The Integration of Technology and Innovation in the Development of Patient-Centered Medicine in the Intensive Care Unit: A Literature Review
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Jan 30, 2024
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Abstract
Since the advent of intensive care units in the twentieth century, several advances have been developed in relation to diagnosis, organ support, and treatment modalities. However, the environment for professionals, patients and their families continues to be stressful and uncomfortable. Optimizing the working conditions and processes of intensive care units is of great significance for improving efficiency and minimizing human errors. Innovations and technological advances can also bring higher quality and safer medicine, as well as greater personalization and a better experience for critically ill patients. This article reviews the progress in the related fields that could be the trend in the coming years for the formation of intelligent intensive care units. It is discussed how thinking about design, structure, equipment, less invasive monitoring, expansion of digital transformation, incorporation of artificial intelligence, in addition to the perspectives of these changes on the multidisciplinary team, can be important in the search for patient-centered care in the future of the intensive care units.
Keywords:
Critical care, technology, patient-centered care, artificial intelligence, intensive care units
Article Details
How to Cite
FILHO, Roberto Rabello; CORRÊA, Thiago Domingos.
The Integration of Technology and Innovation in the Development of Patient-Centered Medicine in the Intensive Care Unit: A Literature Review.
Medical Research Archives, [S.l.], v. 12, n. 1, jan. 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/4950>. Date accessed: 16 may 2024.
doi: https://doi.org/10.18103/mra.v12i1.4950.
Section
Review Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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23. Wang Z, Chen G, Lu K, Zhu Y, Chen Y. Investigation of the accuracy of a noninvasive continuous blood pressure device in different age groups and its ability in detecting hypertension and hypotension: An observational study. BMC Anesthesiol. 2019;19:223-233. doi: 10.1186/s12871-019-0899-z)
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25. Bodys-Pełka A, Kusztal M, Boszko M, Główczyńska R, Grabowski M. Non-Invasive Continuous Measurement of Haemodynamic Parameters-Clinical Utility. J Clin Med. 2021;10(21):4929-4942.
26. Duranteau J, De Backer D, Donadello K, Shapiro NI, Hutchings SD, Rovas A, et al. The future of intensive care: the study of the microcirculation will help to guide our therapies. Crit Care. 2023;27(1):190. doi: 10.1186/s13054-023-04474-x. PMID: 37193993; PMCID: PMC10186296.
27. Filho RR, de Freitas Chaves RC, Assunção MSC, Neto AS, De Freitas FM, Romagnoli ML, et al. Assessment of the peripheral microcirculation in patients with and without shock: a pilot study on different methods. J Clin Monit Comput. 2020;34(6):1167-1176. doi: 10.1007/s10877-019-00423-8. Epub 2019 Nov 21. PMID: 31754965; PMCID: PMC7548274.
28. Michard F. A sneak peek into digital innovations and wearable sensors for cardiac monitoring. J Clin Monit Comput. 2017;31(2):253-259. doi: 10.1007/s10877-016-9925-6. Epub 2016 Aug 26. PMID: 27566472.]
29. Liebo MJ, Israel RL, Lillie EO, Smith MR, Rubenson DS, Topol EJ. Is pocket mobile echocardiography the next-generation stethoscope? A cross-sectional comparison of rapidly acquired images with standard transthoracic echocardiography. Ann Intern Med. 2011;155(1):33-8. doi: 10.7326/0003-4819-155-1-201107050-00005. PMID: 21727291; PMCID: PMC3733444
30. Méndez Hernández R, Ramasco Rueda F. Biomarkers as Prognostic Predictors and Therapeutic Guide in Critically Ill Patients: Clinical Evidence. J Pers Med. 2023;13(2):333. doi: 10.3390/jpm13020333. PMID: 36836567; PMCID: PMC9965041.
31. Sinha P, Delucchi KL, Chen Y, Zhuo H, Abbott J, Wang C, et al. Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study. Thorax. 2022;77(1):13-21. doi: 10.1136/thoraxjnl-2021-217158. Epub 2021 Jul 12. PMID: 34253679; PMCID: PMC8688287.
32. Calfee CS, Delucchi KL, Sinha P, Matthay MA, Hackett J, Shankar-Hari M, et al. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. Lancet Respir Med. 2018;6(9):691-698. doi: 10.1016/S2213-2600(18)30177-2. Epub 2018 Aug 2. PMID: 30078618; PMCID: PMC6201750.
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34. Gattinoni L, Marini JJ, Collino F, Maiolo G, Rapetti F, Tonetti T, et al. The future of mechanical ventilation: lessons from the present and the past. Crit Care. 2017;21(1):183-194. doi: 10.1186/s13054-017-1750-x. PMID: 28701178; PMCID: PMC5508674.
35. Wischmeyer PE, Bear DE, Berger MM, De Waele E, Gunst J, McClave SA, et al. Personalized nutrition therapy in critical care: 10 expert recommendations. Crit Care. 2023; 27(1):261-277. doi: 10.1186/s13054-023-04539-x. PMID: 37403125; PMCID: PMC10318839.
36. Salam MA, Al-Amin MY, Pawar JS, Akhter N, Lucy IB. Conventional methods and future trends in antimicrobial susceptibility testing. Saudi J Biol Sci. 2023;30(3):103582. doi: 10.1016/j.sjbs.2023.103582. Epub 2023 Feb 10. PMID: 36852413; PMCID: PMC9958398.
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38. Bowman S. Impact of electronic health record systems on information integrity: quality and safety implications. Perspect Health Inf Manag. 2013;10(Fall):1c. PMID: 24159271; PMCID: PMC3797550.
39. Higgins TL, Freeseman-Freeman L, Stark MM, Henson KN. Benchmarking Inpatient Mortality Using Electronic Medical Record Data: A Retrospective, Multicenter Analytical Observational Study. Crit Care Med. 2022;50(4):543-553. doi: 10.1097/CCM.0000000000005301. PMID: 34582424.
40. Bulgarelli L, Deliberato RO, Johnson AEW. Prediction on critically ill patients: The role of "big data". J Crit Care. 2020;60:64-68. doi: 10.1016/j.jcrc.2020.07.017. Epub 2020 Jul 23. PMID: 32763775.
41. Saqib M, Iftikhar M, Neha F, Karishma F, Mumtaz H. Artificial intelligence in critical illness and its impact on patient care: a comprehensive review. Front Med (Lausanne). 2023; 10:1176192. doi: 10.3389/fmed.2023.1176192. PMID: 37153088; PMCID: PMC10158493.
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43. Kobayashi N, Shiga T, Ikumi S, Watanabe K, Murakami H, Yamauchi M. Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study. Sci Rep. 2021;11(1):5229-5236. doi: 10.1038/s41598-021-84714-8. PMID: 33664391; PMCID: PMC7933166.
2. Esper AM, Arabi YM, Cecconi M, et al. Systematized and efficient: Organization of Critical Care in the future. Critical Care. 2022;26(1). doi:10.1186/s13054-022-04244-1
3. Mao Z, Liu C, Li Q, Cui Y, Zhou F. Intelligent Intensive Care Unit: Current and future trends. Intensive Care Research. 2023;3(2):182-188. doi:10.1007/s44231-023-00036-5
4. Arabi YM, Azoulay E, Al-Dorzi HM, et al. How the COVID-19 pandemic will change the future of critical care. Intensive Care Med. 2021;47(3):282-291. doi: 10.1007/s00134-021-06352-y. Epub 2021 Feb 22. PMID: 33616696
5. Thompson DR, Hamilton DK, Cadenhead CD, et al. Guidelines for intensive care unit design. Crit Care Med. 2012;40(5):1586-600. doi: 10.1097/CCM.0b013e3182413bb2.
6. Vincent JL, Slutsky AS, Gattinoni L. Intensive care medicine in 2050: the future of ICU treatments. Intensive Care Med. 2017;43(9):1401–2. https://doi.org/10.1007/s00134‐016‐4556‐4.
7. Caruso P, Guardian L, Tiengo T, Dos Santos LS, Junior PM. ICU architectural design affects the delirium prevalence: a comparison between single‐ bed and multibed rooms*. Crit Care Med. 2014;42(10):2204–10. https://doi.org/10.1097/CCM.0000000000000502.
8. Saha S, Noble H, Xyrichis A, et al. Mapping the impact of ICU design on patients, families and the ICU team: a scoping review. J Crit Care. 2022;67:3–13. https://doi.org/10.1016/j.jcrc.2021.07.002.
9. Luetz A, Grunow JJ, Mörgeli R, et al. Innovative ICU solutions to prevent and reduce delirium and post‐intensive care unit syndrome. Semin Respir Crit Care Med. 2019;40(5):673–86. https://doi.org/10.1055/s‐0039‐1698404.
10. Bosch S, Bledsoe T, Jenzarli A. Staff perceptions before and after adding singlefamily rooms in the NICU. HERD Health Environ Res Des J 2012;5(4):64e75. https://doi.org/10.1177/193758671200500406.
11. Maben J, Griffiths P, Penfold C, et al. One size fits all? mixed methods evaluation of the impact of 100% single-room accommodation on staff and patient experience, safety and costs. BMJ Qual Safety. 2015:241e56. https://doi.org/10.1136/bmjqs-2015-004265
12. Garzotto F, Comoretto RI, Ostermann M, et al. Preventing infectious diseases in Intensive Care Unit by medical devices remote control: Lessons from COVID-19. J Crit Care. 2021; 61:119-124. doi: 10.1016/j.jcrc.2020.10.014. Epub 2020 Oct 27. PMID: 33157307; PMCID: PMC7588313.
13. Verderber S, Gray S, Suresh‐Kumar S, Kercz D, Parshuram C. Intensive care unit built environments: a comprehensive literature review (2005–2020). HERD. 2021;14:368–415. 10.1177/19375867211009273. Epub 2021 May 18. PMID: 34000842; PMCID: PMC8597197.
14. Kwon H, An S, Lee HY, et al. Review of Smart Hospital Services in Real Healthcare Environments. Healthc Inform Res. 2022;28(1):3-15. doi: 10.4258/hir.2022.28.1.3. Epub 2022 Jan 31. PMID: 35172086; PMCID: PMC8850169.
15. Ely EW. The ABCDEF Bundle: Science and Philosophy of How ICU Liberation Serves Patients and Families. Crit Care Med. 2017;45(2):321-330. doi: 10.1097/CCM.0000000000002175. PMID: 28098628; PMCID: PMC5830123.
16. Kotfis K, van Diem-Zaal I, Williams Roberson S, et al. The future of intensive care: delirium should no longer be an issue. Crit Care. 2022;26(1):200-211. doi: 10.1186/s13054-022-04077-y. Erratum in: Crit Care. 2022 Sep 21;26(1):285. PMID: 35790979; PMCID: PMC9254432.
17. Chan PY, Tay A, Chen D, et al. Ambient intelligence-based monitoring of staff and patient activity in the intensive care unit. Aust Crit Care. 2023;36(1):92-98. doi: 10.1016/j.aucc.2022.08.011. Epub 2022 Oct 13. PMID: 36244918.
18. Halpern NA, Anderson DC, Kesecioglu J. ICU design in 2050: looking into the crystal ball! Intensive Care Med. 2017;43(5):690-692. doi: 10.1007/s00134-017-4728-x. Epub 2017 Mar 17. PMID: 28315042.
19. Wade DF, Moon Z, Windgassen SS, Harrison AM, Morris L, Weinman JA. Non-pharmacological interventions to reduce ICU-related psychological distress: a systematic review. Minerva Anestesiol. 2016;82(4):465-78. Epub 2015 Oct 27. PMID: 26505225.
20. Zhang L, Hu W, Cai Z, et al. Early mobilization of critically ill patients in the intensive care unit: A systematic review and meta-analysis. PLoS One. 2019;14(10):e0223185. doi: 10.1371/journal.pone.0223185. PMID: 31581205; PMCID: PMC6776357.
21. Wright D, Mackenzie SJ, Buchan I, Cairns CS, Price LE. Critical incidents in the intensive therapy unit. Lancet. 199;338(8768):676-678. doi: 10.1016/0140-6736(91)91243-n. PMID: 1679483.
22. Michard F, Pinsky MR, Vincent JL. Intensive care medicine in 2050: NEWS for hemodynamic monitoring. Intensive Care Med. 2017;43(3):440-442. doi: 10.1007/s00134-016-4674-z. Epub 2017 Jan 25. PMID: 28124086.
23. Wang Z, Chen G, Lu K, Zhu Y, Chen Y. Investigation of the accuracy of a noninvasive continuous blood pressure device in different age groups and its ability in detecting hypertension and hypotension: An observational study. BMC Anesthesiol. 2019;19:223-233. doi: 10.1186/s12871-019-0899-z)
24. Broch O, Renner J, Gruenewald M, Meybohm P, Schöttler J, Caliebe A, et al. A comparison of the Nexfin® and transcardiopulmonary thermodilution to estimate cardiac output during coronary artery surgery. Anaesthesia. 2012;67(4):377-383. doi: 10.1111/j.1365-2044.2011.07018.x. Epub 2012 Feb 11. PMID: 22324797
25. Bodys-Pełka A, Kusztal M, Boszko M, Główczyńska R, Grabowski M. Non-Invasive Continuous Measurement of Haemodynamic Parameters-Clinical Utility. J Clin Med. 2021;10(21):4929-4942.
26. Duranteau J, De Backer D, Donadello K, Shapiro NI, Hutchings SD, Rovas A, et al. The future of intensive care: the study of the microcirculation will help to guide our therapies. Crit Care. 2023;27(1):190. doi: 10.1186/s13054-023-04474-x. PMID: 37193993; PMCID: PMC10186296.
27. Filho RR, de Freitas Chaves RC, Assunção MSC, Neto AS, De Freitas FM, Romagnoli ML, et al. Assessment of the peripheral microcirculation in patients with and without shock: a pilot study on different methods. J Clin Monit Comput. 2020;34(6):1167-1176. doi: 10.1007/s10877-019-00423-8. Epub 2019 Nov 21. PMID: 31754965; PMCID: PMC7548274.
28. Michard F. A sneak peek into digital innovations and wearable sensors for cardiac monitoring. J Clin Monit Comput. 2017;31(2):253-259. doi: 10.1007/s10877-016-9925-6. Epub 2016 Aug 26. PMID: 27566472.]
29. Liebo MJ, Israel RL, Lillie EO, Smith MR, Rubenson DS, Topol EJ. Is pocket mobile echocardiography the next-generation stethoscope? A cross-sectional comparison of rapidly acquired images with standard transthoracic echocardiography. Ann Intern Med. 2011;155(1):33-8. doi: 10.7326/0003-4819-155-1-201107050-00005. PMID: 21727291; PMCID: PMC3733444
30. Méndez Hernández R, Ramasco Rueda F. Biomarkers as Prognostic Predictors and Therapeutic Guide in Critically Ill Patients: Clinical Evidence. J Pers Med. 2023;13(2):333. doi: 10.3390/jpm13020333. PMID: 36836567; PMCID: PMC9965041.
31. Sinha P, Delucchi KL, Chen Y, Zhuo H, Abbott J, Wang C, et al. Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study. Thorax. 2022;77(1):13-21. doi: 10.1136/thoraxjnl-2021-217158. Epub 2021 Jul 12. PMID: 34253679; PMCID: PMC8688287.
32. Calfee CS, Delucchi KL, Sinha P, Matthay MA, Hackett J, Shankar-Hari M, et al. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. Lancet Respir Med. 2018;6(9):691-698. doi: 10.1016/S2213-2600(18)30177-2. Epub 2018 Aug 2. PMID: 30078618; PMCID: PMC6201750.
33. Famous KR, Delucchi K, Ware LB, Kangelaris KN, Liu KD, Thompson BT, et al. Acute Respiratory Distress Syndrome Subphenotypes Respond Differently to Randomized Fluid Management Strategy. Am J Respir Crit Care Med. 2017;195(3):331-338. doi: 10.1164/rccm.201603-0645OC. Erratum in: Am J Respir Crit Care Med. 2018 Dec 15;198(12):1590. Erratum in: Am J Respir Crit Care Med. 2019 Sep 1;200(5):649. PMID: 27513822; PMCID: PMC5328179.
34. Gattinoni L, Marini JJ, Collino F, Maiolo G, Rapetti F, Tonetti T, et al. The future of mechanical ventilation: lessons from the present and the past. Crit Care. 2017;21(1):183-194. doi: 10.1186/s13054-017-1750-x. PMID: 28701178; PMCID: PMC5508674.
35. Wischmeyer PE, Bear DE, Berger MM, De Waele E, Gunst J, McClave SA, et al. Personalized nutrition therapy in critical care: 10 expert recommendations. Crit Care. 2023; 27(1):261-277. doi: 10.1186/s13054-023-04539-x. PMID: 37403125; PMCID: PMC10318839.
36. Salam MA, Al-Amin MY, Pawar JS, Akhter N, Lucy IB. Conventional methods and future trends in antimicrobial susceptibility testing. Saudi J Biol Sci. 2023;30(3):103582. doi: 10.1016/j.sjbs.2023.103582. Epub 2023 Feb 10. PMID: 36852413; PMCID: PMC9958398.
37. Nathan C, Cars O. Antibiotic resistance--problems, progress, and prospects. N Engl J Med. 2014;371(19):1761-1763. doi: 10.1056/NEJMp1408040. Epub 2014 Oct 1. PMID: 25271470.
38. Bowman S. Impact of electronic health record systems on information integrity: quality and safety implications. Perspect Health Inf Manag. 2013;10(Fall):1c. PMID: 24159271; PMCID: PMC3797550.
39. Higgins TL, Freeseman-Freeman L, Stark MM, Henson KN. Benchmarking Inpatient Mortality Using Electronic Medical Record Data: A Retrospective, Multicenter Analytical Observational Study. Crit Care Med. 2022;50(4):543-553. doi: 10.1097/CCM.0000000000005301. PMID: 34582424.
40. Bulgarelli L, Deliberato RO, Johnson AEW. Prediction on critically ill patients: The role of "big data". J Crit Care. 2020;60:64-68. doi: 10.1016/j.jcrc.2020.07.017. Epub 2020 Jul 23. PMID: 32763775.
41. Saqib M, Iftikhar M, Neha F, Karishma F, Mumtaz H. Artificial intelligence in critical illness and its impact on patient care: a comprehensive review. Front Med (Lausanne). 2023; 10:1176192. doi: 10.3389/fmed.2023.1176192. PMID: 37153088; PMCID: PMC10158493.
42. Yoon JH, Pinsky MR. Predicting adverse hemodynamic events in critically ill patients. Curr Opin Crit Care. 2018;24(3):196-203. doi: 10.1097/MCC.0000000000000496. PMID: 29601321; PMCID: PMC6007856.
43. Kobayashi N, Shiga T, Ikumi S, Watanabe K, Murakami H, Yamauchi M. Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study. Sci Rep. 2021;11(1):5229-5236. doi: 10.1038/s41598-021-84714-8. PMID: 33664391; PMCID: PMC7933166.