research-article

Framing Machine Learning Opportunities for Hypotension Prediction in Perioperative Care: A Socio-technical Perspective: Socio-technical perspectives on hypotension prediction

Authors:

Karen L. Posner,

Stephanie L. Hyland,

Melissa Bristow,

Dustin R. Long,

Konstantina Palla,

Christine Fong,

Ronald Pauldine,

Monica S. Vavilala,

Kenton O'HaraAuthors Info & Claims

ACM Transactions on Computer-Human Interaction, Volume 30, Issue 5

Article No.: 79, Pages 1 - 33

https://doi.org/10.1145/3589953

Published: 23 September 2023 Publication History

Abstract

Hypotension during perioperative care, if undetected or uncontrolled, can lead to serious clinical complications. Predictive machine learning models, based on routinely collected EHR data, offer potential for early warning of hypotension to enable proactive clinical intervention. However, while research has demonstrated the feasibility of such machine learning models, little effort is made to ground their formulation and development in socio-technical context of perioperative care work. To address this, we present a study of collaborative work practices of clinical teams during and after surgery with specific emphasis on the organisation of hypotension management. The findings highlight where predictive insights could be usefully deployed to reconfigure care and facilitate more proactive management of hypotension. We further explore how the socio-technical insights help define key parameters of machine learning prediction tasks to align with the demands of collaborative clinical practice. We discuss more general implications for the design of predictive machine learning in hospital care.

References

[1]

J. Abraham and M. Reddy. 2008. Moving patients around: a field study of coordination between clinical and non-clinical staff in hospitals. In Proceedings of the CSCW ’08 Conference on Computer Supported Cooperative Work. 225–228.

Digital Library

[2]

D. Allen. 1997. The nursing-medical boundary: A negotiated order? Sociology of Health and Illness 19, 4 (1997), 498–520.

[3]

P. Atkinson. 1995. Medical Talk and Medical Work. London: Sage.

[4]

J. E. Bardram. 2000. Temporal coordination: On time and coordination of collaborative activities at a surgical department. Computer Supported Cooperative Work 9 (2000), 157–187.

Digital Library

[5]

J. Bardram and C. Bossen. 2005. A web of coordinative artefacts: Collaborative work at a hospital ward. In Proceedings of the Group ’05. 168–176.

Digital Library

[6]

J. Bardram and T. R. Hansen. 2010. Why the plan doesn't hold: a study of situated planning, articulation and coordination work in a Surgical Ward. In Proceedings of the CSCW’10 Conference on Computer Supported Cooperative Work. 331–340.

Digital Library

[7]

S. R. Barley. 1990. The alignment of technology and structure through roles and networks. Administrative Science Quarterly 35, 1 (1990).

[8]

J. Baron, R. Huang, D. McEvoy, and A. Dighe. 2021. Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts. JAMIA Open 4, 1 (2021).

[9]

J. Bassale. 2001. Hypotension Prediction Arterial Blood Pressure Variability. Portland State University Technical Report 01-002.

[10]

M. Bellio, D. Furniss, N.P. Oxtoby, S. Garbarino, N. C. Firth, A. Ribbens, D. C. Alexander, and A. Blandford. 2021. Opportunities and Barriers for Adoption of a Decision-Support Tool for Alzheimer's Disease. ACM Transactions on Computing in Healthcare 2, 4 (2021).

Digital Library

[11]

M. Berg. 1997. Rationalising Medical Work: Decision Support Techniques and Medical Practices. Cambridge, MA: MIT Press.

Digital Library

[12]

P. Bjorn and M. Hertzum. 2011. Artefactual multiplicity: A study of emergency-department whiteboards. Computer Supported Cooperative Work 20 (2011), 93–121.

Digital Library

[13]

J. B. Bijker, W. A. van Klei, Y. Vergouwe, D. J. Eleveld, L. van Wolfswinkel, K.G. Moons, and C. J. Kalkman. 2009. Intraoperative hypotension and 1-year mortality after noncardiac surgery. Anesthesiology 111, 6 (2009), 1217–26.

[14]

C. Bossen. 2002. The parameters of common information spaces: The heterogeneity of cooperative work at a hospital ward. In Proceedings of CSCW ’02 Conference on Computer Supported Cooperative Work. 176–185.

Digital Library

[15]

V. Braun and V. Clarke. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology 3, 2 (2006), 77–101.

[16]

S. Carmel. 2003. High Technology Medicine in Practice. The Organization of Work in Intensive Care. PhD thesis. Faculty of Medicine, University of London.

[17]

A. E. Carroll. 2019. Averting alert fatigue to prevent adverse drug reactions. JAMA 322, 7 (2019), 601.

[18]

J. Cassell. 1987. 'On Control, Certitude, and the “Paranoia” of Surgeons'. Culture, Medicine, and Psychiatry 11 (1987), 229–49.

[19]

J. D. Chaparro, C. Hussain, J. A. Lee, J. Hehmeyer, M. Nguyen and, J. Hoffman. 2020. Reducing interruptive alert burden using quality improvement methodology. Applictions of Clinical Informatics 11, 1 (2020), 46–58.

[20]

X. Chen, D. Xu, G. Zhang, and R. Mukkamala. 2009. Forecasting acute hypotensive episodes in intensive care patients based on a peripheral arterial blood pressure waveform. In Proceedings of the 36th Annual Computers in Cardiology Conference. 545–548.

[21]

M. Cherifa, A. Blet, A. Chambaz, E. Gayat, M. Resche-Rigon, and R. Pirracchio. 2020. Prediction of an acute hypotensive episode during an ICU hospitalization with a super learner machine-learning algorithm. Anesthesia and Analgesia 130, 5 (2020), 1157–1166.

[22]

C. Crespo, J. McNames, N. Aboy, J. Bassale, M. Ellenby, S. Lai, and B. Goldstein. 2002. Precursors in the arterial blood pressure signal to episodes of acute hypotension in sepsis. Proceedings of EURASIP Conference BIOSIGNAL 16, 206–208.

[23]

S. J. Davies, S. T. Vistisen, Z. Jian, F. Hatib, and T. W. L. Scheeren. 2010. Ability of an arterial waveform analysis–derived hypotension prediction index to predict future hypotensive events in surgical patients. Anesthesia and Analgesia 130 (2010), 352–359.

[24]

I. de Keijzer, J. J. Vos, and T. Scheeren. 2020. Hypotension Prediction Index: from proof-of-concept to proof-of-feasibility. Journal of Clinical Monitoring and Computing 34 (2020), 1135–1138.

[25]

G. Fitzpatrick and G. Ellingsen. 2013. A review of 25 years of CSCW research in healthcare: Contributions, challenges and future agendas. Computer Supported Cooperative Work 22 (2013), 609–665.

Digital Library

[26]

E. Futier, J. Y. Lefrant, P. G. Guinot et al. 2017. Effect of individualized vs. standard blood pressure management strategies on postoperative organ dysfunction among high-risk patients undergoing major surgery: a randomized clinical trial. JAMA 318 (2017), 1346–1357.

[27]

R. C. Fox. 2000. Medical uncertainty revisited. G. Albrecht, R. Fitzpatrick, and S. Scrimshaw, (Eds). Handbook of Social Studies in Health and Medicine. London: Sage.

[28]

M. Ghassemi. 2011. Methods and Models For Acute Hypotensive Episode Prediction. Master's thesis. Oxford University, UK.

[29]

D. Goodwin. 2009. Acting in Anaesthesia. Cambridge: Cambridge University Press.

[30]

R. Hanss, J. Renner, C. Ilies, L. Moikow, O. Buell, M. Steinfath, J. Scholz, and B. Bein. 2008. Does heart rate variability predict hypotension and bradycardia after induction of general anaesthesia in high risk cardiovascular patients? Anaesthesia 63 (2008), 129–35.

[31]

J. Harvey. 1996. Achieving the indeterminate: Accomplishing degrees of certainty in life and death situations. The Sociological Review 44, 1 (1996), 78–98.

[32]

F. Hatib, Z. Jian, S. Buddi, C. Lee, J. Settels, K. Sibert, J. Rinehart, and M. Cannesson. 2018. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology 129, 4 (2018), 663–674.

[33]

J. Henriques and T. Rocha. 2009. Prediction of acute hypotensive episodes using neural network multi-models. In Proceedings of the 36th Annual Computers in Cardiology Conference. 549–552.

[34]

S. Hirschauer. 1991. The manufacture of bodies in surgery. Social Studies of Science 21, 2 (1991), 279–319.

[35]

M. Jacobs, J. He, M. F. Pradier, B. Lam, A. Ahn, T. McCoy, R. Perlis, F. Doshi velez, and K. Gajos. 2021. Designing AI for trust and collaboration in time-constrained medical decisions: a sociotechnical lens. In Proceedings of the CHI ’21. 1–14.

Digital Library

[36]

S. L. Kane-Gill, M. F. O'Connor, J. M. Rothschild, et al. 2017. Technologic distractions (part 1): Summary of approaches to manage alert quantity with in- tent to reduce alert fatigue and suggestions for alert fatigue metrics. Critical Care Medicine 45, 9 (2017), 1481–1488.

[37]

S. Kendale, P. Kulkarni, A. D. Rosenberg, and J. Wang. 2018. Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension. Anesthesiology 129, 4 (2018), 675–688.

[38]

E. L. Keuffel, J. Rizzo, M. Stevens, C. Gunnarsson, and K. Maheshwari. 2019. Hospital costs associated with intraoperative hypotension among non-cardiac surgical patients in the US: A simulation model. Journal of Medical Economics 22, 7 (2019), 645–651.

[39]

W. A. Knaus and D. P. Wagner. 1989. APACHE: A non-proprietary measure of severity of illness. Annals of Internal Medicine 110, 4 (1989), 327–328.

[40]

T. Koschmann and C. LeBaron. 2003. Reconsidering common ground: Examining Clark's contribution theory in the OR. In Proceedings of the ECSCW ’03 European Conference on Computer-Supported Cooperative Work. Dordrecht: Kluwer Academic Publishers, 81–98.

[41]

K. Kuutti and T. T. Arvonen. 1992. Identifying potential CSCW applications by means of activity theory concepts: A case example. In Proceedings of the CSCW ’92 Conference on Computer-Supported Cooperative Work. ACM, 233–240.

Digital Library

[42]

J. Lee and R. G. Mark. 2010. An investigation of patterns in hemodynamic data indicative of impending hypotension in intensive care. Biomedical Engineering Online 9 (2010), 62.

[43]

L. H. Lehman, M. Saeed, G. B. Moody, and R. G. Mark. 2008. Similarity based searching in multi parameter time series databases. Computers in Cardiology (2008), 653–656.

[44]

K. Maheshwari, B. H. Nathanson, S. H. Munson, et al. 2018. The relationship between ICU hypotension and in-hospital mortality and morbidity in septic patients. Intensive Care Medicine 44 (2018), 857–867.

[45]

S. Matthiesen, S. Z. Diederichsen, M. Hansen, C. Villumsen, M. Lassen, P. Jacobsen, N. Risum, B. Winkel, B. Philbert, J. Svendsen, and T. Andersen. 2021. Clinician Pre-Implementation perspectives of a decision-support tool for prediction of cardiac arrhythmia based on machine learning: Near-Live feasibility and qualitative study (Preprint). JMIR Human Factors 8, 4 (2021).

[46]

H. Mentis O'Hara, K. A. Sellen, and R. Trivedi. 2012. Interaction proxemics and image use in neurosurgery. In Proceedings of the CHI'12 Conference on Human Factors in Computing Systems. ACM, 927–936.

Digital Library

[47]

M. Mneimneh and R. Povinelli. 2009. A rule-based approach for the prediction of acute hypotensive episodes. In Proceedings of the 36th Annual Computers in Cardiology Conference (CinC). 557–560.

[48]

A. Mol. 2002. The Body Multiple: Ontology in Medical Practice. London: Duke University Press.

[49]

T. G. Monk, M. R. Bronsert, W. G. Henderson, M. P. Mangione, S. T. Sum-Ping, D. R. Bentt, J. D. Nguyen, J. S. Richman, R. A. Meguid, and K. E. Hammermeister. 2015. Association between intraoperative hypotension and hypertension and 30-day postoperative mortality in noncardiac surgery. Anesthesiology 123 (2015), 307–19.

[50]

G. B. Moody and L. H. Lehman. 2009. Predicting acute hypotensive Episodes: 10^th Annual physionet computers in cardiology challenge. In Proceedings of the 2009 36th Annual Computers in Cardiology Conference.

[51]

C. Morrison, G. Fitzpatrick, and A. Blackwell. 2011. Multi-disciplinary collaboration during ward rounds: Embodied aspects of electronic medical record usage. International Journal of Medical Informatics 80, 8 (2011), e96–e111.

[52]

T. Moreira. 2006. Heterogeneity and coordination of blood pressure in neurosurgery. Social Studies of Science 36, 1 (2006), 69–97.

[53]

K. Palla, S. L. Hyland, K. Posner, P. Ghosh, B. Nair, M. Bristow, Y. Paleva, B. Williams, C. Fong, W. Van Cleve, D. R. Long, R. Pauldine, K. O'Hara, K. Takeda, and M. S. Vavilala. 2022. Intraoperative prediction of postanaesthesia care unit hypotension. British Journal of Anaesthesia 128, 4 (2022), 623–635.

[54]

S. A. Paul and M. C. Reddy. 2010. Understanding together: Sensemaking in collaborative information seeking. In Proceedings of the CSCW 2010 Conference on Computer Supported Cooperative Work. 321–330.

Digital Library

[55]

C. Panigutti, A. Beretta, F. Giannotti, and D. Pedreschi. 2022. Understanding the impact of explanations on advice-taking: a user study for AI-based clinical Decision Support Systems. In Proceedings of CHI ’22, Conference on Human Factors in Computing Systems.

Digital Library

[56]

M. Prosperi, Y. Guo, M. Sperrin, J. Koopman, J. Min, X. He, S. Rich, M. Wang, I. Buchan, and J. Bian. 2020. Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nature Machine Intelligence 2 (2020), 369–375.

[57]

R. Randell, S. Wilson, and P. P. Woodward. 2011. Variations and commonalities in processes of collaboration: The need for multi-site workplace studies. Computer Supported Cooperative Work 20, 1–2 (2011), 37–59.

Digital Library

[58]

R. Randell. 2004. Accountability in an alarming environment. In Proceedings of the CSCW'04 Conference on Computer Supported Cooperative Work. Association for Computing Machinery, 125–131.

Digital Library

[59]

M. Reddy, P. Dourish, and W. Pratt. 2006. Temporality in medical work: Time also matters. Computer Supported Cooperative Work 15, 1 (2006), 29–53.

Digital Library

[60]

P. S. Roshanov, T. Sheth, E. Duceppe, V. Tandon, A. Bessissow, M. Chan, C. Butler, B. Chow, J. S. Khan, and P. J. Devereaux. 2019. Relationship between perioperative hypotension and perioperative cardiovascular events in patients with coronary artery disease undergoing major noncardiac surgery. Anesthesiology 130, 5 (2019), 756–766.

[61]

V. Salmasi, K. Maheshwari, D. Yang, et al. 2017. Relationship between intraoperative hypotension, defined by either reduction from baseline or absolute thresholds, and acute kidney and myocardial injury after noncardiac surgery: a retrospective cohort analysis. Anesthesiology Journal of American Society of Anesthesiolists 126 (2017), 47–65.

[62]

B. Saugel, K. Kouz, P. Hoppe, K. Maheshwari, and T. Scheeren. 2019. Predicting hypotension in perioperative and intensive care medicine. Best practice and Research Clinical Anaesthesiology 33, 2 (2019), 189–197.

[63]

P. Scupelli, Y. Xiao, S. Fussell, S. Kiesler, and M. D. Gross. 2010. Supporting coordination in surgical suites: Physical aspects of common information spaces. In Proceedings of the CHI 2010 Conference on Human Factors in Computing Systems. ACM Press, 1777–1786.

Digital Library

[64]

M. P. Sendak, J. D'Arcy, S. Kashyap, M. Gao, and M. Nichols. 2020. A path for translation of machine learning products into healthcare delivery. In Proceedings of the EMJ Innovations.

[65]

M. Sendak, W. Ratliff, D. Sarro, E. Alderton, J. Futoma, M. Gao, M. Nichols, M. Revoir, F. Yashar, C. Miller, K. Kester, S. Sandhu, K. Corey, N. Brajer, C. Tan, A. Lin, T. Brown, S. Engelbosch, K. Anstrom, and C. O'Brien. 2019. Sepsis Watch: A real-world integration of deep learning into routine clinical care (Preprint). JMIR Medical Informatics 8, 7 (2019).

[66]

M. G. Seneviratne, N. H. Shah, and L. Chu. 2020. Bridging the implementation gap of machine learning in healthcare. BMJ Innovations 6, 2 (2020), 45–47.

[67]

D. I. Sessler and A. K. Khanna. 2018. Perioperative myocardial injury and the contribution of hypotension. Intensive Care Medicine 44 (2018), 811–822.

[68]

M. Singer, C. S. Deutschman, C. W. Seymour, et al. 2016. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 315, 8 (2016), 801–810.

[69]

A. Strauss, S. Fragerhaugh, B. Suczek, and C. Wiener. 1985. Social Organisation of Medical Work. Chicago and London: University of Chicago Press.

[70]

L. Y. Sun, D. N. Wijeysundera, G. A. Tait, and W. S. Beattie. 2015. Association of intraoperative hypotension with acute kidney injury after elective noncardiac surgery. Anesthesiology 123 (2015), 515–23.

[71]

M. S. Svensson, C. Heath, and P. Luff. 2007. Instrumental action: the timely exchange of implements during surgical operations. In Proceedings of the ECSCW 2007 European Conference on Computer Cooperative Work. 41–60.

[72]

R. Svensson. 2008. The interplay between doctors and nurses - a negotiated order perspective. Sociology of Health and Illness 18, 3 (2008), 379–398.

[73]

C. Udeh, B. Udeh, N. Rahman, C. Canfield, J. Campbell, and J. S. Hata. 2018. Telemedicine/Virtual ICU: Where are we and where are we going? Methodist Debakey Cardiovascular Journal 14, 2 (2018), 126–133.

[74]

J. A. van Waes, W. A. van Klei, D. N. Wijeysundera, L. van Wolfswinkel, T. F. Lindsay, and W. S. Beattie. 2016. Association between intraoperative hypotension and myocardial injury after vascular surgery. Anesthesiology 124 (2016), 35–44.

[75]

L. Vernooij, W. Van Klei, M. Machina, et al. 2018. Different methods of modelling intraoperative hypotension and their association with postoperative complications in patients undergoing non-cardiac surgery. British Journal of Anaesthesiology 120 (2018), 1080–1089.

[76]

M. P. Walsh, J. Devereaux, A. X. Garg, A. Kurz, A. Turan, R. N. Rodseth, J. Cywinski, L. Thabane, and D. I. Sessler. 2013. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: Toward an empirical definition of hypotension. Anesthesiology 119 (2013), 507–15.

[77]

T. G. Weiser, A. B. Haynes, G. Molina, S. R. Lipsitz, M. M. Esquivel, T. Uribe-Leitz, R. Fu, T. Azad, T. E. Chao, W. R. Berry, and A. A. Gawande. 2015. Estimate of the global volume of surgery in 2012: an assessment supporting improved health outcomes. The Lancet 385 (2015), S11.

[78]

E. M. Wesselink, T. H. Kappen, H. M. Torn, A. Slooter, and W. A. van Klei. 2018. 11. Intraoperative hypotension and the risk of postoperative adverse outcomes: A systematic review. British Journal of Anaesthesia 121, 4 (2018), 706–721.

[79]

M. Wijnberge, B. F. Geerts, l. L. Ho, N. Lemmers, M. P. Mulder, P. Berge, et al. 2020. Effect of a machine learning–derived early warning system for intraoperative hypotension vs. standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery: The HYPE randomized clinical trial. JAMA 323 (2020), 1052.

[80]

Q. Yang, J. Zimmerman, A. Steinfeld, L. Carey, and J. F. Antaki. 2016. Investigating the heart pump implant decision process: Opportunities for decision support tools to Help. In Proceedings of CHI'16, Conference on Human Factors in Computing Systems. Association for Computing Machinery, 4477–4488.

Digital Library

[81]

Q. Yang, A. Steinfeld, and J. Zimmerman. 2019. Unremarkable AI: Fitting intelligent decision support into critical, clinical decision-making processes. In Proceedings of the CHI'19 Conference on Human Factors in Computing Systems. Association for Computing Machinery, 1–11.

Digital Library

[82]

M. Zhang, D. E. Ehrmann, M. Mazwi, D. Eytan, M. Ghassemi, and F. Chevalier. 2022. Get to the Point! Problem-Based curated data views to augment care for critically Ill patients. In Proceedings of the CHI'22 Conference on Human Factors in Computing Systems. Association for Computing Machinery, 1–13.

Digital Library

[83]

R. Zussman. 1992. Intensive Care: Medical Ethics and the Medical Profession. Chicago: Chicago Press.

Cited By

Joshi H(2024)AI and Chronic Diseases From Data Integration to Clinical ImplementationGenerative AI Techniques for Sustainability in Healthcare Security10.4018/979-8-3693-6577-9.ch002(17-40)Online publication date: 22-Nov-2024
https://doi.org/10.4018/979-8-3693-6577-9.ch002
Fuentes CRodríguez ICajamarca GCabrera-Quiros LLucero AHerskovic VO'Hara KFarzan RLópez CCardoso Llach DQuercia DMustafa MNiu SWong-Villacrés M(2024)Opportunities and Challenges of Emerging Human-AI Interactions to Support Healthcare in the Global SouthCompanion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing10.1145/3678884.3681834(724-727)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1145/3678884.3681834

Index Terms

Framing Machine Learning Opportunities for Hypotension Prediction in Perioperative Care: A Socio-technical Perspective: Socio-technical perspectives on hypotension prediction
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory

Recommendations

Perioperative Smartphone Apps and Devices for Patient-Centered Care

Smartphones have grown in ubiquity and computing power, and they play an ever-increasing role in patient-centered health care. The "medicalized smartphone" not only enables web-based access to patient health resources, but also can run patient-oriented ...
Hypotension prediction index for the prevention of hypotension during surgery and critical care: A narrative review
Abstract
Surgeons and anesthesia clinicians commonly face a hemodynamic disturbance known as intraoperative hypotension (IOH), which has been linked to more severe postoperative outcomes and increases mortality rates. Increased occurrence of IOH has been ...
Highlights
- We provide a new monitoring tool that anticipates intraoperative hypotension, which relates to myocardial infarction, acute kidney injury (AKI), composite serious complications and mortality.
- Cardiac patients are included in this ...
Enhancing Intensive Care Patient Prognostics with Machine Learning
SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology

This article delves into the challenge of foreseeing patient discharges and unplanned returns to the intensive care unit. Its primary objective is to enhance the decision-making process for healthcare providers and administrators, facilitate resource ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Computer-Human Interaction

ACM Transactions on Computer-Human Interaction Volume 30, Issue 5

October 2023

593 pages

ISSN:1073-0516

EISSN:1557-7325

DOI:10.1145/3623487

Editors:
Kristina Höök
KTH Royal Institute of Technology, Sweden
,
Kasper Hornbæk
University of Copenhagen, Denmark

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2023

Online AM: 19 April 2023

Accepted: 02 March 2023

Revised: 18 January 2023

Received: 20 September 2021

Published in TOCHI Volume 30, Issue 5

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
545
Total Downloads

Downloads (Last 12 months)276
Downloads (Last 6 weeks)14

Reflects downloads up to 30 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Joshi H(2024)AI and Chronic Diseases From Data Integration to Clinical ImplementationGenerative AI Techniques for Sustainability in Healthcare Security10.4018/979-8-3693-6577-9.ch002(17-40)Online publication date: 22-Nov-2024
https://doi.org/10.4018/979-8-3693-6577-9.ch002
Fuentes CRodríguez ICajamarca GCabrera-Quiros LLucero AHerskovic VO'Hara KFarzan RLópez CCardoso Llach DQuercia DMustafa MNiu SWong-Villacrés M(2024)Opportunities and Challenges of Emerging Human-AI Interactions to Support Healthcare in the Global SouthCompanion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing10.1145/3678884.3681834(724-727)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1145/3678884.3681834

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Figures

Tables

Media

View full text|Download PDF

View Issue’s Table of Contents