cirpublications.com-8vhw-1790899045-z800757605 1790899045 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2024 4 2 Emily Johnson Robert Smith Laura Brown Kevin Miller 2024 10.68159/z800757605 https://cirpublications.com/pub/journal/2/article/z800757605 Ciemins EL, Mohl JT, Moreno CA, Colangelo F, Smith RA, Barton M. Development of a follow-up measure to ensure complete screening for colorectal cancer. JAMA Netw Open. 2024;7(3):e242693. Mohl JT, Ciemins EL, Miller-Wilson LA, Gillen A, Luo R, Colangelo F. Rates of follow-up colonoscopy after a positive stool-based screening test result for colorectal cancer among health care organizations in the US, 2017-2020. JAMA Netw Open. 2023;6(1):e2251384. San Miguel Y, Demb J, Martinez ME, Gupta S, May FP. Time to colonoscopy after abnormal stool-based screening and risk for colorectal cancer incidence and mortality. Gastroenterology. 2021;160(6):1997-2005. Coronado GD, Kihn-Stang A, Slaughter MT, Petrik AF, Thompson JH, Rivelli JS, et al. Follow-up colonoscopy after an abnormal stool-based colorectal cancer screening result: analysis of steps in the colonoscopy completion process. BMC Gastroenterol. 2021;21(1):356. Zorzi M, Battagello J, Selby K, Capodaglio G, Baracco S, Rizzato S, et al. Non-compliance with colonoscopy after a positive faecal immunochemical test doubles the risk of dying from colorectal cancer. Gut. 2022;71(3):561-7. O'Connor EA, Nielson CM, Petrik AF, Green BB, Coronado GD. Prospective cohort study of predictors of follow-up diagnostic colonoscopy from a pragmatic trial of FIT screening. Sci Rep. 2020;10(1):2441. Barbosa EJ Jr, Yang R, Hershman M. Real-world lung cancer CT screening performance, smoking behavior, and adherence to recommendations: Lung-RADS category and smoking status predict adherence. AJR Am J Roentgenol. 2021;216(4):919-26. Powell L, Sittig DF, Chrouser K, Singh H. Assessment of health information technology-related outpatient diagnostic delays in the US Veterans Affairs health care system: a qualitative study of aggregated root cause analysis data. JAMA Netw Open. 2020;3(6):e206752. Varon ML, Geng Y, Fellman BM, Troisi C, Fernandez ME, Li R, et al. Interventions to increase follow-up of abnormal cervical cancer screening results: a systematic literature review and meta-analysis. PLoS One. 2024;19(2):e0291931. Davis M, Simpson K, Lenert LA, Diaz V, Alekseyenko AV. Predicting mammogram screening follow through with electronic health record and geographically linked data. Cancer Res Commun. 2023;3(10):2126-32. Bastani M, Chiuzan C, Silvestri G, Raoof S, Chusid J, Diefenbach M, et al. A predictive model for lung cancer screening nonadherence in a community setting health-care network. JNCI Cancer Spectr. 2023;7(2):pkad019. Atta S, Brown RB, Wasser LM, Mayer N, Cassidy J, Liu PJ, et al. Effect of a patient portal reminder message after no-show on appointment reattendance in ophthalmology: a randomized clinical trial. Am J Ophthalmol. 2024;263:93-8. Berset AE, Burkhardt MC, Xu Y, Mescher A, Brinkman WB. Effect of electronic outreach using patient portal messages on well child care visit completion: a randomized clinical trial. JAMA Netw Open. 2022;5(11):e2242853. Cronin RM, Fabbri D, Denny JC, Rosenbloom ST, Jackson GP. A comparison of rule-based and machine learning approaches for classifying patient portal messages. Int J Med Inform. 2017;105:110-20. Sulieman L, Gilmore D, French C, Cronin RM, Jackson GP, Russell M, et al. Classifying patient portal messages using convolutional neural networks. J Biomed Inform. 2017;74:59-70. Dharod A, Bellinger C, Foley K, Case LD, Miller D. The reach and feasibility of an interactive lung cancer screening decision aid delivered by patient portal. Appl Clin Inform. 2019;10(1):19-27. Steitz BD, Turer RW, Lin CT, MacDonald S, Salmi L, Wright A, et al. Perspectives of patients about immediate access to test results through an online patient portal. JAMA Netw Open. 2023;6(3):e233572. Liu D, Shin WY, Sprecher E, Conroy K, Santiago O, Wachtel G, et al. Machine learning approaches to predicting no-shows in pediatric medical appointments. NPJ Digit Med. 2022;5(1):50. Lee SY, Eagleson RM, Hearld LR, Gibson MJ, Hearld KR, Hall AG, et al. Leveraging machine learning to enhance appointment adherence at a novel post-discharge care transition clinic. JAMIA Open. 2024;7(4):ooae086. Yang Y, Madanian S, Parry D. Enhancing health equity by predicting missed appointments in health care: machine learning study. JMIR Med Inform. 2024;12(1):e48273. Deina C, Fogliatto FS, da Silveira GJ, Anzanello MJ. Decision analysis framework for predicting no-shows to appointments using machine learning algorithms. BMC Health Serv Res. 2024;24(1):37. Elvira C, Ochoa A, Gonzalvez JC, Mochón F. Machine-learning-based no show prediction in outpatient visits. Int J Interact Multimed Artif Intell. 2018;4(7):29-34. AlMuhaideb S, Alswailem O, Alsubaie N, Ferwana I, Alnajem A. Prediction of hospital no-show appointments through artificial intelligence algorithms. Ann Saudi Med. 2019;39(6):373-81. Chong LR, Tsai KT, Lee LL, Foo SG, Chang PC. Artificial intelligence predictive analytics in the management of outpatient MRI appointment no-shows. AJR Am J Roentgenol. 2020;215(5):1155-62. Arndt BG, Beasley JW, Watkinson MD, Temte JL, Tuan WJ, Sinsky CA, et al. Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med. 2017;15(5):419-26. Rittenberg E, Liebman JB, Rexrode KM. Primary care physician gender and electronic health record workload. J Gen Intern Med. 2022;37(13):3295-301. Baxter SL, Saseendrakumar BR, Cheung M, Savides TJ, Longhurst CA, Sinsky CA, et al. Association of electronic health record inbasket message characteristics with physician burnout. JAMA Netw Open. 2022;5(11):e2244363. Ayad A, Hallawa A, Peine A, Martin L, Fazlic LB, Dartmann G, et al. Predicting abnormalities in laboratory values of patients in the intensive care unit using different deep learning models: comparative study. JMIR Med Inform. 2022;10(8):e37658.