Tele-ICUs - Technologies
Technologies used in tele-ICUs include artificial intelligence (AI), machine learning, and audio-visual technology that enables clinicians to offer care to critically ill patients through synchronous, two-way audiovisual communication. The use of AI represents an opportunity for more effective and efficient care delivery by predicting disease trajectory and complications.
- According to SAGE Journals, Tele-ICU is a technology-based model designed to deliver effective critical care in the intensive care unit (ICU).
- The tele-ICU system has been created to address the increasing demand for intensive care services and the shortage of intensivists. The system enables several intensivists from remote locations to provide real-time services to multiple ICUs and assist in the treatment of critically ill patients.
- There are several technologies used in tele-ICUs. The tele‐ICU system includes AI, machine learning, adjustable high‐resolution cameras positioned all over the room, a "two‐way audio system, and a direct digital connection to the monitoring system" that allows the team to view the patient, mechanical ventilation settings, vital signs monitors, "intravenous infusions and rates, and other organs supportive devices, and to communicate with the caring nursing staff from a distant location."
- The tele-ICU environment consists of large computer workstations arranged to enhance communication among members of the tele-ICU team.
- Ergonomic design features are often incorporated because clinicians working in these centers typically work long 8- to 12-hour shifts.
- Adaptable workstations, such as sit-to-stand desks, adjustable computer monitor arms, and chairs of differing sizes that conform to ergonomic standards are used in the stations.
- To support active and continuous surveillance on large populations of high-acuity patients, each workstation is equipped with several monitors displaying clinical data from different health information systems.
- Tele-ICU nurses conduct all their tasks using multiple computer applications and technologies. They are uniquely positioned and equipped to provide continuous monitoring and response to clinical alerts, a job that is difficult to accomplish at the bedside, where alarm fatigue is a significant health hazard.
- The operations centers are outfitted with monitors, PC workstations, and Cisco phone systems, where teams have access to a wealth of data at their fingertips. Platforms pull data from different sources, including bedside monitoring, biodata, and electronic health records, to help identify trends, analyze risks, and produce alerts.
- Tele-ICU involves the offering of care to critically ill patients through synchronous, two-way audiovisual communication.
- Inside patient rooms, teams collaborate via two-way audio and high-definition pan tilt zoom cameras that provide a bird’s-eye view into each room, close enough for a clinician to see a pupil dilate or to read a medicine bottle.
- Using audio/visual conferencing and a real-time data stream of patient information from multiple interfaces, a physician working from a care center in one location can quickly care for a patient in another site, day or night. This connectivity enables an already engaged intensivist to promptly intervene and consistently provide care aligned with best practices.
- The application of artificial intelligence techniques can provide support to health professionals in decision-making related to the patient's treatment. The use of AI represents an opportunity for more effective and efficient care delivery by predicting disease trajectory and complications.
- Machine learning and AI are used in tele-ICUs as risk prediction algorithms, smart alarm systems, and machine learning tools augment conventional coverage and strive to improve the quality of care.
- Tele-ICUs use a combination of Big Data, machine learning, AI, and high-frequency real-time patient-based data, to help deliver accurate predictions for high, moderate, and low-risk patients and to optimize workflow.
- AI-based classification models predict which patients will or will not require intervention within a specified time. The low-risk patient classification allows providers to allocate additional resources to critical and unstable patients.
- For example, Philips created Sentry Score, a predictive algorithm developed as a regression machine learning model, to provide clinicians in tele-ICUs with a more comprehensive view of the patient and to allow them to coordinate assessments and recommend actions with the bedside care team in an efficient, less disruptive way.