{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T03:45:56Z","timestamp":1777347956592,"version":"3.51.4"},"reference-count":17,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T00:00:00Z","timestamp":1658620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Timestamps in the Radiology Information System (RIS) are a readily available and valuable source of information with increasing significance, among others, due to the current focus on the clinical impact of artificial intelligence applications. We aimed to evaluate timestamp-based radiological dictation time, introduce timestamp modeling techniques, and compare those with prospective measured reporting. Dictation time was calculated from RIS timestamps between 05\/2010 and 01\/2021 at our institution (n = 108,310). We minimized contextual outliers by simulating the raw data by iteration (1000, vector size (\u00b5\/sd\/\u03bb) = 100\/loop), assuming normally distributed reporting times. In addition, 329 reporting times were prospectively measured by two radiologists (1 and 4 years of experience). Altogether, 106,127 of 108,310 exams were included after simulation, with a mean dictation time of 16.62 min. Mean dictation time was 16.05 min head CT (44,743\/45,596), 15.84 min for chest CT (32,797\/33,381), 17.92 min for abdominal CT (n = 22,805\/23,483), 10.96 min for CT foot (n = 937\/958), 9.14 min for lumbar spine (881\/892), 8.83 min for shoulder (409\/436), 8.83 min for CT wrist (1201\/1322), and 39.20 min for a polytrauma patient (2127\/2242), without a significant difference to the prospective reporting times. In conclusion, timestamp analysis is useful to measure current reporting practice, whereas body-region and radiological experience are confounders. This could aid in cost\u2013benefit assessments of workflow changes (e.g., AI implementation).<\/jats:p>","DOI":"10.3390\/jimaging8080208","type":"journal-article","created":{"date-parts":[[2022,7,24]],"date-time":"2022-07-24T22:49:02Z","timestamp":1658702942000},"page":"208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Time Is Money: Considerations for Measuring the Radiological Reading Time"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9949-7912","authenticated-orcid":false,"given":"Raphael","family":"Sexauer","sequence":"first","affiliation":[{"name":"Department of Radiology and Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caroline","family":"Bestler","sequence":"additional","affiliation":[{"name":"Department of Radiology and Nuclear Medicine, University Hospital Basel, 4031 Basel, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1056\/NEJM199609263351311","article-title":"Measuring Quality of Care","volume":"335","author":"Brook","year":"1996","journal-title":"N. 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