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Background: Previous studies revealed weight gain is an independent risk factor for hypertension. Hypertension in late middle age increases the risks of dementia and cardiovascular diseases. We aimed to analyze the difference in the risk of hypertension in the late middle age according to the change of body mass index (BMI) using nationwide cohort data of South Korea.Methods: We used 64,136 individuals from National Health Insurance Service-Health Screening Cohort in South Korea. The primary endpoint is newly diagnosed hypertension after observation start date, which was defined as the date of first examination after 50. BMI changes were measured between two consecutive health examinations before and after 50. Cox proportional hazard regression analysis was performed to evaluate the association between the change of BMI and the risk of hypertension.Results: During the mean follow-up period of 3.4±1.9 years, 8,676 individuals were diagnosed with hypertension. Both normal-to-obese group and obese-to-obese group had 19% higher risk for hypertension, and obese-to normal group had 10% higher risk for hypertension in late middle age, compared to normal-to-normal group.Conclusions: We confirmed becoming obese and maintaining obese increase the risk of hypertension in late middle age. Thus, clinicians need to assess obese patients regularly for the possibility of new onset hypertension and take preventive measure to reduce the risk by losing weights before late middle age.

Background: Sleep duration is associated with various health conditions, including chronic kidney disease. However, the association between sleep duration and decline in kidney function in the South Korean population remains unclear. We aimed to investigate the impact of sleep duration on kidney function decline in adult patients with hypertension.Methods: This cohort study was performed using data obtained from the Korean Genome and Epidemiology Study; 2,837 patients with hypertension who initially had normal kidney function were included. Glomerular filtration rates (GFRs) were estimated at baseline and throughout the 16 years of follow-up. A person was considered to have a decline in kidney function if they had a GFR 2. Sleep duration data were obtained through interviewer-assisted questionnaires. Sleep durations were classified as short (

Background: Since the era of “thyroid cancer epidemic,” many Korean academic societies discouraged the use of ultrasonography in healthy individuals and revised the Korean Thyroid Imaging Reporting and Data System to address the overscreening and overdiagnosis issues. This study aimed to evaluate the change in the diagnostic effectiveness of thyroid cancer screening over the last decade.Methods: This single-center, retrospective observational study analyzed the data of 125,962 thyroid nodules obtained during cancer screening at the health promotion center of Seoul National University Bundang Hospital from 2010 to 2019. Only 327 thyroid cancer cases pathologically confirmed by fine-needle aspiration (FNA) were included in the study. The strength of the association between the number of FNA and (1) the number of thyroid cancer diagnoses, (2) the positive predictive values (PPVs), and (3) the difference in PPV from the previous year were evaluated using Pearson’s correlation analysis.Results: The number of thyroid FNA biopsies as well as the thyroid cancer diagnoses decreased from 2010 to 2019 (166 to 48 [-71.1%] vs. 43 to 22 [-48.8%]). The PPV of FNA biopsies increased from 25.9% to 45.8% (+76.8%) and was negatively correlated with the number of FNA biopsies performed (R=-0.87, P

The new IFF mode, Mode-5 replacing Mode-4, has already been established by the US DoD for its allies and NATO forces. A IFF retrofit program for replacing the Mark XII Mode-4 with Mark XIIA Mode-5 is in progress in order to overcome the security limitations of the former in R.O.K. IFF certification test for the new mode, Mode-5, has been performed on medium range surface to air missile platform of the Korean armed forces for the first time in R.O.K and this is regarded as a monumental event in Korean defense industry. The present paper is a discussion on the procedures applied to the IFF system retrofit and integration, lessons learned from AIMS test with the US AIMS PO as observer. The minimum modification from the existing Mark XII Mode-4 to Mark XIIA Mode-5 and evolutional adaption from previous design including interoperability are required for the system retrofit. Letter of Certification was received from the US AIMS PO after the tests. The authors propose that the lessons learned and know-how acquired during the tests are managed by the R.O.K Government as the standard structure of a database. It is expected that the use of the database will reduce the developmental difficulties and risk, also increase efficiency in future developments and other projects.

Background: This study aimed to explore the relationship between fruit intake, changes in fruit intake, and changes in cardiometabolic factors in people with obesity. Methods: A total of 21,270 subjects (8,718 men, 12,552 women) aged 40 years and over, from the Korean-based Genome and Epidemiology Study, were followed up for an average of 4.4 years. Fruit intake was assessed using a food frequency questionnaire at baseline and the second follow-up. The beta coefficient and confidence intervals for changes in cardiometabolic risk factors according to fruit consumption were calculated using a linear regression model. Results: In men, the abdominal circumference decreased with changes in fruit intake (P=0.029). Fruit intake and increased fruit intake in men were associated with a lower systolic blood pressure (P=0.012 and P=0.02, respectively) and lower triglyceride levels (P=0.002 and P

BACKGROUND Despite the widespread adoption of electronic health records (EHR), extracting research data from EHRs depends on a time-consuming manual task. Robotic process automation (RPA) has the potential to solve this problem. OBJECTIVE Analyze the effectiveness of RPA in the abstraction of cancer registry data. METHODS The effect of RPA was analyzed by comparing the required duration of work before and after the implementation of RPA. Furthermore, an in-depth interview was conducted to investigate critical factors for implementing RPA for research data abstraction. RPA was implemented to extract 70 and 83 variables for gastric and breast cancer, respectively. RESULTS The data extraction time per patient was reduced by 74% and 30% for gastric and breast cancer, respectively. During in-depth interviews, participants who implemented RPA in the project emphasized the significance of explicitly defining the target process by identifying small, repeatable unit tasks. CONCLUSIONS In conclusion, RPA effectively extracted gastric and breast cancer registry data from EHRs, thus saving time.

ImportanceDexmedetomidine is a widely used sedative in the intensive care unit (ICU) and has unique properties that may be associated with reduced occurrence of new-onset atrial fibrillation (NOAF).ObjectiveTo investigate whether the use of dexmedetomidine is associated with the incidence of NOAF in patients with critical illness.Design, Setting, and ParticipantsThis propensity score–matched cohort study was conducted using the Medical Information Mart for Intensive Care-IV database, which includes records of patients admitted to the ICU at Beth Israel Deaconess Medical Center in Boston dating 2008 through 2019. Included patients were those aged 18 years or older and hospitalized in the ICU. Data were analyzed from March through May 2022.ExposurePatients were divided into 2 groups according to dexmedetomidine exposure: those who received dexmedetomidine within 48 hours after ICU admission (dexmedetomidine group) and those who never received dexmedetomidine (no dexmedetomidine group).Main Outcomes and MeasuresThe primary outcome was the occurrence of NOAF within 7 days of ICU admission, as defined by the nurse-recorded rhythm status. Secondary outcomes were ICU length of stay, hospital length of stay, and in-hospital mortality.ResultsThis study included 22 237 patients before matching (mean [SD] age, 65.9 [16.7] years; 12 350 male patients [55.5%]). After 1:3 propensity score matching, the cohort included 8015 patients (mean [SD] age, 61.0 [17.1] years; 5240 males [65.4%]), among whom 2106 and 5909 patients were in the dexmedetomidine and no dexmedetomidine groups, respectively. Use of dexmedetomidine was associated with a decreased risk of NOAF (371 patients [17.6%] vs 1323 patients [22.4%]; hazard ratio, 0.80; 95% CI, 0.71-0.90). Although patients in the dexmedetomidine group had longer median (IQR) length of stays in the ICU (4.0 [2.7-6.9] days vs 3.5 [2.5-5.9] days; P P Conclusions and RelevanceThis study found that dexmedetomidine was associated with decreased risk of NOAF in patients with critical illness, suggesting that it may be necessary and warranted to evaluate this association in future clinical trials.

BackgroundNeurodegeneration is associated with pathogenesis of both multiple sclerosis (MS) and neuromyelitis optica (NMOSD). Parkinson’s disease (PD) is a representative neurodegenerative disease, however, whether MS or NMOSD is associated with risk of PD is not known.MethodsMS and NMOSD cohorts were collected from the Korean National Health Insurance Service between 1 January 2010 and 31 December 2017, using International Classification of Diseases 10th revision diagnosis codes and information in the Rare Intractable Disease management programme. The PD incidence rate that occurred after a 1-year lag period was calculated and compared with that of a control cohort matched for age, sex, hypertension, diabetes and dyslipidaemia in a 1:5 ratio.ResultsThe incidence rates of PD in patients with MS and NMOSD were 3.38 and 1.27 per 1000 person-years, respectively, and were higher than that of their matched control groups. The adjusted HR of PD was 7.73 (95% CI, 3.87 to 15.47) in patients with MS and 2.61 (95% CI, 1.13 to 6.02) in patients with NMOSD compared with matched controls. In both patients with MS and NMOSD, there were no significant differences in relative risk when stratified by sex, age, diabetes, hypertension and dyslipidaemia.ConclusionThe PD risk was higher in patients with MS and NMOSD compared with healthy controls and was particularly high in patients with MS. Further investigations should be performed to determine the pathophysiology and occurrence of PD in patients with MS and NMOSD.

BACKGROUND No studies have been conducted on smart devices that provide integrated nursing for patients' urinary & bowel elimination needs. OBJECTIVE To evaluate the feasibility of a smart excretion care system tethered to Electronic Medical Record (EMR) in a tertiary hospital and community care setting and discuss the expectations of the reducing burden of nursing care. METHODS Focus group interviews were conducted based on the Consolidated Criteria for Reporting Qualitative Research Guidelines. The interviews were conducted in March, 2021, involving 67 participants who had worked at Seoul National University Bundang Hospital for more than one year and had experience assisting patients with excretion care. Data were collected using both purposive and snowball sampling methods. RESULTS This research found four themes relevant to the Smart Excretion Care System: expectations of reducing burdens of nursing care, applicable indications (by departments and diseases), preferred features/functions, and expected benefits of using the Smart Excretion Care system at the clinical facilities. Nurses from the Comprehensive Nursing Service wards were found to have the highest burden of excretion care. It was a common opinion that the Smart Excretion Care System would be very useful in the Intensive Care Unit, and it should be applied first to patients with stroke or dementia. CONCLUSIONS Excretion care is one of the most bothersome tasks for nurses that increase their workload. The further development of the Smart Excretion Care System, as a digital health intervention, can help improve nurses’work efficiency and reduce their burden as well as expand to caregivers and guardians.

BACKGROUND Nationwide population-based cohorts provide a new opportunity to build automated risk prediction models at the patient level, and claim data are one of the more useful resources to this end. To avoid unnecessary diagnostic intervention after cancer screening tests, patient-level prediction models should be developed. OBJECTIVE We aimed to develop cancer prediction models using nationwide claim databases with machine learning algorithms, which are explainable and easily applicable in real-world environments. METHODS As source data, we used the Korean National Insurance System Database. Every Korean in ≥40 years old undergoes a national health checkup every 2 years. We gathered all variables from the database including demographic information, basic laboratory values, anthropometric values, and previous medical history. We applied conventional logistic regression methods, light gradient boosting methods, neural networks, survival analysis, and one-class embedding classifier methods to effectively analyze high dimension data based on deep learning–based anomaly detection. Performance was measured with area under the curve and area under precision recall curve. We validated our models externally with a health checkup database from a tertiary hospital. RESULTS The one-class embedding classifier model received the highest area under the curve scores with values of 0.868, 0.849, 0.798, 0.746, 0.800, 0.749, and 0.790 for liver, lung, colorectal, pancreatic, gastric, breast, and cervical cancers, respectively. For area under precision recall curve, the light gradient boosting models had the highest score with values of 0.383, 0.401, 0.387, 0.300, 0.385, 0.357, and 0.296 for liver, lung, colorectal, pancreatic, gastric, breast, and cervical cancers, respectively. CONCLUSIONS Our results show that it is possible to easily develop applicable cancer prediction models with nationwide claim data using machine learning. The 7 models showed acceptable performances and explainability, and thus can be distributed easily in real-world environments.

Background Despite the rapid adoption of electronic health records (EHRs) resulting from the reimbursement program of the US government, EHR adoption in behavioral hospitals is still slow, and there remains a lack of evidence regarding barriers and facilitators to the implementation of mental health care EHRs. Objective The aim of this study is to analyze the experience of mental health professionals to explore the perceived barriers, facilitators, and critical ideas influencing the implementation and usability of a mental health care EHR. Methods In this phenomenological qualitative study, we interviewed physicians, nurses, pharmacists, mental health clinicians, and administrative professionals separately at 4 behavioral hospitals in the United States. We conducted semistructured interviews (N=43) from behavioral hospitals involved in the adoption of the mental health care EHR. Purposeful sampling was used to maximize the diversity. Transcripts were coded and analyzed for emergent domains. An exploratory data analysis was conducted. Results Content analyses revealed 7 barriers and 4 facilitators. The most important barriers to implementing the mental health care EHR were the low levels of computer proficiency among nurses, complexity of the system, alert fatigue, and resistance because of legacy systems. This led to poor usability, low acceptability, and distrust toward the system. The major facilitators to implementing the mental health care EHR were well-executed training programs, improved productivity, better quality of care, and the good usability of the mental health care EHR. Conclusions Health care professionals expected to enhance their work productivity and interprofessional collaboration by introducing the mental health care EHR. Routine education for end users is an essential starting point for the successful implementation of mental health care EHR electronic decision support. When adopting the mental health care EHR, managers need to focus on common practices in behavioral hospitals, such as documenting structured data in their organizations and adopting a seamless workflow of mental health care into the system.

A prompt severity assessment model of patients confirmed for having infectious diseases could enable efficient diagnosis while alleviating burden on the medical system. This study aims to develop a SARS-CoV-2 severity assessment model and establish a medical system that allows patients to check the severity of their cases and informs them to visit the appropriate clinic center based on past treatment data of other patients with similar severity levels. This paper provides the development processes of a severity assessment model using machine learning techniques and its application on SARS-CoV-2 patients. The proposed model is trained on a nationwide dataset provided by a Korean government agency and only requires patients' basic personal data, allowing them to judge the severity of their own cases. After modeling, the boosting-based decision tree model was selected as the classifier while mortality rate was interpreted as the probability score. The dataset was collected from all Korean citizens who were confirmed with COVID-19 between February, 2020 and July, 2021. The experiments achieved high model performance with an approximate precision of $0{\cdot}923$ and AUROC score of $0{\cdot}950$ [$95$% Tolerance Interval $0{\cdot}940$-$0{\cdot}958$, $95$% Confidence Interval $0{\cdot}949$-$0{\cdot}950$]. Moreover, our experiments identified the most important variables affecting the severity in the model via sensitivity analysis. The prompt severity assessment model for managing infectious people has been attained through using a nationwide dataset. It has demonstrated its superior performance by surpassing that of conventional risk assessments. With the model's high performance and easily accessible features, the triage algorithm is expected to be particularly useful when patients monitor their health status by themselves through smartphone applications., Comment: To appear in Infectious Diseases and Therapy

Background Despite the fact that the adoption rate of electronic health records has increased dramatically among high-income nations, it is still difficult to properly disseminate personal health records. Token economy, through blockchain smart contracts, can better distribute personal health records by providing incentives to patients. However, there have been very few studies regarding the particular factors that should be considered when designing incentive mechanisms in blockchain. Objective The aim of this paper is to provide 2 new mathematical models of token economy in real-world scenarios on health care blockchain platforms. Methods First, roles were set for the health care blockchain platform and its token flow. Second, 2 scenarios were introduced: collecting life-log data for an incentive program at a life insurance company to motivate customers to exercise more and recruiting participants for clinical trials of anticancer drugs. In our 2 scenarios, we assumed that there were 3 stakeholders: participants, data recipients (companies), and data providers (health care organizations). We also assumed that the incentives are initially paid out to participants by data recipients, who are focused on minimizing economic and time costs by adapting mechanism design. This concept can be seen as a part of game theory, since the willingness-to-pay of data recipients is important in maintaining the blockchain token economy. In both scenarios, the recruiting company can change the expected recruitment time and number of participants. Suppose a company considers the recruitment time to be more important than the number of participants and rewards. In that case, the company can increase the time weight and adjust cost. When the reward parameter is fixed, the corresponding expected recruitment time can be obtained. Among the reward and time pairs, the pair that minimizes the company’s cost was chosen. Finally, the optimized results were compared with the simulations and analyzed accordingly. Results To minimize the company’s costs, reward–time pairs were first collected. It was observed that the expected recruitment time decreased as rewards grew, while the rewards decreased as time cost grew. Therefore, the cost was represented by a convex curve, which made it possible to obtain a minimum—an optimal point—for both scenarios. Through sensitivity analysis, we observed that, as the time weight increased, the optimized reward increased, while the optimized time decreased. Moreover, as the number of participants increased, the optimization reward and time also increased. Conclusions In this study, we were able to model the incentive mechanism of blockchain based on a mechanism design that recruits participants through a health care blockchain platform. This study presents a basic approach to incentive modeling in personal health records, demonstrating how health care organizations and funding companies can motivate one another to join the platform.

BACKGROUND Despite the fact that the adoption rate of electronic health records has increased dramatically among high-income nations, it is still difficult to properly disseminate personal health records. Token economy, through blockchain smart contracts, can better distribute personal health records by providing incentives to patients. However, there have been very few studies regarding the particular factors that should be considered when designing incentive mechanisms in blockchain. OBJECTIVE The aim of this paper is to provide 2 new mathematical models of token economy in real-world scenarios on health care blockchain platforms. METHODS First, roles were set for the health care blockchain platform and its token flow. Second, 2 scenarios were introduced: collecting life-log data for an incentive program at a life insurance company to motivate customers to exercise more and recruiting participants for clinical trials of anticancer drugs. In our 2 scenarios, we assumed that there were 3 stakeholders: participants, data recipients (companies), and data providers (health care organizations). We also assumed that the incentives are initially paid out to participants by data recipients, who are focused on minimizing economic and time costs by adapting mechanism design. This concept can be seen as a part of game theory, since the willingness-to-pay of data recipients is important in maintaining the blockchain token economy. In both scenarios, the recruiting company can change the expected recruitment time and number of participants. Suppose a company considers the recruitment time to be more important than the number of participants and rewards. In that case, the company can increase the time weight and adjust cost. When the reward parameter is fixed, the corresponding expected recruitment time can be obtained. Among the reward and time pairs, the pair that minimizes the company’s cost was chosen. Finally, the optimized results were compared with the simulations and analyzed accordingly. RESULTS To minimize the company’s costs, reward–time pairs were first collected. It was observed that the expected recruitment time decreased as rewards grew, while the rewards decreased as time cost grew. Therefore, the cost was represented by a convex curve, which made it possible to obtain a minimum—an optimal point—for both scenarios. Through sensitivity analysis, we observed that, as the time weight increased, the optimized reward increased, while the optimized time decreased. Moreover, as the number of participants increased, the optimization reward and time also increased. CONCLUSIONS In this study, we were able to model the incentive mechanism of blockchain based on a mechanism design that recruits participants through a health care blockchain platform. This study presents a basic approach to incentive modeling in personal health records, demonstrating how health care organizations and funding companies can motivate one another to join the platform.

Background Although the electronic health record system adoption rate has reached 96% in the United States, implementation and usage of health information exchange (HIE) is still lagging behind. Blockchain has come into the spotlight as a technology to solve this problem. However, there have been no studies assessing the perspectives of different stakeholders regarding blockchain-based patient-centered HIE. Objective The objective of this study was to analyze the awareness among patients, health care professionals, and information technology developers toward blockchain-based HIE, and compare their different perspectives related to the platform using a qualitative research methodology. Methods In this qualitative study, we applied grounded theory and the Promoting Action on Research Implementation in the Health Service (PARiHS) framework. We interviewed 7 patients, 7 physicians, and 7 developers, for a total of 21 interviewees. Results Regarding the leakage of health information, the patient group did not have concerns in contrast to the physician and developer groups. Physicians were particularly concerned about the fact that errors in the data cannot be easily fixed due to the nature of blockchain technology. Patients were not against the idea of providing information for clinical trials or research institutions. They wished to be provided with the results of clinical research rather than being compensated for providing data. The developers emphasized that blockchain must be technically mature before it can be applied to the health care scene, and standards of medical information to be exchanged must first be established. Conclusions The three groups’ perceptions of blockchain were generally positive about the idea of patients having the control of sharing their own health information. However, they were skeptical about the cooperation among various institutions and implementation for data standardization in the establishment process, in addition to how the service will be employed in practice. Taking these factors into consideration during planning, development, and operation of a platform will contribute to establishing practical treatment plans and tracking in a more convenient manner for both patients and physicians. Furthermore, it will help expand the related research and health management industry based on blockchain.

BACKGROUND The US Health Information Technology for Economic and Clinical Health Act of 2009, which was intended to stimulate the use of electronic health record (EHR) systems, has been amended to cover the behavioral health sector. Consequently, there is an increased need for research on the adoption of behavioral EHR systems by healthcare professionals. Various quantitative studies based on the unified theory of acceptance and use of technology model and technology acceptance model have been conducted in the general medical sectors, but few studies have been conducted in the behavioral sector and they have all been interview-based qualitative studies. OBJECTIVE To evaluate the adoption and implementation of a behavioral EHR system for behavioral clinical professionals through a modified clinical adoption (CA) research model quantitative study. METHODS We modified the existing CA framework to be suitable for evaluating the adoption of the EHR system by behavioral clinical professionals. The existing CA framework did not present questionnaire items. Accordingly, we designed a questionnaire with items that fit into the dimensions of the CA framework and it was verified through the review of behavioral clinical professionals and a pre-survey. The full-scale survey was administered in 5 US behavioral hospitals. The data were analyzed using a structural equation analysis. Additionally, open-ended question responses were qualitatively analyzed. RESULTS We derived a total of 7 dimensions, excluding dimensions that were evaluated as inappropriate for behavioral clinical professionals to respond. In addition, for 2 dimensions, 2 sub-dimensions were classified. A total of 409 behavioral clinical experts from 5 hospitals were surveyed. The ease of use and organizational support significantly influenced the use of the behavioral EHR system. Although the results were not significant, information quality (path coefficient=1.19, P>.05) and service quality (path coefficient=.085, P>.05) tended to influence the ease of use of the system. And Ease of Use (path coefficient=.253, P CONCLUSIONS This study designed a model for evaluating behavioral EHR adoption and conducted a quantitative study to derive the factors associated with the successful introduction of an EHR system in a behavioral environment. CLINICALTRIAL The study was approved by the Institutional Review Board of Seoul National University Bundang Hospital (SNUBH) (IRB No.: B-1904-534-301).

Aims We evaluated the clinical usefulness of a new unified glucose–insulin–potassium (GIK) regimen in a general surgical department. Methods Surgical patients treated under the previous diverse GIK regimens (September 2016 to August 2017) and the new unified GIK regimen (September 2017 to August 2018) were identified in records of the Clinical Data Warehouse of Seoul National University Bundang Hospital. Serial and area under the curve (AUC) glucose levels, and percentages of time within the target glucose levels were compared in propensity score matched patients in the diverse GIK regimen and in the unified GIK regimen (n = 227 in each group). Results The AUC of glucose at 6 h and 12 h was lower under the unified GIK regimen than the diverse GIK regimen. The percentage of target glucose levels was higher in the unified GIK regimen compared to the diverse GIK regimen (81.5% vs. 75.0%, P = 0.026), but the occurrence of hypoglycaemia did not differ significantly between groups. Conclusions The unified GIK regimen was more effective than the diverse GIK regimen for glycaemic control and did not increase the number of patients developing hypoglycaemia. This validated written GIK regimen can be safely used in a general surgical department.

Background The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend. Objective The aim of this study was to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning to respond promptly to the dynamic situation of the outbreak and proactively minimize damage. Methods In this study, we investigated a mathematical model with time-dependent parameters via deep learning based on forward-inverse problems. We used data from the Korea Centers for Disease Control and Prevention (KCDC) and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Because the data consist of confirmed, recovered, and deceased cases, we selected the susceptible-infected-recovered (SIR) model and found approximated solutions as well as model parameters. Specifically, we applied fully connected neural networks to the solutions and parameters and designed suitable loss functions. Results We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional Runge-Kutta fourth order model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from the KCDC, the Korean government, and news media. We also crossvalidated our model using data from the CSSE for Italy, Sweden, and the United States. Conclusions The methodology and new model of this study could be employed for short-term prediction of COVID-19, which could help the government prepare for a new outbreak. In addition, from the perspective of measuring medical resources, our model has powerful strength because it assumes all the parameters as time-dependent, which reflects the exact status of viral spread.

Background Electronic health records (EHRs) are transforming and revolutionizing the healthcare industry. However, whereas developed countries have a high EHR penetration rate, adoption of EHRs in developing countries is lagging behind. Recently, the Korean and Russian governments have been pursuing economic cooperation in the Russian Far East. Thus, since 2009, Russia’s EHR market and healthcare system have been maturing in tandem. Objective To qualitatively investigate and analyze the current status of EHRs in the Russian Far East and derive implementation plans for nationwide EHRs. Methods A qualitative analysis based on semi-structured interviews with healthcare professionals and administrative officers in the Russian Far East was conducted to illuminate the current status of EHRs and to collect various perspectives on barriers and facilitators to implementation. Results The analysis revealed six major barriers and five major facilitators for implementation of nationwide EHRs in the Russian Far East. The barriers include lack of communications, an insufficient system development environment, poor adoption of standard terminology, poor infrastructure, resistance to a new system, and poor functionality. Facilitators include strategic government planning, centrally managed systems, health information exchange, willingness to use new functions, and well-established work processes. Conclusions This study’s results, along with the experiences of developed countries that have already successfully introduced EHRs, will help support successful introduction of EHRs in the Russian Far East.

Background Adverse drug events (ADEs) resulting from medication error are some of the most common causes of iatrogenic injuries in hospitals. With the appropriate use of medication, ADEs can be prevented and ameliorated. Efforts to reduce medication errors and prevent ADEs have been made by implementing a medication decision support system (MDSS) in electronic health records (EHRs). However, physicians tend to override most MDSS alerts. Objective In order to improve MDSS functionality, we must understand what factors users consider essential for the successful implementation of an MDSS into their clinical setting. This study followed the implementation process for an MDSS within a comprehensive EHR system and analyzed the relevant barriers and facilitators. Methods A mixed research methodology was adopted. Data from a structured survey and 15 in-depth interviews were integrated. Structural equation modeling was conducted for quantitative analysis of factors related to user adoption of MDSS. Qualitative analysis based on semistructured interviews with physicians was conducted to collect various opinions on MDSS implementation. Results Quantitative analysis revealed that physicians’ expectations regarding ease of use and performance improvement are crucial. Qualitative analysis identified four significant barriers to MDSS implementation: alert fatigue, lack of accuracy, poor user interface design, and lack of customizability. Conclusions This study revealed barriers and facilitators to the implementation of MDSS. The findings can be applied to upgrade MDSS in the future.

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.

BACKGROUND The COVID-19 pandemic has caused major disruptions worldwide since March 2020. The experience of the 1918 influenza pandemic demonstrated that decreases in the infection rates of COVID-19 do not guarantee continuity of the trend. OBJECTIVE The aim of this study was to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning to respond promptly to the dynamic situation of the outbreak and proactively minimize damage. METHODS In this study, we investigated a mathematical model with time-dependent parameters via deep learning based on forward-inverse problems. We used data from the Korea Centers for Disease Control and Prevention (KCDC) and the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Because the data consist of confirmed, recovered, and deceased cases, we selected the susceptible-infected-recovered (SIR) model and found approximated solutions as well as model parameters. Specifically, we applied fully connected neural networks to the solutions and parameters and designed suitable loss functions. RESULTS We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional Runge-Kutta fourth order model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from the KCDC, the Korean government, and news media. We also crossvalidated our model using data from the CSSE for Italy, Sweden, and the United States. CONCLUSIONS The methodology and new model of this study could be employed for short-term prediction of COVID-19, which could help the government prepare for a new outbreak. In addition, from the perspective of measuring medical resources, our model has powerful strength because it assumes all the parameters as time-dependent, which reflects the exact status of viral spread.

Mathematical modeling is a process aimed at finding a mathematical description of a system and translating it into a relational expression. When a system is continuously changing over time (e.g., infectious diseases) differential equations, which may include parameters, are used for modeling the system. The process of finding those parameters that best fit the given data from the system is called an inverse problem. This study aims at analyzing the novel coronavirus infection (COVID-19) spread in South Korea using the susceptible-infected-recovered (SIR) model. We collect the data from Korea Centers for Disease Control & Prevention (KCDC). We assume that each parameter in the SIR model is a function of time so that we can compute important parameters, such as the basic reproduction number (R0), more delicately. Using neural networks, we propose a method to find the best time-varying parameters and the solution for the model simultaneously. Moreover, using time-dependent parameters, we find that traditional numerical algorithms, such as the Runge-Kutta methods, can successfully approximate the SIR model while fitting the COVID-19 data, thus modeling the propagation patterns of COVID-19 more precisely.