{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T14:15:08Z","timestamp":1788531308171,"version":"build-2803163510"},"reference-count":15,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cloud-based medical imaging systems represent a paradigm shift in healthcare digitization. However, existing evaluations often rely on linear models that overlook critical methodological issues, particularly time-series autocorrelation in longitudinal data and uncertainty quantification in economic assessments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To quantify the geographic, economic, and film-related environmental impacts of cloud medical imaging adoption using ARIMA modeling and Monte Carlo simulation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed 15,709 cleaned cloud access records from November 2021 to March 2025 and 464\u202fmonth-by-format film utilization records covering 114 calendar months from January 2015 to June 2024 at a major tertiary hospital. Time-series autocorrelation was evaluated using ARIMA modeling. Geographic inequality was assessed using the Gini coefficient. Economic robustness was evaluated using a six-scenario deterministic sensitivity analysis and Monte Carlo simulation (10,000 iterations).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The system exhibited marked geographic concentration (Gini coefficient\u202f=\u202f0.980, 95% CI: 0.977\u20130.982), with 85.6% of access originating from local and provincial users. ARIMA(1,1,2) modeling of monthly film savings suggested an upward trend in film savings (monthly increase: 281.5 sheets). Forecasts suggested a plateauing trend rather than indefinite growth. Monte Carlo simulation yielded a mean estimated savings of \u00a515.75 million (95% CI: [\u00a513.16, \u00a518.35] million). Film-related avoided emissions were estimated at 341 tons of CO2e.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This single-center study suggests that cloud imaging can be associated with substantial film-related cost savings and avoided emissions in a large urban tertiary hospital. Broader generalizability, net system-wide economic benefit, and full environmental impact require multicenter validation and more comprehensive life-cycle assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1874190","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T13:23:10Z","timestamp":1788528190000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Longitudinal impact of cloud-based medical imaging: an analysis using ARIMA modeling and Monte Carlo simulation"],"prefix":"10.3389","volume":"14","author":[{"given":"Hui","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiology, Peking University Shenzhen Hospital","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongchao","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Peking University Shenzhen Hospital","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Spine Surgery, Shenzhen Second People\u2019s Hospital, The First Affiliated Hospital of Shenzhen University","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Peking University Shenzhen Hospital","place":["Shenzhen, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.ijcce.2022.06.001","article-title":"Evolutionary trends in progressive cloud computing based healthcare: ideas, enablers, and barriers","volume":"3","author":"Javaid","year":"2022","journal-title":"Int J Cogn Comput Eng"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.rxeng.2023.06.008","article-title":"Green radiology: how to develop sustainable radiology","volume":"66","author":"Lojo-Lendoiro","year":"2024","journal-title":"Radiologia (Madr)"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1016\/j.jbusres.2020.10.030","article-title":"Digital transformation in healthcare: analyzing the current state-of-research","volume":"123","author":"Kraus","year":"2021","journal-title":"J Bus Res"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1038\/s41746-020-00314-2","article-title":"Challenges for the evaluation of digital health solutions\u2014a call for innovative evidence generation approaches","volume":"3","author":"Guo","year":"2020","journal-title":"NPJ Digit. 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