Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective Observational Network Study.

BACKGROUND: Older adults are at an increased risk of postoperative morbidity. Numerous risk stratification tools exist, but effort and manpower are required. OBJECTIVE: This study aimed to develop a predictive model of postoperative adverse outcomes in older patients following general surgery with an open-source, patient-level prediction from the Observational Health Data Sciences and Informatics for internal and external validation. METHODS: We used the Observational Medical Outcomes Partnership common data model and machine learning algorithms. The primary outcome was a composite of 90-day postoperative all-cause mortality and emergency department visits. Secondary outcomes were postoperative delirium, prolonged postoperative stay (≥75th percentile), and prolonged hospital stay (≥21 days). An 80% versus 20% split of the data from the Seoul National University Bundang Hospital (SNUBH) and Seoul National University Hospital (SNUH) common data model was used for model training and testing versus external validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) with a 95% CI. RESULTS: Data from 27,197 (SNUBH) and 32,857 (SNUH) patients were analyzed. Compared to the random forest, Adaboost, and decision tree models, the least absolute shrinkage and selection operator logistic regression model showed good internal discriminative accuracy (internal AUC 0.723, 95% CI 0.701-0.744) and transportability (external AUC 0.703, 95% CI 0.692-0.714) for the primary outcome. The model also possessed good internal and external AUCs for postoperative delirium (internal AUC 0.754, 95% CI 0.713-0.794; external AUC 0.750, 95% CI 0.727-0.772), prolonged postoperative stay (internal AUC 0.813, 95% CI 0.800-0.825; external AUC 0.747, 95% CI 0.741-0.753), and prolonged hospital stay (internal AUC 0.770, 95% CI 0.749-0.792; external AUC 0.707, 95% CI 0.696-0.718). Compared with age or the Charlson comorbidity index, the model showed better prediction performance. CONCLUSIONS: The derived model shall assist clinicians and patients in understanding the individualized risks and benefits of surgery.

Development and validation of a prediction model for evaluating extubation readiness in preterm infants.

Successful early extubation has advantages not only in terms of short-term respiratory morbidities and survival but also in terms of long-term neurodevelopmental outcomes in preterm infants. However, no consensus exists regarding the optimal protocol or guidelines for extubation readiness in preterm infants. Therefore, the decision to extubate preterm infants was almost entirely at the attending physician's discretion. We identified robust and quantitative predictors of success or failure of the first planned extubation attempt before 36 weeks of post-menstrual age in preterm infants (<32 weeks gestational age) and developed a prediction model for evaluating extubation readiness using these predictors. Extubation success was defined as the absence of reintubation within 72 h after extubation. This observational cohort study used data from preterm infants admitted to the neonatal intensive care unit of Seoul National University Bundang Hospital in South Korea between July 2003 and June 2019 to identify predictors and develop and test a predictive model for extubation readiness. Data from preterm infants included in the Medical Informative Medicine for Intensive Care (MIMIC-III) database between 2001 and 2008 were used for external validation. From a machine learning model using predictors such as demographics, periodic vital signs, ventilator settings, and respiratory indices, the area under the receiver operating characteristic curve and average precision of our model were 0.805 (95% confidence interval [CI], 0.802-0.809) and 0.917, respectively in the internal validation and 0.715 (95% CI, 0.713-0.717) and 0.838, respectively in the external validation. Our prediction model (NExt-Predictor) demonstrated high performance in assessing extubation readiness in both internal and external validations.

Exploring the potential of OMOP common data model for process mining in healthcare.

BACKGROUND AND OBJECTIVE: Recently, Electronic Health Records (EHR) are increasingly being converted to Common Data Models (CDMs), a database schema designed to provide standardized vocabularies to facilitate collaborative observational research. To date, however, rare attempts exist to leverage CDM data for healthcare process mining, a technique to derive process-related knowledge (e.g., process model) from event logs. This paper presents a method to extract, construct, and analyze event logs from the Observational Medical Outcomes Partnership (OMOP) CDM for process mining and demonstrates CDM-based healthcare process mining with several real-life study cases while answering frequently posed questions in process mining, in the CDM environment. METHODS: We propose a method to extract, construct, and analyze event logs from the OMOP CDM for process types including inpatient, outpatient, emergency room processes, and patient journey. Using the proposed method, we extract the retrospective data of several surgical procedure cases (i.e., Total Laparoscopic Hysterectomy (TLH), Total Hip Replacement (THR), Coronary Bypass (CB), Transcatheter Aortic Valve Implantation (TAVI), Pancreaticoduodenectomy (PD)) from the CDM of a Korean tertiary hospital. Patient data are extracted for each of the operations and analyzed using several process mining techniques. RESULTS: Using process mining, the clinical pathways, outpatient process models, emergency room process models, and patient journeys are demonstrated using the extracted logs. The result shows CDM's usability as a novel and valuable data source for healthcare process analysis, yet with a few considerations. We found that CDM should be complemented by different internal and external data sources to address the administrative and operational aspects of healthcare processes, particularly for outpatient and ER process analyses. CONCLUSION: To the best of our knowledge, we are the first to exploit CDM for healthcare process mining. Specifically, we provide a step-by-step guidance by demonstrating process analysis from locating relevant CDM tables to visualizing results using process mining tools. The proposed method can be widely applicable across different institutions. This work can contribute to bringing a process mining perspective to the existing CDM users in the changing Hospital Information Systems (HIS) environment and also to facilitating CDM-based studies in the process mining research community.

Coronary Artery Computed Tomography Angiography for Preventing Cardio-Cerebrovascular Disease: Observational Cohort Study Using the Observational Health Data Sciences and Informatics' Common Data Model.

BACKGROUND: Cardio-cerebrovascular diseases (CVDs) result in 17.5 million deaths annually worldwide, accounting for 46.2% of noncommunicable causes of death, and are the leading cause of death, followed by cancer, respiratory disease, and diabetes mellitus. Coronary artery computed tomography angiography (CCTA), which detects calcification in the coronary arteries, can be used to detect asymptomatic but serious vascular disease. It allows for noninvasive and quick testing despite involving radiation exposure. OBJECTIVE: The objective of our study was to investigate the effectiveness of CCTA screening on CVD outcomes by using the Observational Health Data Sciences and Informatics' Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) data and the population-level estimation method. METHODS: Using electronic health record-based OMOP-CDM data, including health questionnaire responses, adults (aged 30-74 years) without a history of CVD were selected, and 5-year CVD outcomes were compared between patients undergoing CCTA (target group) and a comparison group via 1:1 propensity score matching. Participants were stratified into low-risk and high-risk groups based on the American College of Cardiology/American Heart Association atherosclerotic cardiovascular disease (ASCVD) risk score and Framingham risk score (FRS) for subgroup analyses. RESULTS: The 2-year and 5-year risk scores were compared as secondary outcomes between the two groups. In total, 8787 participants were included in both the target group and comparison group. No significant differences (calibration P=.37) were found between the hazard ratios of the groups at 5 years. The subgroup analysis also revealed no significant differences between the ASCVD risk scores and FRSs of the groups at 5 years (ASCVD risk score: P=.97; FRS: P=.85). However, the CCTA group showed a significantly lower increase in risk scores at 2 years (ASCVD risk score: P=.03; FRS: P=.02). CONCLUSIONS: Although we could not confirm a significant difference in the preventive effects of CCTA screening for CVDs over a long period of 5 years, it may have a beneficial effect on risk score management over 2 years.

Effectiveness of the Use of Standardized Vocabularies on Epilepsy Patient Cohort Generation.

OBJECTIVES: This study investigated the effectiveness of using standardized vocabularies to generate epilepsy patient cohorts with local medical codes, SNOMED Clinical Terms (SNOMED CT), and International Classification of Diseases tenth revision (ICD-10)/Korean Classification of Diseases-7 (KCD-7). METHODS: We compared the granularity between SNOMED CT and ICD-10 for epilepsy by counting the number of SNOMED CT concepts mapped to one ICD-10 code. Next, we created epilepsy patient cohorts by selecting all patients who had at least one code included in the concept sets defined using each vocabulary. We set patient cohorts generated by local codes as the reference to evaluate the patient cohorts generated using SNOMED CT and ICD-10/KCD-7. We compared the number of patients, the prevalence of epilepsy, and the age distribution between patient cohorts by year. RESULTS: In terms of the cohort size, the match rate with the reference cohort was approximately 99.2% for SNOMED CT and 94.0% for ICD-10/KDC7. From 2010 to 2019, the mean prevalence of epilepsy defined using the local codes, SNOMED CT, and ICD-10/KCD-7 was 0.889%, 0.891% and 0.923%, respectively. The age distribution of epilepsy patients showed no significant difference between the cohorts defined using local codes or SNOMED CT, but the ICD-9/KCD-7-generated cohort showed a substantial gap in the age distribution of patients with epilepsy compared to the cohort generated using the local codes. CONCLUSIONS: The number and age distribution of patients were substantially different from the reference when we used ICD-10/KCD-7 codes, but not when we used SNOMED CT concepts. Therefore, SNOMED CT is more suitable for representing clinical ideas and conducting clinical studies than ICD-10/KCD-7.

Assessment of the feasibility of developing a clinical pathway using a clinical order log.

A clinical pathway (CP) is a tool for effectively managing a care process. There are several research efforts on developing clinical pathways (CPs) in the process mining domain. However, the nature of the data affects data analysis results, and patient clinical variability makes it challenging to develop CPs. Thus, it is crucial to determine candidate care processes that can be standardized as CPs before applying process mining techniques. This paper proposed a method for assessing CP feasibility regarding clinical complexity using clinical order logs from electronic health records. The proposed method consists of data preparation, activity & trace homogeneity evaluations, and process inspection using process mining. Each step consists of metrics to measure the homogeneity of processes and a visualization method to demonstrate the diversity of processes based on the log. The case study was conducted with five surgical groups of patients from a tertiary hospital in South Korea to validate the proposed method. The five groups of patients were successfully assessed. In addition, the visualization methods helped clinical experts grasp the diversity of care processes.

Management of cardiovascular disease using an mHealth tool: a randomized clinical trial.

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.

Impact of mobile health application on data collection and self-management of epilepsy.

Recent advances in mobile health have enabled health data collection, which includes seizure and medication tracking and epilepsy self-management. We developed a mobile epilepsy management application, integrated with a hospital electronic health record (EHR). In this prospective clinical trial, we assessed whether the mobile application provides quality healthcare data compared to conventional clinic visits, and enhances epilepsy self-management for patients with epilepsy. The study population includes patients with epilepsy (ages 15 years and older) and caregivers for children with epilepsy. Participants were provided access to the application for 90 days. We compared healthcare data collected from the mobile application with data obtained from clinic visits. The healthcare data included seizure records, seizure triggering factors, medication adherence rate, profiles of adverse events resulting from anti-seizure medication (ASM), and comorbidity screenings. In addition, we conducted baseline and follow-up questionnaires after the 90-day period to evaluate how this mobile application improved epilepsy knowledge and self-efficacy in seizure management. Data of 99 participants (18 patients with epilepsy and 81 caregivers) were analyzed. Among 24 individuals who had seizures, we obtained detailed seizure records from 13 individuals through clinic visits and for 18 from the application. Aside from the 6 individuals who reported their medication adherence during clinic visitation, half of the study participants had adherence rates of over 70%, as monitored through the application. However, the adherence rates were not reliable due to high variability. Twenty-three individuals reported 59 adverse reactions on the application, whereas 21 individuals reported 24 adverse reactions during clinic visits. We collected comorbidity data from 4 individuals during clinic visits. In comparison, 64 participants underwent comorbidity self-screening on the application, and 2 of them were referred to neuropsychiatric services. Compared to rare/non-users, app users demonstrated significant improvement in epilepsy knowledge score (p < 0.001) and self-efficacy score (p = 0.038). In conclusion, mobile health technology would help patients and caregivers to record their healthcare data and aid in self-management. Mobile health technology would provide an influential clinical validity in epilepsy care when users engage and actively maintain records on the application.

Transformation of Pathology Reports Into the Common Data Model With Oncology Module: Use Case for Colon Cancer.

BACKGROUND: Common data models (CDMs) help standardize electronic health record data and facilitate outcome analysis for observational and longitudinal research. An analysis of pathology reports is required to establish fundamental information infrastructure for data-driven colon cancer research. The Observational Medical Outcomes Partnership (OMOP) CDM is used in distributed research networks for clinical data; however, it requires conversion of free text-based pathology reports into the CDM's format. There are few use cases of representing cancer data in CDM. OBJECTIVE: In this study, we aimed to construct a CDM database of colon cancer-related pathology with natural language processing (NLP) for a research platform that can utilize both clinical and omics data. The essential text entities from the pathology reports are extracted, standardized, and converted to the OMOP CDM format in order to utilize the pathology data in cancer research. METHODS: We extracted clinical text entities, mapped them to the standard concepts in the Observational Health Data Sciences and Informatics vocabularies, and built databases and defined relations for the CDM tables. Major clinical entities were extracted through NLP on pathology reports of surgical specimens, immunohistochemical studies, and molecular studies of colon cancer patients at a tertiary general hospital in South Korea. Items were extracted from each report using regular expressions in Python. Unstructured data, such as text that does not have a pattern, were handled with expert advice by adding regular expression rules. Our own dictionary was used for normalization and standardization to deal with biomarker and gene names and other ungrammatical expressions. The extracted clinical and genetic information was mapped to the Logical Observation Identifiers Names and Codes databases and the Systematized Nomenclature of Medicine (SNOMED) standard terminologies recommended by the OMOP CDM. The database-table relationships were newly defined through SNOMED standard terminology concepts. The standardized data were inserted into the CDM tables. For evaluation, 100 reports were randomly selected and independently annotated by a medical informatics expert and a nurse. RESULTS: We examined and standardized 1848 immunohistochemical study reports, 3890 molecular study reports, and 12,352 pathology reports of surgical specimens (from 2017 to 2018). The constructed and updated database contained the following extracted colorectal entities: (1) NOTE_NLP, (2) MEASUREMENT, (3) CONDITION_OCCURRENCE, (4) SPECIMEN, and (5) FACT_RELATIONSHIP of specimen with condition and measurement. CONCLUSIONS: This study aimed to prepare CDM data for a research platform to take advantage of all omics clinical and patient data at Seoul National University Bundang Hospital for colon cancer pathology. A more sophisticated preparation of the pathology data is needed for further research on cancer genomics, and various types of text narratives are the next target for additional research on the use of data in the CDM.

A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study.

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.

Developing a mobile epilepsy management application integrated with an electronic health record for effective seizure management.

INTRODUCTION: Epilepsy is a chronic neurological disorder characterized by recurrent spontaneous seizures. Over 70% epilepsy patients can live normally if their seizures can be controlled. For this, many factors should be tracked and managed, but doing so is hard because of individual differences. There are mobile applications to help track these factors; however, no application covers crucial factors comprehensively, and they are complicated to use. Therefore, this study aimed to develop a mobile epilepsy management application covering crucial factors comprehensively in a user-friendly way. We evaluated the pilot version with a usability and satisfaction survey and an interview. METHODS: We established a task force comprising professionals from various fields who participated in all processes of this research. Existing service analysis and professional interviews were conducted to draw a function list. User interface and graphic user interface were designed under the supervision of the task force. After developing the application's pilot version, usability and satisfaction of the application were evaluated with eight patients and caregivers through scenario-based usability test, satisfaction survey, and interview. RESULTS: All existing mobile epilepsy management applications provide seizure and medication diary functions. We decided to provide six main functions: seizure diary, medication reminder, appointments, outpatient survey, education materials, and personal dashboard (My epilepsy). We also integrated the application with the hospital's electronic health record system. To simplify usability, frequently used and relatively important functions are located in the main page as "seizure recording" and "medication diary." Additionally, when designing graphics, art therapy was used to enhance psychological stability. For evaluation, eight participants were recruited. In scenario-based tasks, among 10 tasks, all participants completed six tasks. However, only 37.5% participants could record seizures in detail. System Usability Scale score was 84.5 points, indicating that the system was satisfactory. CONCLUSION: This study confirmed that patients' satisfaction of this application were high. Additionally, it helped them record their seizures accurately, which is very useful for seizure trend analysis, discovering seizure trigger factors, and ensuring efficient management of epilepsy. Through integration with the electronic health record, patient medical information could be utilized to guide physicians' decision-making for setting future medical treatment plan and could contribute greatly to the overall management of epilepsy.

Developing data-driven clinical pathways using electronic health records: The cases of total laparoscopic hysterectomy and rotator cuff tears.

OBJECTIVE: A clinical pathway is one of the tools used to support clinical decision making that provides a standardized care process in a specific context. The objective of this research was to develop a method for building data-driven clinical pathways using electronic health record data. MATERIALS AND METHODS: We proposed a matching rate-based clinical pathway mining algorithm that produces the optimal set of clinical orders for each clinical stage by employing matching rates. To validate the approach, we utilized two different datasets of deidentified inpatient records directly related to total laparoscopic hysterectomy (TLH) and rotator cuff tears (RCTs) from a hospital in South Korea. The derived data-driven clinical pathways were evaluated with knowledge-based models by health professionals using a delta analysis. RESULTS: Two different data-driven clinical pathways, i.e., TLH and RCTs, were produced by applying the matching rate-based clinical pathway mining algorithm. We identified that there were significant differences in clinical orders between the data-driven and knowledge-based models. Additionally, the data-driven clinical pathways based on our algorithm outperformed the models by clinical experts, with average matching rates of 82.02% and 79.66%, respectively. CONCLUSION: The proposed algorithm will be helpful for supporting clinical decisions and directly applicable in medical practices.

Potentials of Smart dynamometer use for clinical and self-management of rehabilitation in breast cancer survivors: a feasibility study.

The aim of this study was to examine the feasibility of the Smart dynamometer as a rehabilitation exercise device in a daily care by comparing with the existing medical devices. We used and analyzed clinical and measurement data of breast cancer survivors who have used Smart dynamometer during their rehabilitation after breast cancer surgery. The Smart dynamometer was compared with the two existing devices of Takei dynamometer and surface electromyography (sEMG) that were used in routine care, respectively. Three key components of the rehabilitation exercise devices were analyzed to validate the feasibility of the Smart dynamometer: grip strength, reaction time, and grip endurance time. Pearson's correlation analysis was performed to compare the statistical significance between the devices. The data of 12 and 15 female breast cancer patients were analyzed for comparing the Smart dynamometer with Takei dynamometer and sEMG, respectively. There was a very weak correlation between the maximum values from the Takei and the Smart dynamometers in the affected and non-affected arms of breast cancer patients (r = 0.5321, 0.4733). Comparisons of 3 features between the Smart dynamometer and sEMG showed that there were strong positive correlations for both reaction time and endurance time in the affected and non-affected arms (r > 0.9). The feasibility of the Smart dynamometer for the possible use in a daily rehabilitation exercise was partially verified. Moreover, since the Smart dynamometer was highly correlated with time-related variables, it was important and significant to measure both grip strength and time-related information.

Survey on the demand for adoption of Internet of Things (IoT)-based services in hospitals: Investigation of nurses' perception in a tertiary university hospital.

In hospitals, while the opportunities and challenges of Internet of Things (IoT) applications are continuously increasing, research on what IoT services are actually in demand in hospitals has not been conducted. In this study, a survey of working hospital nurses was conducted to confirm the demand for IoT services. A total of 1086 (90.2%) participants responded. Five out of seven points for all service questions were obtained, which indicates a high demand for all services. The highest demand was shown for a vital sign device interface system. A comparison between ward and non-ward nurses showed that individuals working in wards had a high demand for patient care related IoT services, and individuals working in non-ward departments demonstrated a high demand for IoT services to improve work efficiency. Overall, the results provide a framework for future directions of services that can improve the efficiency of medical staff and health outcomes of patients.

Laboratory Environment Monitoring: Implementation Experience and Field Study in a Tertiary General Hospital.

OBJECTIVES: To 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. METHODS: In 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. RESULTS: A total of five issues were identified: sensing and measuring, transmission method, power supply, sensor module shape, and accessibility. CONCLUSIONS: It 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.

Analysis of length of hospital stay using electronic health records: A statistical and data mining approach.

BACKGROUND: The 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 METHODS: Research 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. RESULTS: Overall, 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. CONCLUSIONS: Accurate 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.

Validation of the mobile wireless digital automatic blood pressure monitor using the cuff pressure oscillometric method, for clinical use and self-management, according to international protocols.

The purpose of this study was to evaluate the accuracy of a mobile wireless digital automatic blood pressure monitor for clinical use and mobile health (mHealth). In this study, a manual sphygmomanometer and a digital blood pressure monitor were tested in 100 participants in a repetitive and sequential manner to measure blood pressure. The guidelines for measurement used the Korea Food & Drug Administration protocol, which reflects international standards, such as the American National Standard Institution/Association for the Advancement of Medical Instrumentation SP 10: 1992 and the British Hypertension Society protocol. Measurements were generally consistent across observers according to the measured mean ± SD, which ranged in 0.1 ± 2.6 mmHg for systolic blood pressure (SBP) and 0.5 ± 2.2 mmHg for diastolic blood pressure (DBP). For the device and the observer, the difference in average blood pressure (mean ± SD) was 2.3 ± 4.7 mmHg for SBP and 2.0 ± 4.2 mmHg for DBP. The SBP and DBP measured in this study showed accurate measurements that satisfied all criteria, including an average difference that did not exceed 5 mmHg and a standard deviation that did not exceed 8 mmHg. The mobile wireless digital blood pressure monitor has the potential for clinical use and managing one's own health.

Enhancing User Experience Through User Study: Design of an mHealth Tool for Self-Management and Care Engagement of Cardiovascular Disease Patients.

BACKGROUND: As 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. OBJECTIVE: The 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. METHODS: To 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. RESULTS: The 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. CONCLUSIONS: Insights 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.

Development and User Research of a Smart Bedside Station System toward Patient-Centered Healthcare System.

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.

Establishing a Personal Health Record System in an Academic Hospital: One Year's Experience.

BACKGROUND: Personal health records (PHRs) are web based tools that help people to access and manage their personalized medical information. Although needs for PHR are increasing, current serviced PHRs are unsatisfactory and researches on them remain limited. The purpose of this study is to show the process of developing Seoul National University Bundang Hospital (SNUBH)'s own PHR system and to analyze consumer's use pattern after providing PHR service. METHODS: Task force team was organized to decide service range and set the program. They made the system available on both mobile application and internet web page. The study enrolled PHR consumers who assessed PHR system between June 2013 and June 2014. We analyzed the total number of users on a monthly basis and the using pattern according to each component. RESULTS: The PHR service named Health4U has been provided from June 2013. Every patient who visited SNUBH could register Health4U service and view their medical data. The PHR user has been increasing, especially they tend to approach via one way of either web page or mobile application. The most frequently used service is to check laboratory test result. CONCLUSION: For paradigm shift toward patient-centered care, there is a growing interest in PHR. This study about experience of establishing and servicing the Health4U would contribute to development of interconnected PHR.