• 1.

    Pastores SM, Kvetan V, Coopersmith CM, et al. Workforce, workload, and burnout among Intensivists and advanced practice providers: a narrative review. Crit Care Med. 2019;47(4):550–7. https://doi.org/10.1097/CCM.0000000000003637.

    Article 
    PubMed 

    Google Scholar
     

  • 2.

    Dietz AS, Pronovost PJ, Mendez-Tellez PA, et al. A systematic review of teamwork in the intensive care unit: what do we know about teamwork, team tasks, and improvement strategies? J Crit Care. 2014;29(6):908–14. https://doi.org/10.1016/j.jcrc.2014.05.025.

    Article 
    PubMed 

    Google Scholar
     

  • 3.

    Donchin Y, Gopher D, Olin M, et al. A look into the nature and causes of human errors in the intensive care unit. BMJ Quality & Safety. 2003;12(2):143–7. https://doi.org/10.1136/qhc.12.2.143.

    CAS 
    Article 

    Google Scholar
     

  • 4.

    Harry E, Pierce RG, Kneeland P, Huang G, Stein J, Sweller J. Cognitive load and its implications for health care. NEJM Catalyst Published online March 14, 2018. https://catalyst.nejm.org/doi/full/10.1056/CAT.18.0233

  • 5.

    Zhang Y, Padman R, Levin JE. Reducing Provider Cognitive Workload in CPOE Use: Optimizing Order Sets. MEDINFO 2013. Published online 2013:734–738. doi:https://doi.org/10.3233/978-1-61499-289-9-734.

  • 6.

    Avansino J, Leu MG. Effects of CPOE on provider cognitive workload: a randomized crossover trial. Pediatrics. 2012;130(3):e547–52. https://doi.org/10.1542/peds.2011-3408.

    Article 
    PubMed 

    Google Scholar
     

  • 7.

    Gabler NB, Ratcliffe SJ, Wagner J, et al. Mortality among patients admitted to strained intensive care units. Am J Respir Crit Care Med. 2013;188(7):800–6. https://doi.org/10.1164/rccm.201304-0622OC.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 8.

    Wagner J, Gabler NB, Ratcliffe SJ, Brown SES, Strom BL, Halpern SD. Outcomes among patients discharged from busy intensive care units. Ann Intern Med. 2013;159(7):447–55. https://doi.org/10.7326/0003-4819-159-7-201310010-00004.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 9.

    Kohn R, Harhay MO, Bayes B, et al. Ward capacity strain: a novel predictor of 30-Day hospital readmissions. J Gen Intern Med. 2018;33(11):1851–3. https://doi.org/10.1007/s11606-018-4564-x.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 10.

    Leviatan I, Oberman B, Zimlichman E, Stein GY. Associations of physicians’ prescribing experience, work hours, and workload with prescription errors. J Am Med Inform Assoc. 2021;28(6):1074–80. https://doi.org/10.1093/jamia/ocaa219.

    Article 
    PubMed 

    Google Scholar
     

  • 11.

    Neuraz A, Guérin C, Payet C, et al. Patient mortality is associated with staff resources and workload in the ICU: a multicenter observational study*. Crit Care Med. 2015;43(8):1587–94. https://doi.org/10.1097/CCM.0000000000001015.

    Article 

    Google Scholar
     

  • 12.

    Carayon P, Gürses AP. A human factors engineering conceptual framework of nursing workload and patient safety in intensive care units. Intensive and Critical Care Nursing. 2005;21(5):284–301. https://doi.org/10.1016/j.iccn.2004.12.003.

    Article 
    PubMed 

    Google Scholar
     

  • 13.

    Aziz S, Arabi YM, Alhazzani W, et al. Managing ICU surge during the COVID-19 crisis: rapid guidelines. Intensive Care Med Published online June 8, 2020:1–23. doi:https://doi.org/10.1007/s00134-020-06092-5.

  • 14.

    Grimm CA. Hospitals reported that the COVID-19 pandemic has significantly strained health care delivery. [https://oig.hhs.gov/oei/reports/oei-06-20-00300.pdf] Accessed 31 Dec 2021.

  • 15.

    Rausand M, Hoyland A. System Reliability Theory: Models, Statistical Methods, and Applications. Hoboken: Wiley; 2003.

  • 16.

    Holden RJ, Carayon P, Gurses AP, et al. SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients. Ergonomics. 2013;56(11):1669–86. https://doi.org/10.1080/00140139.2013.838643.

    Article 
    PubMed 

    Google Scholar
     

  • 17.

    Bhalla T, Dairo OO, Martin D, et al. A proactive risk assessment by utilizing ‘Healthcare Failure Mode and Effect Analysis’ (HFMEA) for safe implementation of peripheral nerve catheters in pediatric patients. Anaesthesia Pain Intensive Care. Published online March 1, 2021:21–24. doi:https://doi.org/10.35975/apic.v0i0.723

  • 18.

    Caballero-Romero Á, Fernández S, Morillo AB, Zaragoza-Rascón M, Jaramillo-Pérez C, Del Pozo-Rubio R. Healthcare failure mode and effects analysis and cost-minimization analysis of three pharmaceutical services. Farm Hosp. 2021;45(2):66–72. https://doi.org/10.7399/fh.11532.

    Article 
    PubMed 

    Google Scholar
     

  • 19.

    Lumley C, Ellis A, Ritchings S, Venes T, Ede J. Using the systems engineering initiative for patient safety (SEIPS) model to describe critical care nursing during the SARS-CoV-2 pandemic (2020). Nurs Crit Care. 2020;25(4):203–5. https://doi.org/10.1111/nicc.12514.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 20.

    Kwan WM, Mok CK, Kwok YT, et al. Using the Systems Engineering Initiative for Patient Safety (SEIPS) model to describe the planning and management of the COVID-19 pandemic in Hong Kong. Int J Quality Health Care. 2021;33(1). doi:https://doi.org/10.1093/intqhc/mzab042

  • 21.

    Banks AP, Millward LJ. Running shared mental models as a distributed cognitive process. Br J Psychol. 2000;91(4):513–31. https://doi.org/10.1348/000712600161961.

    Article 
    PubMed 

    Google Scholar
     

  • 22.

    Mälstam J, Lind L. Therapeutic intervention scoring system (TISS) — a method for measuring workload and calculating costs in the ICU. Acta Anaesthesiol Scand. 1992;36(8):758–63. https://doi.org/10.1111/j.1399-6576.1992.tb03559.x.

    Article 
    PubMed 

    Google Scholar
     

  • 23.

    Hoonakker P, Carayon P, Gurses AP, et al. Measuring workload of ICU nurses with a questionnaire survey: the NASA task load index (TLX). IIE Trans Healthcare Systems Eng. 2011;1(2):131–43. https://doi.org/10.1080/19488300.2011.609524.

    Article 

    Google Scholar
     

  • 24.

    Mohammadi M, Mazloumi A, Kazemi Z, Zeraati H. Evaluation of Mental Workload among ICU Ward’s Nurses. Health Promot Perspect. 2016;5(4):280–287. doi:https://doi.org/10.15171/hpp.2015.033

  • 25.

    Van Groningen N, Prasad PA, Najafi N, Rajkomar A, Khanna RR, Fang MC. Electronic Order Volume as a Meaningful Component in Estimating Patient Complexity and Resident Physician Workload. J Hosp Med. Published online August 29, 2018. doi:https://doi.org/10.12788/jhm.3069

  • 26.

    Wright M, Dunbar S, Macpherson B, et al. Toward designing information display to support critical care: a qualitative contextual evaluation and visioning effort. Appl Clin Inform. 2016;07(04):912–29. https://doi.org/10.4338/ACI-2016-03-RA-0033.

    Article 

    Google Scholar
     

  • 27.

    Nolan M, Siwani R, Helmi H, Pickering B, Moreno-Franco P, Herasevich V. Health IT usability focus section: data use and navigation patterns among medical ICU clinicians during electronic chart review. Appl Clin Inform. 2017;08(04):1117–26. https://doi.org/10.4338/ACI-2017-06-RA-0110.

    Article 

    Google Scholar
     

  • 28.

    Nolan M, Cartin-Ceba R, Moreno-Franco P, Pickering B, Herasevich V. A multisite survey study of EMR review habits, information needs, and display preferences among medical ICU clinicians evaluating new patients. Appl Clin Inform. 2017;08(04):1197–207. https://doi.org/10.4338/ACI-2017-04-RA-0060.

    Article 

    Google Scholar
     

  • 29.

    Kiekkas P, Brokalaki H, Manolis E, Samios A, Skartsani C, Baltopoulos G. Patient severity as an indicator of nursing workload in the intensive care unit. Nurs Crit Care. 2007;12(1):34–41. https://doi.org/10.1111/j.1478-5153.2006.00193.x.

    Article 
    PubMed 

    Google Scholar
     

  • 30.

    Kiekkas P, Sakellaropoulos GC, Brokalaki H, et al. Association between nursing workload and mortality of intensive care unit patients. J Nurs Scholarsh. 2008;40(4):385–90. https://doi.org/10.1111/j.1547-5069.2008.00254.x.

    Article 
    PubMed 

    Google Scholar
     

  • 31.

    Herasevich V, Pickering BW, Dong Y, Peters SG, Gajic O. Informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness. Mayo Clin Proc. 2010;85(3):247–54. https://doi.org/10.4065/mcp.2009.0479.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 32.

    Hazlehurst B, McMullen C, Gorman P, Sittig D. How the ICU follows orders: care delivery as a complex activity system. AMIA Annu Symp Proc. 2003;2003:284–8.

    PubMed Central 

    Google Scholar
     

  • 33.

    Hazlehurst B, Gorman PN, McMullen CK. Distributed cognition: an alternative model of cognition for medical informatics. Int J Med Inform. 2008;77(4):226–34. https://doi.org/10.1016/j.ijmedinf.2007.04.008.

    Article 
    PubMed 

    Google Scholar
     

  • 34.

    Collins SA, Stein DM, Vawdrey DK, Stetson PD, Bakken S. Content overlap in nurse and physician handoff artifacts and the potential role of electronic health records: a systematic review. J Biomed Inform. 2011;44(4):704–12. https://doi.org/10.1016/j.jbi.2011.01.013.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 35.

    Miller A, Scheinkestel C, Limpus A, Joseph M, Karnik A, Venkatesh B. Uni- and interdisciplinary effects on round and handover content in intensive care units. Hum Factors. 2009;51(3):339–53. https://doi.org/10.1177/0018720809338188.

    Article 
    PubMed 

    Google Scholar
     

  • 36.

    Biron AD, Loiselle CG, Lavoie-Tremblay M. Work interruptions and their contribution to medication administration errors: an evidence review. Worldviews Evid-Based Nurs. 2009;6(2):70–86. https://doi.org/10.1111/j.1741-6787.2009.00151.x.

    Article 
    PubMed 

    Google Scholar
     

  • 37.

    Westbrook JI, Woods A, Rob MI, Dunsmuir WTM, Day RO. Association of Interruptions with an increased risk and severity of medication administration errors. Arch Intern Med. 2010;170(8):683–90. https://doi.org/10.1001/archinternmed.2010.65.

    Article 
    PubMed 

    Google Scholar
     

  • 38.

    Radley DC, Wasserman MR, Olsho LE, Shoemaker SJ, Spranca MD, Bradshaw B. Reduction in medication errors in hospitals due to adoption of computerized provider order entry systems. J Am Med Inform Assoc. 2013;20(3):470–6. https://doi.org/10.1136/amiajnl-2012-001241.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 39.

    Gajic O, Afessa B, Hanson AC, et al. Effect of 24-hour mandatory versus on-demand critical care specialist presence on quality of care and family and provider satisfaction in the intensive care unit of a teaching hospital*. Crit Care Med. 2008;36(1):36–44. https://doi.org/10.1097/01.CCM.0000297887.84347.85.

    Article 
    PubMed 

    Google Scholar
     

  • 40.

    Jones AE, Trzeciak S, Kline JA. The Sequential Organ Failure Assessment score for predicting outcome in patients with severe sepsis and evidence of hypoperfusion at the time of emergency department presentation*: Critical Care Medicine. 2009;37(5):1649–1654. doi:https://doi.org/10.1097/CCM.0b013e31819def97

  • 41.

    Aakre C, Franco PM, Ferreyra M, Kitson J, Li M, Herasevich V. Prospective validation of a near real-time EHR-integrated automated SOFA score calculator. Int J Med Inform. 2017;103:1–6. https://doi.org/10.1016/j.ijmedinf.2017.04.001.

    Article 
    PubMed 

    Google Scholar
     

  • 42.

    Ferreira FL. Serial evaluation of the SOFA score to predict outcome in critically ill patients. JAMA. 2001;286(14):1754. https://doi.org/10.1001/jama.286.14.1754.

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  • 43.

    Wunsch H. ICU Bed Utilization. In: Oropello JM, Pastores SM, Kvetan V, eds. Critical Care. McGraw-Hill Education; 1. Accessed 15 Nov 2021. accessmedicine.mhmedical.com/content.aspx?aid=1136418526.

  • 44.

    Naseer N, Hong KS. fNIRS-based brain-computer interfaces: a review. Front Hum Neurosci. 2015;9:3. https://doi.org/10.3389/fnhum.2015.00003.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 45.

    NASA Task Load Index | Digital Healthcare Research. Accessed 14 Nov 2021. https://digital.ahrq.gov/health-it-tools-and-resources/evaluation-resources/workflow-assessment-health-it-toolkit/all-workflow-tools/nasa-task-load-index

  • 46.

    Pinti P, Aichelburg C, Lind F, et al. Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks J Vis Exp 2015;(106):53336. doi:https://doi.org/10.3791/53336.

  • 47.

    Schulz CM, Endsley MR, Kochs EF, Gelb AW, Wagner KJ. Situation awareness in anesthesia. Anesthesiology. 2013;118(3):729–42. https://doi.org/10.1097/ALN.0b013e318280a40f.

    Article 
    PubMed 

    Google Scholar
     

  • 48.

    Medar S, Cassel-Choudhury G, Weingarten-Arams J, Ushay HM. Preventing cardiac arrest in a pediatric cardiac ICU—situational awareness and early intervention work together!. Crit Care Med. 2020;48(7):1093–5. https://doi.org/10.1097/CCM.0000000000004379.

  • 49.

    Goedken CC, Moeckli J, Cram PM, Reisinger HS. Introduction of Tele-ICU in rural hospitals: changing organisational culture to harness benefits. Intensive Critical Care Nursing. 2017;40:51–6. https://doi.org/10.1016/j.iccn.2016.10.001.

    Article 
    PubMed 

    Google Scholar
     

  • 50.

    Garland A, Gershengorn HB. Staffing in ICUs: physicians and alternative staffing models. Chest. 2013;143(1):214–21. https://doi.org/10.1378/chest.12-1531.

    Article 
    PubMed 

    Google Scholar
     

  • 51.

    Foster CB, Simone S, Bagdure D, Garber NA, Bhutta A. Optimizing team dynamics: an assessment of physician trainees and advanced practice providers collaborative practice*. Pediatr Crit Care Med. 2016;17(9):e430–6. https://doi.org/10.1097/PCC.0000000000000881.

    Article 
    PubMed 

    Google Scholar
     

  • 52.

    Vijay SA. REDUCING AND OPTIMIZING THE CYCLE TIME OF PATIENTS DISCHARGE PROCESS IN A HOSPITAL USING SIX SIGMA DMAIC APPROACH. Int J Quality Res. 2014;8(2).

  • 53.

    Kapoor R, Gupta N, Roberts SD, Naum C, Perkins AJ, Khan BA. Impact of geographical Cohorting in the ICU: an academic tertiary care center experience. Crit Care Explor. 2020;2(10):e0212. https://doi.org/10.1097/CCE.0000000000000212.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 54.

    Berner ES. Clinical decision support systems. New York: Springer Science+ Business Media, LLC; 2007.

  • 55.

    Xue Y, Klabjan D, Luo Y. Predicting ICU readmission using grouped physiological and medication trends. Artif Intell Med. 2019;95:27–37. https://doi.org/10.1016/j.artmed.2018.08.004.

    Article 
    PubMed 

    Google Scholar
     

  • 56.

    Fialho AS, Cismondi F, Vieira SM, Reti SR, Sousa JMC, Finkelstein SN. Data mining using clinical physiology at discharge to predict ICU readmissions. Expert Syst Appl. 2012;39(18):13158–65. https://doi.org/10.1016/j.eswa.2012.05.086.

    Article 

    Google Scholar
     

  • 57.

    Weng SF, Reps J, Kai J, Garibaldi JM, Qureshi N. Can machine-learning improve cardiovascular risk prediction using routine clinical data? Liu B, ed. PLoS One. 2017;12(4):e0174944. https://doi.org/10.1371/journal.pone.0174944.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 58.

    Chen M, Hao Y, Hwang K, Wang L, Wang L. Disease prediction by machine learning over big data from healthcare communities. IEEE Access. 2017;5:8869–79. https://doi.org/10.1109/ACCESS.2017.2694446.

    Article 

    Google Scholar
     

  • 59.

    Trautner BW, Bhimani RD, Amspoker AB, et al. Development and validation of an algorithm to recalibrate mental models and reduce diagnostic errors associated with catheter-associated bacteriuria. BMC Med Inform Decis Mak. 2013;13:48. https://doi.org/10.1186/1472-6947-13-48.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 60.

    O’Halloran HM, Kwong K, Veldhoen RA, Maslove DM. Characterizing the patients, hospitals, and data quality of the eICU collaborative research database*. Crit Care Med. 2020;48(12):1737–43. https://doi.org/10.1097/CCM.0000000000004633.

    Article 
    PubMed 

    Google Scholar
     

  • 61.

    Hsiang EY, Mehta SJ, Small DS, et al. Association of Primary Care Clinic Appointment Time with Clinician Ordering and Patient Completion of breast and colorectal Cancer screening. JAMA Netw Open. 2019;2(5):e193403. https://doi.org/10.1001/jamanetworkopen.2019.3403.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 62.

    Kim RH, Day SC, Small DS, Snider CK, Rareshide CAL, Patel MS. Variations in influenza vaccination by clinic appointment time and an active choice intervention in the electronic health record to increase influenza vaccination. JAMA Netw Open. 2018;1(5):e181770. https://doi.org/10.1001/jamanetworkopen.2018.1770.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 63.

    Auriemma C, McKenzie M, Olenik J, Wang W, Halpern SD, Weissman GE. Patterns of Decision Fatigue During Rounds in the Medical Intensive Care Unit. In: B21. OPTIMIZING ICU CARE AND SURVIVORSHIP. American Thoracic Society; 2020:A2793-A2793. doi:https://doi.org/10.1164/ajrccm-conference.2020.201.1_MeetingAbstracts.A2793.

  • 64.

    Persson E, Barrafrem K, Meunier A, Tinghög G. The effect of decision fatigue on surgeons’ clinical decision making. Health Econ. 2019;28(10):1194–203. https://doi.org/10.1002/hec.3933.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 65.

    Keers RN, Williams SD, Cooke J, Ashcroft DM. Causes of medication administration errors in hospitals: a systematic review of quantitative and qualitative evidence. Drug Saf. 2013;36(11):1045–67. https://doi.org/10.1007/s40264-013-0090-2.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

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