A system automatically collects and integrates patient data generated by bedside equipment and can be used as training data sets for AI systems in disease prediction.
Nursing staff often face heavy workloads while managing manual patient records. Although hospitals no longer rely on traditional paper-based methods, typing data into computers remains time-consuming. Smart healthcare improves diagnostic efficiency and reduces costs through ICT integration.
Our team developed the Patient Integrated Data Service (PIDS) to automate the parsing, storage, and transfer of data from bedside monitors. This system supports various units, including the ICU, ER, and recovery rooms. It also handles diverse physiological parameters, even when monitors use different transmission protocols.
Hospitals can configure PIDS easily to meet specific connection requirements via a simple file. PIDS automatically parses and uploads patient data through hospital APIs once deployed. Additionally, the local database stores all vital signs, alarms, and waveforms. These features provide a robust foundation for clinical research and future AI-driven early warning systems.
Through the APIs provided by hospitals, the system can connect to receiving port of the hospital then parse and upload the data of patients continuously and automatically.
The system stores integrated data of patients, and it can be a data source to provide the hospital systems such as HIS, NIS, CIS and CDSS applications.
The structured data stored in the database is easy to query and export, and it can be used for further clinical research.
Modular design and fast deployment make the system easily maintain.
Due to Microsoft’s tool for development and installation, the system is safe and stable.
Technical alarms from monitors provide information to engineering staff of hospital to watch the status of the equipment remotely followed by a quick respond for maintenance needs. Besides, the alarms statistics is used for management purpose.
Assisting in collecting and storing patients’ medical records reduce the burden for nursing staff. The query and retrieval of physiological waveform segemnts of system functions provide as a record in nursing staff shift. The complete alarm can provide a source of data for patient monitoring and equipment maintenance, and respond quickly to the occurrence of alarms. Its complete set of patient physiological data can be used for training AI early warning systems for certain diseases and provide clinical research.
