Cystic lesions of the retroperitoneum can be classified as either neoplastic or nonneoplastic. Neoplastic lesions include cystic lymphangioma, mucinous cystadenoma, cystic teratoma, cystic mesothelioma, müllerian cyst, epidermoid cyst, tailgut cyst, bronchogenic cyst, cystic change in solid neoplasms, pseudomyxoma retroperitonei, and perianal mucinous carcinoma. Nonneoplastic lesions include pancreatic pseudocyst, nonpancreatic pseudocyst, lymphocele, urinoma, and hematoma. Because the clinical implications of and therapeutic strategies for retroperitoneal cystic masses vary depending on the cause, the ability to noninvasively differentiate between masses is important. Although there is substantial overlap of computed tomographic (CT) findings in various retroperitoneal cysts, some CT features, along with clinical characteristics, may suggest a specific diagnosis. CT may provide important information regarding lesion location, size, and shape; the presence and thickness of a wall; the presence of septa, calcifications, or fat; and involvement of adjacent structures. The most important clinical parameters include patient gender, age, symptoms, and clinical history. Familiarity with the CT and clinical features of various retroperitoneal cystic masses facilitates accurate diagnosis and treatment.

BackgroundAlthough the factors that affect the end-user’s intention to use a new system and technology have been researched, the previous studies have been theoretical and do not verify the factors that affected the adoption of a new system. Thus, this study aimed to confirm the factors that influence users’ intentions to utilize a mobile electronic health records (EMR) system using both a questionnaire survey and a log file analysis that represented the real use of the system.MethodsAfter observing the operation of a mobile EMR system in a tertiary university hospital for seven months, we performed an offline survey regarding the user acceptance of the system based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology Acceptance Model (TAM). We surveyed 942 healthcare professionals over two weeks and performed a structural equation modeling (SEM) analysis to identify the intention to use the system among the participants. Next, we compared the results of the SEM analysis with the results of the analyses of the actual log files for two years to identify further insights into the factors that affected the intention of use. For these analyses, we used SAS 9.0 and AMOS 21.ResultsOf the 942 surveyed end-users, 48.3 % (23.2 % doctors and 68.3 % nurses) responded. After eliminating six subjects who completed the survey insincerely, we conducted the SEM analyses on the data from 449 subjects (65 doctors and 385 nurses). The newly suggested model satisfied the standards of model fitness, and the intention to use it was especially high due to the influences of Performance Expectancy on Attitude and Attitude. Based on the actual usage log analyses, both the doctors and nurses used the menus to view the inpatient lists, alerts, and patients’ clinical data with high frequency. Specifically, the doctors frequently retrieved laboratory results, and the nurses frequently retrieved nursing notes and used the menu to assume the responsibilities of nursing work.ConclusionIn this study, the end-users’ intentions to use the mobile EMR system were particularly influenced by Performance Expectancy and Attitude. In reality, the usage log revealed high-frequency use of the functions to improve the continuity of care and work efficiency. These results indicate the influence of the factor of performance expectancy on the intention to use the mobile EMR system. Consequently, we suggest that when determining the implementation of mobile EMR systems, the functions that are related to workflow with ability to increase performance should be considered first.

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.

Implementation of the AKI alert system was associated with beneficial effects in terms of an improved rate of recovery from AKI. Therefore, widespread adoption of such systems could be considered in general hospitals.

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ObjectivesSeoul National University Bundang Hospital, which is the first Stage 7 hospital outside of North America, has adopted and utilized an innovative and emerging information technology system to improve the efficiency and quality of patient care. The objective of this paper is to briefly introduce the major components of the SNUBH information system and to describe our progress toward a next-generation hospital information system (HIS).MethodsSNUBH opened in 2003 as a fully digital hospital by successfully launching a new HIS named BESTCare, "Bundang hospital Electronic System for Total Care". Subsequently, the system has been continuously improved with new applications, including close-loop medication administration (CLMA), clinical data warehouse (CDW), health information exchange (HIE), and disaster recovery (DR), which have resulted in the achievement of Stage 7 status.ResultsThe BESTCare system is an integrated system for a university hospital setting. BESTCare is mainly composed of three application domains: the core applications, an information infrastructure, and channel domains. The most critical and unique applications of the system, such as the electronic medical record (EMR), computerized physician order entry (CPOE), clinical decision support system (CDSS), CLMA, CDW, HIE, and DR applications, are described in detail.ConclusionsBeyond our achievement of Stage 7 hospital status, we are currently developing a next-generation HIS with new goals of implementing infrastructure that is flexible and innovative, implementing a patient-centered system, and strengthening the IT capability to maximize the hospital value.

BackgroundPersonal health record (PHR)–based health care management systems can improve patient engagement and data-driven medical diagnosis in a clinical setting.ObjectiveThe purpose of this study was (1) to demonstrate the development of an electronic health record (EHR)–tethered PHR app named MyHealthKeeper, which can retrieve data from a wearable device and deliver these data to a hospital EHR system, and (2) to study the effectiveness of a PHR data-driven clinical intervention with clinical trial results.MethodsTo improve the conventional EHR-tethered PHR, we ascertained clinicians’ unmet needs regarding PHR functionality and the data frequently used in the field through a cocreation workshop. We incorporated the requirements into the system design and architecture of the MyHealthKeeper PHR module. We constructed the app and validated the effectiveness of the PHR module by conducting a 4-week clinical trial. We used a commercially available activity tracker (Misfit) to collect individual physical activity data, and developed the MyHealthKeeper mobile phone app to record participants’ patterns of daily food intake and activity logs. We randomly assigned 80 participants to either the PHR-based intervention group (n=51) or the control group (n=29). All of the study participants completed a paper-based survey, a laboratory test, a physical examination, and an opinion interview. During the 4-week study period, we collected health-related mobile data, and study participants visited the outpatient clinic twice and received PHR-based clinical diagnosis and recommendations.ResultsA total of 68 participants (44 in the intervention group and 24 in the control group) completed the study. The PHR intervention group showed significantly higher weight loss than the control group (mean 1.4 kg, 95% CI 0.9-1.9; P<.001) at the final week (week 4). In addition, triglyceride levels were significantly lower by the end of the study period (mean 2.59 mmol/L, 95% CI 17.6-75.8; P=.002).ConclusionsWe developed an innovative EHR-tethered PHR system that allowed clinicians and patients to share lifelog data. This study shows the effectiveness of a patient-managed and clinician-guided health tracker system and its potential to improve patient clinical profiles.Trial RegistrationClinicalTrials.gov NCT03200119; https://clinicaltrials.gov/ct2/show/NCT03200119 (Archived by WebCite at http://www.webcitation.org/6v01HaCdd)

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