Potential uses of AI for perioperative nursing handoffs: a qualitative study
Abstract Objective Situational awareness and anticipatory guidance for nurses receiving a patient after surgery are keys to patient safety. Little work has defined the role of artificial intelligence (AI) to support these functions during nursing handoff communication or patient assessment. We used...
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Veröffentlicht in: | JAMIA open 2023-04, Vol.6 (1), p.ooad015-ooad015 |
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description | Abstract
Objective
Situational awareness and anticipatory guidance for nurses receiving a patient after surgery are keys to patient safety. Little work has defined the role of artificial intelligence (AI) to support these functions during nursing handoff communication or patient assessment. We used interviews to better understand how AI could work in this context.
Materials and Methods
Eleven nurses participated in semistructured interviews. Mixed inductive-deductive thematic analysis was used to extract major themes and subthemes around roles for AI supporting postoperative nursing.
Results
Five themes were generated from the interviews: (1) nurse understanding of patient condition guides care decisions, (2) handoffs are important to nurse situational awareness, but multiple barriers reduce their effectiveness, (3) AI may address barriers to handoff effectiveness, (4) AI may augment nurse care decision making and team communication outside of handoff, and (5) user experience in the electronic health record and information overload are likely barriers to using AI. Important subthemes included that AI-identified problems would be discussed at handoff and team communications, that AI-estimated elevated risks would trigger patient re-evaluation, and that AI-identified important data may be a valuable addition to nursing assessment.
Discussion and Conclusion
Most research on postoperative handoff communication relies on structured checklists. Our results suggest that properly designed AI tools might facilitate postoperative handoff communication for nurses by identifying specific elevated risks faced by a patient, triggering discussion on those topics. Limitations include a single center, many participants lacking of applied experience with AI, and limited participation rate.
Lay Summary
Nurses caring for patients after surgery make many decisions about what complications to look for and how to treat issues that arise. They rely on handoffs from prior clinicians to understand the patient's background, relevant events, and care plans so far. We interviewed nurses to ask if and how artificial intelligence (AI) might help them focus their handoff communication on likely problems and generally understand the patient. Our participants stated that if AI identified likely issues, they would discuss those topics in handoff, communicate about those problems with physicians, and modify their monitoring and treatment to the level of risk faced by the patient. This findin |
doi_str_mv | 10.1093/jamiaopen/ooad015 |
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Objective
Situational awareness and anticipatory guidance for nurses receiving a patient after surgery are keys to patient safety. Little work has defined the role of artificial intelligence (AI) to support these functions during nursing handoff communication or patient assessment. We used interviews to better understand how AI could work in this context.
Materials and Methods
Eleven nurses participated in semistructured interviews. Mixed inductive-deductive thematic analysis was used to extract major themes and subthemes around roles for AI supporting postoperative nursing.
Results
Five themes were generated from the interviews: (1) nurse understanding of patient condition guides care decisions, (2) handoffs are important to nurse situational awareness, but multiple barriers reduce their effectiveness, (3) AI may address barriers to handoff effectiveness, (4) AI may augment nurse care decision making and team communication outside of handoff, and (5) user experience in the electronic health record and information overload are likely barriers to using AI. Important subthemes included that AI-identified problems would be discussed at handoff and team communications, that AI-estimated elevated risks would trigger patient re-evaluation, and that AI-identified important data may be a valuable addition to nursing assessment.
Discussion and Conclusion
Most research on postoperative handoff communication relies on structured checklists. Our results suggest that properly designed AI tools might facilitate postoperative handoff communication for nurses by identifying specific elevated risks faced by a patient, triggering discussion on those topics. Limitations include a single center, many participants lacking of applied experience with AI, and limited participation rate.
Lay Summary
Nurses caring for patients after surgery make many decisions about what complications to look for and how to treat issues that arise. They rely on handoffs from prior clinicians to understand the patient's background, relevant events, and care plans so far. We interviewed nurses to ask if and how artificial intelligence (AI) might help them focus their handoff communication on likely problems and generally understand the patient. Our participants stated that if AI identified likely issues, they would discuss those topics in handoff, communicate about those problems with physicians, and modify their monitoring and treatment to the level of risk faced by the patient. This finding runs against most research on improving communication, which focuses on fixed checklists of topics to discuss. Most uses of AI for nurses focus on making specific to-do recommendations and documentation reminders, but we find that nurses would benefit from AI which focuses more on their understanding of the patient’s condition.</description><identifier>ISSN: 2574-2531</identifier><identifier>EISSN: 2574-2531</identifier><identifier>DOI: 10.1093/jamiaopen/ooad015</identifier><identifier>PMID: 36935899</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>Analysis ; Artificial intelligence ; Decision-making ; Electronic records ; Medical records ; Medical research ; Medicine, Experimental ; Research and Applications ; Surgical nursing</subject><ispartof>JAMIA open, 2023-04, Vol.6 (1), p.ooad015-ooad015</ispartof><rights>The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. 2023</rights><rights>The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.</rights><rights>COPYRIGHT 2023 Oxford University Press</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c504t-c451b68a83fc8ca76a36f8c6aa6200c3ea3456bcd15a31cdb01c7ebed830ca423</citedby><cites>FETCH-LOGICAL-c504t-c451b68a83fc8ca76a36f8c6aa6200c3ea3456bcd15a31cdb01c7ebed830ca423</cites><orcidid>0000-0002-4574-8616 ; 0000-0003-0235-1632</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019806/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10019806/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,1598,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36935899$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>King, Christopher Ryan</creatorcontrib><creatorcontrib>Shambe, Ayanna</creatorcontrib><creatorcontrib>Abraham, Joanna</creatorcontrib><title>Potential uses of AI for perioperative nursing handoffs: a qualitative study</title><title>JAMIA open</title><addtitle>JAMIA Open</addtitle><description>Abstract
Objective
Situational awareness and anticipatory guidance for nurses receiving a patient after surgery are keys to patient safety. Little work has defined the role of artificial intelligence (AI) to support these functions during nursing handoff communication or patient assessment. We used interviews to better understand how AI could work in this context.
Materials and Methods
Eleven nurses participated in semistructured interviews. Mixed inductive-deductive thematic analysis was used to extract major themes and subthemes around roles for AI supporting postoperative nursing.
Results
Five themes were generated from the interviews: (1) nurse understanding of patient condition guides care decisions, (2) handoffs are important to nurse situational awareness, but multiple barriers reduce their effectiveness, (3) AI may address barriers to handoff effectiveness, (4) AI may augment nurse care decision making and team communication outside of handoff, and (5) user experience in the electronic health record and information overload are likely barriers to using AI. Important subthemes included that AI-identified problems would be discussed at handoff and team communications, that AI-estimated elevated risks would trigger patient re-evaluation, and that AI-identified important data may be a valuable addition to nursing assessment.
Discussion and Conclusion
Most research on postoperative handoff communication relies on structured checklists. Our results suggest that properly designed AI tools might facilitate postoperative handoff communication for nurses by identifying specific elevated risks faced by a patient, triggering discussion on those topics. Limitations include a single center, many participants lacking of applied experience with AI, and limited participation rate.
Lay Summary
Nurses caring for patients after surgery make many decisions about what complications to look for and how to treat issues that arise. They rely on handoffs from prior clinicians to understand the patient's background, relevant events, and care plans so far. We interviewed nurses to ask if and how artificial intelligence (AI) might help them focus their handoff communication on likely problems and generally understand the patient. Our participants stated that if AI identified likely issues, they would discuss those topics in handoff, communicate about those problems with physicians, and modify their monitoring and treatment to the level of risk faced by the patient. This finding runs against most research on improving communication, which focuses on fixed checklists of topics to discuss. Most uses of AI for nurses focus on making specific to-do recommendations and documentation reminders, but we find that nurses would benefit from AI which focuses more on their understanding of the patient’s condition.</description><subject>Analysis</subject><subject>Artificial intelligence</subject><subject>Decision-making</subject><subject>Electronic records</subject><subject>Medical records</subject><subject>Medical research</subject><subject>Medicine, Experimental</subject><subject>Research and Applications</subject><subject>Surgical nursing</subject><issn>2574-2531</issn><issn>2574-2531</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><recordid>eNqNkcFL5DAUxoMoKuofsJclsBcPO5o0TZp6kUF0VxjQw-45vKbJGGmTmrSC__1m6Oyg4EFyeCHv933kvQ-hb5RcUFKzy2foHYTB-MsQoCWU76HjglflouCM7r-7H6GzlJ4JIbSua8HIITpiomZc1vUxWj2G0fjRQYenZBIOFi_vsQ0RDya6bB9hdK8G-ykm59f4CXwbrE1XGPDLBJ0b534ap_btFB1Y6JI529YT9Pfu9s_N78Xq4df9zXK10JyU40KXnDZCgmRWSw2VACas1AJAFIRoZoCVXDS6pRwY1W1DqK5MY1rJiIayYCfoevYdpqY3rc4DROjUEF0P8U0FcOpjx7sntQ6vim6WIInIDudbhxheJpNG1bukTdeBN2FKqqiklITSimf0x4yuoTPKeRuypd7gallVgnJOWZWpi0-ofFrTOx28sS6_fxDQWaBjSCkau_s-JWoTsNoFrLYBZ83393PvFP_jzMDPGQjT8AW_fw8HtSE</recordid><startdate>20230401</startdate><enddate>20230401</enddate><creator>King, Christopher Ryan</creator><creator>Shambe, Ayanna</creator><creator>Abraham, Joanna</creator><general>Oxford University Press</general><scope>TOX</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-4574-8616</orcidid><orcidid>https://orcid.org/0000-0003-0235-1632</orcidid></search><sort><creationdate>20230401</creationdate><title>Potential uses of AI for perioperative nursing handoffs: a qualitative study</title><author>King, Christopher Ryan ; Shambe, Ayanna ; Abraham, Joanna</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c504t-c451b68a83fc8ca76a36f8c6aa6200c3ea3456bcd15a31cdb01c7ebed830ca423</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Analysis</topic><topic>Artificial intelligence</topic><topic>Decision-making</topic><topic>Electronic records</topic><topic>Medical records</topic><topic>Medical research</topic><topic>Medicine, Experimental</topic><topic>Research and Applications</topic><topic>Surgical nursing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>King, Christopher Ryan</creatorcontrib><creatorcontrib>Shambe, Ayanna</creatorcontrib><creatorcontrib>Abraham, Joanna</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>JAMIA open</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>King, Christopher Ryan</au><au>Shambe, Ayanna</au><au>Abraham, Joanna</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Potential uses of AI for perioperative nursing handoffs: a qualitative study</atitle><jtitle>JAMIA open</jtitle><addtitle>JAMIA Open</addtitle><date>2023-04-01</date><risdate>2023</risdate><volume>6</volume><issue>1</issue><spage>ooad015</spage><epage>ooad015</epage><pages>ooad015-ooad015</pages><issn>2574-2531</issn><eissn>2574-2531</eissn><abstract>Abstract
Objective
Situational awareness and anticipatory guidance for nurses receiving a patient after surgery are keys to patient safety. Little work has defined the role of artificial intelligence (AI) to support these functions during nursing handoff communication or patient assessment. We used interviews to better understand how AI could work in this context.
Materials and Methods
Eleven nurses participated in semistructured interviews. Mixed inductive-deductive thematic analysis was used to extract major themes and subthemes around roles for AI supporting postoperative nursing.
Results
Five themes were generated from the interviews: (1) nurse understanding of patient condition guides care decisions, (2) handoffs are important to nurse situational awareness, but multiple barriers reduce their effectiveness, (3) AI may address barriers to handoff effectiveness, (4) AI may augment nurse care decision making and team communication outside of handoff, and (5) user experience in the electronic health record and information overload are likely barriers to using AI. Important subthemes included that AI-identified problems would be discussed at handoff and team communications, that AI-estimated elevated risks would trigger patient re-evaluation, and that AI-identified important data may be a valuable addition to nursing assessment.
Discussion and Conclusion
Most research on postoperative handoff communication relies on structured checklists. Our results suggest that properly designed AI tools might facilitate postoperative handoff communication for nurses by identifying specific elevated risks faced by a patient, triggering discussion on those topics. Limitations include a single center, many participants lacking of applied experience with AI, and limited participation rate.
Lay Summary
Nurses caring for patients after surgery make many decisions about what complications to look for and how to treat issues that arise. They rely on handoffs from prior clinicians to understand the patient's background, relevant events, and care plans so far. We interviewed nurses to ask if and how artificial intelligence (AI) might help them focus their handoff communication on likely problems and generally understand the patient. Our participants stated that if AI identified likely issues, they would discuss those topics in handoff, communicate about those problems with physicians, and modify their monitoring and treatment to the level of risk faced by the patient. This finding runs against most research on improving communication, which focuses on fixed checklists of topics to discuss. Most uses of AI for nurses focus on making specific to-do recommendations and documentation reminders, but we find that nurses would benefit from AI which focuses more on their understanding of the patient’s condition.</abstract><cop>United States</cop><pub>Oxford University Press</pub><pmid>36935899</pmid><doi>10.1093/jamiaopen/ooad015</doi><orcidid>https://orcid.org/0000-0002-4574-8616</orcidid><orcidid>https://orcid.org/0000-0003-0235-1632</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Artificial intelligence Decision-making Electronic records Medical records Medical research Medicine, Experimental Research and Applications Surgical nursing |
title | Potential uses of AI for perioperative nursing handoffs: a qualitative study |
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