Abstract
Background
Methods
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- Waste in the US health care system: estimated costs and potential for savings.JAMA. 2019; 322 (Available at) (Accessed July 24, 2020): 1501-1509
- The landscape of inappropriate laboratory testing: a 15-year meta-analysis.PLoS One. 2013; 8 (Available at:) (Accessed January 28, 2021): e78962
Janowiak D, Hannon T. The laboratory's role in delivering high-value care. 2018. Available at: https://clpmag.com/diagnostic-technologies/hematology-serology/laboratorys-role-delivering-high-value-care/. Accessed September 30, 2021.
- Blood loss from laboratory testing, anemia, and red blood cell transfusion in the intensive care unit: a retrospective study.Transfusion (Paris). 2020; 60 (Available at:) (Accessed January 7, 2021): 256-261
- RBC transfusion in the ICU: is there a reason?.Chest. 1995; 108 (Available at: http://www.sciencedirect.com/science/article/pii/S0012369216342295. Accessed January 7, 2021): 767-771
- Do blood tests cause anemia in hospitalized patients? The effect of diagnostic phlebotomy on hemoglobin and hematocrit levels.J Gen Intern Med. 2005; 20 (Available at:) (Accessed July 24, 2020): 520-524
- Anemia, blood loss, and blood transfusions in North American children in the intensive care unit.Am J Respir Crit Care Med. 2008; 178 (Available at:) (Accessed July 24, 2020): 26-33
- Hospital-acquired anemia: Prevalence, outcomes, and healthcare implications.J Hosp Med. 2013; 8 (Available at:) (Accessed July 24, 2020): 506-512
- Incidence, predictors, and outcomes of hospital-acquired anemia.J Hosp Med. 2017; 12 (Available at:) (Accessed January 7, 2021): 317-322
- Hospital-acquired anemia and in-hospital mortality in patients with acute myocardial infarction.Am Heart J. 2011; 162: 300-309.e3
Choosing Wisely: An Initiative of the ABIM Foundation. American Association of Blood Banks - serial blood counts. Available at: https://www.choosingwisely.org/clinician-lists/american-association-blood-banks-serial-blood-counts-on-clinically-stable-patients/. Accessed July 24, 2020.
Choosing Wisely: An Initiative of the ABIM Foundation. Critical Care Societies Collaborative – Critical Care: responsive diagnostic tests. Available at: https://www.choosingwisely.org/clinician-lists/critical-care-societies-collaborative-regular-diagnostic-tests/. Accessed July 24, 2020.
- Reducing test utilization in hospital settings: a narrative review.Ann Lab Med. 2018; 38 (Available at:) (Accessed January 7, 2021): 402-412
- Introduction of cost display reduces laboratory test utilization.Am J Manag Care. 2018; 24: e164-e169
- Reducing the number of unnecessary routine laboratory tests through education of internal medicine residents.Postgrad Med J. 2018; 94 (Available at:) (Accessed July 24, 2020): 716-719
- Reducing unnecessary inpatient laboratory testing in a teaching hospital.Am J Clin Pathol. 2006; 126 (Available at:) (Accessed July 24, 2020): 200-206
- Reducing unnecessary lab testing in the ICU with artificial intelligence.Int J Med Inf. 2013; 82 (Available at:) (Accessed December 21, 2020): 345-358
- A machine learning approach to predicting the stability of inpatient lab test results.AMIA Jt Summits Transl Sci Proc. 2019; (Available at:) (Accessed September 18, 2019): 515-523
- Using machine learning to predict laboratory test results.Am J Clin Pathol. 2016; 145 (Available at:) (Accessed September 1, 2020): 778-788
- A deep learning solution to recommend laboratory reduction strategies in ICU.Int J Med Inf. 2020; (144. Available at:) (Accessed October 8, 2020)104282
- MIMIC-III, a freely accessible critical care database.Sci Data. 2016; 3 (Available at:) (Accessed September 18, 2019)160035
- Predict or draw blood: an integrated method to reduce lab tests.J Biomed Inform. 2020; 104 (Available at:) (Accessed June 1, 2020)103394
Gupta P, Malhotra P, Narwariya J, Vig L, Shroff G. Transfer learning for clinical time series analysis using deep neural networks. arXiv:190400655. 2021. Available at: http://arxiv.org/abs/1904.00655. Accessed October 14, 2021.
Mokrii I, Boytsov L, Braslavski P. A systematic evaluation of transfer learning and pseudo-labeling with BERT-based ranking models. arXiv:2013.03335. 2021. Available at: http://arxiv.org/abs/2103.03335. Accessed October 14, 2021.
- The ethics of machine learning-based clinical decision support: an analysis through the lens of professionalisation theory.BMC Med Ethics. 2021; 22 (Available at: Accessed November 18, 2021): 112https://doi.org/10.1186/s12910-021-00679-3
Article Info
Publication History
Footnotes
Funding: This work was supported in part by the National Center for Advancing Translational Sciences (NCATS) under awards U01TR002062, UL1TR000371 and U01TR002393; the National Institute of Aging (NIA) under award (R01AG066749), the Cancer Prevention and Research Institute of Texas (CPRIT), under award RP170668, RR180012 and the Reynolds and Reynolds Professorship in Clinical Informatics.
Conflicts of Interest: The authors have no competing interests or financial relationships relevant to this article to disclose.
Authorship: All authors had access to the data and a role in the manuscript writing. LTL: Conceptualization, project administration, roles/writing – original draft. TH: Data curation, formal analysis, validation; writing – review & editing. EVB: Conceptualization, resources, writing – review & editing. XJ: Conceptualization, formal analysis, methodology, supervision, writing – review & editing.