Many studies have indicated that computing technology can enable off-site cardiologists to read patients’ electrocardiograph (ECG), echocardiography (ECHO), and relevant images via smart phones during pre-hospital, in-hospital, and post-hospital teleconsultation, which not only identifies emergency cases in need of immediate treatment, but also prevents the unnecessary re-hospitalizations. Meanwhile, several studies have combined cloud computing and mobile computing to facilitate better storage, delivery, retrieval, and management of medical files for telecardiology. In the future, the aggregated ECG and images from hospitals worldwide will become big data, which should be used to develop an e-consultation program helping on-site practitioners deliver appropriate treatment. With information technology, real-time tele-consultation and tele-diagnosis of ECG and images can be practiced via an e-platform for clinical, research, and educational purposes. While being devoted to promote the application of information technology onto telecardiology, we need to resolve several issues: (1) data confidentiality in the cloud, (2) data interoperability among hospitals, and (3) network latency and accessibility. If these challenges are overcome, tele-consultation will be ubiquitous, easy to perform, inexpensive, and beneficial. Most importantly, these services will increase global collaboration and advance clinical practice, education, and scientific research in cardiology.

Due to the great variability of 12-lead ECG instruments and medical specialists’ interpretation skills, it remains a challenge to deliver rapid and accurate 12-lead ECG reports with senior cardiologists’ decision making support in emergency telecardiology.

This study demonstrates transmission of 12-lead electrocardiography (ECG) in an ambulance to the cell phone of the attendant emergency medical technician and then to the hospital and to cell phones of off-site cardiologists. The emergency medical technician cell phone receives Extensible Markup Language files generated by a Phillips Extensible Markup Language ECG instrument via Wi-Fi-based wireless network and then sends them to an ECG-processing server at the hospital over the mobile telephone network. After reducing ECG noises and artifacts, the server converts files to Digital Imaging and Communications in Medicine-based ECG reports stored in Picture Archiving and Communication System. These reports are sent to the cell phones of off-site cardiologists. Consequently, on-site Emergency Department physicians and off-site cardiologists can discuss ECG reports via Picture Archiving and Communication System on their computers or cell phones to prepare for the most appropriate treatment while the patient is on the way to the hospital. In conclusion, this 12-lead ECG transmission e-technology expands the functions of a 12-lead ECG instrument and facilitates more efficient prehospital cardiac care.

Among various kinds of falling prevention measures, bed exit alarm mechanism has raised serious attention recently. In particular, the recent inflow of innovative ICT advancement from Internet of Things, wearable technology, and artificial intelligence have shed on more possibility in realizing effective bed exit alarm systems. This research proposes a deep learning algorithm to construct the bed exit detection model using monitored behavior information collected from the infrared array sensor. Based on the preliminary experiment results, the bed-exit events can be recognized with 92% accuracy, 99% for precision and 97% for recall rate. This approach also has its advantages in low device costs, less data storage needed, less spacial resolution without privacy and legal concerns, and unaffected performance in various lighting conditions.