Background: Medication adherence is critical for patient treatment. This study involved evaluating how implementing Short Message Service (SMS) reminders affected patient medication adherence and related factors. Methods: We used a structured questionnaire to survey outpatients at three medical centers. Patients aged 20 years and older who were prescribed more than 7 days of a prescription medication were randomized into SMS intervention or control groups. The intervention group received daily messages reminding them of aspects regarding taking their medication; the control group received no messages. A phone follow-up was performed to assess outcomes after 8 days. Data were collected from 763 participants in the intervention group and 435 participants in the control group. Results: After participants in the intervention group received SMS reminders to take medication or those in the control group received no messages, incidences of delayed doses were decreased by 46.4 and 78.8% for those in the control and intervention groups, respectively. The rate of missed doses was decreased by 90.1% for participants in the intervention group and 61.1% for those in the control group. We applied logistic regression analysis and determined that participants in the intervention group had a 3.2-fold higher probability of having a decrease in delayed doses compared with participants in the control group. Participants in the intervention group also showed a 2.2-fold higher probability of having a decrease in missed doses compared with participants in the control group. Conclusions: Use of SMS significantly affected the rates of taking medicine on schedule. Therefore, daily SMS could be useful for reminding patients to take their medicine on schedule.

In this era of ubiquitous information, patient record exchange among hospitals still has technological and individual barriers including resistance to information sharing. Most research on user attitudes has been limited to one type of user or aspect. Because few analyses of attitudes toward electronic patient records (EPRs) have been conducted, understanding the attitudes among different users in multiple aspects is crucial to user acceptance. This proof-of-concept study investigated the attitudes of users toward the inter-hospital EPR exchange system implemented nationwide and focused on discrepant behavioral intentions among three user groups. The system was designed by combining a Health Level 7-based protocol, object-relational mapping, and other medical informatics techniques to ensure interoperability in realizing patient-centered practices. After implementation, three user-specific questionnaires for physicians, medical record staff, and patients were administered, with a 70 % response rate. The instrument showed favorable convergent construct validity and internal consistency reliability. Two dependent variables were applied: the attitudes toward privacy and support. Independent variables comprised personal characteristics, work characteristics, human aspects, and technology aspects. Major statistical methods included exploratory factor analysis and general linear model. The results from 379 respondents indicated that the patients highly agreed with privacy protection by their consent and support for EPRs, whereas the physicians remained conservative toward both. Medical record staff was ranked in the middle among the three groups. The three user groups demonstrated discrepant intentions toward privacy protection and support. Experience of computer use, level of concerns, usefulness of functions, and specifically, reason to use electronic medical records and number of outpatient visits were significantly associated with the perceptions. Overall, four categories of independent variables were associated with the mean difference in the perceptions. Discrepant attitudes toward privacy and support among the three user groups are identified. Patients may require further education and communication regarding the system. Culturally fit e-Consent should be incorporated into the system to fully utilize the computing power of the Internet when also considering workload. The concern for misuse of EPRs might lead to low support among physicians. Highly readable EPR documents and managerial incentives for information exchange may improve system use.

In this paper, an efficient instance segmentation scheme based on deep convolutional neural networks is proposed to deal with unconstrained psoriasis images for computer-aided diagnosis. To achieve instance segmentation, the You Only Look At CoefficienTs (YOLACT) network composed of backbone, feature pyramid network (FPN), Protonet, and prediction head is used to deal with psoriasis images. The backbone network is used to extract feature maps from an image, and FPN is designed to generate multiscale feature maps for effectively classifying and localizing objects with multiple sizes. The prediction head is used to predict the classification information, bounding box information, and mask coefficients of objects. Some prototypes generated by Protonet are combined with mask coefficients to estimate the pixel-level shapes for objects. To achieve instance segmentation for unconstrained psoriasis images, YOLACT++ with a pretrained model is retrained via transfer learning. To evaluate the performance of the proposed scheme, unconstrained psoriasis images with different severity levels are collected for testing. As for subjective testing, the psoriasis regions and normal skin areas can be located and classified well. The four performance indices of the proposed scheme were higher than 93% after cross validation. About object localization, the Mean Average Precision (mAP) rates of the proposed scheme were at least 85.9% after cross validation. As for efficiency, the frames per second (FPS) rate of the proposed scheme reached up to 15. In addition, the F1_score and the execution speed of the proposed scheme were higher than those of the Mask Region-Based Convolutional Neural Networks (R-CNN)-based method. These results show that the proposed scheme based on YOLACT++ can not only detect psoriasis regions but also distinguish psoriasis pixels from background and normal skin pixels well. Furthermore, the proposed instance segmentation scheme outperforms the Mask R-CNN-based method for unconstrained psoriasis images.

We established a web-based ubiquitous health management (UHM) system, “ECG4UHM”, for processing ECG signals with AI-enabled models to recognize hybrid arrhythmia patterns, including atrial premature atrial complex (APC), atrial fibrillation (AFib), ventricular premature complex (VPC), and ventricular tachycardia (VT), versus normal sinus rhythm (NSR). The analytical model coupled machine learning methods, such as multiple layer perceptron (MLP), random forest (RF), support vector machine (SVM), and naive Bayes (NB), to process the hybrid patterns of four arrhythmia symptoms for AI computation. The data pre-processing used Hilbert–Huang transform (HHT) with empirical mode decomposition to calculate ECGs’ intrinsic mode functions (IMFs). The area centroids of the IMFs’ marginal Hilbert spectrum were suggested as the HHT-based features. We engaged the MATLAB^TM compiler and runtime server in the ECG4UHM to build the recognition modules for driving AI computation to identify the arrhythmia symptoms. The modeling extracted the crucial data sets from the MIT-BIH arrhythmia open database. The validated models, including the premature pattern (i.e., APC–VPC) and the fibril-rapid pattern (i.e., AFib–VT) against NSR, could reach the best area under the curve (AUC) of the receiver operating characteristic (ROC) of approximately 0.99. The models for all hybrid patterns, without VPC versus AFib and VT, achieved an average accuracy of approximately 90%. With the prediction test, the respective AUCs of the NSR and APC versus the AFib, VPC, and VT were 0.94 and 0.93 for the RF and SVM on average. The average accuracy and the AUC of the MLP, RF, and SVM models for APC–VT reached the value of 0.98. The self-developed system with AI computation modeling can be the backend of the intelligent social-health system that can recognize hybrid arrhythmia patterns in the UHM and home-isolated cares.