OBJECTIVES: Recent advances in mobile technology have opened up possibilities to provide strongly integrated mobile-based services in healthcare and telemedicine. Although the number of mobile Electronic Health Record (EHR) applications is large and growing, there is a paucity of evidence demonstrating the usage patterns of these mobile applications by healthcare providers. This study aimed to illustrate the deployment process for an integrated mobile EHR application and to analyze usage patterns after provision of the mobile EHR service. METHODS: We developed an integrated mobile application that aimed to enhance the mobility of healthcare providers by improving access to patient- and hospital-related information during their daily medical activities. The study included mobile EHR users who accessed patient healthcare records between May 2013 and May 2014. We performed a data analysis using a web server log file analyzer from the integrated EHR system. Cluster analysis was applied to longitudinal user data based on their application usage pattern. RESULTS: The mobile EHR service named M-UMIS has been in service since May 2013. Every healthcare provider in the hospital could access the mobile EHR service and view the medical charts of their patients. The frequency of using services and network packet transmission on the M-UMIS increased gradually during the study period. The most frequently accessed service in the menu was the patient list. CONCLUSIONS: A better understanding regarding the adoption of mobile EHR applications by healthcare providers in patient-centered care provides useful information to guide the design and implementation of future applications.
Delivery of Health Care ; Electronic Health Records* ; Health Personnel ; Humans ; Korea* ; Medical Informatics Applications ; Mobile Applications ; Patient-Centered Care ; Statistics as Topic ; Telemedicine ; Tertiary Care Centers* ; Wireless Technology

OBJECTIVES: A number of technologies can reduce overall costs for the prevention or management of chronic illnesses. These include devices that constantly monitor health indicators, devices that auto-administer therapies, or devices that track real-time health data when a patient self-administers a therapy. Because they have increased access to high-speed Internet and smartphones, many patients have started to use mobile applications (apps) to manage various health needs. These devices and mobile apps are now increasingly used and integrated with telemedicine and telehealth via the medical Internet of Things (mIoT). This paper reviews mIoT and big data in healthcare fields. METHODS: mIoT is a critical piece of the digital transformation of healthcare, as it allows new business models to emerge and enables changes in work processes, productivity improvements, cost containment and enhanced customer experiences. RESULTS: Wearables and mobile apps today support fitness, health education, symptom tracking, and collaborative disease management and care coordination. All those platform analytics can raise the relevancy of data interpretations, reducing the amount of time that end users spend piecing together data outputs. Insights gained from big data analysis will drive the digital disruption of the healthcare world, business processes and real-time decision-making. CONCLUSIONS: A new category of "personalised preventative health coaches" (Digital Health Advisors) will emerge. These workers will possess the skills and the ability to interpret and understand health and well-being data. They will help their clients avoid chronic and diet-related illness, improve cognitive function, achieve improved mental health and achieve improved lifestyles overall. As the global population ages, such roles will become increasingly important.
Chronic Disease ; Cognition ; Commerce ; Cost Control ; Delivery of Health Care* ; Disease Management ; Efficiency ; Health Education ; Humans ; Internet* ; Life Style ; Mental Health ; Mobile Applications ; Smartphone ; Statistics as Topic ; Telemedicine ; Wireless Technology

PURPOSE: The purpose of this study was to develop a smartphone app for use in patient controlled analgesia (PCA) education and to identify PCA knowledge and pain management following lower extremity orthopaedic surgery under spinal anesthesia in patients who received smartphone app education. METHODS: Participants were 150 patients in an orthopaedic hospital located in Busan. The measurement variables used in this study were PCA knowledge, pain management and pain level. For data analysis, SPSS/WIN 21.0 program was used in the analysis of the relation of frequencies. In addition, percentage, mean and standard deviation, t-test, ANOVA, Duncan, Pearson's correlation coefficients were also assessed. RESULTS: The score for knowledge regarding PCA was 4.27±1.64. The correlations between knowledge and pain management (button push times Analgesia ; Analgesia, Patient-Controlled* ; Anesthesia, Spinal* ; Busan ; Education* ; Humans ; Lower Extremity* ; Methods ; Mobile Applications ; Orthopedics* ; Pain Management* ; Passive Cutaneous Anaphylaxis ; Smartphone* ; Statistics as Topic

BACKGROUND: To combat coronavirus disease 2019 (COVID-19), many countries have used contact tracing apps, including Japan's voluntary-use contact-confirming application (COCOA). The current study aimed to identify industry and workplace characteristics associated with the downloading of this COVID-19 contact tracing app. METHODS: This cross-sectional study of full-time workers used an online survey. Multiple logistic regression analysis was used to evaluate the associations of industry and workplace characteristics with contact tracing app use. RESULTS: Of the 27,036 participants, 25.1% had downloaded the COCOA. Workers in the public service (adjusted odds ratio [aOR] = 1.29, 95% confidence interval [CI] 1.14-1.45) and information technology (aOR = 1.38, 95% CI 1.20-1.58) industries were more likely to use the app than were those in the manufacturing industry. In contrast, app usage was less common among workers in the retail and wholesale (aOR = 0.87, 95% CI 0.76-0.99) and food/beverage (aOR = 0.81, 95% CI 0.70-0.94) industries, but further adjustment for company size attenuated these associations. Workers at larger companies were more likely to use the app. Compared with permanent employees, the odds of using the app were higher for managers and civil servants but lower for those who were self-employed. CONCLUSIONS Downloading of COCOA among Japanese workers was insufficient; thus, the mitigating effect of COCOA on the COVID-19 pandemic is considered to be limited. One possible reason for the under-implementation of the contact tracing app in the retail and wholesale and food/beverage industries is small company size, as suggested by the fully adjusted model results. An awareness campaign should be conducted to promote the widespread use of the contact tracing app in these industries.
Adult ; COVID-19/prevention & control* ; Contact Tracing/methods* ; Cross-Sectional Studies ; Female ; Humans ; Industry/classification* ; Japan/epidemiology* ; Male ; Middle Aged ; Mobile Applications/statistics & numerical data* ; SARS-CoV-2 ; Smartphone ; Workplace/statistics & numerical data*

OBJECTIVES: Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients’ needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems. METHODS: We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed. RESULTS: Data mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients’ self-management. CONCLUSIONS: Embedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.
Artificial Intelligence ; Data Collection ; Data Mining* ; Decision Trees ; Delivery of Health Care ; Early Diagnosis ; Education ; Follow-Up Studies ; Fuzzy Logic ; Humans ; Information Systems* ; Mass Screening ; Methods ; Mobile Applications ; Patient Care ; Self Care ; Smartphone ; Statistics as Topic ; Support Vector Machine ; Telemedicine