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1. A Novel mHealth Approach for a Patient-Centered Medication and Health Management System in Taiwan: Pilot Study

Author

Hsieh, Wen-Ting, Su, Yung-Cheng, Han, Hsin-Lien, and Huang, Ming-Yuan

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundMobile health (mHealth) apps have recently demonstrated the potential to engage and empower people to improve their own health. Although the availability of health-related apps is increasing, their adoption rate in Taiwan is exceptionally low mainly due to the preponderance of Western culture-based app designs that are challenging for non-English-speaking individuals. To our knowledge, no mHealth app is available in Taiwan that is culturally tailored for Chinese-speaking users and that applies a patient-centered approach to self-manage medication and health. ObjectiveThe purpose of this study was to design and deploy a culturally tailored mHealth system that could be easily integrated into current clinical practice and to evaluate how this mHealth system could support the continuity of patient care in Taiwan. MethodsAn mHealth information system and a mobile app were designed. To promote the best patient experience, a Quick Response (QR) code system was developed to enable efficient registration of personal medication information through the mobile app. The app also supported notifications for drug utilization, refills, and symptom checks. Patients were encouraged to record medication use, symptoms, and self-assessments in the app during their treatment period. Evaluation of the novel mHealth system was conducted from August 1, 2016 to December 31, 2016 at MacKay Memorial Hospital, Taipei, Taiwan. Population data and app usage statistics were analyzed. ResultsDuring the 5-month implementation period, a total of 25,909 users downloaded the app with an overall 7-day retention rate of 15.4% (SD 3.9). Young male adults (range 25-44 years) were the predominant user population. Patients’ feedback on app usability and design, QR code system as drug input method, medication reminders, and linking family or friends into care networks was generally positive. Physicians showed great interest in utilizing patient-generated data in their care process, and the positive medication adherence rate was the most highly valued component of this system. ConclusionsThis pilot study demonstrated the value of a novel mHealth approach for individualized medication and health management in Taiwan. The mHealth system shows the potential to optimize personalized care into existing clinical services and may help hospitals and health authorities perform continuous quality improvement and policy development.

Published
2018
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25. Using Artificial Intelligence to Establish Chest X-Ray Image Recognition Model to Assist Crucial Diagnosis in Elder Patients With Dyspnea

Author

Liu, Liong-Rung, Chiu, Hung-Wen, Huang, Ming-Yuan, Huang, Shu-Tien, Tsai, Ming-Feng, Chang, Chia-Yu, and Chang, Kuo-Song

Subjects
General Medicine
Abstract

Pneumonia and pulmonary edema are the most common causes of acute respiratory failure in emergency and intensive care. Airway maintenance and heart function preservation are two foundations for resuscitation. Laboratory examinations have been utilized for clinicians to early differentiate pneumonia and pulmonary edema; however, none can provide results as prompt as radiology examinations, such as portable chest X-ray (CXR), which can quickly deliver results without mobilizing patients. However, similar features between pneumonia and pulmonary edema are found in CXR. It remains challenging for Emergency Department (ED) physicians to make immediate decisions as radiologists cannot be on-site all the time and provide support. Thus, Accurate interpretation of images remains challenging in the emergency setting. References have shown that deep convolutional neural networks (CNN) have a high sensitivity in CXR readings. In this retrospective study, we collected the CXR images of patients over 65 hospitalized with pneumonia or pulmonary edema diagnosis between 2016 and 2020. After using the ICD-10 codes to select qualified patient records and removing the duplicated ones, we used keywords to label the image reports found in the electronic medical record (EMR) system. After that, we categorized their CXR images into five categories: positive correlation, negative correlation, no correlation, low correlation, and high correlation. Subcategorization was also performed to better differentiate characteristics. We applied six experiments includes the crop interference and non-interference categories by GoogLeNet and applied three times of validations. In our best model, the F1 scores for pneumonia and pulmonary edema are 0.835 and 0.829, respectively; accuracy rate: 83.2%, Recall rate: 83.2%, positive predictive value: 83.3%, and F1 Score: 0.832. After the validation, the best accuracy rate of our model can reach up to 73%. The model has a high negative predictive value of excluding pulmonary edema, meaning the CXR shows no sign of pulmonary edema. At the time, there was a high positive predictive value in pneumonia. In that way, we could use it as a clinical decision support (CDS) system to rule out pulmonary edema and rule in pneumonia contributing to the critical care of the elderly.

Published
2022

49. Is the choice of renewable portfolio standards random?

Author

Huang, Ming-Yuan, Alavalapati, Janaki R.R., Carter, Douglas R., and Langholtz, Matthew H.

Subjects
Alternative energy sources -- Laws, regulations and rules, Alternative energy sources -- Production management, Gross state product -- Influence, Energy policy -- Management, Population -- Growth, Population -- Influence, Government regulation, Company business management, Business, Environmental issues, Petroleum, energy and mining industries
Published
2007

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