the standardization of handoff tools and the use of bedside handoff, and evaluate their effects on patient safety outcomes.

After the COVID-19 pandemic occurred in South Korea in 2020, medical institutions have dealt with the epidemiological crisis. The institutions’ strategies in response to the crisis were classified into four stages depending on the change of epidemic circumstances. Efficiently responding to the pandemic, close cooperation between the government and medical institutions is essential.

This study aims to provide a bibliometric overview of research at nursing informatics and understand the state in nursing informatics in the last ten years. We used the Web of Science to extract relevant literature published from 2009 to 2018. A total of 455 articles were retrieved and analyzed. The total of the top 5 institutions, countries, journals was discussed. This study will help researchers to understand trends and the situation in nursing informatics research.

The purpose of this study is to extract features and structure them using text mining and to analyze changes over time on consultation records accumulated in a cancer consultation and support center database from 2009 to 2018. The text-mining approach worked effectively under conditions of expanding data, and a co-occurrence network revealed patterns and trends in the content of consultations.

This study aimed to understand the status of nursing informatics in Mainland China. Articles on nursing informatics, published between 2009 and 2018, were retrieved from the CNKI (China National Knowledge Infrastructure) database. A total of 51 papers were identified and analyzed. The journals, annual publications, and co-occurrence of keywords were analyzed with the bibliometric analysis. The result will help us better understand the nursing informatics research in mainland China.

Medication errors occur during clinical learning for nursing students. This study aimed to develop learning cases to prevent medication errors using analysis of data from an incident reporting system. This study utilized an action research approach to develop learning cases. These learning cases were implemented with problem-based learning (PBL) method and self-learning materials strategies. The results showed that repeated occurrences of medication errors and near misses were reduced after implementing the new teaching strategy.

This study analyzed collected social media data from South Korea containing keywords related to “pregnancy” using ontology-based natural language processing. Of the 504,725 documents, those containing concepts related to “maternal emotion” were the most frequent, followed by “family support”. Social media were used as a means of exchanging information and expressing emotions.

We analyzed the spatial and temporal patterns of inpatient falls in a tertiary hospital. Data were obtained from the adverse-event self-reporting system and the electronic nursing record system of the hospital. Through chart reviews we reclassified the cases. Most falls occurred by the bedside, followed by the restroom and hallway, and they occurred most often between 2 a.m. and 6 a.m. These findings indicate when and where nurses should be most alert about falls.

The purpose of the study is to identify research topics and hotspots in nursing informatics education during the period of 2009–2018. The relevant literature of nursing informatics education was retrieved from the Web of Science Core Collection (WoSCC). This study identified three research themes and hotspots in nursing informatics education using co-word analysis. The themes are curriculum, technology, and nursing informatics. The results provide useful information for researchers to research topic choices and in-depth research.

Early detection of chronic kidney disease (CKD) for high-risk population adults is very important. It has a common risk factor and causal relationship with chronic diseases such as diabetes, hypertension and cardiovascular disease etc. The results of this study provide that for early high-risk factors detection in CKD healthy population can be used by home care to recommend adjuvant treatment.

Manual theatre performance measurement is resource yearning and inaccurate. To automate the process, we built a dashboard which provides interactive visualisation of key performance metrics related to operating theatres. The aim is to assist in the efficient management of surgical services and provide visibility on metrics trending over time for health service facilities.

The activities/interventions performed by the nursing team and the time required for their performance have become a widely discussed topic due to their impact on the quality of patient safety and staff working conditions. Nursing Interventions Classification (NIC) was used as a more accurate indicator as it identified activities/interventions. This study analyzed the workload distribution of nursing team in a pediatric oncology center.