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

      research-article
      , MD, MBA 1 , , MA 1 , , MD 1 ,
      Anesthesia and Analgesia
      Lippincott Williams & Wilkins

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          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.

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          Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension.

          Intraoperative hypotension may contribute to postoperative acute kidney injury (AKI) and myocardial injury, but what blood pressures are unsafe is unclear. The authors evaluated the association between the intraoperative mean arterial pressure (MAP) and the risk of AKI and myocardial injury.
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            The Rise of Consumer Health Wearables: Promises and Barriers

            Lukasz Piwek and colleagues consider whether wearable technology can become a valuable asset for health care.
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              Artificial Intelligence in Precision Cardiovascular Medicine.

              Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine.
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                Author and article information

                Journal
                Anesth Analg
                Anesth. Analg
                ANE
                Anesthesia and Analgesia
                Lippincott Williams & Wilkins
                0003-2999
                1526-7598
                September 2019
                21 November 2018
                : 129
                : 3
                : 726-734
                Affiliations
                [1]From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts.
                Author notes
                Address correspondence to Jeanine P. Wiener-Kronish, MD, Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, 55 Fruit St, GRB 4–444, Boston, MA 02114. Address e-mail to jwiener-kronish@ 123456partners.org .
                Article
                00019
                10.1213/ANE.0000000000003948
                6693927
                31425213
                1ddfb257-ac91-4429-847f-73c28a87f762
                Copyright © 2018 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Anesthesia Research Society.

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

                History
                : 18 October 2018
                Categories
                Technology, Computing, and Simulation
                Narrative Review Article
                Custom metadata
                TRUE

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