Mona Choi

Computer and Internet Interventions for Loneliness and Depression in Older Adults: A Meta-Analysis

Objectives This study conducted a meta-analysis to examine the effectiveness of computer and Internet training interventions intended to reduce loneliness and depression in older adults. Methods Searches were performed to retrieve studies that had been published in peer-reviewed journals from January 2001 to July 2012 and written in English or Korean from PubMed, CINAHL, Scopus, the Cochrane Library, PsycINFO, KoreaMed, KMbase, KISS, MEDLIS, and RISS. We used combinations of the keywords for population, intervention, and psychosocial problems. A meta-analysis was employed to summarize the findings of studies on computer and Internet interventions for older adults. An overall mean weighted effect size for each outcome was calculated, and Q statistics were used to test the heterogeneity of variance in the effect sizes of the selected studies. Results As the Q statistics revealed heterogeneity, random effects models were chosen for the meta-analysis. The overall mean weighted effect size for loneliness from five studies was statistically significant for decreased loneliness (Z = 2.085, p = 0.037). However, the overall mean weighted effect size for depression from five studies was not statistically significant (Z = 1.528, p = 0.126). Conclusions These results suggest that computer and Internet programs were effective in managing loneliness among older adults. Therefore, further computer-mediated social support should be considered to help manage loneliness in this population.

Big Data and Healthcare: Building an Augmented World

Starting from July of 2016, literally bumping into people on the streets trying to catch Pokemon has become a common sight. Pokemon, of course, are fictional creatures based on the popular Japanese video game franchise that has become the basis of animated series and films [1]. Unlike other fictional characters, however, Pokemon now exist in an augmented reality (AR) in the new mobile game, Pokemon Go, which transforms your physical location into a world that hosts characters superimposed on the reality that we see through our smart phones.

Reusability of EMR Data for Applying Cubbin and Jackson Pressure Ulcer Risk Assessment Scale in Critical Care Patients

Objectives The purposes of this study were to examine the predictive validity of the Cubbin and Jackson pressure ulcer risk assessment scale for the development of pressure ulcers in intensive care unit (ICU) patients retrospectively and to evaluate the reusability of Electronic Medical Records (EMR) data. Methods A retrospective design was used to examine 829 cases admitted to four ICUs in a tertiary care hospital from May 2010 to April 2011. Patients who were without pressure ulcers at admission to ICU, 18 years or older, and had stayed in ICU for 24 hours or longer were included. Sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) were calculated. Results The reported incidence rate of pressure ulcers among the study subjects was 14.2%. At the cut-off score of 24 of the Cubbin and Jackson scale, the sensitivity, specificity, positive predictive value, negative predictive value, and AUC were 72.0%, 68.8%, 27.7%, 93.7%, and 0.76, respectively. Eight items out 10 of the Cubbin and Jackson scale were readily available in the EMR data. Conclusions The Cubbin and Jackson scale performed slightly better than the Braden scale to predict pressure ulcer development. Eight items of the Cubbin and Jackson scale except mobility and hygiene can be extracted from the EMR, which initially demonstrated the reusability of EMR data for pressure ulcer risk assessment. If the Cubbin and Jackson scale is a part of the EMR assessment form, it would help nurses perform tasks to effectively prevent pressure ulcers with an EMR alert for high-risk patients.

Factors Associated with the Timeliness of Electronic Nursing Documentation

Objectives To investigate the factors associated with the timeliness of electronic nursing documentation using the entry time on the Electronic Medical Record (EMR) system. Methods As a retrospective study, data were extracted from January 1 to February 28, 2014 from a hospital EMR system and a nurses’ personnel information system. The timeliness of instances of nursing documentation was categorized into ‘timely’ or ‘untimely’ according to whether the entry time was time-stamped within the working hours during each day, evening, or night shift. Factors associated with the timeliness of the electronic nursing documentation were included in the logistic regression models as nurse- and patient-associated factors. Results Among 1,700,247 instances of electronic nursing documentation, 79.3% (n = 1,347,711) were completed within the working hours. Years of nursing experience, nursing shift, days of the week, patients’ age, and medical department had a statistically significant associated with the timeliness of nursing records. Nurses with experience of more than 1 year entered nursing records over 2 times more during their working hours than did less experienced nurses. During the evening and night shifts, nurses were 1.49 times and 9.19 times more likely to enter nursing documents in a timely manner, respectively, as compared to those in the day shift. Conclusions Nursing documentation was typically completed outside of working hours when a nurse had little experience, worked during the day shift or weekdays, and when tasks were unpredictable. This shows that new nurses need support to familiarize them with various tasks and the overall workflow.

Psychometric evaluation of the Korean Version of the Self-Efficacy for Exercise Scale for older adults

We evaluated the psychometric properties of the Korean version of the Self-Efficacy for Exercise Scale (SEE-K). The SEE-K consists of nine items and was translated into Korean using the forward-backward translation method. We administered it to 212 community-dwelling older adults along with measures of outcome expectation for exercise, quality of life, and physical activity. The validity was determined using confirmatory factor analysis and Rasch analysis with INFIT and OUTFIT statistics, which showed acceptable model fit. The concurrent validity was confirmed according to positive correlations between the SEE-K, outcome expectation for exercise, and quality of life. Furthermore, the high physical activity group had higher SEE-K scores. Finally, the reliability of the SEE-K was deemed acceptable based on Cronbachs alpha, coefficients of determination, and person and item separation indices with reliability. Thus, the SEE-K appears to have satisfactory validity and reliability among older adults in South Korea.

Citation Analysis for Biomedical and Health Sciences Journals Published in Korea

Objectives A citation analysis of biomedical and health sciences journals was conducted based on their enlistment in journal databases to identify the factors contributing to the citation metrics. Methods Among the 1,219 academic journals managed by the National Center for Medical Information and Knowledge at the Korea Centers for Disease Control and Prevention, 556 journals were included for analysis as of July 2016. The characteristics of the journals include history years, publication media, language, open-access policy as well as the status enlisted in international and domestic databases, such as Science Citation Index (SCI), Scopus, Medline, PubMed Central, Embase, and Korea Citation Index (KCI). Six bibliometric measures were collected from SCI, Scopus, and KCI as of 2015, the most recent disclosure year. Analyses of group differences and influential factors were conducted using t-tests, Mann-Whitney tests, and multiple regression. Results Journal characteristics, such as history years, publication media, and open-access policy, were not significant factors influencing global or domestical citation of the journals. However, global citations were higher for SCI and Medline enlisted journals than for their counterparts. Among KCI journals, the KCI impact factors of journals published in English only were lower. Conclusions Efforts by journals to be enlisted in international databases, especially in SCI and Medline, are critical to enhance their global circulation. However, articles published in English only hinder the use of domestic researchers. Different strategies are required for enhancing international and domestic readerships.

Assessment of the Need to Integrate Academic Electronic Medical Records Into the Undergraduate Clinical Practicum: A Focus Group Interview.

As healthcare systems demand that nurses be competent in using electronic medical records for patient care, the integration of electronic medical records into nursing curricula has become necessary. The purpose of this study was to explore how students, new nurses, clinical instructors, and faculty perceive the integration of academic electronic medical records into the undergraduate clinical practicum. From January to February 2014, four focus group interviews with 18 participants were conducted based on purposive sampling. Content analysis was used on the unabridged transcripts to extract themes and develop meaningful categories. Three major themes and eight subthemes were revealed from the focus group interviews. The major themes were “electronic medical record as a learning tool for clinical practicum,” “essential functions of academic electronic medical records,” and “expected outcomes of academic electronic medical record.” Participants expected academic electronic medical records to enhance students’ nursing informatics competencies. The findings of this study can inform the process of developing academic electronic medical records for clinical practicum, which will then augment students’ informatics competencies.

Healthcare Informatics Research's Journey of Paving the Road to Excellence in Global Healthcare Informatics

identity in and influence on its field [1], Healthcare Informatics Research (HIR), the official journal of the Korean Society of Medical Informatics (KOSMI), forms the core of KOSMI’s academic activities on healthcare informatics. The transition from the Journal of KOSMI to HIR in 2010 has allowed an expansion of KOSMI’s vision through by its contribution to the field of global healthcare informatics. HIR has accomplished many milestones in the years since. It was indexed in the National Library of Medicine (NLM) Catalog, PubMed Central (PMC), and Scopus [2]. In 2016, it was indexed in the Emerging Sources Citation Index (ESCI) [3]. Of course, as HIR has grown externally, its internal growth has also continued since 2010. This can be confirmed by the journal metrics provided by Scopus and ESCI. Scopus is the largest database of abstracts and citations of peer-reviewed literature [4]. Of all Scopus’s journal metrics, h-index and SCImago Journal Rank (SJR) are the most widely used measures of journal quality. SJR is a citation index indicator that computes a value reflecting the topic, quality, and reputation of the journal by citing another specific journal [5]. In conclusion, citations from famous journals are very important, and each quotation yields other influencing factors [5]. As of 2016, HIR’s h-index was 14, and its SJR increased from 0.123 in 2011 to 0.588 in 2016 [6]. Scopus divides the indexed journals into quadrants according to the SJR of each journal, with the highest being in Q1 and the lowest in Q4. HIR is classified into three categories: Biomedical Engineering, Health Informatics, and Health Information Management in Scopus. In 2016, HIR was placed in Q2 for all three categories [6]. In addition, such as the total number of citations, citations per document, and ratio of international collaborations, are steadily increasing. Subsequent to HIR’s major efforts to gain inclusion in SCIE quite some time ago, a recent achievement was being indexed among another critical database, ESCI. As a new index among the Web of Science (WoS) databases, ESCI expands the citation universe and reflects the growing global body of science and scholarly activity. ESCI complements the highly selective indexes by providing earlier visibility for sources under evaluation as part of SCIE (Science Citation Index Expanded), SSCI (Social Sciences Citation Index), and AHCI’s (Arts & Humanities Citation Index) rigorous journal selection process. Inclusion in ESCI provides greater opportunities for exposure to the scientific community, which leads to measurable citations and more transparency [7]. Since HIR was included in ESCI in November 2015, it is now possible to calculate its 2-year impact factor (IF) in the Web of Science database. Although we are aware of various viewpoints and opinions in considering IFs, many would agree that the IF is still one of the most important indicators of the status of academic journals. The IF calculation for 2017 is performed using this method: cited items from 2015 to 2016 divided by citable publications from 2015 to 2016, and this has yielded an IF of 1.438. As described above, this grand achievement has been made Healthcare Informatics Research’s Journey of Paving the Road to Excellence in Global Healthcare Informatics

Critical Patient Severity Classification System predicts outcomes in intensive care unit patients.

BACKGROUND The CPSCS was developed to assess the nursing care demands of patients in intensive care units (ICUs). AIM This study aimed to examine the Critical Patient Severity Classification System (CPSCS) score as an independent predictor of patient hospital outcomes. DESIGN This study was a secondary analysis. METHODS Data from 6380 cases were extracted from the electronic medical records in ICUs at a tertiary hospital in Korea during 2010-2012. To examine the association of the CPSCS score with 30-day ICU mortality, the Cox proportional hazards model and Kaplan-Meier survival curves were used, and generalized linear regression models of gamma distribution were developed for ICU length of stay (LOS). RESULTS More patients were admitted to surgical ICUs than medical ICUs (4664 versus 1716) during the study period. Medical ICU patients had longer ICU LOS, higher 30-day ICU mortality and a higher mean CPSCS score than surgical ICU patients. Cox analysis indicated that the mid and high CPSCS score groups had 1·687 and 2·913 times higher mortality risk, respectively, than the low CPSCS score group after adjusting for age, sex and primary diagnosis. The CPSCS score significantly predicted ICU mortality in both medical and surgical ICUs. Multivariate generalized linear regression indicated that CPSCS score was a significant predictor of ICU LOS after adjusting for other covariates. CONCLUSIONS The CPSCS score can be used to efficiently predict ICU mortality and LOS in patients admitted to the medical and surgical ICUs, although only the high CPSCS score group had significantly high mortality than the low CPSCS score group in the medical ICU. RELEVANCE TO CLINICAL PRACTICE The findings of this study contribute to valuable evidence that nursing-related factors have an impact on patient outcomes such as ICU mortality and LOS and that they have implications for hospital management, clinical practice and future research.

Book Review: Spatial Analysis in Epidemiology

place, however, has always been the most difficult and time consuming to analyze and illustrate [1]. In addition, the majority of data for demographic, socioeconomic, environmental pollution, and health outcomes have a common aspect; all of these data can be located within space as a point, line, or area [2]. Because where people live, work, learn, and play can affect their health and well-being, these location data are gaining momentum as an important part of epidemiology and can be assessed with advanced technology and analytic methods. Spatial analysis in epidemiology is a field dealing with spatial or spatiotemporal data, which can be linked to the phenomenon of disease spread or population at risk [3]. This book, Spatial Analysis in Epidemiology, consists of 8 chapters. Chapter 1 is an introduction to the concepts and framework for spatial analysis, available software, and the basics of spatial data by providing readers with many references, Web sites, and information sources. Chapter 2 dives into more detail about spatial data and spatial effects in conjunction with the geographic information system (GIS), such as data types, collection, and management. Especially for someone who is not familiar with GIS as a powerful tool for dealing with such spatial data, chapter 2 will be an essential read. Chapter 3 focuses on the visualization and representation of spatial data. Examples of geographical data were described as point data (e.g., disease cases, attribute values), aggregated data (e.g., total, mean, and median), and continuous data (e.g., temperature, air pollution). Chapters 4 and 5 introduce spatial clustering for disease at global and local levels, which is very useful for spatial aggregation of disease cases. These 2 chapters provide various methods and statistics dealing with aggregated and point data for spatial clustering that has great potential as an alarm system for disease clustering. Chapter 6 deals with spatial variations in risk, Spatial Analysis in Epidemiology