The Risk of COVID-19 and Its Outcomes in Korean Patients With Gout: A Multicenter, Retrospective, Observational Study.

This retrospective cohort study aimed to compare coronavirus disease 2019 (COVID-19)-related clinical outcomes between patients with and without gout. Electronic health record-based data from two centers (Seoul National University Hospital [SNUH] and Boramae Medical Center [BMC]), from January 2021 to April 2022, were mapped to a common data model. Patients with and without gout were matched using a large-scale propensity-score algorithm based on population-level estimation methods. At the SNUH, the risk for COVID-19 diagnosis was not significantly different between patients with and without gout (hazard ratio [HR], 1.07; 95% confidence interval [CI], 0.59-1.84). Within 30 days after COVID-19 diagnosis, no significant difference was observed in terms of hospitalization (HR, 0.57; 95% CI, 0.03-3.90), severe outcomes (HR, 2.90; 95% CI, 0.54-13.71), or mortality (HR, 1.35; 95% CI, 0.06-16.24). Similar results were obtained from the BMC database, suggesting that gout does not increase the risk for COVID-19 diagnosis or severe outcomes.

Clinical Genomic Sequencing Reports in Electronic Health Record Systems Based on International Standards: Implementation Study.

BACKGROUND: To implement standardized machine-processable clinical sequencing reports in an electronic health record (EHR) system, the International Organization for Standardization Technical Specification (ISO/TS) 20428 international standard was proposed for a structured template. However, there are no standard implementation guidelines for data items from the proposed standard at the clinical site and no guidelines or references for implementing gene sequencing data results for clinical use. This is a significant challenge for implementation and application of these standards at individual sites. OBJECTIVE: This study examines the field utilization of genetic test reports by designing the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) for genomic data elements based on the ISO/TS 20428 standard published as the standard for genomic test reports. The goal of this pilot is to facilitate the reporting and viewing of genomic data for clinical applications. FHIR Genomics resources predominantly focus on transmitting or representing sequencing data, which is of less clinical value. METHODS: In this study, we describe the practical implementation of ISO/TS 20428 using HL7 FHIR Genomics implementation guidance to efficiently deliver the required genomic sequencing results to clinicians through an EHR system. RESULTS: We successfully administered a structured genomic sequencing report in a tertiary hospital in Korea based on international standards. In total, 90 FHIR resources were used. Among 41 resources for the required fields, 26 were reused and 15 were extended. For the optional fields, 28 were reused and 21 were extended. CONCLUSIONS: To share and apply genomic sequencing data in both clinical practice and translational research, it is essential to identify the applicability of the standard-based information system in a practical setting. This prototyping work shows that reporting data from clinical genomics sequencing can be effectively implemented into an EHR system using the existing ISO/TS 20428 standard and FHIR resources.

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.

Patient-Level Prediction of Cardio-Cerebrovascular Events in Hypertension Using Nationwide Claims Data.

BACKGROUND: Prevention and management of chronic diseases are the main goals of national health maintenance programs. Previously widely used screening tools, such as Health Risk Appraisal, are restricted in their achievement this goal due to their limitations, such as static characteristics, accessibility, and generalizability. Hypertension is one of the most important chronic diseases requiring management via the nationwide health maintenance program, and health care providers should inform patients about their risks of a complication caused by hypertension. OBJECTIVE: Our goal was to develop and compare machine learning models predicting high-risk vascular diseases for hypertensive patients so that they can manage their blood pressure based on their risk level. METHODS: We used a 12-year longitudinal dataset of the nationwide sample cohort, which contains the data of 514,866 patients and allows tracking of patients' medical history across all health care providers in Korea (N=51,920). To ensure the generalizability of our models, we conducted an external validation using another national sample cohort dataset, comprising one million different patients, published by the National Health Insurance Service. From each dataset, we obtained the data of 74,535 and 59,738 patients with essential hypertension and developed machine learning models for predicting cardiovascular and cerebrovascular events. Six machine learning models were developed and compared for evaluating performances based on validation metrics. RESULTS: Machine learning algorithms enabled us to detect high-risk patients based on their medical history. The long short-term memory-based algorithm outperformed in the within test (F1-score=.772, external test F1-score=.613), and the random forest-based algorithm of risk prediction showed better performance over other machine learning algorithms concerning generalization (within test F1-score=.757, external test F1-score=.705). Concerning the number of features, in the within test, the long short-term memory-based algorithms outperformed regardless of the number of features. However, in the external test, the random forest-based algorithm was the best, irrespective of the number of features it encountered. CONCLUSIONS: We developed and compared machine learning models predicting high-risk vascular diseases in hypertensive patients so that they may manage their blood pressure based on their risk level. By relying on the prediction model, a government can predict high-risk patients at the nationwide level and establish health care policies in advance.

Impact of an Electronic Health Record-Integrated Personal Health Record on Patient Participation in Health Care: Development and Randomized Controlled Trial of MyHealthKeeper.

BACKGROUND: Personal health record (PHR)-based health care management systems can improve patient engagement and data-driven medical diagnosis in a clinical setting. OBJECTIVE: The 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. METHODS: To 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. RESULTS: A 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). CONCLUSIONS: We 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 REGISTRATION: ClinicalTrials.gov NCT03200119; https://clinicaltrials.gov/ct2/show/NCT03200119 (Archived by WebCite at http://www.webcitation.org/6v01HaCdd).

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.

Snoring-related polygenic risk and its relationship with lifestyle factors in a Korean population: KoGES study.

Whereas lifestyle-related factors are recognized as snoring risk factors, the role of genetics in snoring remains uncertain. One way to measure the impact of genetic risk is through the use of a polygenic risk score (PRS). In this study, we aimed to investigate whether genetics plays a role in snoring after adjusting for lifestyle factors. Since the effect of polygenic risks may differ across ethnic groups, we calculated the PRS for snoring from the UK Biobank and applied it to a Korean cohort. We sought to evaluate the reproducibility of the UK Biobank PRS for snoring in the Korean cohort and to investigate the interaction of lifestyle factors and genetic risk on snoring in the Korean population. In this study, we utilized a Korean cohort obtained from the Korean Genome Epidemiology Study (KoGES). We computed the snoring PRS for the Korean cohort based on the UK Biobank PRS. We investigated the relationship between polygenic risks and snoring while controlling for lifestyle factors, including sex, age, body mass index (BMI), alcohol consumption, smoking, physical activity, and sleep time. Additionally, we analyzed the interaction of each lifestyle factor and the genetic odds of snoring. We included 3526 snorers and 1939 nonsnorers from the KoGES cohort and found that the PRS, a polygenic risk factor, was an independent factor for snoring after adjusting for lifestyle factors. In addition, among lifestyle factors, higher BMI, male sex, and older age were the strongest lifestyle factors for snoring. In addition, the highest adjusted odds ratio for snoring was higher BMI (OR 1.98, 95% CI 1.76-2.23), followed by male sex (OR 1.54, 95% CI 1.28-1.86), older age (OR 1.23, 95% CI 1.03-1.35), polygenic risks such as higher PRS (OR 1.18, 95% CI 1.08-1.29), drinking behavior (OR 1.18, 95% CI 1.03-1.35), late sleep mid-time (OR 1.17, 95% CI 1.02-1.33), smoking behavior (OR 0.99, 95% CI 0.82-1.19), and lower physical activity (OR 0.92, 95% CI 0.85-1.00). Our study identified that the UK Biobank PRS for snoring was reproducible in the Korean cohort and that genetic risk served as an independent risk factor for snoring in the Korean population. These findings may help to develop personalized approaches to reduce snoring in individuals with high genetic risk.

Machine learning-based prediction of in-ICU mortality in pneumonia patients.

Conventional severity-of-illness scoring systems have shown suboptimal performance for predicting in-intensive care unit (ICU) mortality in patients with severe pneumonia. This study aimed to develop and validate machine learning (ML) models for mortality prediction in patients with severe pneumonia. This retrospective study evaluated patients admitted to the ICU for severe pneumonia between January 2016 and December 2021. The predictive performance was analyzed by comparing the area under the receiver operating characteristic curve (AU-ROC) of ML models to that of conventional severity-of-illness scoring systems. Three ML models were evaluated: (1) logistic regression with L2 regularization, (2) gradient-boosted decision tree (LightGBM), and (3) multilayer perceptron (MLP). Among the 816 pneumonia patients included, 223 (27.3%) patients died. All ML models significantly outperformed the Simplified Acute Physiology Score II (AU-ROC: 0.650 [0.584-0.716] vs 0.820 [0.771-0.869] for logistic regression vs 0.827 [0.777-0.876] for LightGBM 0.838 [0.791-0.884] for MLP; P < 0.001). In the analysis for NRI, the LightGBM and MLP models showed superior reclassification compared with the logistic regression model in predicting in-ICU mortality in all length of stay in the ICU subgroups; all age subgroups; all subgroups with any APACHE II score, PaO2/FiO2 ratio < 200; all subgroups with or without history of respiratory disease; with or without history of CVA or dementia; treatment with mechanical ventilation, and use of inotropic agents. In conclusion, the ML models have excellent performance in predicting in-ICU mortality in patients with severe pneumonia. Moreover, this study highlights the potential advantages of selecting individual ML models for predicting in-ICU mortality in different subgroups.

Current Status and Key Issues of Data Management in Tertiary Hospitals: A Case Study of Seoul National University Hospital.

OBJECTIVES: In the era of the Fourth Industrial Revolution, where an ecosystem is being developed to enhance the quality of healthcare services by applying information and communication technologies, systematic and sustainable data management is essential for medical institutions. In this study, we assessed the data management status and emerging concerns of three medical institutions, while also examining future directions for seamless data management. METHODS: To evaluate the data management status, we examined data types, capacities, infrastructure, backup methods, and related organizations. We also discussed challenges, such as resource and infrastructure issues, problems related to government regulations, and considerations for future data management. RESULTS: Hospitals are grappling with the increasing data storage space and a shortage of management personnel due to costs and project termination, which necessitates countermeasures and support. Data management regulations on the destruction or maintenance of medical records are needed, and institutional consideration for secondary utilization such as long-term treatment or research is required. Government-level guidelines for facilitating hospital data sharing and mobile patient services should be developed. Additionally, hospital executives at the organizational level need to make efforts to facilitate the clinical validation of artificial intelligence software. CONCLUSIONS: This analysis of the current status and emerging issues of data management reveals potential solutions and sets the stage for future organizational and policy directions. If medical big data is systematically managed, accumulated over time, and strategically monetized, it has the potential to create new value.

Different treatment response to systemic corticosteroids according to white blood cell counts in severe COVID-19 patients.

BACKGROUND: Limited data are available in COVID-19 patients on the prediction of treatment response to systemic corticosteroid therapy based on systemic inflammatory markers. There is a concern whether the response to systemic corticosteroid is different according to white blood cell (WBC) counts in COVID-19 patients. We aimed to assess whether WBC count is related with the clinical outcomes after treatment with systemic corticosteroids in severe COVID-19. METHODS: We conducted a retrospective cohort study and analysed the patients hospitalised for severe COVID-19 and received systemic corticosteroids between July 2020 and June 2021. The primary endpoint was to compare the composite poor outcome of mechanical ventilation, extracorporeal membrane oxygenation, and mortality among the patients with different WBC counts. RESULTS: Of the 585 COVID-19 patients who required oxygen supplementation and systemic corticosteroids, 145 (24.8%) belonged to the leukopoenia group, 375 (64.1%) belonged to the normal WBC group, and 65 (11.1%) belonged to the leukocytosis group. In Kaplan-Meier curve, the composite poor outcome was significantly reduced in leukopoenia group compared to leukocytosis group (log-rank p-value < 0.001). In the multivariable Cox regression analysis, leukopoenia group was significantly associated with a lower risk of the composite poor outcome compared to normal WBC group (adjusted hazard ratio [aHR] = 0.32, 95% CI 0.14-0.76, p-value = 0.009) and leukocytosis group (aHR = 0.30, 95% CI = 0.12-0.78, p-value = 0.013). There was no significant difference in aHR for composite poor outcome between leukocytosis and normal WBC group. CONCLUSION: Leukopoenia may be related with a better response to systemic corticosteroid therapy in COVID-19 patients requiring oxygen supplementation.KEY MESSAGESIn severe COVID-19 treated with systemic corticosteroids, patients with leukopoenia showed a lower hazard for composite poor outcome compared to patients with normal white blood cell counts or leukocytosis.Leukopoenia may be a potential biomarker for better response to systemic corticosteroid therapy in COVID-19 pneumonia.

Thirty-day hospital readmission prediction model based on common data model with weather and air quality data.

Although several studies have attempted to develop a model for predicting 30-day re-hospitalization, few attempts have been made for sufficient verification and multi-center expansion for clinical use. In this study, we developed a model that predicts unplanned hospital readmission within 30 days of discharge; the model is based on a common data model and considers weather and air quality factors, and can be easily extended to multiple hospitals. We developed and compared four tree-based machine learning methods: decision tree, random forest, AdaBoost, and gradient boosting machine (GBM). Above all, GBM showed the highest AUC performance of 75.1 in the clinical model, while the clinical and W-score model showed the best performance of 73.9 for musculoskeletal diseases. Further, PM10, rainfall, and maximum temperature were the weather and air quality variables that most impacted the model. In addition, external validation has confirmed that the model based on weather and air quality factors has transportability to adapt to other hospital systems.

Development of Prediction Models for Unplanned Hospital Readmission within 30 Days Based on Common Data Model: A Feasibility Study.

BACKGROUND: Unplanned hospital readmission after discharge reflects low satisfaction and reliability in care and the possibility of potential medical accidents, and is thus indicative of the quality of patient care and the appropriateness of discharge plans. OBJECTIVES: The purpose of this study was to develop and validate prediction models for all-cause unplanned hospital readmissions within 30 days of discharge, based on a common data model (CDM), which can be applied to multiple institutions for efficient readmission management. METHODS: Retrospective patient-level prediction models were developed based on clinical data of two tertiary general university hospitals converted into a CDM developed by Observational Medical Outcomes Partnership. Machine learning classification models based on the LASSO logistic regression model, decision tree, AdaBoost, random forest, and gradient boosting machine (GBM) were developed and tested by manipulating a set of CDM variables. An internal 10-fold cross-validation was performed on the target data of the model. To examine its transportability, the model was externally validated. Verification indicators helped evaluate the model performance based on the values of area under the curve (AUC). RESULTS: Based on the time interval for outcome prediction, it was confirmed that the prediction model targeting the variables obtained within 30 days of discharge was the most efficient (AUC of 82.75). The external validation showed that the model is transferable, with the combination of various clinical covariates. Above all, the prediction model based on the GBM showed the highest AUC performance of 84.14 ± 0.015 for the Seoul National University Hospital cohort, yielding in 78.33 in external validation. CONCLUSIONS: This study showed that readmission prediction models developed using machine-learning techniques and CDM can be a useful tool to compare two hospitals in terms of patient-data features.

Transforming electronic health record polysomnographic data into the Observational Medical Outcome Partnership's Common Data Model: a pilot feasibility study.

Well-defined large-volume polysomnographic (PSG) data can identify subgroups and predict outcomes of obstructive sleep apnea (OSA). However, current PSG data are scattered across numerous sleep laboratories and have different formats in the electronic health record (EHR). Hence, this study aimed to convert EHR PSG into a standardized data format-the Observational Medical Outcome Partnership (OMOP) common data model (CDM). We extracted the PSG data of a university hospital for the period from 2004 to 2019. We designed and implemented an extract-transform-load (ETL) process to transform PSG data into the OMOP CDM format and verified the data quality through expert evaluation. We converted the data of 11,797 sleep studies into CDM and added 632,841 measurements and 9,535 observations to the existing CDM database. Among 86 PSG parameters, 20 were mapped to CDM standard vocabulary and 66 could not be mapped; thus, new custom standard concepts were created. We validated the conversion and usefulness of PSG data through patient-level prediction analyses for the CDM data. We believe that this study represents the first CDM conversion of PSG. In the future, CDM transformation will enable network research in sleep medicine and will contribute to presenting more relevant clinical evidence.

Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network.

OBJECTIVE: Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed 10 phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Data Sciences and Informatics (OHDSI) network. MATERIALS AND METHODS: We constructed classifiers using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE) R-package, an open-source framework for learning phenotype classifiers using datasets in the Observational Medical Outcomes Partnership Common Data Model. We labeled training data based on the presence of multiple mentions of disease-specific codes. Performance was evaluated on cohorts derived using rule-based definitions and real-world disease prevalence. Classifiers were developed and evaluated across 3 medical centers, including 1 international site. RESULTS: Compared to the multiple mentions labeling heuristic, classifiers showed a mean recall boost of 0.43 with a mean precision loss of 0.17. Performance decreased slightly when classifiers were shared across medical centers, with mean recall and precision decreasing by 0.08 and 0.01, respectively, at a site within the USA, and by 0.18 and 0.10, respectively, at an international site. DISCUSSION AND CONCLUSION: We demonstrate a high-throughput pipeline for constructing and sharing phenotype classifiers across sites within the OHDSI network using APHRODITE. Classifiers exhibit good portability between sites within the USA, however limited portability internationally, indicating that classifier generalizability may have geographic limitations, and, consequently, sharing the classifier-building recipe, rather than the pretrained classifiers, may be more useful for facilitating collaborative observational research.

Impact of Personal Health Records and Wearables on Health Outcomes and Patient Response: Three-Arm Randomized Controlled Trial.

BACKGROUND: Although using the technologies for a variety of chronic health conditions such as personal health record (PHR) is reported to be acceptable and useful, there is a lack of evidence on the associations between the use of the technologies and the change of health outcome and patients' response to a digital health app. OBJECTIVE: This study aimed to examine the impact of the use of PHR and wearables on health outcome improvement and sustained use of the health app that can be associated with patient engagement. METHODS: We developed an Android-based mobile phone app and used a wristband-type activity tracker (Samsung Charm) to collect data on health-related daily activities from individual patients. Dietary record, daily step counts, sleep log, subjective stress amount, blood pressure, and weight values were recorded. We conducted a prospective randomized clinical trial across 4 weeks on those diagnosed with obstructive sleep apnea (OSA) who had visited the outpatient clinic of Seoul National University Bundang Hospital. The trial randomly assigned 60 patients to 3 subgroups including 2 intervention groups: (1) mobile app and wearable device users (n=20), (2) mobile app-only users (n=20), and (3) controls (n=20). The primary outcome measure was weight change. Body weights before and after the trial were recorded and analyzed during clinic visits. Changes in OSA-related respiratory parameters such as respiratory disturbance, apnea-hypopnea, and oxygenation desaturation indexes and snoring comprised the secondary outcome and were analyzed for each participant. RESULTS: We collected the individual data for each group during the trial, specifically anthropometric measurement and laboratory test results for health outcomes, and the app usage logs for patient response were collected and analyzed. The body weight showed a significant reduction in the 2 intervention groups after intervention, and the mobile app-only group showed more weight loss compared with the controls (P=.01). There were no significant changes in sleep-related health outcomes. From a patient response point of view, the average daily step counts (8165 steps) from the app plus wearable group were significantly higher than those (6034 steps) from the app-only group because they collected step count data from different devices (P=.02). The average rate of data collection was not different in physical activity (P=.99), food intake (P=.98), sleep (P=.95), stress (P=.70), and weight (P=.90) in the app plus wearable and app-only groups, respectively. CONCLUSIONS: We tried to integrate PHR data that allow clinicians and patients to share lifelog data with the clinical workflow to support lifestyle interventions. Our results suggest that a PHR-based intervention may be successful in losing body weight and improvement in lifestyle behavior. TRIAL REGISTRATION: ClinicalTrials.gov NCT03200223; https://clinicaltrials.gov/ct2/show/NCT03200223 (Archived by WebCite at http://www.webcitation.org/74baZmnCX).

Effect of Lifestyle Modification Using a Smartphone Application on Obesity With Obstructive Sleep Apnea: A Short-term, Randomized Controlled Study.

OBJECTIVES: To investigate the short-term effects of a lifestyle modification intervention based on a mobile application (app) linked to a hospital electronic medical record (EMR) system on weight reduction and obstructive sleep apnea (OSA). METHODS: We prospectively enrolled adults (aged >20 years) with witnessed snoring or sleep apnea from a sleep clinic. The patients were randomized into the app user (n=24) and control (n=23) groups. The mobile app was designed to collect daily lifestyle data by wearing a wrist activity tracker and reporting dietary intake. A summary of the lifestyle data was displayed on the hospital EMR and was reviewed. In the control group, the lifestyle modification was performed as per usual practice. All participants underwent peripheral arterial tonometry (WatchPAT) and body mass index (BMI) measurements at baseline and after 4 weeks of follow-up. RESULTS: Age and BMI did not differ significantly between the two groups. While we observed a significant decrease in the BMI of both groups, the decrease was greater in the app user group (P <0.001). Apnea-hypopnea index, respiratory distress index, and oxygenation distress index did not change significantly in both groups. However, the proportion of sleep spent snoring at >45 dB was significantly improved in the app user group alone (P =0.014). In either group, among the participants with successful weight reduction, the apnea-hypopnea index was significantly reduced after 4 weeks (P =0.015). Multiple regression analyses showed that a reduction in the apnea-hypopnea index was significantly associated with BMI. CONCLUSION: Although a short-term lifestyle modification approach using a mobile app was more effective in achieving weight reduction, improvement in OSA was not so significant. Long-term efficacy of this mobile app should be evaluated in the future studies.

Inpatient satisfaction and usage patterns of personalized smart bedside station system for patient-centered service at a tertiary university hospital.

OBJECTIVE: Bedside stations, also known as bedside terminals, are in place to enhance the quality and experience of a hospital's healthcare service delivery. The purpose of this study was to identify information needs and overall satisfaction with the personalized patient bedside system, called Smart Bedside Station (SBS) system, embedded in a tertiary general university hospital. METHODS: End-user responses on the satisfaction survey and system usage logs of the SBS system were collected and analyzed. For the user opinion survey, 156 nurses and 1914 patients, their family members, or caregivers participated during the evaluation period of 2013 to 2014 in this study. All working nurses in the SBS-installed ward were answered the paper-based evaluation, for complete enumeration survey. Inpatients were voluntary participated to deliver the online questionnaire on the SBS menu. We also explored system log data including page calls and usage time from December 2013 to 2015. RESULTS: Regarding the relationship of overall satisfaction of the SBS with patient's characteristics, patient's education status and degree of familiarity with the smart device were statistically significant. From the analysis of system logs, Personalized My Menu(28.0%) was the most frequently used menu item (except for TV and Internet entertainment service use of 62.7%),it provides individual health information, such as laboratory test results, hospital fee check, message logs, daily medication information, and meal information. Next frequently used menus were information support(4.9%) which deliver hospital guide and health information and convenience service ordering(4.4%) such as meal order, bed sheet change. Satisfaction survey results and log data results show that the personalized service enhances the user satisfaction during hospital admission. CONCLUSIONS: Our post-implementation experience and subsequent assessment of SBS system is capable of providing insights into improving the hospital information system and service contents for patient-centered services. Further research should be directed at developing sophisticated patient-centered services as a communication tool between the hospital and the patient.

An Evaluation of Multiple Query Representations for the Relevance Judgments used to Build a Biomedical Test Collection.

OBJECTIVES: The purpose of this study is to validate a method that uses multiple queries to create a set of relevance judgments used to indicate which documents are pertinent to each query when forming a biomedical test collection. METHODS: The aspect query is the major concept of this research; it can represent every aspect of the original query with the same informational need. Manually generated aspect queries created by 15 recruited participants where run using the BM25 retrieval model in order to create aspect query based relevance sets (QRELS). In order to demonstrate the feasibility of these QRELSs, The results from a 2004 genomics track run supported by the National Institute of Standards and Technology (NIST) were used to compute the mean average precision (MAP) based on Text Retrieval Conference (TREC) QRELSs and aspect-QRELSs. The rank correlation was calculated using both Kendall's and Spearman's rank correlation methods. RESULTS: We experimentally verified the utility of the aspect query method by combining the top ranked documents retrieved by a number of multiple queries which ranked the order of the information. The retrieval system correlated highly with rankings based on human relevance judgments. CONCLUSIONS: Substantial results were shown with high correlations of up to 0.863 (p < 0.01) between the judgment-free gold standard based on the aspect queries and the human-judged gold standard supported by NIST. The results also demonstrate that the aspect query method can contribute in building test collections used for medical literature retrieval.