{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T19:58:13Z","timestamp":1790798293974,"version":"4.1.0"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,11,21]],"date-time":"2020-11-21T00:00:00Z","timestamp":1605916800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,11,21]],"date-time":"2020-11-21T00:00:00Z","timestamp":1605916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Technische Universit\u00e4t Hamburg"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"published-print":{"date-parts":[[2021,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTATs) for critical findings in chest radiographs (CXRs). Furthermore, we investigate a method to counteract the effect of false negative predictions by AI\u2014resulting in an extremely and dangerously long RTAT, as CXRs are sorted to the end of the worklist.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a simulation framework that models the current workflow at a university hospital by incorporating hospital-specific CXR generation rates and reporting rates and pathology distribution. Using this, we simulated the standard worklist processing \u201cfirst-in, first-out\u201d (FIFO) and compared it with a worklist prioritization based on urgency. Examination prioritization was performed by the AI, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass, and foreign object. Furthermore, we introduced an upper limit for the maximum waiting time, after which the highest urgency is assigned to the examination.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The average RTAT for all critical findings was significantly reduced in all prioritization simulations compared to the FIFO simulation (e.g., pneumothorax: 35.6\u00a0min vs. 80.1\u00a0min;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001), while the maximum RTAT for most findings increased at the same time (e.g., pneumothorax: 1293\u00a0min vs 890\u00a0min;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001). Our \u201cupper limit\u201d substantially reduced the maximum RTAT in all classes (e.g., pneumothorax: 979\u00a0min vs. 1293\u00a0min\/1178\u00a0min;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our simulations demonstrate that smart worklist prioritization by AI can reduce the average RTAT for critical findings in CXRs while maintaining a small maximum RTAT as FIFO.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:italic>\u2022 Development of a realistic clinical workflow simulator based on empirical data from a hospital allowed precise assessment of smart worklist prioritization using artificial intelligence.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:italic>\u2022 Employing a smart worklist prioritization without a threshold for maximum waiting time runs the risk of false negative predictions of the artificial intelligence greatly increasing the report turnaround time.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:italic>\u2022 Use of a state-of-the-art convolution neural network can reduce the average report turnaround time almost to the upper limit of a perfect classification algorithm (e.g., pneumothorax: 35.6\u00a0min<\/jats:italic>\n                      vs.\n                      <jats:italic>30.4\u00a0min).<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-020-07480-7","type":"journal-article","created":{"date-parts":[[2020,11,21]],"date-time":"2020-11-21T11:02:36Z","timestamp":1605956556000},"page":"3837-3845","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation"],"prefix":"10.1007","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8748-3820","authenticated-orcid":false,"given":"Ivo","family":"Baltruschat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonhard","family":"Steinmeister","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannes","family":"Nickisch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Axel","family":"Saalbach","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Grass","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerhard","family":"Adam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Knopp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harald","family":"Ittrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,21]]},"reference":[{"issue":"4","key":"7480_CR1","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/s10278-013-9609-4","volume":"26","author":"BI Reiner","year":"2013","unstructured":"Reiner BI (2013) Innovation opportunities in critical results communication: theoretical concepts. 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Baltruschat, Leonhard Steinmeister, Gerhard Adam, Tobias Knopp, and Harald Ittrich of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"One of the authors, Axel Saalbach, has significant statistical expertise.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Only if the study is on human subjects:Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"In view of the fact that the patient data, that is, the subject of the study, can no longer be assigned to a person, our project does not constitute a \u201cresearch project on people\u201d within the meaning of Article 9 para. 2 of the Hamburg Chamber Act for the Medical Professions and does not fall under the research projects requiring consultation according to Article 15 para. 1 of the Professional Code of Conduct for Hamburg Doctors. There is therefore no advisory responsibility for the \u201cEthikkommission der \u00c4rztekammer Hamburg\u201d for our project.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"\u2022 retrospective\u2022 performed at one institution","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}