Computers, Informatics, Nursing

Evaluation of Electronic Health Record–Generated Work Intensity Scores and Nurse Perceptions of Workload Appropriateness Electronic health record–generated work intensity scores represent state-of-the art functionality for dynamic nursing workload estimation in the hospital setting. In contrast to traditional stand-alone patient classification and acuity tools, electronic health record–based tools eliminate the need for dedicated data entry, and scores are automatically updated as new information is entered into patient records. This paper summarizes the method and results of evaluation of electronic health record–generated work intensity scores on six hospital patient care units in a single academic medical center. The correlation between beginning-of-shift work intensity scores and self-reported registered nurse rating of appropriateness of patient assignment was assessed using Spearman rank correlation. A weak negative correlation (−0.09 to −0.23) was observed on all study units, indicating that nurse appropriateness ratings decrease as work intensity scores increase. Electronic health record–generated work intensity scores provide useful information that can augment existing data sources used by charge nurses to create equitable nurse-patient assignments. Additional research is needed to explain observed variation in nurses' appropriateness ratings across similar work intensity point ranges. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Funding for D. Womack was provided through the National Library of Medicine Training Grant T15-LM007088 and Agency for Healthcare Research and Quality award 1K12HS026370. Human subjects protections: The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the OHSU Institutional Review Board. Corresponding author: Dana Womack, PhD, RN, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Rd, Portland, OR 97230 (womacda@ohsu.edu). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Use of Personal Health Records to Support Diabetes Self-management: An Integrative Review More than 30 million persons in the United States have diagnosed or undiagnosed diabetes. Persons with chronic types of diabetes must learn self-management principles and techniques and perform self-care behaviors to reduce the risk of diabetes-related complications. An electronic personal health record is one type of technology commonly used to support diabetes self-management. This integrative review examines research on how personal health records incorporate or address the American Association of Diabetes Educators self-care behaviors, diabetes-related psychosocial concerns, and the diabetes-related clinical quality-of-care measures of hemoglobin A1c, low-density lipoprotein cholesterol, and blood pressure. In the majority of studies reviewed, participants showed improvement in the self-care behavior or physiological outcome examined. Findings were inconclusive about the impact of personal health record use on diabetes distress. Results also revealed a lack evidence of patient specific factors influencing intention to use a personal health record for management of type 2 diabetes mellitus. Despite evidence that personal health record use improves diabetes self-management, they are underutilized. Implications for practice include understanding what influences intention to use a personal health record. Further research is also needed to determine the impact of personal health record use on diabetes distress. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Khaliah Fisher-Grace, MSN, RN, Duquesne University School of Nursing, 5th Floor Fisher Hall, 600 Forbes Avenue, Pittsburgh, PA 15282 (fishergracek@duq.edu). Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.cinjournal.com). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Using a Systems Engineering Approach to Design an Interactive Mobile Health Application for Improving Asthma Self-management Mobile health applications are in demand. According to the Grand Review Research group, there were US $12.4 billion in mobile health app sales in 2018. Increasingly, providers are seeking new ways to engage their pediatric patients. One approach is through mobile health apps. Nearly 10 000 mobile health apps target youth and teens, especially for children with conditions that require complex treatment and self-management. One such chronic illness is asthma. Children with asthma who lack social support are particularly vulnerable to exacerbations because they often are not focused on self-care. For this reason, successful asthma programs for children engage family members, encouraging them to play an active role on the healthcare team. The Just-in-Time Asthma Self-management Intervention is unique in several ways compared to other asthma management mobile applications. The app uses gold standard, evidence-based asthma care practices and extends the support infrastructure beyond family and healthcare providers to engage the child, their peers, and school personnel. Further, the app was built using a systems engineering approach. This article reviews the basis for developing an asthma care mobile application including the conceptual framework supporting a systems model, how the Just-in-Time Asthma Self-management Intervention is unique, and how it was built using a systems engineering design. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. This article is based, in part, on a master's thesis: Pradeep Velur Rajashekaran, "A Systems Engineering Approach to a Just-In-Time Intervention System," University of Tennessee, 2018. https://trace.tennessee.edu/utk_gradthes/5121 Corresponding author: Tami H. Wyatt, PhD, RN, ANEF, FAAN, College of Nursing, The University of Tennessee, Knoxville, Room 307, 1200 Volunteer Blvd, Knoxville, TN 37996 (twyatt@utk.edu). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Exploratory Factor Analysis for a Nursing Workaround Instrument in Korean and Interpretations of Statistical Decision Points The use of workarounds by nurses is prevalent in clinical settings. Understanding how work processes are adjusted as a result of implementing an electronic medical record is important because of its impact on patient care. The purpose of this study was to conduct an exploratory factor analysis of a 20-item nursing workaround instrument translated into Korean. The responses from 104 nurses were analyzed. Examinations of sample size, factorability of a correlation matrix, the Kaiser-Meyer-Olkin value, the P value of Bartlett test of sphericity, anti-image correlation coefficients, and communality were acceptable to proceed with exploratory factor analysis. The original dimensionality of four groups was retained. However, the number of items loaded onto each group was reduced from five to three. These four factors explained 66% of the total variance between the items. Cronbach's α for the internal consistency reliability of the instrument was .70. The 12-item nursing workaround instrument was specific to an electronic medical record, which was the strongest point of the instrument. Further confirmatory factor analysis of this instrument is needed. This translated instrument is expected to contribute to the proliferation of studies examining nurses' workarounds related to the use of electronic medical records in Korean clinical settings, thereby improving clinical information systems for clinical practice. Ethical approval details: The study protocol was approved by the institutional review board of the Chonnam National University (approval no. 1040198-190517-HR-041-02). Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.cinjournal.com). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Interdisciplinary Optimization of Admission Documentation: Reducing the Bloat Moving toward the electronic health record increases the quality of information gathered. However, nurses argue that the electronic health record is an added burden. The aim of this study was to evaluate the removal of duplicative or unnecessary fields and reordering fields on the admission form to increase documentation that is meaningful to the patient story. A team of approximately 60 interdisciplinary clinicians engaged in document review to evaluate the importance of each field and removal or modification based on those findings. After a review of the 251 fields, the authors reduced the form to 124 fields, and the percentage of unfields by 31%. After outlier removal, the average time to complete the admission form decreased by 2.88 minutes. The new form showed a reduction of 36.71% of the use of the free text advance directive. Additionally, nurses' perceptions of the form significantly improved from pretest to posttest in terms of satisfaction with the form, time to complete, usability and usefulness, question flow, and length of the form. This study shows that an interdisciplinary team can effectively work together to optimize the Adult Admission History Form, increasing the quality of documentation while reducing the time to complete. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Tina L. Rylee, BA, Dignity Health Phoenix System Office 3033 N 3rd Ave. Phoenix, AZ 85013 (tina.l.rylee@gmail.com). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Automated Fall and Pressure Injury Risk Assessment Systems: Nurses' Experiences, Perspectives, and Lessons Learned This study examined the clinical usability of two automated risk assessment systems—the Automated Fall Risk Assessment System and Automated Pressure Injury Risk Assessment System. The clinical usability of automated assessment systems was tested in three ways: agreement between the scales that nurses generally use and the automated assessment systems, focus group interviews, and the predicted amount of time saved for risk assessment and documentation. For the analysis of agreement, 1160 patients and 1000 patients were selected for falls and pressure injuries, respectively. A total of 60 nurses participated in focus group interviews. The nurses personally checked the time taken to assess and document the risks of falls and pressure injury for 271 and 251 patient cases, respectively. The results for the agreement showed a κ index of 0.43 and a percentage of agreement of 71.55% between the Automated Fall Risk Assessment System and the Johns Hopkins Fall Risk Assessment Tool. For the agreement between the Automated Pressure Injury Risk Assessment System and the Braden scale, the κ index was 0.52 and the percentage of agreement was 80.60%. The focus group interviews showed that participants largely perceived the automated risk assessment systems positively. The time it took for assessment and documentation were about 5 minutes to administer the Johns Hopkins Fall Risk Assessment Tool and 2 to 3 minutes to administer the Braden scale per day to all patients. Overall, the automated risk assessment systems may help in obtaining time devoted to directly preventing falls and pressure injuries and thereby contribute to better quality care. Yinji Jin and Heejeong Kim contributed equally to this work. This study was supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science, and Technology (NRF-2010-0027077; NRF-2014R1A2A2A01003313). Ethical approval: Approval to conduct the study was obtained from the study hospital and the Institutional Review Board (approval no. MC13RIS10066). The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Author contributions: All the authors contributed to the design of the study, collection of data, analysis and interpretation of data, and writing and approval of the manuscript. Corresponding author: Sun-Mi Lee, College of Nursing, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, Korea 137-701 (leesunmi@catholic.ac.kr). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Using Mobile Technologies Among Undergraduate Nursing Students for Academic Purposes in Tertiary Education: A Quantitative Survey Mobile devices are increasingly part of daily life, with the benefits of using the technology in nursing education widely recognized. This study explored the use of mobile devices among undergraduate nursing students for academic purposes in South Africa, using a quantitative survey. The majority of participants owned smartphones (87.6%), followed by laptops (76%) and tablets (47.1%). Mobile devices were used to perform academic tasks and communicate and collaborate with peers and teachers, as well as search and access electronic resources. Few of the first year nursing students owned laptops and tablets and used them less frequently than the students from other levels of the study. Equipping nursing students with mobile devices, such as laptops and tablets, particularly first year students, and ensuring that they have adequate skills to use them, is essential to training future nurses who are expected to work in a technology-mediated health environment. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Alexis Harerimana, RN, BNAP, MNE, Grad CertEd, PhD, James Cook University/College of Healthcare Sciences, 1 James Cook Drive, Townsville, QLD 4811, Australia (alexis.harerimana@my.jcu.edu.au; haralexis@yahoo.fr). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

The Self-management Smartphone Application for Cancer Survivors, ReLive: Development and Usability Testing ReLive is a nursing theory–driven and evidence-based smartphone application that aims to promote self-management among cancer survivors. It has been designed to display symptom measurement results in different traffic light colors, depending on the severity of a user's symptoms (eg, severe symptoms are presented in red). Therefore, it is easy for users to draw inferences about changes in their symptoms. Further, users can simultaneously set several physical activity goals and monitor their performance. Social support, self-efficacy, and quality of life of a user can also be monitored regularly. This study investigated the usability of this application. An iterative formative test, including a cognitive walkthrough and face-to-face interviews, was conducted. Participants were seven individuals with a diagnosis of chronic myeloid leukemia. The ease of use and understanding, acceptability, and usefulness of the application were evaluated. The results revealed that the participants had evaluated ReLive positively. This program could be used as an intervention to deliver health information and manage their performance. Further research is needed to assess the application's effects on self-management among survivors of various types of cancers. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Source of funding: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A3B03031697). Ethical approval: This study was conducted under the approval of the institutional review board from Chonnam National University Hwasun Hospital, Jeonnam, South Korea (CNUHH-2018-074). Corresponding author: Minjeong An, PhD, RN, FNP-BC, Chonnam National University College of Nursing, 160 Baekseoro, Donggu, Gwangju 61469, South Korea (anminjeong@jnu.ac.kr). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Predicting Falls Among Community-Dwelling Older Adults: A Demonstration of Applied Machine Learning Data science skills are increasingly needed by informatics nurses and nurse scientists, but techniques such as machine learning can be daunting for those with clinical, rather than computer science or technical, backgrounds. With the increasing quantity of publicly available population-level datasets, identification of factors that predict clinical outcomes is possible using machine learning algorithms. This study demonstrates how to apply a machine learning approach to nursing-relevant questions, specifically an approach to predict falls among community-dwelling older adults, based on data from the 2014 Behavioral Risk Factor Surveillance System. A random forest algorithm, a common approach to machine learning, was compared to a logistic regression model. Explanations of how to interpret the models and their associated performance characteristics are included to serve as a tutorial to readers. Machine learning methods constitute an increasingly important approach for nursing as population-level data are increasingly being made available to the public. R.Y. and J.M.P. contributed equally to this work. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Funding: Nanjing Medical University, Nanjing, China, JX10631803 & NMUR2020006. National Natural Science Foundation of China, grant number 72004098. Corresponding author: Rumei Yang, PhD, RN, School of Nursing, Nanjing Medical University, 101 Longmian Ave, Jiangning District, Jiangsu, Nanjing, China 211166; and University of Utah College of Nursing 10 S 2000 E, Salt Lake City, UT 84112 (Rumei.yang@hsc.utah.edu; rumeiyang@njmu.edu.cn). Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.cinjournal.com). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Status and Influential Factors of Intelligent Healthcare in Nursing Homes in China With the support of the Chinese government, nursing homes have increasingly adopted Internet and intelligent information technology to provide daily healthcare services to residents. However, no research has reported the status of intelligent healthcare in nursing homes. From September 2017 to May 2018, we conducted a survey of 197 nursing homes and collected information on their general characteristics, the intelligent healthcare services provided, the effectiveness of the application products used, and the attitudes of the staff and residents toward intelligent healthcare. Overall, 79.69% of the surveyed nursing homes have provided intelligent healthcare services, including medical care services (eg, chronic disease management and intelligent nursing) and daily life services (eg, fall monitoring and wireless positioning). Portable health monitoring devices and data management and service platforms are the most used healthcare products. The attitudes of staff probably affected the development of intelligent healthcare. Financial investment and the attitudes of staff and residents are factors that influence the effectiveness of the application of intelligent healthcare products in nursing homes. With the support of national policies, nursing homes have implemented primary intelligent healthcare. Stakeholders play pivotal roles in the provision of intelligent healthcare services. Fanli Meng and Fengbin Song contributed equally to this work. Corresponding authors: Dahui Wang, PhD, Medical School, Hangzhou Normal University, Hangzhou 310036, Zhejiang, PR China (dahui230@vip.163.com); and Liangwen Xu, PhD, Medical School, Hangzhou Normal University, Hangzhou 310036, Zhejiang, PR China (tougaoscihz@163.com). The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. This study was supported by the National Natural Science Foundation of China (no. 71603068) and Department of Education of Zhejiang Province (no. Y201533217). This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

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