Automatic Forecasting of Radiology Examination Volume Trends for Optimal Resource Planning and Allocation
The aim of the study was to evaluate the performance of the Prophet forecasting procedure, part of the Facebook open-source Artificial Intelligence portfolio, for forecasting variations in radiological examination volumes. Daily CT and MRI examination volumes from our institution were extracted from...
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| Veröffentlicht in: | Journal of digital imaging 2022-02, Vol.35 (1), p.1-8 |
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| creator | Becker, Anton S. Erinjeri, Joseph P. Chaim, Joshua Kastango, Nicholas Elnajjar, Pierre Hricak, Hedvig Vargas, H. Alberto |
| description | The aim of the study was to evaluate the performance of the Prophet forecasting procedure, part of the Facebook open-source Artificial Intelligence portfolio, for forecasting variations in radiological examination volumes. Daily CT and MRI examination volumes from our institution were extracted from the radiology information system (RIS) database. Data from January 1, 2015, to December 31, 2019, was used for training the Prophet algorithm, and data from January 2020 was used for validation. Algorithm performance was then evaluated prospectively in February and August 2020. Total error and mean error per day were evaluated, and computational time was logged using different Markov chain Monte Carlo (MCMC) samples. Data from 610,570 examinations were used for training; the majority were CTs (82.3%). During retrospective testing, prediction error was reduced from 19 to |
| doi_str_mv | 10.1007/s10278-021-00532-4 |
| format | Article |
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p
= 0.001). Inference with MCMC added no substantial improvements while vastly increasing computational time. Prophet accurately models weekly, seasonal, and overall trends paving the way for optimal resource allocation for radiology exam acquisition and interpretation.</description><identifier>ISSN: 0897-1889</identifier><identifier>EISSN: 1618-727X</identifier><identifier>DOI: 10.1007/s10278-021-00532-4</identifier><identifier>PMID: 34755249</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Algorithms ; Artificial Intelligence ; Computational efficiency ; Computer applications ; Computing time ; Error reduction ; Forecasting ; Humans ; Imaging ; Magnetic resonance imaging ; Markov chains ; Medicine ; Medicine & Public Health ; Performance evaluation ; Predictions ; Prospective Studies ; Radiology ; Resource allocation ; Retrospective Studies ; Training ; Trends</subject><ispartof>Journal of digital imaging, 2022-02, Vol.35 (1), p.1-8</ispartof><rights>The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2021</rights><rights>2021. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.</rights><rights>The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2021.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-3379d6dfcc405a4f774e280bbafd18756991c1df5aa5d5300a608ab6dbf06aa23</citedby><cites>FETCH-LOGICAL-c474t-3379d6dfcc405a4f774e280bbafd18756991c1df5aa5d5300a608ab6dbf06aa23</cites><orcidid>0000-0001-8372-6496</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8577854/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8577854/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34755249$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Becker, Anton S.</creatorcontrib><creatorcontrib>Erinjeri, Joseph P.</creatorcontrib><creatorcontrib>Chaim, Joshua</creatorcontrib><creatorcontrib>Kastango, Nicholas</creatorcontrib><creatorcontrib>Elnajjar, Pierre</creatorcontrib><creatorcontrib>Hricak, Hedvig</creatorcontrib><creatorcontrib>Vargas, H. Alberto</creatorcontrib><title>Automatic Forecasting of Radiology Examination Volume Trends for Optimal Resource Planning and Allocation</title><title>Journal of digital imaging</title><addtitle>J Digit Imaging</addtitle><addtitle>J Digit Imaging</addtitle><description>The aim of the study was to evaluate the performance of the Prophet forecasting procedure, part of the Facebook open-source Artificial Intelligence portfolio, for forecasting variations in radiological examination volumes. Daily CT and MRI examination volumes from our institution were extracted from the radiology information system (RIS) database. Data from January 1, 2015, to December 31, 2019, was used for training the Prophet algorithm, and data from January 2020 was used for validation. Algorithm performance was then evaluated prospectively in February and August 2020. Total error and mean error per day were evaluated, and computational time was logged using different Markov chain Monte Carlo (MCMC) samples. Data from 610,570 examinations were used for training; the majority were CTs (82.3%). During retrospective testing, prediction error was reduced from 19 to < 1 per day in CT (total 589 to 17) and from 5 to < 1 per day (total 144 to 27) in MRI by fine-tuning the Prophet procedure. Prospective prediction error in February was 11 per day in CT (9934 predicted, 9667 actual) and 1 per day in MRI (2484 predicted, 2457 actual) and was significantly better than manual weekly predictions (
p
= 0.001). Inference with MCMC added no substantial improvements while vastly increasing computational time. Prophet accurately models weekly, seasonal, and overall trends paving the way for optimal resource allocation for radiology exam acquisition and interpretation.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Computational efficiency</subject><subject>Computer applications</subject><subject>Computing time</subject><subject>Error reduction</subject><subject>Forecasting</subject><subject>Humans</subject><subject>Imaging</subject><subject>Magnetic resonance imaging</subject><subject>Markov chains</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Performance evaluation</subject><subject>Predictions</subject><subject>Prospective Studies</subject><subject>Radiology</subject><subject>Resource allocation</subject><subject>Retrospective Studies</subject><subject>Training</subject><subject>Trends</subject><issn>0897-1889</issn><issn>1618-727X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kU9rFTEUxYMo9ln9Ai4k4MbN6M3_zEZ4lFaFQkup4i5kksxzykzyTGbEfnvz-mpbXXSVxf3dk3PuQeg1gfcEQH0oBKjSDVDSAAhGG_4ErYgkulFUfX-KVqBb1RCt2wP0opQrAKKE4s_RAeNKCMrbFRrWy5wmOw8On6QcnC3zEDc49fjC-iGNaXONj3_baYiVSRF_S-MyBXyZQ_QF9ynjs-08THbEF6GkJbuAz0cb407ERo_X45jczepL9Ky3Ywmvbt9D9PXk-PLoc3N69unL0fq0cVzxuWFMtV763jkOwvJeKR6ohq6zvSdaCdm2xBHfC2uFFwzAStC2k77rQVpL2SH6uNfdLt0UvAtxznY021xd5muT7GD-ncThh9mkX0YLpbTgVeDdrUBOP5dQZjMNxYWxxgppKYaKVgKhiqiKvv0Pvao3iDWeoZK2gjKQrFJ0T7mcSsmhvzNDwOyaNPsmTW3S3DRpdi7ePIxxt_K3ugqwPVDqKG5Cvv_7Edk_pEyrpA</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Becker, Anton S.</creator><creator>Erinjeri, Joseph P.</creator><creator>Chaim, Joshua</creator><creator>Kastango, Nicholas</creator><creator>Elnajjar, Pierre</creator><creator>Hricak, Hedvig</creator><creator>Vargas, H. 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Alberto</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automatic Forecasting of Radiology Examination Volume Trends for Optimal Resource Planning and Allocation</atitle><jtitle>Journal of digital imaging</jtitle><stitle>J Digit Imaging</stitle><addtitle>J Digit Imaging</addtitle><date>2022-02-01</date><risdate>2022</risdate><volume>35</volume><issue>1</issue><spage>1</spage><epage>8</epage><pages>1-8</pages><issn>0897-1889</issn><eissn>1618-727X</eissn><abstract>The aim of the study was to evaluate the performance of the Prophet forecasting procedure, part of the Facebook open-source Artificial Intelligence portfolio, for forecasting variations in radiological examination volumes. Daily CT and MRI examination volumes from our institution were extracted from the radiology information system (RIS) database. Data from January 1, 2015, to December 31, 2019, was used for training the Prophet algorithm, and data from January 2020 was used for validation. Algorithm performance was then evaluated prospectively in February and August 2020. Total error and mean error per day were evaluated, and computational time was logged using different Markov chain Monte Carlo (MCMC) samples. Data from 610,570 examinations were used for training; the majority were CTs (82.3%). During retrospective testing, prediction error was reduced from 19 to < 1 per day in CT (total 589 to 17) and from 5 to < 1 per day (total 144 to 27) in MRI by fine-tuning the Prophet procedure. Prospective prediction error in February was 11 per day in CT (9934 predicted, 9667 actual) and 1 per day in MRI (2484 predicted, 2457 actual) and was significantly better than manual weekly predictions (
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| subjects | Algorithms Artificial Intelligence Computational efficiency Computer applications Computing time Error reduction Forecasting Humans Imaging Magnetic resonance imaging Markov chains Medicine Medicine & Public Health Performance evaluation Predictions Prospective Studies Radiology Resource allocation Retrospective Studies Training Trends |
| title | Automatic Forecasting of Radiology Examination Volume Trends for Optimal Resource Planning and Allocation |
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