Prospective External Validation of a Commercial Model Predicting the Likelihood of Inpatient Admission From the Emergency Department
Tài liệu tham khảo
Forster, 2003, The effect of hospital occupancy on emergency department length of stay and patient disposition, Acad Emerg Med, 10, 127, 10.1197/aemj.10.2.127
Richardson, 2009, Myths versus facts in emergency department overcrowding and hospital access block, Med J Aust, 190, 369, 10.5694/j.1326-5377.2009.tb02451.x
Kelen GD, Wolfe R, D’Onofrio G, et al. Emergency department crowding: the canary in the health care system. NEJM Catalyst. Published online September 28, 2021.
Bernstein, 2009, The effect of emergency department crowding on clinically oriented outcomes, Acad Emerg Med, 16, 1, 10.1111/j.1553-2712.2008.00295.x
Stoyanov, 2020, Effects of crowding in the emergency department on the diagnosis and management of suspected acute coronary syndrome using rapid algorithms: an observational study, BMJ Open, 10, 10.1136/bmjopen-2020-041757
Hoot, 2008, Systematic review of emergency department crowding: causes, effects, and solutions, Ann of Emerg Med, 52, 126, 10.1016/j.annemergmed.2008.03.014
Hobbs, 2000, Hospital factors associated with emergency center patients leaving without being seen, Am J Emerg Med, 18, 767, 10.1053/ajem.2000.18075
Gorski, 2021, Crowding is the strongest predictor of left without being seen risk in a pediatric emergency department, Am J Emerg Med, 48, 73, 10.1016/j.ajem.2021.04.005
Bursch, 1993, Emergency department satisfaction: what matters most?, Ann Emerg Med, 22, 586, 10.1016/S0196-0644(05)81947-X
Schull, 2004, Emergency department crowding and thrombolysis delays in acute myocardial infarction, Ann Emerg Med, 44, 577, 10.1016/j.annemergmed.2004.05.004
Lee, 2020, Using emergency physicians’ abilities to predict patient admission to decrease admission delay time, Emerg Med J, 37, 417, 10.1136/emermed-2019-208859
Peck, 2014, Characterizing the value of predictive analytics in facilitating hospital patient flow, IIE Trans Healthc Syst Eng, 4, 135, 10.1080/19488300.2014.930765
Qiu, 2015, A cost sensitive inpatient bed reservation approach to reduce emergency department boarding times, Health Care Manag Sci, 18, 67, 10.1007/s10729-014-9283-1
Resar, 2011, Using real-time demand capacity management to improve hospitalwide patient flow, Jt Comm J Qual Patient Saf, 37
Cameron, 2015, A simple tool to predict admission at the time of triage, Emerg Med J, 32, 174, 10.1136/emermed-2013-203200
Sun, 2011, Predicting hospital admissions at emergency department triage using routine administrative data, Acad Emerg Med, 18, 844, 10.1111/j.1553-2712.2011.01125.x
Parker, 2019, Predicting hospital admission at the emergency department triage: a novel prediction model, Am J Emerg Med, 37, 1498, 10.1016/j.ajem.2018.10.060
Peck, 2012, Predicting emergency department inpatient admissions to improve same-day patient flow, Acad Emerg Med, 19, E1045, 10.1111/j.1553-2712.2012.01435.x
Hong, 2018, Predicting hospital admission at emergency department triage using machine learning, PloS One, 13, 10.1371/journal.pone.0201016
Barak-Corren, 2017, Progressive prediction of hospitalisation in the emergency department: uncovering hidden patterns to improve patient flow, Emerg Med J, 34, 308, 10.1136/emermed-2014-203819
Lucke, 2018, Early prediction of hospital admission for emergency department patients: a comparison between patients younger or older than 70 years, Emerg Med J, 35, 18, 10.1136/emermed-2016-205846
Brink, 2022, Predicting inhospital admission at the emergency department: a systematic review, Emerg Med J, 39, 191, 10.1136/emermed-2020-210902
Kraaijvanger, 2018, Development and validation of an admission prediction tool for emergency departments in the Netherlands, Emerg Med J, 35, 464, 10.1136/emermed-2017-206673
Fenn, 2021, Development and validation of machine learning models to predict admission from emergency department to inpatient and intensive care units, Ann Emerg Med, 78, 290, 10.1016/j.annemergmed.2021.02.029
King, 2022, Machine learning for real-time aggregated prediction of hospital admission for emergency patients, NPJ Digital Med, 5, 104, 10.1038/s41746-022-00649-y
Cabitza, 2021, The importance of being external. methodological insights for the external validation of machine learning models in medicine, Comput Methods Programs Biomed, 208, 10.1016/j.cmpb.2021.106288
Beam, 2020, Challenges to the reproducibility of machine learning models in health care, JAMA, 323, 305, 10.1001/jama.2019.20866
Calster, 2019, Predictive analytics in health care: how can we know it works?, J Am Med Inform Assoc, 26, 1651, 10.1093/jamia/ocz130
Blauer
Singh, 2021, Evaluating a widely implemented proprietary deterioration index model among hospitalized patients with COVID-19, Ann Am Thorac Soc, 18, 1129, 10.1513/AnnalsATS.202006-698OC
Wong, 2021, External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients, JAMA Intern Med, 181, 1065, 10.1001/jamainternmed.2021.2626
Moons, 2015, Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration, Ann Intern Med, 162, W1, 10.7326/M14-0698
Riley, 2021, Minimum sample size for external validation of a clinical prediction model with a binary outcome, Stat Med, 40, 4230, 10.1002/sim.9025
Saito, 2015, The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets, PLoS One, 10bh
Huang, 2020, A tutorial on calibration measurements and calibration models for clinical prediction models, J Am Med Inform Assoc, 27, 621, 10.1093/jamia/ocz228
Allen, 2019, Raincloud plots: a multi-platform tool for robust data visualization, Wellcome Open Res, 4, 63, 10.12688/wellcomeopenres.15191.1
Wickham, 2019, Welcome to the Tidyverse, J Open Source Softw, 4, 1686, 10.21105/joss.01686
Pedersen
Kuhn
Wickham, 2016
Singh
Zhang, 2017, Prediction of emergency department hospital admission based on natural language processing and neural networks∗, Method Inform Med, 56, 377, 10.3414/ME17-01-0024
Lee, 2020, Prediction of emergency department patient disposition decision for proactive resource allocation for admission, Health Care Manag Sci, 23, 339, 10.1007/s10729-019-09496-y
Zwank, 2021, Provider-in-triage prediction of hospital admission after brief patient interaction, Am J Emerg Med, 40, 60, 10.1016/j.ajem.2020.11.072
Chang, 2018, Hospital strategies for reducing emergency department crowding: a mixed-methods study, Ann Emerg Med, 71, 497, 10.1016/j.annemergmed.2017.07.022
Baugh, 2021, The cases not seen: Patterns of emergency department visits and procedures in the era of COVID-19, Am J Emerg Med, 46, 476, 10.1016/j.ajem.2020.10.081
Wong, 2021, Quantification of sepsis model alerts in 24 US hospitals before and during the COVID-19 pandemic, JAMA Netw Open, 4, 10.1001/jamanetworkopen.2021.35286
Finlayson, 2021, The clinician and dataset shift in artificial intelligence, N Engl J Med, 385, 283, 10.1056/NEJMc2104626
Duckworth, 2021, Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19, Sci Rep-uk, 11
Kamran, 2022, Early identification of patients admitted to hospital for COVID-19 at risk of clinical deterioration: model development and multisite external validation study, BMJ, 376
Dayan, 2021, Federated learning for predicting clinical outcomes in patients with COVID-19, Nat Med, 27, 1735, 10.1038/s41591-021-01506-3