Digital Transformation: The Smart ICU

  • Javier Pérez-Fernández
  • Nestor A. Raimondi
  • Francisco Murillo Cabezas


Digital transformation in healthcare is affecting all levels of care. Conceptually speaking, it involves technology but also cultural changes and acceptance. Adapting technology is beneficial in healthcare. Patient safety and the delivery of care have certainly improved with the advent of technological advances. Also, our ability to manage extended databases and have readily available resources for its use at all levels has also impacted healthcare. However, technology has also created obstacles and limitations such us alarm fatigue and lack of interoperability among systems, making it difficult to navigate information pathways. Smart ICU design contemplates the ability of the systems to communicate, facilitate, and re-create all elements of the delivery of medical care in the ICU. Concepts as action-reaction, real-time data collection, and integration of information as well as predictive algorithms are subject to growing research and discussion. A change on the culture of medical care from our actual system of delivery to the patient-centered one also benefits from technological innovation. The future of medical care, including ICU care, is personalized, centered in the singularity of the human being.


Smart ICU Artificial intelligence Interoperability Patient-centered care Personalized medicine Alarms Predictive algorithms 


  1. 1.
    De Georgia MA, Kaffashi F, Jacono F, Loparo K. Information technology in critical care: review of monitoring and data acquisition systems for patient care and research. Sci World J. 2015;2015:1–9: 727694.CrossRefGoogle Scholar
  2. 2.
    What is patient-centered care? NEJM Catalyst; 2017. Accessed on line.Google Scholar
  3. 3.
    Kim MS, Barnato AE, Angus DC. Care teams on intensive care unit mortality. Arch Intern Med. 2010;170(4):369–76.CrossRefGoogle Scholar
  4. 4.
    Halpern NA. Innovative designs for the smart ICU. Part 3: advanced ICU informatics. Chest. 2014;145(4):903–12.CrossRefGoogle Scholar
  5. 5.
    Alyssas A, Turcotte M, Meyre D. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med Genet. 2015;8:33–45.Google Scholar
  6. 6.
    Burdick H, Pino E, Gabel-Comeau D, Gu C, et al. Evaluating a sepsis prediction machine learning algorithm in the emergency department and intensive care unit: a before and after comparative study. bioRxiv:224014.
  7. 7.
    Hooper MH, Weavind L, Wheeler AP, Martin JB, et al. Randomized trial of automated, electronic monitoring to facilitate early detection of sepsis in the intensive care unit. Crit Care Med. 2012;40:2096–101.CrossRefGoogle Scholar
  8. 8.
    Bell B, Thornton K. From promise to reality achieving the value of an EHR. Healthc Financ Manage. 2011;65(2):51–6.Google Scholar
  9. 9.
    Tubaishat A. The effect of electronic health records on patient safety: a qualitative exploratory study. Inform Health Soc Care. 2019;44(1):79–91.CrossRefGoogle Scholar
  10. 10.
    Duffy L, et al. Effects of electronic prescribing on the clinical practice of a family medicine residency. Fam Med. 2010;42(5):358–63.PubMedGoogle Scholar
  11. 11.
    Schädler D, Miestinger G, Becher T, Frerichs I, et al. Automated control of mechanical ventilation during general anaesthesia: study protocol of a bicentric observational study (AVAS). BMJ Open. 2017;7(5):e014742.CrossRefGoogle Scholar
  12. 12.
    Gordo F, Abella A. Intensive care unit without walls: seeking patient safety by improving the efficiency of the system. Med Int. 2014;38(7):438–43.Google Scholar
  13. 13.
    Poissant L, Pereira J, Tamblyn R, Kawasumi Y. The impact of electronic health records on time efficiency of physicians and nurses: a systematic review. J Am Med Inform Assoc. 2005;12(5):505–16.CrossRefGoogle Scholar
  14. 14.
    Fernando SM, Neilipovitz D, Sarti AJ, et al. Monitoring intensive care unit performance-impact of a novel individualised performance scorecard in critical care medicine: a mixed-methods study protocol. BMJ Open. 2018;8(1):e019165.CrossRefGoogle Scholar
  15. 15.
    Demartini C, Trucco S. Are performance measurement systems useful? Perceptions from health care. BMC Health Serv Res. 2017;17:96. Scholar
  16. 16.
    McGinn, et al. Comparison of user groups’ perspectives of barriers and facilitators to implementing electronic health records: a systematic review. BMC Med. 2011;9:46.CrossRefGoogle Scholar
  17. 17.
    Murray E, Burns J, May C, et al. Why is it difficult to implement e-health initiatives? A qualitative study. Implement Sci. 2011;6:6. Scholar
  18. 18.
    Leslie GD. Living in a glasshouse. Embracing care issues beyond ICU. Aust Crit Care. 2017;20(3):85–6.CrossRefGoogle Scholar
  19. 19.
    Grundy BL, Crawford P, Jones PK, Kiley ML, et al. Telemedicine in critical care: an experiment in health care delivery. JACEP. 1977;6(10):439–44.CrossRefGoogle Scholar
  20. 20.
    Grundy BL, Jones PK, Lovitt A. Telemedicine in critical care: problems in design, implementation, and assessment. Crit Care Med. 1982;10(7):471–5.CrossRefGoogle Scholar
  21. 21.
    Embriaco N, Azoulay E, Barrau K, et al. High level of burnout in intensivists: prevalence and associated factors. Am J Respir Crit Care Med. 2007;175:686–92.CrossRefGoogle Scholar
  22. 22.
    Kahn JM, Cicero BD, Wallace DJ, et al. Adoption of ICU telemedicine in the United States. Crit Care Med. 2014;42:362–8.CrossRefGoogle Scholar
  23. 23.
    Reynolds HN, Bander J. Options for tele-intensive care unit design: centralized versus decentralized and other considerations: it is not just a “another black sedan”. Crit Care Clin. 2015;31:335–50.CrossRefGoogle Scholar
  24. 24.
    Merrell RC. The journal, telemedicine, and the internet. Telemed J E Health. 2014;20(4):293–4.CrossRefGoogle Scholar
  25. 25.
    Thornton K, Schwarz J, Gross J, Gross K, et al. Preventing harm in the ICU-building a culture of safety and engaging patients and families. Crit Care Med. 2017;45. Scholar
  26. 26.
    Gonzalez Mendez MI. Smart ICU project. Enferm Clin. 2017;27(Espec Congr 2):7.Google Scholar
  27. 27.
    Well MH, Tang W. From intensive care to critical care medicine. A historical perspective. Am J Respir Crit Care Med. 2011;183(11):1451–3.CrossRefGoogle Scholar
  28. 28.
    Ruskin KJ, Hueske-Kraus D. Alarm fatigue: impacts on patient safety. Curr Opin Anaesthesiol. 2015;26(6):685–90.CrossRefGoogle Scholar
  29. 29.
    The Joint Commission. National patient safety goals effective January 2019. Hospital Accreditation program. Accessed online, August 2019.Google Scholar
  30. 30.
    Malykh VL, Rudetskiy SV. Approaches to medical decision-making based on big clinical data. J Healthc Eng. 2018;2018:3917659.CrossRefGoogle Scholar
  31. 31.
    Cuadros Carlesi K, Grillo K, Tofoletto MC, et al. Patient safety incidents and nursing workload. Rev Lat Am Enfermagem. 2017;25:e2841.Google Scholar
  32. 32.
    MacPhee M, Dahinten VS, Havael F. The impact of heavy perceived nurse workloads on patient and nurse outcomes. Adm Sci. 2017;7:7. Scholar
  33. 33.
    Koinis A, Giannou V, Drantaki V, et al. The impact of healthcare workers job environment on their mental-emotional health. Coping strategies: the case of a local general hospital. Health Psychol Res. 2015;3(1):1984.CrossRefGoogle Scholar
  34. 34.
    U.S. Food and Drug Administration. Medical device interoperability. Accessed online. Content current as of Sept 27, 2018.Google Scholar
  35. 35.
    De Moraes L, Garcia R, Ensslin L, et al. The multicriteria analysis for construction of benchmarkers to support the clinical engineering in the healthcare technology management. Eur J Oper Res. 2010;200:607–15.CrossRefGoogle Scholar
  36. 36.
    Cho OM, Kim H, Lee YW, et al. Clinical alarms in intensive care units: perceived obstacles of alarm management and alarm fatigue in nurses. Healthc Inform Res. 2016;22(1):46–53.CrossRefGoogle Scholar
  37. 37.
    Islam S, Hassam M, Wang X, et al. A systematic review on healthcare analytics: application and theoretical perspective of data mining. Healthcare (Basel). 2018;6(2):54.CrossRefGoogle Scholar
  38. 38.
    Lovejoy CA, Buch V, Maruhappu M. Artificial intelligence in the intensive care unit. Crit Care. 2019;23:7.CrossRefGoogle Scholar
  39. 39.
    Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, et al. Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR Med Inform. 2016;4(3):e28. pmid:27694098.CrossRefGoogle Scholar
  40. 40.
    Kamel Boulos MN, Berry G. Real-time locating systems (RTLS) in healthcare: a condensed primer. Int J Health Geogr. 2012;11:25.CrossRefGoogle Scholar
  41. 41.
    Daily M, Medasani S, Behringer R, Trivedi M. Self-driving cars. Computer. 2017;50:18–23.CrossRefGoogle Scholar
  42. 42.
    Jalali A, Bender D, Rehman M, Nadkanri V, Nataraj C. Advanced analytics for outcome prediction in intensive care units. In Engineering in Medicine and Biology Society (EMBC), IEEE 38th annual international conference of the, 2520–2524 (IEEE); 2016.Google Scholar
  43. 43.
    Amos B, Ludwiczuk B, Satyanarayanan M. Openface: a general-purpose face recognition library with mobile applications. Report, CMU School of Computer Science 2016.Google Scholar
  44. 44.
    Ma AJ, et al. Measuring patient mobility in the ICU using a novel noninvasive sensor. Crit Care Med. 2017;45:630–6. Scholar
  45. 45.
    Nasr Reem. Autopilot: what the system can and can’t do in CNBC explains. Accessed on line Aug 2019. Updated March 2015.Google Scholar
  46. 46.
    Hale K. Predictive analytics for marketing: what it can do and why you should be using it. Towards Data Science. Published May 7, 2018. Accessed on line Aug 2019.Google Scholar
  47. 47.
    Slotman GJ. Prospectively validated prediction of physiologic variables and organ failure in septic patients: the systemic mediator associated response test (SMART). Crit Care Med. 2002;30(5):1035–45.CrossRefGoogle Scholar
  48. 48.
    Ogundele O, Clermont G, Sileanu F, Pinsky M. Use of derived physiologic variables to predict individual Patients’ probability of hemodynamic instability. Am J Respir Crit Care Med. 2013;187:A5067.Google Scholar
  49. 49.
    Vincent J-L, Creteur J. Paradigm shifts in critical care medicine: the progress we have made. Crit Care. 2015;19:S10. Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Javier Pérez-Fernández
    • 1
  • Nestor A. Raimondi
    • 2
  • Francisco Murillo Cabezas
    • 3
  1. 1.Department of Critical Care ServicesBaptist Hospital of MiamiMiamiUSA
  2. 2.Intensive Care MedicineJuan A. FernandezBuenos AiresArgentina
  3. 3.Department of Critical CareHospital Universitario Virgen del Rocío. Seville UniversitySevilleSpain

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