BackgroundThe length of stay (LOS) is an important indicator of the efficiency of hospital management. Reduction in the number of inpatient days results in decreased risk of infection and medication side effects, improvement in the quality of treatment, and increased hospital profit with more efficient bed management. The purpose of this study was to determine which factors are associated with length of hospital stay, based on electronic health records, in order to manage hospital stay more efficiently.Materials and methodsResearch subjects were retrieved from a database of patients admitted to a tertiary general university hospital in South Korea between January and December 2013. Patients were analyzed according to the following three categories: descriptive and exploratory analysis, process pattern analysis using process mining techniques, and statistical analysis and prediction of LOS.ResultsOverall, 55% (25,228) of inpatients were discharged within 4 days. The department of rehabilitation medicine (RH) had the highest average LOS at 15.9 days. Of all the conditions diagnosed over 250 times, diagnoses of I63.8 (cerebral infarction, middle cerebral artery), I63.9 (infarction of middle cerebral artery territory) and I21.9 (myocardial infarction) were associated with the longest average hospital stay and high standard deviation. Patients with these conditions were also more likely to be transferred to the RH department for rehabilitation. A range of variables, such as transfer, discharge delay time, operation frequency, frequency of diagnosis, severity, bed grade, and insurance type was significantly correlated with the LOS.ConclusionsAccurate understanding of the factors associating with the LOS and progressive improvements in processing and monitoring may allow more efficient management of the LOS of inpatients.

User experience design that reflects real-world application and aims to support suitable service solutions has arisen as one of the current issues in the medical informatics research domain. The Smart Bedside Station (SBS) is a screen that is installed on the bedside for the personal use and provides a variety of convenient services for the patients. Recently, bedside terminal systems have been increasingly adopted in hospitals due to the rapid growth of advanced technology in healthcare at the point of care. We designed user experience (UX) research to derive users' unmet needs and major functions that are frequently used in the field. To develop the SBS service, a service design methodology, the Double Diamond Design Process Model, was undertaken. The problems or directions of the complex clinical workflow of the hospital, the requirements of stakeholders, and environmental factors were identified through the study. The SBS system services provided to patients were linked to the hospital's main services or to related electronic medical record (EMR) data. Seven key services were derived from the results of the study. The primary services were as follows: Bedside Check In and Out, Bedside Room Service, Bedside Scheduler, Ready for Rounds, My Medical Chart, Featured Healthcare Content, and Bedside Community. This research developed a patient-centered SBS system with improved UX using service design methodology applied to complex and technical medical services, providing insights to improve the current healthcare system.

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BackgroundAs patient communication, engagement, personal health data tracking, and up-to-date information became more efficient through mobile health (mHealth), cardiovascular diseases (CVD) and other diseases that require behavioral improvements in daily life are now capable of being managed and prevented more effectively. However, to increase patient engagement through mHealth, it is important for the initial design to consider functionality and usability factors and accurately assess user demands during the developmental process so that the app can be used continuously.ObjectiveThe purpose of the study was to provide insightful information for developing mHealth service for patients with CVD based on user research to help enhance communication between patients and doctors.MethodsTo drive the mobile functions and services needed to manage diseases in CVD patients, user research was conducted on patients and doctors at a tertiary general university hospital located in the Seoul metropolitan area of South Korea. Interviews and a survey were performed on patients (35 participants) and a focus group interview was conducted with doctors (5 participants). A mock-up mobile app was developed based on the user survey results, and a usability test was then conducted (8 participants) to identify factors that should be considered to improve usability.ResultsThe majority of patients showed a positive response in terms of their interest or intent to use an app for managing CVD. Functional features, such as communication with doctors, self-risk assessment, exercise, tailored education, blood pressure management, and health status recording had a score of 4.0 or higher on a 5-point Likert scale, showing that these functions were perceived to be useful to patients. The results of the mock-up usability test showed that inputting and visualizing blood pressure and other health conditions was required to be easier. The doctors requested a function that offered a comprehensive view of the patient’s daily health status by linking the mHealth app data with the hospital’s electronic health record system.ConclusionsInsights derived from a user study for developing an mHealth tool for CVD management, such as self-assessment and a communication channel between patients and doctors, may be helpful to improve patient engagement in care.

Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death and morbidity worldwide. This randomized controlled, single-center, open-label trial tested the impact of a mobile health (mHealth) service tool optimized for ASCVD patient care. Patients with clinical ASCVD were enrolled and randomly assigned to the intervention or control group. Participants in the intervention group were provided with a smartphone application named HEART4U, while a dedicated interface integrated into the electronic healthcare record system was provided to the treating physicians. A total of 666 patients with ASCVD were enrolled, with 333 patients in each group. The estimated baseline 10-year risk of cardiovascular disease was 9.5% and 10.8% in the intervention and control groups, respectively, as assessed by the pooled cohort risk equations. The primary study endpoint was the change in the estimated risk at six months. The estimated risk increased by 1.3% and 1.1%, respectively, which did not differ significantly (P = 0.821). None of the secondary study endpoints showed significant differences between the groups. A post-hoc subgroup analysis showed the benefit was greater if a participant in the intervention group accessed the application more frequently. The present study demonstrated no significant benefits associated with the use of the mHealth tool in terms of the predefined study endpoints in stable patients with ASCVD. However, it also suggested that motivating patients to use the mHealth tool more frequently may lead to greater clinical benefit. Better design with a positive user experience needs to be considered for developing future mHealth tools for ASCVD patient care.Trial Registration: ClinicalTrials.gov NCT03392259

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Background Neonatal sepsis is associated with most cases of mortalities and morbidities in the neonatal intensive care unit (NICU). Many studies have developed prediction models for the early diagnosis of bloodstream infections in newborns, but there are limitations to data collection and management because these models are based on high-resolution waveform data. Objective The aim of this study was to examine the feasibility of a prediction model by using noninvasive vital sign data and machine learning technology. Methods We used electronic medical record data in intensive care units published in the Medical Information Mart for Intensive Care III clinical database. The late-onset neonatal sepsis (LONS) prediction algorithm using our proposed forward feature selection technique was based on NICU inpatient data and was designed to detect clinical sepsis 48 hours before occurrence. The performance of this prediction model was evaluated using various feature selection algorithms and machine learning models. Results The performance of the LONS prediction model was found to be comparable to that of the prediction models that use invasive data such as high-resolution vital sign data, blood gas estimations, blood cell counts, and pH levels. The area under the receiver operating characteristic curve of the 48-hour prediction model was 0.861 and that of the onset detection model was 0.868. The main features that could be vital candidate markers for clinical neonatal sepsis were blood pressure, oxygen saturation, and body temperature. Feature generation using kurtosis and skewness of the features showed the highest performance. Conclusions The findings of our study confirmed that the LONS prediction model based on machine learning can be developed using vital sign data that are regularly measured in clinical settings. Future studies should conduct external validation by using different types of data sets and actual clinical verification of the developed model.

ObjectivesTo successfully introduce an Internet of Things (IoT) system in the hospital environment, this study aimed to identify issues that should be considered while implementing an IoT based on a user demand survey and practical experiences in implementing IoT environment monitoring systems.MethodsIn a field test, two types of IoT monitoring systems (on-premises and cloud) were used in Department of Laboratory Medicine and tested for approximately 10 months from June 16, 2016 to April 30, 2017. Information was collected regarding the issues that arose during the implementation process.ResultsA total of five issues were identified: sensing and measuring, transmission method, power supply, sensor module shape, and accessibility.ConclusionsIt is expected that, with sufficient consideration of the various issues derived from this study, IoT monitoring systems can be applied to other areas, such as device interconnection, remote patient monitoring, and equipment/environmental monitoring.