{"id":3909,"date":"2023-11-29T13:12:06","date_gmt":"2023-11-29T02:12:06","guid":{"rendered":"https:\/\/harrison.ai\/?p=3909"},"modified":"2025-10-07T06:31:30","modified_gmt":"2025-10-06T19:31:30","slug":"effect-of-machine-learning-assisted-triage-on-ct-brain-report-turn-around-time-in-australian-emergency-departments","status":"publish","type":"post","link":"https:\/\/harrison.ai\/effect-of-machine-learning-assisted-triage-on-ct-brain-report-turn-around-time-in-australian-emergency-departments\/","title":{"rendered":"Effect of Machine Learning Assisted Triage on CT Brain Report Turn-Around Time in Australian Emergency Departments"},"content":{"rendered":"    <section id=\"evidence-block-block_815198f982159d6e0af3e17f1b528288\" class=\"study-block   text-\" >\n        <div class=\"container  container--tab\">\n            <div class=\"container container--tab\">\n                <div class=\"connect__decor hide-md\">\n                    <span class=\"pixel-decor\" style=\"background-color: rgba(9, 114, 241, 0.75)\"><\/span>\n                    <span class=\"pixel-decor\" style=\"background-color: #0972f1\"><\/span>\n                    <span class=\"pixel-decor\" style=\"background-color: rgba(9, 114, 241, 0.5)\"><\/span>\n                    <span class=\"pixel-decor hide-sm\" style=\"background-color: rgba(9, 114, 241, 0.5)\"><\/span>\n                <\/div>\n                <div class=\"study__share hide-sm\" data-aos=\"fade-up\">\n                                    <\/div>\n                <div class=\"study__row\">\n                    <div class=\"study__left\" data-aos=\"fade-up\"><\/div>\n                    <div class=\"study__right\" data-aos=\"fade-up\">\n                        <div class=\"text b1\">\n                        <h5>Authors<\/h5>\n<p>H Ahmad, M Ryan, G Crow, CM Jones, M Bartlett<\/p>\n<p>Poster presented at ACEM, 2023<\/p>\n                        <\/div>\n                                            <\/div>\n                <\/div>\n                                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Background<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p>Timely availability of diagnostic imaging results is critical to the clinical decision-making process in the Emergency Department (ED), particularly after-hours when resources and experience are limited. Faster report turnaround time (RTAT) enabled by machine learning solutions may contribute to reduced delays and improved outcomes for patients with time-sensitive pathology. <\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Objectives<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p>This analysis evaluates the impact of an AI driven radiology worklist triage tool on RTAT of after-hours non-contrast CTB (NCCTB) scans. <\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Method<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p>A commercially available AI tool (Annalise Enterprise CTB) was deployed across a large teleradiology service. Accuracy of the model has been established in prior work (1). The AI assigned a triage priority to NCCTB scans (\u2018Critical,\u2019 \u2018Urgent\u2019, \u2018High\u2019 and \u2018Standard\u2019) based on the presence of automatically detected time-sensitive findings. Radiologist worklist prioritisation was changed for studies showing critical and urgent findings on AI triage. Data was collected three months pre- and post-intervention (Jan-June 2023), and analysis restricted to ED studies between 1700 and 0700 weekdays and on weekends. RTAT was defined as the time between \u2018tech ready\u2019 and \u2018report signed\u2019 timestamps. Mann-Whitney U test was used to compare RTAT pre- and post-triage implementation within each triage classification group.  <\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Results<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p>A total of 4,880 studies reported from 21 EDs were included in the analysis. Use of machine learning triage assistance resulted in a decrease in median RTAT for AI predicted \u2018Critical\u2019 studies (median (IQR) = 27.6 (11.4 \u2013 56.1) vs 16.4 (8.0 \u2013 33.1) mins, p < 0.001). An increase in median RTAT was observed for AI predicted  \u2018Standard\u2019 studies 26.8 (8.1 \u2013 60.6) vs 34.4 (12.0 \u2013 63.1), p = 0.001) (Figure 1). \n\n<\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Conclusions<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p>The use of a machine learning worklist triage tool led to significant reductions in RTAT for after-hours NCCTB scans containing time-sensitive pathology. Earlier availability of CTB reports may positively impact key performance metrics relating to treatment, disposition, and discharge of ED patients. <\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                \n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/harrison.ai\/wp-content\/uploads\/2025\/09\/Screen-Shot-2025-09-29-at-1.10.32-pm-460x284.png\" alt=\"\" width=\"460\" height=\"284\" class=\"alignnone size-medium wp-image-3907\" srcset=\"https:\/\/harrison.ai\/wp-content\/uploads\/2025\/09\/Screen-Shot-2025-09-29-at-1.10.32-pm-460x284.png 460w, https:\/\/harrison.ai\/wp-content\/uploads\/2025\/09\/Screen-Shot-2025-09-29-at-1.10.32-pm-1024x632.png 1024w, https:\/\/harrison.ai\/wp-content\/uploads\/2025\/09\/Screen-Shot-2025-09-29-at-1.10.32-pm-768x474.png 768w, https:\/\/harrison.ai\/wp-content\/uploads\/2025\/09\/Screen-Shot-2025-09-29-at-1.10.32-pm.png 1222w\" sizes=\"auto, (max-width: 460px) 100vw, 460px\" \/><\/p>\n<p>Figure 1: Box-plot illustrating the change in report turn-around time (RTAT) in minutes when AI driven triage is used (cyan) vs when it is not (red) <\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">References<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p>1. Buchlak Q, Tang C, Seah J, Johnson A, Holt X, Bottrell G, et al. Effects of a comprehensive brain computed tomography deep-learning model on radiologist detection accuracy: a multireader, multicase study [Internet]. Research Square. 2022. Available from: http:\/\/dx.doi.org\/10.21203\/rs.3.rs-1588540\/v1 <\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Disclaimer<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p>Harrison.ai Radiology Solutions were previously marketed as Annalise.ai solutions.<\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                                                <\/div>\n        <\/div>\n    <\/section>\n\n\n\n    <section id=\"cta-block-block_9949e580296a83c20aa1f813c2329b4d\" class=\"cta-block   text-\" >\n        <div class=\"container  container-\">\n            <div 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