{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T23:48:33Z","timestamp":1772840913485,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T00:00:00Z","timestamp":1605744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01HL126896"],"award-info":[{"award-number":["R01HL126896"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Monitoring healthcare providers\u2019 cognitive workload during surgical procedures can provide insight into the dynamic changes of mental states that may affect patient clinical outcomes. The role of cognitive factors influencing both technical and non-technical skill are increasingly being recognized, especially as the opportunities to unobtrusively collect accurate and sensitive data are improving. Applying sensors to capture these data in a complex real-world setting such as the cardiac surgery operating room, however, is accompanied by myriad social, physical, and procedural constraints. The goal of this study was to investigate the feasibility of overcoming logistical barriers in order to effectively collect multi-modal psychophysiological inputs via heart rate (HR) and near-infrared spectroscopy (NIRS) acquisition in the real-world setting of the operating room. The surgeon was outfitted with HR and NIRS sensors during aortic valve surgery, and validation analysis was performed to detect the influence of intra-operative events on cardiovascular and prefrontal cortex changes. Signals collected were significantly correlated and noted intra-operative events and subjective self-reports coincided with observable correlations among cardiovascular and cerebral activity across surgical phases. The primary novelty and contribution of this work is in demonstrating the feasibility of collecting continuous sensor data from a surgical team member in a real-world setting.<\/jats:p>","DOI":"10.3390\/s20226616","type":"journal-article","created":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T06:23:52Z","timestamp":1605767032000},"page":"6616","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Sensors for Continuous Monitoring of Surgeon\u2019s Cognitive Workload in the Cardiac Operating Room"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2696-3943","authenticated-orcid":false,"given":"Lauren R.","family":"Kennedy-Metz","sequence":"first","affiliation":[{"name":"Division of Cardiac Surgery, Medical Robotics and Computer Assisted Surgery Lab, VA Boston Healthcare System, West Roxbury, MA 02132, USA"},{"name":"Department of Surgery, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4959-5052","authenticated-orcid":false,"given":"Roger D.","family":"Dias","sequence":"additional","affiliation":[{"name":"STRATUS Center for Medical Simulation, Department of Emergency Medicine, Brigham and Women\u2019s Hospital, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rithy","family":"Srey","sequence":"additional","affiliation":[{"name":"Division of Cardiac Surgery, VA Boston Healthcare System, West Roxbury, MA 02132, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geoffrey C.","family":"Rance","sequence":"additional","affiliation":[{"name":"Division of Cardiac Surgery, VA Boston Healthcare System, West Roxbury, MA 02132, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cesare","family":"Furlanello","sequence":"additional","affiliation":[{"name":"HK3 Lab, 20129 Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7139-0323","authenticated-orcid":false,"given":"Marco A.","family":"Zenati","sequence":"additional","affiliation":[{"name":"Division of Cardiac Surgery, Medical Robotics and Computer Assisted Surgery Lab, VA Boston Healthcare System, West Roxbury, MA 02132, USA"},{"name":"Department of Surgery, Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1136\/bmjqs-2014-003482","article-title":"Role of cognition in generating and mitigating clinical errors","volume":"24","author":"Patel","year":"2015","journal-title":"BMJ Qual. 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