Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Background: Challenges associated with turnover time are magnified in robotic surgery. The introduction of advanced technology increases the complexity of an already intricate perioperative environment. We applied a human factors approach to develop systematic, data-driven interventions to reduce robotic surgery turnover time.
Methods: Researchers observed 40 robotic surgery turnovers at a tertiary hospital [20 pre-intervention (Jan 2018 to Apr 2018), 20 post-intervention (Jan 2019 to Jun 2019)]. Components of turnover time, including cleaning, instrument and room set-up, robot preparation, flow disruptions, and major delays, were documented and analyzed. Surveys and focus groups were used to investigate staff perceptions of robotic surgery turnover time. A multidisciplinary team of human factors experts and physicians developed targeted interventions. Pre- and post-intervention turnovers were compared.
Results: Median turnover time was 67 min (mean: 72, SD: 24) and 22 major delays were noted (1.1/case). The largest contributors were instrument setup (25.5 min) and cleaning (25 min). Interventions included an electronic dashboard for turnover time reporting, clear designation of roles and simultaneous completion of tasks, process standardization of operating room cleaning, and data transparency through monthly reporting. Post-intervention turnovers were significantly shorter (U = 57.5, p = .000) and ten major delays were noted.
Conclusions: Human factors analysis generated interventions to improve turnover time. Significant improvements were seen post-intervention with a reduction in turnover time by a 26 min and decrease in major delays by over 50%. Future opportunities to intervene and further improve turnover time include targeting pre- and post-operative care phases.
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Source |
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http://dx.doi.org/10.1007/s00268-022-06487-z | DOI Listing |
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