Using augmented intelligence to improve long term outcomes.


Journal

Current opinion in critical care
ISSN: 1531-7072
Titre abrégé: Curr Opin Crit Care
Pays: United States
ID NLM: 9504454

Informations de publication

Date de publication:
04 Jul 2024
Historique:
medline: 16 8 2024
pubmed: 16 8 2024
entrez: 16 8 2024
Statut: aheadofprint

Résumé

For augmented intelligence (AI) tools to realize their potential, critical care clinicians must ensure they are designed to improve long-term outcomes. This overview is intended to align professionals with the state-of-the art of AI. Many AI tools are undergoing preliminary assessment of their ability to support the care of survivors and their caregivers at multiple time points after intensive care unit (ICU) discharge. The domains being studied include early identification of deterioration (physiological, mental), management of impaired physical functioning, pain, sleep and sexual dysfunction, improving nutrition and communication, and screening and treatment of cognitive impairment and mental health disorders.Several technologies are already being marketed and many more are in various stages of development. These technologies mostly still require clinical trials outcome testing. However, lacking a formal regulatory approval process, some are already in use. Plans for long-term management of ICU survivors must account for the development of a holistic follow-up system that incorporates AI across multiple platforms. A tiered post-ICU screening program may be established wherein AI tools managed by ICU follow-up clinics provide appropriate assistance without human intervention in cases with less pathology and refer severe cases to expert treatment.

Identifiants

pubmed: 39150034
doi: 10.1097/MCC.0000000000001185
pii: 00075198-990000000-00192
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.

Références

Needham DM, Davidson J, Cohen H, Hopkins RO, et al. Improving long-term outcomes after discharge from intensive care unit. Crit Care Med 2012; 40:502–509.
Marra A, Pandharipande PP, Girard TD, et al. Co-occurrence of post-intensive care syndrome problems among 406 survivors of critical illness. Crit Care Med 2018; 46:1393–1401.
Flaws D, Fraser JF, Laupland K, et al. Time in ICU and postintensive care syndrome: how long is long enough? Crit Care 2024; 28:34.
Rousseau AF, Prescott HC, Brett SJ, et al. Long-term outcomes after critical illness: recent insights. Crit Care 2021; 25:108.
Ridley EJ, Parke RL, Davies AR, et al. What happens to nutrition intake in the post–intensive care unit hospitalization period? An observational cohort study in critically ill adults. JPEN J Parenter Enteral Nutr 2018; 43:88–95.
Zuercher P, Moret CS, Dziewas R, Schefold JC. Dysphagia in the intensive care unit: epidemiology, mechanisms, and clinical management. Crit Care 2019; 23:103.
McIntyre M, Doeltgen S, Dalton N, et al. Postextubation dysphagia incidence in critically ill patients: a systematic review and meta-analysis. Aust Crit Care 2020; 34:67–75.
Ali Abdelhamid Y, Deane AM. Post-ICU diabetes. lessons from the ICU. Cham:Springer; 2019.
Schiffl H, Lang SM, Fischer R. Long-term outcomes of survivors of ICU acute kidney injury requiring renal replacement therapy: a 10-year prospective cohort study. Clin Kidney J 2012; 5:297–302.
Fleischmann-Struzek C, Rose N, Freytag A, et al. Epidemiology and costs of postsepsis morbidity, nursing care dependency, and mortality in Germany, 2013 to 2017. JAMA Netw Open 2021; 4:e2134290.
Van Aerde N, Meersseman P, Debaveye Y, et al. Aerobic exercise capacity in long-term survivors of critical illness: secondary analysis of the post-EPaNIC Follow-up Study. Intensive Care Med 2021; 47:1462–1471.
Cuthbertson BH, Roughton S, Jenkinson D, et al. Quality of life in the five years after intensive care: a cohort study. Crit Care 2010; 14:R6.
Gamberini L, Mazzoli CA, Prediletto I, et al. Health-related quality of life profiles, trajectories, persistent symptoms and pulmonary function one year after ICU discharge in invasively ventilated COVID-19 patients, a prospective follow-up study. Respir Med 2021; 189:106665.
Alfheim HB, Småstuen MC, Hofsø K, et al. Quality of life in family caregivers of patients in the intensive care unit: a longitudinal study. Aust Crit Care 2019; 32:479–485.
Bohm M, Cronberg T, Årestedt K, et al. Caregiver burden and health-related quality of life amongst caregivers of out-of-hospital cardiac arrest survivors. Resuscitation 2021; 167:118–127.
Kang J, Lee KM. Three-year mortality, readmission, and medical expenses in critical care survivors: a population-based cohort study. Aust Crit Care 2024; 37:251–257.
McPeake J, Quasim T, Henderson P, et al. Multimorbidity and its relationship with long-term outcomes after critical care discharge: a prospective cohort study. Chest 2021; 160:1681–1692.
Connolly B, Milton-Cole R, Adams C, et al. Recovery, rehabilitation and follow-up services following critical illness: an updated UK national cross-sectional survey and progress report. BMJ Open 2021; 11:e052214.
Hosein FS, Roberts DJ, Turin TC, et al. A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care. Crit Care 2014; 18:715.
Schmidt KFR, Gensichen JS, Schroevers M, et al. Trajectories of posttraumatic stress in sepsis survivors two years after ICU discharge: a secondary analysis of a randomized controlled trial. Crit Care 2024; 28:35.
Cuschieri J, Kornblith L, Pati S, Piliponsky A. The injured monocyte: the link to chronic critical illness and mortality following injury. J Trauma Acute Care Surg 2024; 96:195–202.
Bertram K, Cox C, Alam H, et al. Insights from CTTACC: immune system reset by cellular therapies for chronic illness after trauma, infection, and burn. Cytotherapy 2024; 26:714–718.
Vollam S, Gustafson O, Young JD, et al. Problems in care and avoidability of death after discharge from intensive care: a multicentre retrospective case record review study. Critical Care 2021; 25:10.
Pant U, Vyas K, Meghani S, et al. Screening tools for post–intensive care syndrome and posttraumatic symptoms in intensive care unit survivors: a scoping review. Aust Crit Care 2022; 36:863–871.
Spies CD, Krampe H, Paul N, et al. Instruments to measure outcomes of postintensive care syndrome in outpatient care settings – results of an expert consensus and feasibility field test. J Intensive Care Soc 2020; 22:159–174.
Taylor SP, Chou SH, Sierra MF, et al. Association between adherence to recommended care and outcomes for adult survivors of sepsis. Ann Am Thorac Soc 2020; 17:89–97.
Yanagi N, Kamiya K, Hamazaki N, et al. Postintensive care syndrome as a predictor of mortality in patients with critical illness: a cohort study. PLoS One 2021; 16:e0244564.
Rosa RG, Ferreira GE, Viola TW, et al. Effects of post-ICU follow-up on subject outcomes: a systematic review and meta-analysis. J Crit Care 2019; 52:115–125.
Sevin CM, Bloom SL, Jackson JC, et al. Comprehensive care of ICU survivors: development and implementation of an ICU recovery center. J Crit Care 2018; 46:141–148.
Teixeira C, Rosa RG. Postintensive care outpatient clinic: is it feasible and effective? A literature Review. Rev Brasil Terapia Intensiva 2018; 30:98–111.
Nakanishi N, Liu K, Hatakeyama J, et al. Postintensive care syndrome follow-up system after hospital discharge: a narrative review. J Intensive Care 2024; 12:2.
Johanna Josepha op’t Hoog SA, Eskes AM, Johanna van Mersbergen-de Bruin MP, et al. The effects of intensive care unit-initiated transitional care interventions on elements of postintensive care syndrome: a systematic review and meta-analysis. Aust Crit Care 2021; 35:309–320.
Van Sleeuwen D, Zegers M, Ramjith J, et al. Prediction of long-term physical, mental, and cognitive problems following critical illness: development and external validation of the PROSPECT Prediction Model. Crit Care Med 2024; 52:200–209.
Dimopoulos S, Leggett NE, Deane AM, et al. Models of intensive care unit follow-up care and feasibility of intervention delivery: a systematic review. Aust Crit Care 2024; 37:508–516.
Fleming KA, Horton S, Wilson ML, et al. The Lancet Commission on diagnostics: transforming access to diagnostics. Lancet 2021; 398:1997–2050.
Adams SJ, Babyn P, Burbridge B, et al. Access to ultrasound imaging: a qualitative study in two northern, remote, indigenous communities in Canada. Int J Circumpolar Health 2021; 80:1961392.
Metra M, Adamo M, Tomasoni D, et al. Predischarge and early postdischarge management of patients hospitalized for acute heart failure: a scientific statement by the Heart Failure Association of the ESC. Eur J Heart Fail 2023; 25:1115–1131.
McDonagh TA, Metra M, Adamo M, et al. 2021 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J 2021; 42:3599–3726.
Elmi N, Sadri Y, Myslik F, et al. Self-administered at-home lung ultrasound with remote guidance in patients without clinical training. Respir Res 2024; 25:111.
Malia L, Nye ML, Kessler DO. Exploring the feasibility of at-home lung ultra-portable ultrasound: parent-performed pediatric lung imaging. J Ultrasound Med 2024; 43:723–728.
Chiem AT, Lim GW, Tabibnia AP, et al. Feasibility of patient-performed lung ultrasound self-exams (Patient-PLUS) as a potential approach to telemedicine in heart failure. ESC Heart Fail 2021; 8:3997–4006.
Pivetta E, Ravetti A, Paglietta G, et al. Feasibility of self-performed lung ultrasound with remote teleguidance for monitoring at home COVID-19 patients. Biomedicines 2022; 10:2569.
Schneider E, Maimon N, Hasidim A, et al. Can dialysis patients identify and diagnose pulmonary congestion using self-lung ultrasound? J Clin Med 2023; 12:3829.
Haines RW, Powell-Tuck J, Leonard H, et al. Long-term kidney function of patients discharged from hospital after an intensive care admission: observational cohort study. Sci Rep 2021; 11:9928.
Rosa RG, Robinson CC, Veiga VC, et al. Quality of life and long-term outcomes after hospitalization for COVID-19: protocol for a prospective cohort study (Coalition VII). Rev Brasil Ter Intensiva 2021; 33:31–37.
Erez DL, Derwick H, Furth S, et al. Dipping at home: is it better, easier, and more convenient? A feasibility and acceptability study of a novel home urinalysis using a smartphone application. Pediatr Nephrol 2022; 38:139–143.
Siu VS, Lu M, Hsieh KY, et al. Development of a quantitative digital urinalysis tool for detection of nitrite, protein, creatinine, and pH. Biosensors 2024; 14:70.
Capstick A, Palermo F, Zakka K, et al. Digital remote monitoring for screening and early detection of urinary tract infections. NPJ Digit Med 2024; 7:11.
Burke AE, Thaler KM, Geva M, Adiri Y. Feasibility and acceptability of home use of a smartphone-based urine testing application among women in prenatal care. Am J Obstet Gynecol 2019; 221:527–528.
Nie J, Shao H, Fan Y, et al. LLM-based conversational AI therapist for daily functioning screening and psychotherapeutic intervention via everyday smart devices (preprint). arXivorg 2024.
Becker ML, Hurkmans HLP, Verhaar JAN, Bussmann JBJ. Validation of the Activ8 activity monitor for monitoring postures, motions, transfers, and steps of hospitalized patients. Sensors 2024; 24:180.
di Biase L, Pecoraro PM, Pecoraro G, et al. Markerless radio frequency indoor monitoring for telemedicine: gait analysis, indoor positioning, fall detection, tremor analysis, vital signs and sleep monitoring. Sensors 2022; 22:8486.
Echocare Home Page. EchoCare Technologies. [cited 2024 Apr 27]. Available at: https://echocare.ai/.
Denehy L, Hough CL. Critical illness, disability, and the road home. Intensive Care Med 2017; 43:1881–1883.
Major ME, Dettling-Ihnenfeldt D, Ramaekers SPJ, et al. Feasibility of a home-based interdisciplinary rehabilitation program for patients with post-intensive care syndrome: the REACH Study. Crit Care 2021; 25:279.
Pereira MF, Prahm C, Kolbenschlag J, et al. Application of AR and VR in hand rehabilitation: a systematic review. J Biomed Inform 2020; 111:103584.
Vélez-Guerrero MA, Callejas-Cuervo M, Mazzoleni S. Artificial intelligence-based wearable robotic exoskeletons for upper limb rehabilitation: a review. Sensors 2021; 21:2146.
Xiao X, Fang Y, Xiao X, et al. Machine-learning-aided self-powered assistive physical therapy devices. ACS Nano 2021; 15:18633–18646.
Feng H, Li C, Liu J, et al. Virtual reality rehabilitation versus conventional physical therapy for improving balance and gait in Parkinson's disease patients: a randomized controlled trial. Med Sci Monitor 2019; 25:4186–4192.
Wu CL, Liu SF, Yu TL, et al. Deep learning-based pain classifier based on the facial expression in critically ill patients. Front Med 2022; 9:851690.
Piette JD, Newman S, Krein SL, et al. Patient-centered pain care using artificial intelligence and mobile health tools. JAMA Intern Med 2022; 182:975.
Rabbi M, Aung MS, Gay G, et al. Feasibility and acceptability of mobile phone-based auto-personalized physical activity recommendations for chronic pain self-management: pilot study on adults. J Med Internet Res 2018; 20:e10147.
Schwartz AR, Cohen-Zion M, Pham LV, et al. Brief digital sleep questionnaire powered by machine learning prediction models identifies common sleep disorders. Sleep Med 2020; 71:66–76.
Griffiths J, Gager M, Alder N, et al. A self-report-based study of the incidence and associations of sexual dysfunction in survivors of intensive care treatment. Intensive Care Med 2006; 32:445–451.
Lin H, Zhao L, Wu H, et al. Sexual life and medication taking behaviours in young men: an online survey of 92 620 respondents in China. Int J Clin Pract 2019; 74:e13417.
Grover S, Shouan A. Assessment scales for sexual disorders—a review. J Psychosex Health 2020; 2:263183182091958.
Abdel Hady DA, Abd El-Hafeez T. Revolutionizing core muscle analysis in female sexual dysfunction based on machine learning. Sci Rep 2024; 14:4795.
Seyam RM, Khan BS, Aljazaeri SA, et al. (033) Artificial intelligence ChatGPT and GPT4 performance on male and female sexual dysfunction, sexually transmitted infection, and male factor infertility in the 2019 to 2023 American Urological Association Self-Assessment Study Programs. J Sex Med 2024; 21: (Suppl 2): qdae002.033.
Brodsky MB, Huang M, Shanholtz C, et al. Recovery from dysphagia symptoms after oral endotracheal intubation in acute respiratory distress syndrome survivors. A 5-year longitudinal study. Ann Am Thorac Soc 2017; 14:376–383.
Hongo T, Yamamoto R, Liu K, et al. Association between timing of speech and language therapy initiation and outcomes among postextubation dysphagia patients: a multicenter retrospective cohort study. Crit Care 2022; 26:98.
Martin-Martinez A, Miró J, Amadó C, et al. A systematic and universal artificial intelligence screening method for oropharyngeal dysphagia: improving diagnosis through risk management. Dysphagia 2022; 38:1224–1237.
Rousseau AF, Lucania S, Fadeur M, et al. Adequacy of nutritional intakes during the year after critical illness: an observational study in a post-ICU follow-up clinic. Nutrients 2022; 14:3797–3807.
Wang J, He C, Long Z. Establishing a machine learning model for predicting nutritional risk through facial feature recognition. Front Nutr 2023; 10:1219193.
Bond A, McCay KD, Lal S. Artificial intelligence & clinical nutrition: what the future might have in store. Clin Nutr ESPEN 2023; 57:542–549.
Ponzo V, Goitre I, Favaro E, et al. Is ChatGPT an effective tool for providing dietary advice? Nutrients 2024; 16:469.
Willett FR, Kunz EM, Fan C, et al. A high-performance speech neuroprosthesis. Nature 2023; 620:1031–1036.
Metzger SL, Littlejohn KT, Silva AB, et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature 2023; 620:1–10.
Luo S, Rabbani Q, Crone NE. Brain-computer interface: applications to speech decoding and synthesis to augment communication. Neurotherapeutics 2022; 19:263–273.
Wen F, Zhang Z, He T, Lee C. AI enabled sign language recognition and vr space bidirectional communication using triboelectric smart glove. Nat Commun 2021; 12:5378.
Müller A, von Hofen-Hohloch J, Mende M, et al. Long-term cognitive impairment after ICU treatment: a prospective longitudinal cohort study (Cog-I-CU). Sci Rep 2020; 10:15518.
Wang C, Liu S, Li A, Liu J. Text dialogue analysis for primary screening of mild cognitive impairment: development and validation study. J Med Internet Res 2023; 25:e51501.
Kalafatis C, Modarres MH, Apostolou P, et al. Validity and cultural generalisability of a 5-minute ai-based, computerised cognitive assessment in mild cognitive impairment and Alzheimer's dementia. Front Psychiatry 2021; 12:706695.
Jung HT, Daneault JF, Lee H, et al. Remote assessment of cognitive impairment level based on serious mobile game performance: an initial proof of concept. IEEE J Biomed Health Inform 2019; 23:1269–1277.
Rawtaer I, Mahendran R, Kua EH, et al. Early detection of mild cognitive impairment with in-home sensors to monitor behavior patterns in community-dwelling senior citizens in singapore: cross-sectional feasibility study. J Med Internet Rese 2020; 22:e16854.
Patel BK, Wolfe KS, Patel SB, et al. Effect of early mobilisation on long-term cognitive impairment in critical illness in the USA: a randomised controlled trial. Lancet Respir Med 2023; 11:563–572.
Muradov O, Petrovskaya O, Papathanassoglou E. Effectiveness of cognitive interventions on cognitive outcomes of adult intensive care unit survivors: a scoping review. Aust Crit Care 2021; 34:473–485.
Zhu S, Sui Y, Shen Y, et al. Effects of virtual reality intervention on cognition and motor function in older adults with mild cognitive impairment or dementia: a systematic review and meta-analysis. Front Aging Neurosci 2021; 13:586999.
Parker AM, Sricharoenchai T, Raparla S, et al. Posttraumatic stress disorder in critical illness survivors. Crit Care Med 2015; 43:1121–1129.
Rabiee A, Nikayin S, Hashem MD, et al. Depressive symptoms after critical illness. Crit Care Med 2016; 44:1744–1753.
Habicht J, Viswanathan S, Carrington B, et al. Closing the accessibility gap to mental health treatment with a personalized self-referral Chatbot. Nat Med 2024; 30:595–602.
Rollwage M, Habicht J, Juchems K, et al. Using conversational AI to facilitate mental health assessments and improve clinical efficiencies within psychotherapy services in a large real-world dataset (preprint). JMIR AI 2023; 2:e44358–e44368.
Kaywan P, Ahmed K, Ibaida A, et al. Early detection of depression using a conversational AI Bot: a nonclinical trial. PLoS One 2023; 18:e0279743.
Caulley D, Alemu Y, Burson S, et al. Objectively quantifying pediatric psychiatric severity using artificial intelligence, voice recognition technology, and universal emotions: pilot study for artificial intelligence-enabled innovation to address youth mental health crisis. JMIR Res Protoc 2023; 12:e51912.
Low DM, Bentley KH, Ghosh SS. Automated assessment of psychiatric disorders using speech: a systematic review. Laryngoscope Investig Otolaryngol 2020; 5:96–116.
Zheng L, Wang O, Hao S, et al. Development of an early warning system for high-risk patients for suicide attempt using deep learning and electronic health records. Transl Psychiatry 2020; 10:72.
De Angel V, Lewis S, White K, et al. Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ Digit Med 2022; 5:1–14.
Oxevision US Homepage. www.oxehealth.com. [cited 2024 Apr 27]. Available at: https://www.oxehealth.com/evidence.
Binah.ai Homepage Binah. [cited 2024 Apr 27]. Available at: https://www.binah.ai/.
Hoffmann JA, Attridge MM, Carroll MS, et al. Association of youth suicides and county-level mental health professional shortage areas in the US. JAMA Pediatr 2022; 177:71–80.
Kim J, Aryee LMD, Bang H, et al. Effectiveness of digital mental health tools to reduce depressive and anxiety symptoms in low- and middle-income countries: systematic review and meta-analysis (preprint). JMIR Mental Health 2022; 10:e43066.
Levkovich I, Elyoseph Z. Identifying depression and its determinants upon initiating treatment: ChatGPT versus primary care physicians. Fam Med Community Health 2023; 11:e002391.
Sharma A, Lin IW, Miner AS, et al. Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nat Mach Intell 2023; 5:46–57.
Fulmer R, Joerin A, Gentile B, et al. Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: randomized controlled trial. JMIR Ment Health 2018; 5:e64.
Kanschik D, Bruno RR, Wolff G, et al. Virtual and augmented reality in intensive care medicine: a systematic review. Ann Intensive Care 2023; 13:81.
Vlake JH, Van Bommel J, Wils EJ, et al. Virtual reality for relatives of ICU patients to improve psychological sequelae: study protocol for a multicentre, randomised controlled trial. BMJ Open 2021; 11:e049704.
Vlake JH, Van Bommel J, Wils EJ, et al. Intensive care unit–specific virtual reality for critically ill patients with COVID-19: multicenter randomized controlled trial. J Med Internet Res 2022; 24:e32368.
Woebot Health Homepage. Woebot. [cited 2024 Apr 27]. Available at: https://woebothealth.com/.
Limbic ai Homepage. limbic.ai. [cited 2024 Apr 27]. Available at: https://limbic.ai/.
XRHealth Homepage. XRHealth. [cited 2024 Apr 27]. Available at: https://www.xr.health/.
Stade EC, Wiltsey Stirman S, Ungar LH, et al. Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation. NPJ Ment Health Res 2024; 3:12.
Coghlan S, Leins K, Sheldrick S, et al. To chat or bot to chat: ethical issues with using chatbots in mental health. Digital Health 2023; 9:20552076231183542.
Javakhishvili J, Makhashvili N, Winkler P, et al. Providing immediate digital mental health interventions and psychotrauma support during political crises. Lancet Psychiatry 2023; 10:727–732.
Renner C, Jeitziner M, Tran A, et al. Guideline on multimodal rehabilitation for patients with postintensive care syndrome. Crit Care 2023; 27:301.

Auteurs

Itay Zahavi (I)

Bruce and Ruth Rappaport Faculty of Medicine, Technion - Israel Institute of Technology Haifa.

Itamar Ben Shitrit (I)

Joyce and Irving Goldman Medical School and Clinical Research Center, Soroka University Medical Center, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva.

Sharon Einav (S)

Maccabi Healthcare System, Sharon Region, and Hebrew University Faculty of Medicine, Jerusalem, Israel.

Classifications MeSH