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Review
. 2019 Sep;129(3):726-734.
doi: 10.1213/ANE.0000000000003948.

Remote Surveillance Technologies: Realizing the Aim of Right Patient, Right Data, Right Time

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Free PMC article
Review

Remote Surveillance Technologies: Realizing the Aim of Right Patient, Right Data, Right Time

Kyan C Safavi et al. Anesth Analg. .
Free PMC article

Abstract

The convergence of multiple recent developments in health care information technology and monitoring devices has made possible the creation of remote patient surveillance systems that increase the timeliness and quality of patient care. More convenient, less invasive monitoring devices, including patches, wearables, and biosensors, now allow for continuous physiological data to be gleaned from patients in a variety of care settings across the perioperative experience. These data can be bound into a single data repository, creating so-called data lakes. The high volume and diversity of data in these repositories must be processed into standard formats that can be queried in real time. These data can then be used by sophisticated prediction algorithms currently under development, enabling the early recognition of patterns of clinical deterioration otherwise undetectable to humans. Improved predictions can reduce alarm fatigue. In addition, data are now automatically queriable on a real-time basis such that they can be fed back to clinicians in a time frame that allows for meaningful intervention. These advancements are key components of successful remote surveillance systems. Anesthesiologists have the opportunity to be at the forefront of remote surveillance in the care they provide in the operating room, postanesthesia care unit, and intensive care unit, while also expanding their scope to include high-risk preoperative and postoperative patients on the general care wards. These systems hold the promise of enabling anesthesiologists to detect and intervene upon changes in the clinical status of the patient before adverse events have occurred. Importantly, however, significant barriers still exist to the effective deployment of these technologies and their study in impacting patient outcomes. Studies demonstrating the impact of remote surveillance on patient outcomes are limited. Critical to the impact of the technology are strategies of implementation, including who should receive and respond to alerts and how they should respond. Moreover, the lack of cost-effectiveness data and the uncertainty of whether clinical activities surrounding these technologies will be financially reimbursed remain significant challenges to future scale and sustainability. This narrative review will discuss the evolving technical components of remote surveillance systems, the clinical use cases relevant to the anesthesiologist's practice, the existing evidence for their impact on patients, the barriers that exist to their effective implementation and study, and important considerations regarding sustainability and cost-effectiveness.

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1.
Figure 1.
Components of effective remote surveillance systems. EMR indicates electronic medical record.
Figure 2.
Figure 2.
Example data and information systems architecture for effective remote surveillance. A and B indicate data acquisition pathways for 2 different types of medical devices: a traditional patient monitor (“medical device”) and a wearable patient monitor (“wearable medical device”). (1) In pathway A, patient data collected on the medical device are fed into “Medical Device Drivers,” a software application that enables connection between medical devices and downstream software applications. The “device gateway” then receives the data and transforms it into a Healthcare Language-7 message. The “Healthcare Language-7 integration engine” (C) receives the Healthcare Language-7 messages from the device gateway and then splits and distributes them to (D) the “data lake” (composed of the “enterprise data warehouse” that includes clinical and nonclinical high-resolution data) and (F) the electronic medical record. Data from the enterprise data warehouse are sent to the “remote monitoring and predictive analytics servers” on which analytics can be performed and alerts generated. These alerts are fed to (E) downstream systems that alert the clinician, such as pagers or visual display systems. (2) Alternatively, in pathway B, the “wearable medical device” may send its data to a “cloud-based storage” system. Using an “application programming interface (API),” the data can then be delivered to the data lake.

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