{"title":"Using an AI-Powered Solution to Transform Nursing Workflow and Improve Inpatient Care: A Retrospective Observational Study.","authors":"Luana Llagostera Sillano Gentil, Vanessa Aparecida Luz Pires, Jéssica Andrade-Silva, Youri Eliphas Almeida, Paulo Gurgel Pinheiro, Claudio Gurgel Pinheiro, Claudia Regina Laselva","doi":"10.1097/AJN.0000000000000070","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Nurses face an escalating workload, including tasks not directly related to patient care, such as responding to patients' requests for water or extra blankets, and adjusting room conditions like air conditioning, which can contribute to burnout and pose potential risks for both professionals and patients.</p><p><strong>Purpose: </strong>We aimed to evaluate an artificial intelligence (AI)-driven technology that automates patient requests and directs them to the appropriate teams in other departments instead of to the nurses' station, thereby eliminating the need for nursing intervention.</p><p><strong>Methods: </strong>This retrospective observational study, conducted in the surgical clinic of a large hospital over 30 days, examined data gathered from patients occupying 16 beds (as our focus was on the volume and type of patient requests generated per bed rather than per patient, patient turnover did not affect the study). During this period, the AI-driven platform, known as Neonpass Room, was implemented in addition to the traditional bell-activation request system.</p><p><strong>Results: </strong>During the 30-day implementation of Neonpass Room, 2,113 requests were recorded, mostly from patients, and the AI-powered system directed 35.4% of those requests, which required clinical attention, to the nursing staff. The remaining requests were routed to other appropriate teams, such as housekeeping or maintenance, according to the nature of the requests. Among the top 10 most frequent request types, only two were explicitly directed to the nursing team: \"check IV fluids\" and \"support with restroom use.\" The AI platform also measured the response times among the various departments (the nursing team had the shortest response time and the cleaning staff the longest) and enabled the visualization of patterns in patient-initiated requests by request type over a 24-hour period. The AI solution effectively reduced the nursing team's workload by nearly three out of five patient requests, resulting in considerable time savings for nurses.</p><p><strong>Conclusion: </strong>Neonpass Room allowed the nursing staff to improve their focus on patient care by optimizing task delegation. Once integrated into the hospital's operations, an AI-driven platform like Neonpass Room would provide managers with valuable strategic insights into the allocation of hospital services.</p>","PeriodicalId":7622,"journal":{"name":"American Journal of Nursing","volume":"125 5","pages":"38-43"},"PeriodicalIF":2.5000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Nursing","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/AJN.0000000000000070","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/4/24 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"NURSING","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Nurses face an escalating workload, including tasks not directly related to patient care, such as responding to patients' requests for water or extra blankets, and adjusting room conditions like air conditioning, which can contribute to burnout and pose potential risks for both professionals and patients.
Purpose: We aimed to evaluate an artificial intelligence (AI)-driven technology that automates patient requests and directs them to the appropriate teams in other departments instead of to the nurses' station, thereby eliminating the need for nursing intervention.
Methods: This retrospective observational study, conducted in the surgical clinic of a large hospital over 30 days, examined data gathered from patients occupying 16 beds (as our focus was on the volume and type of patient requests generated per bed rather than per patient, patient turnover did not affect the study). During this period, the AI-driven platform, known as Neonpass Room, was implemented in addition to the traditional bell-activation request system.
Results: During the 30-day implementation of Neonpass Room, 2,113 requests were recorded, mostly from patients, and the AI-powered system directed 35.4% of those requests, which required clinical attention, to the nursing staff. The remaining requests were routed to other appropriate teams, such as housekeeping or maintenance, according to the nature of the requests. Among the top 10 most frequent request types, only two were explicitly directed to the nursing team: "check IV fluids" and "support with restroom use." The AI platform also measured the response times among the various departments (the nursing team had the shortest response time and the cleaning staff the longest) and enabled the visualization of patterns in patient-initiated requests by request type over a 24-hour period. The AI solution effectively reduced the nursing team's workload by nearly three out of five patient requests, resulting in considerable time savings for nurses.
Conclusion: Neonpass Room allowed the nursing staff to improve their focus on patient care by optimizing task delegation. Once integrated into the hospital's operations, an AI-driven platform like Neonpass Room would provide managers with valuable strategic insights into the allocation of hospital services.
期刊介绍:
The American Journal of Nursing is the oldest and most honored broad-based nursing journal in the world. Peer reviewed and evidence-based, it is considered the profession’s premier journal. AJN adheres to journalistic standards that require transparency of real and potential conflicts of interests that authors,editors and reviewers may have. It follows publishing standards set by the International Committee of Medical Journal Editors (ICMJE; www.icmje.org), the World Association of Medical Editors (WAME; www.wame.org), and the Committee on Publication Ethics (COPE; http://publicationethics.org/).
AJN welcomes submissions of evidence-based clinical application papers and descriptions of best clinical practices, original research and QI reports, case studies, narratives, commentaries, and other manuscripts on a variety of clinical and professional topics. The journal also welcomes submissions for its various departments and columns, including artwork and poetry that is relevant to nursing or health care. Guidelines on writing for specific departments—Art of Nursing, Viewpoint, Policy and Politics, and Reflections—are available at http://AJN.edmgr.com.
AJN''s mission is to promote excellence in nursing and health care through the dissemination of evidence-based, peer-reviewed clinical information and original research, discussion of relevant and controversial professional issues, adherence to the standards of journalistic integrity and excellence, and promotion of nursing perspectives to the health care community and the public.