Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting
Background Transoral robotic surgery has been successfully used by head and neck surgeons for a variety of procedures but is limited by rigid instrumentation and line-of-sight visualization. Non-linear systems specifically designed for the aerodigestive tract are needed. Ease of use of these new sys...
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Veröffentlicht in: | Surgical endoscopy 2022-02, Vol.36 (2), p.1563-1572 |
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description | Background
Transoral robotic surgery has been successfully used by head and neck surgeons for a variety of procedures but is limited by rigid instrumentation and line-of-sight visualization. Non-linear systems specifically designed for the aerodigestive tract are needed. Ease of use of these new systems in both training and clinical environments is critical in its widespread adoption.
Methods
Residents, fellows, and junior faculty performed four tasks on an anatomical airway mannequin using the Medrobotics FLEX™ Robotic System: expose and incise the tonsil, grasp the epiglottis, palpate the vocal processes, and grasp the interarytenoid space. These tasks were performed once a day for four days; after a 4-month time gap, subjects were asked to perform these same tasks for three more days. Time to task completion and total distance driven were tracked. In addition, a retrospective analysis was performed analyzing one attending physician’s experience with clinical usage of the robot.
Results
13 subjects completed the initial round of the mannequin simulation and 8 subjects completed the additional testing 4 months later. Subjects rapidly improved their speed and efficiency at task completion. Junior residents were slower in most tasks initially compared to senior trainees but quickly reached similar levels of efficiency. Following the break there was minimal degradation in skills and continued improvement in efficiency was observed with additional trials. There was significant heterogeneity in the analyzed clinical cases, but when analyzing cases of similar complexity and pathology, clear decreases in overall operative times were demonstrable.
Conclusion
Novice users quickly gained proficiency with the FLEX™ Robotic System in a training environment, and these skills are retained after several months. This learning could translate to the clinical setting if a proper training regimen is developed. The Medrobotics FLEX™ Robotic System shows promise as a surgical tool in head and neck surgery in this study. |
doi_str_mv | 10.1007/s00464-021-08445-7 |
format | Article |
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Transoral robotic surgery has been successfully used by head and neck surgeons for a variety of procedures but is limited by rigid instrumentation and line-of-sight visualization. Non-linear systems specifically designed for the aerodigestive tract are needed. Ease of use of these new systems in both training and clinical environments is critical in its widespread adoption.
Methods
Residents, fellows, and junior faculty performed four tasks on an anatomical airway mannequin using the Medrobotics FLEX™ Robotic System: expose and incise the tonsil, grasp the epiglottis, palpate the vocal processes, and grasp the interarytenoid space. These tasks were performed once a day for four days; after a 4-month time gap, subjects were asked to perform these same tasks for three more days. Time to task completion and total distance driven were tracked. In addition, a retrospective analysis was performed analyzing one attending physician’s experience with clinical usage of the robot.
Results
13 subjects completed the initial round of the mannequin simulation and 8 subjects completed the additional testing 4 months later. Subjects rapidly improved their speed and efficiency at task completion. Junior residents were slower in most tasks initially compared to senior trainees but quickly reached similar levels of efficiency. Following the break there was minimal degradation in skills and continued improvement in efficiency was observed with additional trials. There was significant heterogeneity in the analyzed clinical cases, but when analyzing cases of similar complexity and pathology, clear decreases in overall operative times were demonstrable.
Conclusion
Novice users quickly gained proficiency with the FLEX™ Robotic System in a training environment, and these skills are retained after several months. This learning could translate to the clinical setting if a proper training regimen is developed. The Medrobotics FLEX™ Robotic System shows promise as a surgical tool in head and neck surgery in this study.</description><identifier>ISSN: 0930-2794</identifier><identifier>EISSN: 1432-2218</identifier><identifier>DOI: 10.1007/s00464-021-08445-7</identifier><identifier>PMID: 33751213</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Abdominal Surgery ; Clinical Competence ; Efficiency ; Endoscopy ; Epiglottis ; Gastroenterology ; Gynecology ; Hepatology ; Humans ; Learning Curve ; Medicine ; Medicine & Public Health ; Otolaryngology ; Proctology ; Retrospective Studies ; Robotic surgery ; Robotic Surgical Procedures - methods ; Robotics ; Simulation ; Surgeons ; Surgery ; Training</subject><ispartof>Surgical endoscopy, 2022-02, Vol.36 (2), p.1563-1572</ispartof><rights>The Author(s) 2021</rights><rights>2021. The Author(s).</rights><rights>The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-98d68434d984d44944ed89bd82db609d7618702d214de74027dfbbd895f4682a3</citedby><cites>FETCH-LOGICAL-c474t-98d68434d984d44944ed89bd82db609d7618702d214de74027dfbbd895f4682a3</cites><orcidid>0000-0001-9264-9701</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00464-021-08445-7$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00464-021-08445-7$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33751213$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhu, Toby S.</creatorcontrib><creatorcontrib>Godse, Neal</creatorcontrib><creatorcontrib>Clayburgh, Daniel R.</creatorcontrib><creatorcontrib>Duvvuri, Umamaheswar</creatorcontrib><title>Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting</title><title>Surgical endoscopy</title><addtitle>Surg Endosc</addtitle><addtitle>Surg Endosc</addtitle><description>Background
Transoral robotic surgery has been successfully used by head and neck surgeons for a variety of procedures but is limited by rigid instrumentation and line-of-sight visualization. Non-linear systems specifically designed for the aerodigestive tract are needed. Ease of use of these new systems in both training and clinical environments is critical in its widespread adoption.
Methods
Residents, fellows, and junior faculty performed four tasks on an anatomical airway mannequin using the Medrobotics FLEX™ Robotic System: expose and incise the tonsil, grasp the epiglottis, palpate the vocal processes, and grasp the interarytenoid space. These tasks were performed once a day for four days; after a 4-month time gap, subjects were asked to perform these same tasks for three more days. Time to task completion and total distance driven were tracked. In addition, a retrospective analysis was performed analyzing one attending physician’s experience with clinical usage of the robot.
Results
13 subjects completed the initial round of the mannequin simulation and 8 subjects completed the additional testing 4 months later. Subjects rapidly improved their speed and efficiency at task completion. Junior residents were slower in most tasks initially compared to senior trainees but quickly reached similar levels of efficiency. Following the break there was minimal degradation in skills and continued improvement in efficiency was observed with additional trials. There was significant heterogeneity in the analyzed clinical cases, but when analyzing cases of similar complexity and pathology, clear decreases in overall operative times were demonstrable.
Conclusion
Novice users quickly gained proficiency with the FLEX™ Robotic System in a training environment, and these skills are retained after several months. This learning could translate to the clinical setting if a proper training regimen is developed. The Medrobotics FLEX™ Robotic System shows promise as a surgical tool in head and neck surgery in this study.</description><subject>Abdominal Surgery</subject><subject>Clinical Competence</subject><subject>Efficiency</subject><subject>Endoscopy</subject><subject>Epiglottis</subject><subject>Gastroenterology</subject><subject>Gynecology</subject><subject>Hepatology</subject><subject>Humans</subject><subject>Learning Curve</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Otolaryngology</subject><subject>Proctology</subject><subject>Retrospective Studies</subject><subject>Robotic surgery</subject><subject>Robotic Surgical Procedures - methods</subject><subject>Robotics</subject><subject>Simulation</subject><subject>Surgeons</subject><subject>Surgery</subject><subject>Training</subject><issn>0930-2794</issn><issn>1432-2218</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNp9UcuKFDEUDaI4besPuJCAGzeleVUl2QjD4AsG3Og6pCq3ujOkkzZJ9ejfm54e28fCRQiX87jnchB6TslrSoh8UwgRg-gIox1RQvSdfIBWVHDWMUbVQ7QimpOOSS0u0JNSbkjja9o_Rhecy54yylcoXJYCpfi4wXULOIDN8ThMSz4AtqWkydsKDt_6usUWx3SAgOcA3_0YAOc0pop9vBPvM3RT8NFPNmAbHT4PBWptrk_Ro9mGAs_u_zX6-v7dl6uP3fXnD5-uLq-7SUhRO63coAQXTivhhNBCgFN6dIq5cSDayYEqSZhjVDiQgjDp5rHBup_FoJjla_T25Ltfxh24CWLNNph99jubf5hkvfkbiX5rNulglOT8-Nbo1b1BTt8WKNXsfJkgBBshLcWwnrR8quVo1Jf_UG_SkmM7z7CB6uYnyNGQnVhTTqVkmM9hKDHHMs2pTNPKNHdlGtlEL_484yz51V4j8BOhNChuIP_e_R_bn9F_q0k</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Zhu, Toby S.</creator><creator>Godse, Neal</creator><creator>Clayburgh, Daniel R.</creator><creator>Duvvuri, Umamaheswar</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>M1P</scope><scope>NAPCQ</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-9264-9701</orcidid></search><sort><creationdate>20220201</creationdate><title>Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting</title><author>Zhu, Toby S. ; Godse, Neal ; Clayburgh, Daniel R. ; Duvvuri, Umamaheswar</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-98d68434d984d44944ed89bd82db609d7618702d214de74027dfbbd895f4682a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Abdominal Surgery</topic><topic>Clinical Competence</topic><topic>Efficiency</topic><topic>Endoscopy</topic><topic>Epiglottis</topic><topic>Gastroenterology</topic><topic>Gynecology</topic><topic>Hepatology</topic><topic>Humans</topic><topic>Learning Curve</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Otolaryngology</topic><topic>Proctology</topic><topic>Retrospective Studies</topic><topic>Robotic surgery</topic><topic>Robotic Surgical Procedures - methods</topic><topic>Robotics</topic><topic>Simulation</topic><topic>Surgeons</topic><topic>Surgery</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Toby S.</creatorcontrib><creatorcontrib>Godse, Neal</creatorcontrib><creatorcontrib>Clayburgh, Daniel R.</creatorcontrib><creatorcontrib>Duvvuri, Umamaheswar</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Surgical endoscopy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhu, Toby S.</au><au>Godse, Neal</au><au>Clayburgh, Daniel R.</au><au>Duvvuri, Umamaheswar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting</atitle><jtitle>Surgical endoscopy</jtitle><stitle>Surg Endosc</stitle><addtitle>Surg Endosc</addtitle><date>2022-02-01</date><risdate>2022</risdate><volume>36</volume><issue>2</issue><spage>1563</spage><epage>1572</epage><pages>1563-1572</pages><issn>0930-2794</issn><eissn>1432-2218</eissn><abstract>Background
Transoral robotic surgery has been successfully used by head and neck surgeons for a variety of procedures but is limited by rigid instrumentation and line-of-sight visualization. Non-linear systems specifically designed for the aerodigestive tract are needed. Ease of use of these new systems in both training and clinical environments is critical in its widespread adoption.
Methods
Residents, fellows, and junior faculty performed four tasks on an anatomical airway mannequin using the Medrobotics FLEX™ Robotic System: expose and incise the tonsil, grasp the epiglottis, palpate the vocal processes, and grasp the interarytenoid space. These tasks were performed once a day for four days; after a 4-month time gap, subjects were asked to perform these same tasks for three more days. Time to task completion and total distance driven were tracked. In addition, a retrospective analysis was performed analyzing one attending physician’s experience with clinical usage of the robot.
Results
13 subjects completed the initial round of the mannequin simulation and 8 subjects completed the additional testing 4 months later. Subjects rapidly improved their speed and efficiency at task completion. Junior residents were slower in most tasks initially compared to senior trainees but quickly reached similar levels of efficiency. Following the break there was minimal degradation in skills and continued improvement in efficiency was observed with additional trials. There was significant heterogeneity in the analyzed clinical cases, but when analyzing cases of similar complexity and pathology, clear decreases in overall operative times were demonstrable.
Conclusion
Novice users quickly gained proficiency with the FLEX™ Robotic System in a training environment, and these skills are retained after several months. This learning could translate to the clinical setting if a proper training regimen is developed. The Medrobotics FLEX™ Robotic System shows promise as a surgical tool in head and neck surgery in this study.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>33751213</pmid><doi>10.1007/s00464-021-08445-7</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-9264-9701</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Abdominal Surgery Clinical Competence Efficiency Endoscopy Epiglottis Gastroenterology Gynecology Hepatology Humans Learning Curve Medicine Medicine & Public Health Otolaryngology Proctology Retrospective Studies Robotic surgery Robotic Surgical Procedures - methods Robotics Simulation Surgeons Surgery Training |
title | Assessing the learning curve associated with a novel flexible robot in the pre-clinical and clinical setting |
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