Deep-learning-based personalized prediction of absolute neutrophil count recovery and comparison with clinicians for validation.

Neutropenia and its complications are major adverse effects of cytotoxic chemotherapy. The time to recovery from neutropenia varies from patient to patient, and cannot be easily predicted even by experts. Therefore, we trained a deep learning model using data from 525 pediatric patients with solid tumors to predict the day when patients recover from severe neutropenia after high-dose chemotherapy. We validated the model with data from 99 patients and compared its performance to those of clinicians. The accuracy of the model at predicting the recovery day, with a 1-day error, was 76%; its performance was better than those of the specialist group (58.59%) and the resident group (32.33%). In addition, 80% of clinicians changed their initial predictions at least once after the model's prediction was conveyed to them. In total, 86 prediction changes (90.53%) improved the recovery day estimate.

Preliminary Attainability Assessment of Real-World Data for Answering Major Clinical Research Questions in Breast Cancer Brain Metastasis: Framework Development and Validation Study.

BACKGROUND: In recent decades, real-world evidence (RWE) in oncology has rapidly gained traction for its potential to answer clinical questions that cannot be directly addressed by randomized clinical trials. Integrating real-world data (RWD) into clinical research promises to contribute to more sustainable research designs, including extension, augmentation, enrichment, and pragmatic designs. Nevertheless, clinical research using RWD is still limited because of concerns regarding the shortage of best practices for extracting, harmonizing, and analyzing RWD. In particular, pragmatic screening methods to determine whether the content of a data source is sufficient to answer the research questions before conducting the research with RWD have not yet been established. OBJECTIVE: We examined the PAR (Preliminary Attainability Assessment of Real-World Data) framework and assessed its utility in breast cancer brain metastasis (BCBM), which has an unmet medical need for data attainability screening at the preliminary step of observational studies that use RWD. METHODS: The PAR framework was proposed to assess data attainability from a particular data source during the early research process. The PAR framework has four sequential stages, starting with clinical question clarification: (1) operational definition of variables, (2) data matching (structural/semantic), (3) data screening and extraction, and (4) data attainability diagramming. We identified 5 clinical questions to be used for PAR framework evaluation through interviews and validated them with a survey of breast cancer experts. We used the Samsung Medical Center Breast Cancer Registry, a hospital-based real-time registry implemented in March 2021, leveraging the institution's anonymized and deidentified clinical data warehouse platform. The number of breast cancer patients in the registry was 45,129; it covered the period from June 1995 to December 2021. The registry consists of 24 base data marts that represent disease-specific breast cancer characteristics and care pathways. The outcomes included screening results of the clinical questions via the PAR framework and a procedural diagram of data attainability for each research question. RESULTS: Data attainability was tested for study feasibility according to the PAR framework with 5 clinical questions for BCBM. We obtained data sets that were sufficient to conduct studies with 4 of 5 clinical questions. The research questions stratified into 3 types when we developed data fields for clearly defined research variables. In the first, only 1 question could be answered using direct data variables. In the second, the other 3 questions required surrogate definitions that combined data variables. In the third, the question turned out to be not feasible for conducting further analysis. CONCLUSIONS: The adoption of the PAR framework was associated with more efficient preliminary clinical research using RWD from BCBM. Furthermore, this framework helped accelerate RWE generation through clinical research by enhancing transparency and reproducibility and lowering the entry barrier for clinical researchers.

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.

Evaluation of the Antiviral Activity of Tabamide A and Its Structural Derivatives against Influenza Virus.

Influenza viruses cause severe endemic respiratory infections in both humans and animals worldwide. The emergence of drug-resistant viral strains requires the development of new influenza therapeutics. Tabamide A (TA0), a phenolic compound isolated from tobacco leaves, is known to have antiviral activity. We investigated whether synthetic TA0 and its derivatives exhibit anti-influenza virus activity. Analysis of structure-activity relationship revealed that two hydroxyl groups and a double bond between C7 and C8 in TA0 are crucial for maintaining its antiviral action. Among its derivatives, TA25 showed seven-fold higher activity than TA0. Administration of TA0 or TA25 effectively increased survival rate and reduced weight loss of virus-infected mice. TA25 appears to act early in the viral infection cycle by inhibiting viral mRNA synthesis on the template-negative strand. Thus, the anti-influenza virus activity of TA0 can be expanded by application of its synthetic derivatives, which may aid in the development of novel antiviral therapeutics.

Who is more likely to adopt and comply with the electronic patient-reported outcome measure (ePROM) mobile application? A real-world study with cancer patients undergoing active treatment.

PURPOSE: This study aims to identify factors associated with the adoption and compliance of electronic patient-reported outcome measure (ePROM) use among cancer patients in a real-world setting. METHODS: This prospective cohort study was conducted at the Samsung Medical Center in Seoul, Korea, from September 2018 to January 2019. Cancer patients aged 18 years or older who owned smartphones and who were receiving chemotherapy or radiation therapy were eligible for this study. Patients were asked to use the app to report their symptoms every 7 days for a total of 21 days (3 weeks). Logistic regression was performed to identify the factors associated with the adoption and compliance. RESULTS: Among 580 patients, 417 (71.9%) adopted the ePROM app and 159 (27.4%) out of 417 had good compliance. Patients who had greater expectations regarding the ease of use (adjusted odds ratio [aOR] 2.67, 95% CI: 1.28-5.57) and usefulness (aOR 1.69, 95% CI: 1.05-2.72) of the ePROM app were more likely to adopt the app than those who did not. Patients who had greater satisfaction with usefulness (aOR 1.89, 95% CI 1.10-3.25) were more likely to comply with using the app, but satisfaction with ease of use was not related to the compliance. CONCLUSION: While expectation regarding the ease of use and usefulness of the ePROM app was associated with the adoption of the app, satisfaction with ease of use was not related to compliance with the ePROM app. Satisfaction with usefulness was associated with the compliance of ePROM app use.

Sodium-Glucose Cotransporter-2 Inhibitor-Related Diabetic Ketoacidosis: Accuracy Verification of Operational Definition.

BACKGROUND: The most important aspect of a retrospective cohort study is the operational definition (OP) of the disease. We developed a detailed OP for the detection of sodium-glucose cotransporter-2 inhibitors (SGLT2i) related to diabetic ketoacidosis (DKA). The OP was systemically verified and analyzed. METHODS: All patients prescribed SGLT2i at four university hospitals were enrolled in this experiment. A DKA diagnostic algorithm was created and distributed to each hospital; subsequently, the number of SGLT2i-related DKAs was confirmed. Then, the algorithm functionality was verified through manual chart reviews by an endocrinologist using the same OP. RESULTS: A total of 8,958 patients were initially prescribed SGLT2i. According to the algorithm, 0.18% (16/8,958) were confirmed to have SGLT2i-related DKA. However, based on manual chart reviews of these 16 cases, there was only one case of SGLT2i-related DKA (positive predictive value = 6.3%). Even after repeatedly narrowing the diagnosis range of the algorithm, the effect of a positive predictive value was insignificant (6.3-10.0%, P > 0.999). CONCLUSION: Owing to the nature of electronic medical record data, we could not create an algorithm that clearly differentiates SGLT2i-related DKA despite repeated attempts. In all retrospective studies, a portion of the samples should be randomly selected to confirm the accuracy of the OP through chart review. In retrospective cohort studies in which chart review is not possible, it will be difficult to guarantee the reliability of the results.

Development and Validation of Digital Health Technology Literacy Assessment Questionnaire.

In clinical practice, assessing digital health literacy is important to identify patients who may encounter difficulties adapting to digital health using digital technology and service. We developed the Digital Health Technology Literacy Assessment Questionnaire (DHTL-AQ) to assess the ability to use digital health technology, services, and data. The DHTL-AQ was developed in three phases. In the first phase, the conceptual framework and domains and items were generated from a systematic literature review using relevant theory and surveys. In the second phase, a cross-sectional survey with 590 adults age ≥ 18 years was conducted at an academic hospital in Seoul, Korea in January and February 2020 to test face validity of the items. Then, psychometric validation was conducted to determine the final items and cut-off scores of the DHTL-AQ. The eHealth literacy scale, the Newest Vital Sign, and 10 mobile app task ability assessments were examined to test validity. The final DHTL-AQ includes 34 items in two domains (digital functional and digital critical literacy) and 4 categories (Information and Communications Technology terms, Information and Communications Technology icons, use of an app, evaluating reliability and relevance of health information). The DHTL-AQ had excellent internal consistency (overall Cronbach's α = 0.95; 0.87-0.94 for subtotals) and acceptable model fit (CFI = 0.821, TLI = 0.807, SRMR = 0.065, RMSEA = 0.090). The DHTL-AQ was highly correlated with task ability assessment (r = 0.7591), and moderately correlated with the eHealth literacy scale (r = 0.5265) and the Newest Vital Sign (r = 0.5929). The DHTL-AQ is a reliable and valid instrument to measure digital health technology literacy.

Data Pseudonymization in a Range That Does Not Affect Data Quality: Correlation with the Degree of Participation of Clinicians.

Personal medical information is an essential resource for research; however, there are laws that regulate its use, and it typically has to be pseudonymized or anonymized. When data are anonymized, the quantity and quality of extractable information decrease significantly. From the perspective of a clinical researcher, a method of achieving pseudonymized data without degrading data quality while also preventing data loss is proposed herein. As the level of pseudonymization varies according to the research purpose, the pseudonymization method applied should be carefully chosen. Therefore, the active participation of clinicians is crucial to transform the data according to the research purpose. This can contribute to data security by simply transforming the data through secondary data processing. Case studies demonstrated that, compared with the initial baseline data, there was a clinically significant difference in the number of datapoints added with the participation of a clinician (from 267,979 to 280,127 points, P < 0.001). Thus, depending on the degree of clinician participation, data anonymization may not affect data quality and quantity, and proper data quality management along with data security are emphasized. Although the pseudonymization level and clinical use of data have a trade-off relationship, it is possible to create pseudonymized data while maintaining the data quality required for a given research purpose. Therefore, rather than relying solely on security guidelines, the active participation of clinicians is important.

Federated Learning on Clinical Benchmark Data: Performance Assessment.

BACKGROUND: Federated learning (FL) is a newly proposed machine-learning method that uses a decentralized dataset. Since data transfer is not necessary for the learning process in FL, there is a significant advantage in protecting personal privacy. Therefore, many studies are being actively conducted in the applications of FL for diverse areas. OBJECTIVE: The aim of this study was to evaluate the reliability and performance of FL using three benchmark datasets, including a clinical benchmark dataset. METHODS: To evaluate FL in a realistic setting, we implemented FL using a client-server architecture with Python. The implemented client-server version of the FL software was deployed to Amazon Web Services. Modified National Institute of Standards and Technology (MNIST), Medical Information Mart for Intensive Care-III (MIMIC-III), and electrocardiogram (ECG) datasets were used to evaluate the performance of FL. To test FL in a realistic setting, the MNIST dataset was split into 10 different clients, with one digit for each client. In addition, we conducted four different experiments according to basic, imbalanced, skewed, and a combination of imbalanced and skewed data distributions. We also compared the performance of FL to that of the state-of-the-art method with respect to in-hospital mortality using the MIMIC-III dataset. Likewise, we conducted experiments comparing basic and imbalanced data distributions using MIMIC-III and ECG data. RESULTS: FL on the basic MNIST dataset with 10 clients achieved an area under the receiver operating characteristic curve (AUROC) of 0.997 and an F1-score of 0.946. The experiment with the imbalanced MNIST dataset achieved an AUROC of 0.995 and an F1-score of 0.921. The experiment with the skewed MNIST dataset achieved an AUROC of 0.992 and an F1-score of 0.905. Finally, the combined imbalanced and skewed experiment achieved an AUROC of 0.990 and an F1-score of 0.891. The basic FL on in-hospital mortality using MIMIC-III data achieved an AUROC of 0.850 and an F1-score of 0.944, while the experiment with the imbalanced MIMIC-III dataset achieved an AUROC of 0.850 and an F1-score of 0.943. For ECG classification, the basic FL achieved an AUROC of 0.938 and an F1-score of 0.807, and the imbalanced ECG dataset achieved an AUROC of 0.943 and an F1-score of 0.807. CONCLUSIONS: FL demonstrated comparative performance on different benchmark datasets. In addition, FL demonstrated reliable performance in cases where the distribution was imbalanced, skewed, and extreme, reflecting the real-life scenario in which data distributions from various hospitals are different. FL can achieve high performance while maintaining privacy protection because there is no requirement to centralize the data.

Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study.

BACKGROUND: Screening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests. OBJECTIVE: The aim of this study was to develop a machine learning-based screening tool using patient-generated health data (PGHD) obtained from a mobile health (mHealth) app. METHODS: We trained a deep learning model based on a gated recurrent unit to screen influenza using PGHD, including each patient's fever pattern and drug administration records. We used meteorological data and app-based surveillance of the weekly number of patients with influenza. We defined a single episode as the set of consecutive days, including the day the user was diagnosed with influenza or another disease. Any record a user entered 24 hours after his or her last record was considered to be the start of a new episode. Each episode contained data on the user's age, gender, weight, and at least one body temperature record. The total number of episodes was 6657. Of these, there were 3326 episodes within which influenza was diagnosed. We divided these episodes into 80% training sets (2664/3330) and 20% test sets (666/3330). A 5-fold cross-validation was used on the training set. RESULTS: We achieved reliable performance with an accuracy of 82%, a sensitivity of 84%, and a specificity of 80% in the test set. After the effect of each input variable was evaluated, app-based surveillance was observed to be the most influential variable. The correlation between the duration of input data and performance was not statistically significant (P=.09). CONCLUSIONS: These findings suggest that PGHD from an mHealth app could be a complementary tool for influenza screening. In addition, PGHD, along with traditional clinical data, could be used to improve health conditions.

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.

Artificial Intelligence in Health Care: Current Applications and Issues.

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low; moreover, there are various concerns regarding the safety and reliability of AI technology implementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.

The 2017 Network Tools and Applications in Biology (NETTAB) workshop: aims, topics and outcomes.

The 17th International NETTAB workshop was held in Palermo, Italy, on October 16-18, 2017. The special topic for the meeting was "Methods, tools and platforms for Personalised Medicine in the Big Data Era", but the traditional topics of the meeting series were also included in the event. About 40 scientific contributions were presented, including four keynote lectures, five guest lectures, and many oral communications and posters. Also, three tutorials were organised before and after the workshop. Full papers from some of the best works presented in Palermo were submitted for this Supplement of BMC Bioinformatics. Here, we provide an overview of meeting aims and scope. We also shortly introduce selected papers that have been accepted for publication in this Supplement, for a complete presentation of the outcomes of the meeting.

PEATH: single-individual haplotyping by a probabilistic evolutionary algorithm with toggling.

Motivation: Single-individual haplotyping (SIH) is critical in genomic association studies and genetic diseases analysis. However, most genomic analysis studies do not perform haplotype-phasing analysis due to its complexity. Several computational methods have been developed to solve the SIH problem, but these approaches have not generated sufficiently reliable haplotypes. Results: Here, we propose a novel SIH algorithm, called PEATH (Probabilistic Evolutionary Algorithm with Toggling for Haplotyping), to achieve more accurate and reliable haplotyping. The proposed PEATH method was compared to the most recent algorithms in terms of the phased length, N50 length, switch error rate and minimum error correction. The PEATH algorithm consistently provides the best phase and N50 lengths, as long as possible, given datasets. In addition, verification of the simulation data demonstrated that the PEATH method outperforms other methods on high noisy data. Additionally, the experimental results of a real dataset confirmed that the PEATH method achieved comparable or better accuracy. Availability and implementation: Source code of PEATH is available at https://github.com/jcna99/PEATH. Contact: jkrhee@catholic.ac.kr or sooyong.shin@gmail.com. Supplementary information: Supplementary data are available at Bioinformatics online.

Why Do Data Users Say Health Care Data Are Difficult to Use? A Cross-Sectional Survey Study.

BACKGROUND: There has been significant effort in attempting to use health care data. However, laws that protect patients' privacy have restricted data use because health care data contain sensitive information. Thus, discussions on privacy laws now focus on the active use of health care data beyond protection. However, current literature does not clarify the obstacles that make data usage and deidentification processes difficult or elaborate on users' needs for data linking from practical perspectives. OBJECTIVE: The objective of this study is to investigate (1) the current status of data use in each medical area, (2) institutional efforts and difficulties in deidentification processes, and (3) users' data linking needs. METHODS: We conducted a cross-sectional online survey. To recruit people who have used health care data, we publicized the promotion campaign and sent official documents to an academic society encouraging participation in the online survey. RESULTS: In total, 128 participants responded to the online survey; 10 participants were excluded for either inconsistent responses or lack of demand for health care data. Finally, 118 participants' responses were analyzed. The majority of participants worked in general hospitals or universities (62/118, 52.5% and 51/118, 43.2%, respectively, multiple-choice answers). More than half of participants responded that they have a need for clinical data (82/118, 69.5%) and public data (76/118, 64.4%). Furthermore, 85.6% (101/118) of respondents conducted deidentification measures when using data, and they considered rigid social culture as an obstacle for deidentification (28/101, 27.7%). In addition, they required data linking (98/118, 83.1%), and they noted deregulation and data standardization to allow access to health care data linking (33/98, 33.7% and 38/98, 38.8%, respectively). There were no significant differences in the proportion of responded data needs and linking in groups that used health care data for either public purposes or commercial purposes. CONCLUSIONS: This study provides a cross-sectional view from a practical, user-oriented perspective on the kinds of data users want to utilize, efforts and difficulties in deidentification processes, and the needs for data linking. Most users want to use clinical and public data, and most participants conduct deidentification processes and express a desire to conduct data linking. Our study confirmed that they noted regulation as a primary obstacle whether their purpose is commercial or public. A legal system based on both data utilization and data protection needs is required.

Current status and future direction of digital health in Korea.

Recently, digital health has gained the attention of physicians, patients, and healthcare industries. Digital health, a broad umbrella term, can be defined as an emerging health area that uses brand new digital or medical technologies involving genomics, big data, wearables, mobile applications, and artificial intelligence. Digital health has been highlighted as a way of realizing precision medicine, and in addition is expected to become synonymous with health itself with the rapid digitization of all health-related data. In this article, we first define digital health by reviewing the diverse range of definitions among academia and government agencies. Based on these definitions, we then review the current status of digital health, mainly in Korea, suggest points that are missing from the discussion or ought to be added, and provide future directions of digital health in clinical practice by pointing out certain key points.

Depression Screening Using Daily Mental-Health Ratings from a Smartphone Application for Breast Cancer Patients.

BACKGROUND: Mobile mental-health trackers are mobile phone apps that gather self-reported mental-health ratings from users. They have received great attention from clinicians as tools to screen for depression in individual patients. While several apps that ask simple questions using face emoticons have been developed, there has been no study examining the validity of their screening performance. OBJECTIVE: In this study, we (1) evaluate the potential of a mobile mental-health tracker that uses three daily mental-health ratings (sleep satisfaction, mood, and anxiety) as indicators for depression, (2) discuss three approaches to data processing (ratio, average, and frequency) for generating indicator variables, and (3) examine the impact of adherence on reporting using a mobile mental-health tracker and accuracy in depression screening. METHODS: We analyzed 5792 sets of daily mental-health ratings collected from 78 breast cancer patients over a 48-week period. Using the Patient Health Questionnaire-9 (PHQ-9) as the measure of true depression status, we conducted a random-effect logistic panel regression and receiver operating characteristic (ROC) analysis to evaluate the screening performance of the mobile mental-health tracker. In addition, we classified patients into two subgroups based on their adherence level (higher adherence and lower adherence) using a k-means clustering algorithm and compared the screening accuracy between the two groups. RESULTS: With the ratio approach, the area under the ROC curve (AUC) is 0.8012, indicating that the performance of depression screening using daily mental-health ratings gathered via mobile mental-health trackers is comparable to the results of PHQ-9 tests. Also, the AUC is significantly higher (P=.002) for the higher adherence group (AUC=0.8524) than for the lower adherence group (AUC=0.7234). This result shows that adherence to self-reporting is associated with a higher accuracy of depression screening. CONCLUSIONS: Our results support the potential of a mobile mental-health tracker as a tool for screening for depression in practice. Also, this study provides clinicians with a guideline for generating indicator variables from daily mental-health ratings. Furthermore, our results provide empirical evidence for the critical role of adherence to self-reporting, which represents crucial information for both doctors and patients.

Which Users Should Be the Focus of Mobile Personal Health Records? Analysis of User Characteristics Influencing Usage of a Tethered Mobile Personal Health Record.

BACKGROUND: This study was conducted to analyze the usage pattern of a hospital-tethered mobile personal health records (m-PHRs) application named My Chart in My Hand (MCMH) and to identify user characteristics that influence m-PHR usage. MATERIALS AND METHODS: Access logs to MCMH and its menus were collected for a total of 18 months, from August 2011 to January 2013. Usage patterns between users without a patient identification number (ID) and users with a patient ID were compared. Users with a patient ID were divided into light and heavy user groups by the median number of monthly access. Multiple linear regression models were used to assess MCMH usage pattern by characteristics of MCMH user with a patient ID. RESULTS: The total number of MCMH logins was 105,603, and the median number of accesses was 15 times. Users (n = 7,096) mostly accessed the "My Chart" menu, but "OPD [outpatient department] Service Support" and "Health Management" menus were also frequently used. Patients with chronic diseases, experience of hospital visits including emergency room and OPD, and age group of 0-19 years were more frequently found among users with a patient ID (n = 2,186) (p < 0.001). A similar trend was found in the heavy user group (n = 1,123). Submenus of laboratory result, online appointment, and medication lists that were accessed mostly by users with a patient ID were associated with OPD visit and chronic diseases. CONCLUSIONS: This study showed that focuses on patients with chronic disease and more hospital visits and empowerment functions in a tethered m-PHR would be helpful to pursue the extensive use.

A De-identification method for bilingual clinical texts of various note types.

De-identification of personal health information is essential in order not to require written patient informed consent. Previous de-identification methods were proposed using natural language processing technology in order to remove the identifiers in clinical narrative text, although these methods only focused on narrative text written in English. In this study, we propose a regular expression-based de-identification method used to address bilingual clinical records written in Korean and English. To develop and validate regular expression rules, we obtained training and validation datasets composed of 6,039 clinical notes of 20 types and 5,000 notes of 33 types, respectively. Fifteen regular expression rules were constructed using the development dataset and those rules achieved 99.87% precision and 96.25% recall for the validation dataset. Our de-identification method successfully removed the identifiers in diverse types of bilingual clinical narrative texts. This method will thus assist physicians to more easily perform retrospective research.