Status of MyHealthWay and Suggestions for Widespread Implementation, Emphasizing the Utilization and Practical Use of Personal Medical Data

OBJECTIVES

In the Fourth Industrial Revolution, there is a focus on managing diverse medical data to improve healthcare and prevent disease. The challenges include tracking detailed medical records across multiple institutions and the necessity of linking domestic public medical entities for efficient data sharing. This study explores MyHealthWay, a Korean healthcare platform designed to facilitate the integration and transfer of medical data from various sources, examining its development, importance, and legal implications.

METHODS

To evaluate the management status and utilization of MyHealthWay, we analyzed data types, security, legal issues, domestic versus international issues, and infrastructure. Additionally, we discussed challenges such as resource and infrastructure constraints, regulatory hurdles, and future considerations for data management.

RESULTS

The secure sharing of medical information via MyHealthWay can reduce the distance between patients and healthcare facilities, fostering personalized care and self-management of health. However, this approach faces legal challenges, particularly relating to data standardization and access to personal health information. Legal challenges in data standardization and access, particularly for secondary uses such as research, necessitate improved regulations. There is a crucial need for detailed governmental guidelines and clear data ownership standards at institutional levels.

CONCLUSIONS

This report highlights the role of Korea's MyHealthWay, which was launched in 2023, in transforming healthcare through systematic data integration. Challenges include data privacy and legal complexities, and there is a need for data standardization and individual empowerment in health data management within a systematic medical big data framework.

Deep learning approach to detection of colonoscopic information from unstructured reports

BACKGROUND

Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text. The information embedded in the reports can be used for various purposes, including colorectal cancer risk prediction, follow-up recommendation, and quality measurement. However, the availability and accessibility of unstructured text data are still insufficient despite the large amounts of accumulated data. We aimed to develop and apply deep learning-based natural language processing (NLP) methods to detect colonoscopic information.

METHODS

This study applied several deep learning-based NLP models to colonoscopy reports. Approximately 280,668 colonoscopy reports were extracted from the clinical data warehouse of Samsung Medical Center. For 5,000 reports, procedural information and colonoscopic findings were manually annotated with 17 labels. We compared the long short-term memory (LSTM) and BioBERT model to select the one with the best performance for colonoscopy reports, which was the bidirectional LSTM with conditional random fields. Then, we applied pre-trained word embedding using large unlabeled data (280,668 reports) to the selected model.

RESULTS

The NLP model with pre-trained word embedding performed better for most labels than the model with one-hot encoding. The F1 scores for colonoscopic findings were: 0.9564 for lesions, 0.9722 for locations, 0.9809 for shapes, 0.9720 for colors, 0.9862 for sizes, and 0.9717 for numbers.

CONCLUSIONS

This study applied deep learning-based clinical NLP models to extract meaningful information from colonoscopy reports. The method in this study achieved promising results that demonstrate it can be applied to various practical purposes.

Standard Document Development for Health Information Exchange in Korea

BACKGROUND

Health information exchange (HIE) allows healthcare providers to access a patient's medical information to improve patient care continuity. The standardized data realize the HIE values. Since the Health Level 7 Clinical Document Architecture (CDA) is flexible, implementation guides (IG) are needed for use cases. Although many CDA IGs have been developed, they did not describe how these CDA IGs were developed. A national CDA IG that meets the local requirements is demanded since the data differs according to the digital divide and social-cultural background of the country that wants to establish HIE. Due to their localized contents, other countries cannot directly adopt the published CDA IGs.

OBJECTIVES

We developed the national CDA IG, namely, Korean (K)-CDA IG that meets the local requirement, including reusable structured templates, value sets, and object identifiers (OIDs). We present a detailed description of the development process and the technical methods of the national CDA IG in the Korean context.

METHODS

The K-CDA IG was developed in the following stages: analysis, development, and evaluation. First, we investigated the health information environment and electronic health record (EHR) systems and conducted a gap analysis with published CDA IGs. Second, a templated CDA approach was taken for designing modular. Lastly, we consulted a technical advisory group for comments on the validity of the K-CDA IG.

RESULTS

A total of 35 CDA templates were developed. We improved 28 value sets of which 13 were Korea specific and 15 were based on the ones used in other IGs, and made a set of rules to establish the OID structure.

CONCLUSION

We presented the development process and the technical specifications of K-CDA IG. We explored how the results can be used as interoperability criteria in the national EHR systems certification program. Finally, we provided recommendations that could guide other entities planning their HIE programs.

Development of eClaim system for private indemnity health insurance in South Korea: Compatibility and interoperability

South Korea has the mandatory National Health Insurance (NHI) and supplemental Private Indemnity Health Insurance (PIHI). According to the Financial Supervisory Service, the share of the population with PIHI increased to 66% in 2018 due to the financial burden. However, since the traditional PIHI claim workflow is based on the paper attachment method, it is a big burden to every stakeholder and limits the usability and accessibility of the claims data. To improve the traditional PIHI claim workflow, we developed the electronic claim (eClaim) service for the PIHI in Korea. We also applied the HL7^® (Health Level Seven) FHIR^® (Fast Healthcare Interoperability Resources) standard to ensure interoperability of the claims data. The proposed eClaim Service has been launched in 2017. It has been increased from 8155 in the first half of 2018 to 114,087 in the second half of 2020. Currently, 60 healthcare providers and 22 payers participated in this service. In this study, we proposed an eClaim workflow and service to improve the legacy system. The proposed method can be helpful to other entities planning for their own health insurance system and also applied to various practical purposes including value-based care, automated claim review, and clinical research.

Fast Healthcare Interoperability Resources (FHIR)-Based Quality Information Exchange for Clinical Next-Generation Sequencing Genomic Testing: Implementation Study

Background

Next-generation sequencing (NGS) technology has been rapidly adopted in clinical practice, with the scope extended to early diagnosis, disease classification, and treatment planning. As the number of requests for NGS genomic testing increases, substantial efforts have been made to deliver the testing results clearly and unambiguously. For the legitimacy of clinical NGS genomic testing, quality information from the process of producing genomic data should be included within the results. However, most reports provide insufficient quality information to confirm the reliability of genomic testing owing to the complexity of the NGS process.

Objective

The goal of this study was to develop a Fast Healthcare Interoperability Resources (FHIR)–based web app, NGS Quality Reporting (NGS-QR), to report and manage the quality of the information obtained from clinical NGS genomic tests.

Methods

We defined data elements for the exchange of quality information from clinical NGS genomic tests, and profiled a FHIR genomic resource to enable information exchange in a standardized format. We then developed the FHIR-based web app and FHIR server to exchange quality information, along with statistical analysis tools implemented with the R Shiny server.

Results

Approximately 1000 experimental data entries collected from the targeted sequencing pipeline CancerSCAN designed by Samsung Medical Center were used to validate implementation of the NGS-QR app using real-world data. The user can share the quality information of NGS genomic testing and verify the quality status of individual samples in the overall distribution.

Conclusions

This study successfully demonstrated how quality information of clinical NGS genomic testing can be exchanged in a standardized format. As the demand for NGS genomic testing in clinical settings increases and genomic data accumulate, quality information can be used as reference material to improve the quality of testing. This app could also motivate laboratories to perform diagnostic tests to provide high-quality genomic data.

Developing a Standardization Algorithm for Categorical Laboratory Tests for Clinical Big Data Research: Retrospective Study

Background

Data standardization is essential in electronic health records (EHRs) for both clinical practice and retrospective research. However, it is still not easy to standardize EHR data because of nonidentical duplicates, typographical errors, or inconsistencies. To overcome this drawback, standardization efforts have been undertaken for collecting data in a standardized format as well as for curating the stored data in EHRs. To perform clinical big data research, the stored data in EHR should be standardized, starting from laboratory results, given their importance. However, most of the previous efforts have been based on labor-intensive manual methods.

Objective

We aimed to develop an automatic standardization method for eliminating the noises of categorical laboratory data, grouping, and mapping of cleaned data using standard terminology.

Methods

We developed a method called standardization algorithm for laboratory test–categorical result (SALT-C) that can process categorical laboratory data, such as pos +, 250 4+ (urinalysis results), and reddish (urinalysis color results). SALT-C consists of five steps. First, it applies data cleaning rules to categorical laboratory data. Second, it categorizes the cleaned data into 5 predefined groups (urine color, urine dipstick, blood type, presence-finding, and pathogenesis tests). Third, all data in each group are vectorized. Fourth, similarity is calculated between the vectors of data and those of each value in the predefined value sets. Finally, the value closest to the data is assigned.

Results

The performance of SALT-C was validated using 59,213,696 data points (167,938 unique values) generated over 23 years from a tertiary hospital. Apart from the data whose original meaning could not be interpreted correctly (eg, ** and _^), SALT-C mapped unique raw data to the correct reference value for each group with accuracy of 97.6% (123/126; urine color tests), 97.5% (198/203; (urine dipstick tests), 95% (53/56; blood type tests), 99.68% (162,291/162,805; presence-finding tests), and 99.61% (4643/4661; pathogenesis tests).

Conclusions

The proposed SALT-C successfully standardized the categorical laboratory test results with high reliability. SALT-C can be beneficial for clinical big data research by reducing laborious manual standardization efforts.

Clinical Decision Support Functions and Digitalization of Clinical Documents of Electronic Medical Record Systems

Objectives

The objective of this study was to investigate the clinical decision support (CDS) functions and digitalization of clinical documents of Electronic Medical Record (EMR) systems in Korea. This exploratory study was conducted focusing on current status of EMR systems.

Methods

This study used a nationwide survey on EMR systems conducted from July 25, 2018 to September 30, 2018 in Korea. The unit of analysis was hospitals. Respondents of the survey were mainly medical recorders or staff members in departments of health insurance claims or information technology. This study analyzed data acquired from 132 hospitals that participated in the survey.

Results

This study found that approximately 80% of clinical documents were digitalized in both general and small hospitals. The percentages of general and small hospitals with 100% paperless medical charts were 33.7% and 38.2%, respectively. The EMR systems of general hospitals are more likely to have CDS functions of warnings regarding drug dosage, reminders of clinical schedules, and clinical guidelines compared to those of small hospitals; this difference was statistically significant. For the lists of digitalized clinical documents, almost 93% of EMR systems in general hospitals have the inpatient progress note, operation records, and discharge summary notes digitalized.

Conclusions

EMRs are becoming increasingly important. This study found that the functions and digital documentation of EMR systems still have a large gap, which should be improved and made more sophisticated. We hope that the results of this study will contribute to the development of more sophisticated EMR systems.

Association between Electronic Medical Record System Adoption and Healthcare Information Technology Infrastructure

Objectives

The objective of this study was to investigate the relationship between the level of Electronic Medical Record (EMR) system adoption and healthcare information technology (IT) infrastructure.

Methods

Both survey and various healthcare administrative datasets in Korea were used. The survey was conducted during the period from June 13 to September 25, 2017. The chief information officers of hospitals were respondents. Among them, 257 general hospitals and 273 small hospitals were analyzed. A logistic regression analysis was conducted using the SAS program.

Results

The odds of having full EMR systems in general hospitals statistically significantly increased as the number of IT department staff members increased (odds ratio [OR] = 1.058, confidence interval [CI], 1.003–1.115; p = 0.038). The odds of having full EMR systems was significantly higher for small hospitals that had an IT department than those of small hospitals with no IT department (OR = 1.325; CI, 1.150–1.525; p < 0.001). Full EMR system adoption had a positive relationship with IT infrastructure in both general hospitals and small hospitals, which was statistically significant in small hospitals. The odds of having full EMR systems for small hospitals increased as IT infrastructure increased after controlling the covariates (OR = 1.527; CI, 1.317–4.135; p = 0.004).

Conclusions

This study verified that full EMR adoption was closely associated with IT infrastructure, such as organizational structure, human resources, and various IT subsystems. This finding suggests that political support related to these areas is indeed necessary for the fast dispersion of EMR systems into the healthcare industry.

Non-alcoholic fatty liver disease and progression of coronary artery calcium score: a retrospective cohort study

BACKGROUND AND AIM

Non-alcoholic fatty liver disease (NAFLD), a hepatic manifestation of the metabolic syndrome, was associated with subclinical atherosclerosis in many cross-sectional studies, but the prospective association between NAFLD and the progression of atherosclerosis has not been evaluated. This study was conducted to evaluate the association between NAFLD and the progression of coronary atherosclerosis.

METHODS

This retrospective cohort study included 4731 adult men and women with no history of cardiovascular disease (CVD), liver disease or cancer at baseline who participated in a repeated regular health screening examination between 2004 and 2013. Fatty liver was diagnosed by ultrasound based on standard criteria, including parenchymal brightness, liver-to-kidney contrast, deep beam attenuation and bright vessel walls. Progression of coronary artery calcium (CAC) scores was measured using multidetector CT scanners.

RESULTS

The average duration of follow-up was 3.9 years. During follow-up, the annual rate of CAC progression in participants with and without NAFLD were 22% (95% CI 20% to 23%) and 17% (16% to 18%), respectively (p<0.001). The multivariable ratio of progression rates comparing participants with NAFLD with those without NAFLD was 1.04 (1.02 to 1.05; p<0.001). The association between NAFLD and CAC progression was similar in most subgroups analysed, including in participants with CAC 0 and in those with CAC >0 at baseline.

CONCLUSIONS

In this large cohort study of adult men and women with no history of CVD, NAFLD was significantly associated with the development of CAC independent of cardiovascular and metabolic risk factors. NAFLD may play a pathophysiological role in atherosclerosis development and may be useful to identify subjects with a higher risk of subclinical disease progression.

Persistent Nonalcoholic Fatty Liver Disease Increases Risk for Carotid Atherosclerosis

BACKGROUND & AIMS

Nonalcoholic fatty liver disease (NAFLD) has been associated with subclinical atherosclerosis in cross-sectional studies. We investigated the longitudinal association of NAFLD with the development of subclinical carotid atherosclerosis.

METHODS

We performed a retrospective cohort study of 8020 adult men (average age, 49.2 y) without carotid atherosclerosis at baseline who underwent repeated health check-up examinations from January 1, 2005, through December 31, 2013. NAFLD status was diagnosed by ultrasonography and classified into 4 groups based on baseline and follow-up findings: none, developed, regressed, or persistent NAFLD. Subclinical carotid atherosclerosis was measured by ultrasound.

RESULTS

The age-adjusted hazard ratio for subclinical carotid atherosclerosis development comparing participants with persistent NAFLD with those without NAFLD was 1.23 (95% confidence interval [CI], 1.13-1.35; P < .001). The association persisted after adjustment for smoking, alcohol, body mass index, and weight change (hazard ratio, 1.13; 95% CI, 1.03-1.25; P = .014), but disappeared after adjustment for metabolic variables. The hazard ratio, comparing subjects with regression of NAFLD vs those with persistent NAFLD, was 0.82 (95% CI, 0.69-0.96; P = .013). The risk of subclinical carotid atherosclerosis development also was higher among participants with a high NAFLD fibrosis score, fibrosis-4 scores, or levels of γ-glutamyl transferase at baseline.

CONCLUSIONS

In a large cohort study, persistent NAFLD was associated with an increased risk of subclinical carotid atherosclerosis development. This association was explained by metabolic factors that could be potential mediators of the effect of NAFLD. Markers of liver fibrosis also were associated with subclinical carotid atherosclerosis development. Prospective studies are needed to determine whether treatment of NAFLD can reduce this risk.

Nonalcoholic Fatty Liver Disease for Identification of Preclinical Carotid Atherosclerosis

Nonalcoholic fatty liver disease (NAFLD) is associated with cardiovascular disease, yet whether identification of NAFLD in asymptomatic individuals is helpful over established risk factors remains unknown. A total of 37,799 asymptomatic adults aged 20 years or older who underwent comprehensive health check-up examination, including abdominal and carotid artery duplex ultrasonography (US) were included in the analysis. Nonalcoholic fatty liver disease was diagnosed with US and exclusion of secondary causes of fat accumulation or other causes of chronic liver disease, and graded as mild or moderate to severe fatty liver. Individuals with carotid plaque identified on carotid artery US were considered at risk for cardiovascular disease. Metabolic syndrome (MetS) was defined by the adult treatment panel III criteria. Nonalcoholic fatty liver disease was an independent factor associated with carotid plaque in a dose-dependent manner (odds ratio [OR]; 95% confidence interval [CI]: 1.09 [1.03-1.16] and 1.13 [1.06-1.21] for mild and ≥ moderate degree of NAFLD). Among clinically-relevant subgroups, NAFLD was more closely associated with carotid plaque in young adults (aged < 60 years) without MetS (OR [95% CI]: 1.13 [1.03-1.19] and 1.16 [1.06-1.27] for mild and ≥ moderate degree of NAFLD) than old adults (aged ≥ 60 years) or with MetS (OR [95% CI]: 1.06 [0.97-1.17] and 1.07 [0.97-1.19] for mild and ≥ moderate degree NAFLD). In young adults without MetS, the prevalence of carotid plaques was 32.8% and the sensitivity and specificity of NAFLD for carotid plaque was 0.38 and 0.67, respectively. In conclusion, NAFLD is associated with carotid plaque independent of traditional risk factors, especially in young adults without MetS. Nonalcoholic fatty liver disease could help identify additional individuals with preclinical atherosclerosis in asymptomatic young adults without MetS, yet, showed suboptimal performance as a screening tool.

Improving chronic disease management with mobile health platform

In modern society, aging and chronic disease is becoming common phenomenon due to the increasing numbers of elderly patients. To best treat this growing segment of the population, medical care should be based on constant vital sign monitoring. In this study, we propose a mobile vital sign measurement and data collection system for chronic disease management.. And we implemented a middle ware using Multi-Agent platform in SOS (Self-Organizing System) platform that transmits patient clinical data for services. We also implemented a HL7 messaging interface for interoperability of clinical data exchange. We propose health services on a self-organized software platform.

Embedding the shapes of regions of interest into a Clinical Document Architecture document

Sharing a medical image visually annotated by a region of interest with a remotely located specialist for consultation is a good practice. It may, however, require a special-purpose (and most likely expensive) system to send and view them, which is an unfeasible solution in developing countries such as Vietnam. In this study, we design and implement interoperable methods based on the HL7 Clinical Document Architecture and the eXtensible Markup Language Stylesheet Language for Transformation standards to seamlessly exchange and visually present the shapes of regions of interest using web browsers. We also propose a new integration architecture for a Clinical Document Architecture generator that enables embedding of regions of interest and simultaneous auto-generation of corresponding style sheets. Using the Clinical Document Architecture document and style sheet, a sender can transmit clinical documents and medical images together with coordinate values of regions of interest to recipients. Recipients can easily view the documents and display embedded regions of interest by rendering them in their web browser of choice.

Preliminary comparison of mHealth architectures based on MU2 criteria

Mobile Health (MH) is a hot topic in the health IT industry because it can make a big difference in healthcare services. Before adopting Mobile Health technology, however, we must ensure that it provides safe and reliable services to users and it should be evaluated by authoritative criteria. In US, Electronic Health Record (EHR) systems are evaluated through Meaningful Use (MU). Although MU focuses on EHR systems, it can't be thought of without MH. So in this paper we derive proper evaluation criteria for MH from MU criteria and evaluate existing MH architectures that are currently being surveyed in ISO/AWI TR 17522 - Provisions for Health Applications on Mobile/Smart Devices.

Development tool for generating optimized HL7 libraries for embedded medical devices

Embedded medical devices with constraints on the CPU power and memory capacity must communicate with HIS only in a few HL7 messages. Therefore, it is not desirable to deploy one big library that processes all HL7 messages to all types of devices. We present a development tool that automatically generates an optimized library with a small memory footprint that only processes a subset of HL7 messages for each target device type.

POSBIOTM-NER: a trainable biomedical named-entity recognition system

SUMMARY

POSBIOTM-NER is a trainable biomedical named-entity recognition system. POSBIOTM-NER can be automatically trained and adapted to new datasets without performance degradation, using CRF (conditional random field) machine learning techniques and automatic linguistic feature analysis. Currently, we have trained our system on three different datasets. GENIA-NER was trained based on GENIA Corpus, GENE-NER based on BioCreative data and GPCR-NER based on our own POSBIOTM/NE corpus, respectively, which would be used in GPCR-related pathway extraction.