Hospitals provide a considerable amount of questionnaires to patients, and their result data can be one of the significant measures to check a patient's current health. In many cases, however, such data utilization in another kind of healthcare services is unsatisfactory because patients cannot manage the data by themselves. We propose a blockchain-based medical questionnaire management system for data sharing. This system guarantees the integrity of questionnaire result data using characteristics of the blockchain. Furthermore, data in this system can be in interoperable with other systems because it is generated based on the international standard Health Level 7 Fast Healthcare Interoperability Resources. This paper explores how to use medical questionnaire result data for the lifelong healthcare of patient and better quality of health care services and enhance the security of personal medical records.

The amount of healthcare data is increasing. However, issues with unstandardized data formats and the reliability of provenance make the data challenging to share with other institutions. This paper proposes a standards-based sharing framework, SHAREChain, which incorporates two features to deal with reliability and interoperability issues. First, it improves reliability by using the data integrity of a Blockchain-registry and constitutes a Consortium Blockchain Network to share data solely between authenticated institutions. The second feature improves interoperability with standards relating to healthcare data sharing: Fast Healthcare Interoperability Resources and Cross-Enterprise Document Sharing.

With the advent of digital healthcare without borders, enormous amounts of health information are captured and computerized. As healthcare quality largely depends on the reliability of given health information, personal health records should be accessible according to patients’ mobility, even as they travel or migrate to other countries. However, since all the health information is scattered in multiple places, it is an onerous task to carry it whenever people move to other countries. To effectively and efficiently utilize health information, interoperability, which is the ability of various healthcare information technologies to exchange, to interpret, and to use data, is needed. Hence, building a robust transnational health information infrastructure with clear interoperability guidelines considering heterogeneous aspects is necessary. For this purpose, this study proposes a Transnational Health Record framework, which enables access to personal health records anywhere. We review related literature and define level-specific interoperability guidelines, business processes, and requirements for the Transnational Health Record system framework.

Objectives: This paper aimed to use machine learning to identify a new group of factors predicting frailty in the elderly population by utilizing the existing frailty criteria as a basis, as well as to validate the obtained results. Methods: This study was conducted using data from the Korean Frailty and Aging Cohort Study (KFACS). The KFACS participants were classified as robust or frail based on Fried’s frailty phenotype and excluded if they did not properly answer the questions, resulting in 1,066 robust and 165 frail participants. We then selected influential features through feature selection and trained the model using support vector machine, random forest, and gradient boosting algorithms with the prepared dataset. Due to the imbalanced distribution in the dataset with a low sample size, holdout was applied with stratified 10-fold and cross-validation for estimating the model performance. The reliability of the constructed model was validated using an unseen test set. The model was then trained with hyperparameter optimization. Results: During the feature selection process, 27 features were identified as meaningful factors for frailty. The model was trained based on the selected features, and the weighted average F1-score reached 95.30% with the random forest algorithm. Conclusions: The results of the study demonstrated the possibility of adopting machine learning to strengthen existing frailty criteria. As the method analyzes questionnaire responses in a short time, it can support higher volumes of data on participants’ health conditions and alert them regarding potential risks in advance.

Medication errors are one of the problems to be solved not only in Korea but also across the world. To prevent a medication accident in advance, the comprehensive management of individual medication history is essential. Currently, in the hospital-centric Personal Health Record system, if the patient is prescribed medicines at multiple hospitals, he/she needs to go to the hospital where a medicine was prescribed to get the corresponding needs such as prescription or medical certification to record the prescription information on his/her own. Alternatively, the patient needs to record the prescription information on his/her own. This is very time consuming, inconvenient, and even worse, not reliable method because of input errors. Therefore, in this study, we developed Patient-centric medication history recording system using blockchain, which is directly capturing QR code printed on the envelop by drug store based on prescription. All the information are stored using the hash value of the data in a blockchain and it prevents the tampering of the data. In addition, this system adopted Fast Healthcare Interoperability Resources, which is the international health information exchange standard, as way to improve interoperability.

Background: Patients with lung cancer are among the most frequent visitors to emergency departments due to cancer-related problems, and the prognosis for those who seek emergency care is dismal. Given that patients with lung cancer frequently visit health care facilities for treatment or follow-up, the ability to predict emergency department visits based on clinical information gleaned from their routine visits would enhance hospital resource utilization and patient outcomes. Objective: This study proposed a machine learning–based prediction model to identify risk factors for emergency department visits by patients with lung cancer. Methods: This was a retrospective observational study of patients with lung cancer diagnosed at Seoul National University Bundang Hospital, a tertiary general hospital in South Korea, between January 2010 and December 2017. The primary outcome was an emergency department visit within 30 days of an outpatient visit. This study developed a machine learning–based prediction model using a common data model. In addition, the importance of features that influenced the decision-making of the model output was analyzed to identify significant clinical factors. Results: The model with the best performance demonstrated an area under the receiver operating characteristic curve of 0.73 in its ability to predict the attendance of patients with lung cancer in emergency departments. The frequency of recent visits to the emergency department and several laboratory test results that are typically collected during cancer treatment follow-up visits were revealed as influencing factors for the model output. Conclusions: This study developed a machine learning–based risk prediction model using a common data model and identified influencing factors for emergency department visits by patients with lung cancer. The predictive model contributes to the efficiency of resource utilization and health care service quality by facilitating the identification and early intervention of high-risk patients. This study demonstrated the possibility of collaborative research among different institutions using the common data model for precision medicine in lung cancer.