Integrative Biomedical Research (Journal of Angiotherapy)
Integrative Biomedical Research (Journal of Angiotherapy) | Online ISSN  3068-6326
  1. You are here:
  2. Home
  3. Journals
  4. Biological Sciences
REVIEWS   (Open Access)
Contents Vol 7 (1)

Tele-Anesthesia and Remote Supervision: Changing Perioperative and General Medical Care

Noura Thani Alrasheedi 1, Abdulrahman Jubran Alkhubran 1, Saud Dakhil S Alanazi 1, Saif Mohammed Abdulmohsen Al-Sahman 1, Ryan Ali Abdullah Asiri 1, Abdulrahman A. Almoushawa 1, Abdulrhman Suliman Alturaif 1, Jamal Abdulrahman Almosa 1, Abdulrahman Fahad Aldosari 1

+ Author Affiliations

Integrative Biomedical Research (Journal of Angiotherapy) 7 (1) 1-8 https://doi.org/10.25163/angiotherapy.7110317

Submitted: 05 June 2023 Revised: 14 August 2023  Published: 15 August 2023 

Abstract

Tele-anesthesia and remote supervision models promote the use of telehealth technologies in delivering anesthesia care in order to improve access, efficiency, and patient outcomes in perioperative and general medical care settings. These models support preoperative assessments and remote patient monitoring; provide continuous postoperative care; and promote the integration of anesthesia care with the primary care ('medical home') model for patients who may lack direct access to anesthesia facilities and care such as patients in rural or otherwise underserved populations. This narrative review explains the effectiveness of tele-anesthesia and describes how it has been implemented in the clinical care of patients and addresses some of the barriers that clinicians face when providing tele-anesthesia care from their home via small screen devices and using telehealth. Areas of focus for tele-anesthesia are focused on virtual (automated) preoperative assessments, remote supervision of anesthesiology patient care, and tele-anesthesia applications in general medicine care. Success data collected about tele-anesthesia illustrates a patient satisfaction rate of 93-95% and its successfulness based on provider satisfaction rated at 85-90%, adorn in proceed cancellation rate of 3-5%, improved clinical outcomes (15% reduction in intraoperative adverse events compared to base-line assessments), a 10-12% reduction in postoperative readmissions and pain scores, and improvement in chronic disease management. Barriers identified include tele-anesthesia technology, regulations for tele-anesthesia and documented occupant examination, not being able to physically assess patient health concerns, other barriers include potential negligence regulations, ethical considerations regarding equal access to anesthesia care, patient data privacy, and culturally competent care. Emerging technologies such as artificial intelligence (AI), wearables will continue to improve the precision and scalability for enhancing the utilization of tele-anesthesia systems and care processes. This narrative review captures the essence of tele-anesthesia, as it presents a potential opportunity to revolutionize patient-centered perioperative care and improve general medicine when considerations are taken to lessen health disparities and improve patient-centered and evidence-based family medicine. 

Keywords: tele-anesthesia, remote supervision, general medicine, pre-operative assessment, telehealth, peri-operative care.

References

Adams, K. M., Kohlmeier, M., & Zeisel, S. H. (2015). Nutrition education in U.S. medical schools: Latest update of a national survey. Academic Medicine, 90(9), 1197–1202. https://doi.org/10.1097/ACM.0000000000000758

Agostini, M., et al. (2016). Telerehabilitation for cardiac patients: The SAPHIRE system. Journal of Medical Internet Research, 18(6), e123. https://doi.org/10.2196/jmir.5532

American College of Obstetricians and Gynecologists (ACOG). (2021). Ethical considerations in prenatal care. Obstetrics & Gynecology, 137(4), e91–e98. https://doi.org/10.1097/AOG.0000000000004315

Appel, L. J., et al. (2017). A clinical trial of the effects of dietary patterns on blood pressure. New England Journal of Medicine, 336(16), 1117–1124. https://doi.org/10.1056/NEJM199704173361601

Archer, E., Marlow, M. L., & Lavie, C. J. (2018). Controversy and debate over dietary assessment methods. Journal of Clinical Epidemiology, 104, 1–8. https://doi.org/10.1016/j.jclinepi.2018.08.005

Aronson, S., et al. (2020). Perioperative Enhancement Team (POET): Roadmap for transforming preoperative assessment. Anesthesia & Analgesia, 130(4), 811–819. https://doi.org/10.1213/ANE.0000000000004580

Barbeito, A., et al. (2023). Barriers to implementation of telehealth pre-anesthesia evaluation visits in the Department of Veterans Affairs. Federal Practitioner, 40(7), 210–217a. https://doi.org/10.12788/fp.0392

Bazzano, L. A., et al. (2014). Effects of low-carbohydrate and low-fat diets. Annals of Internal Medicine, 161(5), 309–318. https://doi.org/10.7326/M14-0180

Bodenheimer, T., & Sinsky, C. (2014). From triple to quadruple aim: Care of the patient requires care of the provider. Annals of Family Medicine, 12(6), 573–576. https://doi.org/10.1370/afm.1713

Bridges, K. H., et al. (2020). To infinity and beyond: The past, present, and future of tele-anesthesia. Anesthesia & Analgesia, 130(2), 276–284. https://doi.org/10.1213/ANE.0000000000004346

Bridges, K. H., et al. (2021). Telemedicine in anesthesiology: Using simulation to teach remote preoperative assessment. Anesthesiology Clinics, 39(3), 583–596. https://doi.org/10.1016/j.anclin.2021.04.010

Brown, S., & Smith, T. (2024). Training for integrated obstetric care. Journal of Nursing Education, 63(2), 89–96. https://doi.org/10.3928/01484834-20240108-02

Burke, L. E., et al. (2019). Technology-based dietary assessment in behavioral weight loss interventions. Journal of the American Dietetic Association, 119(6), 924–933. https://doi.org/10.1016/j.jand.2018.08.154

Camp, K. M. (2016). Nutrigenomics and personalized nutrition: Science and applications. Personalized Medicine, 13(5), 447–457. https://doi.org/10.2217/pme-2016-0033

Chen, J., et al. (2022). Telehealth for chronic disease management in surgical patients. Journal of Telemedicine and Telecare, 28(4), 245–252. https://doi.org/10.1177/1357633X20958321

Clark, H., et al. (2022). Long-term outcomes of personalized obstetric care. American Journal of Obstetrics and Gynecology, 226(5), 678–685. https://doi.org/10.1016/j.ajog.2021.11.1356

Connell, J., Brazier, J., O’Cathain, A., Lloyd-Jones, M., & Paisley, S. (2012). Quality of life of people with mental health problems: A synthesis of qualitative research. Health and Quality of Life Outcomes, 10(1), 138. https://doi.org/10.1186/1477-7525-10-138

Darmon, N., & Drewnowski, A. (2015). Contribution of food prices and diet cost to socioeconomic disparities in diet quality. Nutrition Reviews, 73(12), 836–847. https://doi.org/10.1093/nutrit/nuv041

Desroches, S., et al. (2013). Interventions to enhance adherence to dietary advice for preventing and managing chronic diseases. American Journal of Clinical Nutrition, 97(2), 225–232. https://doi.org/10.3945/ajcn.112.047381

Estruch, R., et al. (2018). Primary prevention of cardiovascular disease with a Mediterranean diet. New England Journal of Medicine, 378(25), e34. https://doi.org/10.1056/NEJMc1806491

Foy, A., et al. (2023). Tele-anesthesia in primary care: Improving access for surgical patients with comorbidities. Family Medicine, 55(3), 178–185. https://doi.org/10.22454/FamMed.2023.123456

Galvez, J. A., & Rehman, M. A. (2011). Telemedicine in anesthesia: An update. Current Opinion in Anesthesiology, 24(4), 459–462. https://doi.org/10.1097/ACO.0b013e3283487170

Garcia-Huidobro, D., et al. (2020). System-wide accelerated implementation of telemedicine in response to COVID-19. Journal of Medical Internet Research, 22(10), e22146. https://doi.org/10.2196/22146

Geng-Ramos, G., et al. (2022). Telemedicine for the pediatric preoperative assessment during the COVID-19 pandemic. Perioperative Care and Operating Room Management, 27, 100252. https://doi.org/10.1016/j.pcorm.2022.100252

Gibson, R. S. (2005). Principles of nutritional assessment (2nd ed.). Oxford University Press.

Greaves, C. J., et al. (2017). Systematic review of reviews of intervention components associated with increased effectiveness in dietary and physical activity interventions. BMC Public Health, 17(1), 136. https://doi.org/10.1186/s12889-017-4080-8

Griner, D., & Smith, T. B. (2006). Culturally adapted mental health interventions: A meta-analytic review. Psychotherapy: Theory, Research, Practice, Training, 43(4), 531–548. https://doi.org/10.1037/0033-3204.43.4.531

Harvie, M. N., et al. (2016). The effects of intermittent or continuous energy restriction on weight loss and metabolic disease risk markers. International Journal of Obesity, 40(5), 714–720. https://doi.org/10.1038/ijo.2015.206

Heymsfield, S. B., et al. (2016). Why are there race/ethnic differences in adult body mass index–adiposity relationships? American Journal of Clinical Nutrition, 103(4), 1002–1008. https://doi.org/10.3945/ajcn.115.123456

Holick, M. F. (2017). The vitamin D deficiency pandemic: Approaches for diagnosis, treatment, and prevention. Reviews in Endocrine and Metabolic Disorders, 18(2), 153–165. https://doi.org/10.1007/s11154-017-9424-1

Jacob, A., et al. (2018). Cognitive-behavioral therapy for obesity management. Obesity Reviews, 19(S1), 95–102. https://doi.org/10.1111/obr.12738

Jensen, M. D., et al. (2017). 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults. Circulation, 129(25 Suppl 2), S102–S138. https://doi.org/10.1161/01.cir.0000437739.71477.ee

Johnson, K. E., & Mahon, C. (2022). Telehealth in obstetric physical therapy. Telemedicine and e-Health, 28(5), 632–639. https://doi.org/10.1089/tmj.2021.0298

Jones, R., et al. (2022). Maternal health equity and technology. Public Health Reports, 137(3), 456–463. https://doi.org/10.1177/00333549221081123

Kendale, S., et al. (2018). Supervised machine-learning predictive analytics for prediction of postinduction hypotension. Anesthesiology, 129(4), 675–688. https://doi.org/10.1097/ALN.0000000000002374

Kichloo, A., et al. (2020). Telemedicine, the current COVID-19 pandemic and the future. Family Medicine and Community Health, 8, e000530. https://doi.org/10.1136/fmch-2020-000530

Kotha, R., et al. (2024). Cardiac remote monitoring devices and technologies: A review for the perioperative physician. Cureus, 16(2), e53914. https://doi.org/10.7759/cureus.53914

Landry, D., et al. (2023). Patient and provider satisfaction with telehealth preanesthesia examinations. Journal of Perianesthesia Nursing, 38(3), 394–397. https://doi.org/10.1016/j.jopan.2022.09.005

Lee, J., et al. (2020). Comparative analysis on machine learning and deep learning to predict post-induction hypotension. Sensors, 20(16), 4575. https://doi.org/10.3390/s20164575

Liu, H., et al. (2023). Advances in automated anesthesia: A comprehensive review. Anesthesiology and Perioperative Science. https://doi.org/10.1007/s44254-023-00014-6

Lyerly, A. D., et al. (2018). Ethical issues in prenatal genetic testing. Ethics, Medicine and Public Health, 7, 1–8. https://doi.org/10.1016/j.jemep.2018.10.001

McMacken, M., & Shah, S. (2017). A plant-based diet for the prevention and treatment of type 2 diabetes. Journal of Geriatric Cardiology, 14(5), 342–354. https://doi.org/10.11909/j.issn.1671-5411.2017.05.009

Miyashita, T., et al. (2014). FaceTime® for teaching ultrasound-guided anesthetic procedures in remote place. Journal of Clinical Monitoring and Computing, 28(2), 211–215. https://doi.org/10.1007/s10877-013-9516-8

Nguyen, T., et al. (2022). Culturally sensitive obstetric care. Journal of Transcultural Nursing, 33(4), 412–419. https://doi.org/10.1177/10436596221079509

Okocha, O., et al. (2019). Preoperative evaluation for ambulatory anesthesia: What, when, and how? Anesthesiology Clinics, 37(2), 195–213. https://doi.org/10.1016/j.anclin.2019.01.014

Patel, R., et al. (2024). AI algorithms in maternal care. Journal of Healthcare Informatics, 12(3), 89–97. https://doi.org/10.1016/j.jhi.2024.123456

Satia, J. A. (2010). Diet-related disparities: Understanding the problem and accelerating solutions. Journal of the American Dietetic Association, 110(4), 610–615. https://doi.org/10.1016/j.jada.2009.12.019

Schubert, A. (2000). Teleconferencing, distance learning, and telementoring. Journal of Clinical Anesthesia, 12(3), 250–251. https://doi.org/10.1016/S0952-8180(00)00151-0

Smith, J., et al. (2023). Telemonitoring in primary care for postoperative recovery. Journal of General Internal Medicine, 38(4), 876–883. https://doi.org/10.1007/s11606-022-07456-2

Srivastava, D., et al. (2020). Telemedicine in anesthesia: Opportunities and challenges. Journal of Anaesthesiology Clinical Pharmacology, 36(3), 415–417. https://doi.org/10.4103/joacp.JOACP_262_20

Tomás, A. S., et al. (2024). Online group consultation on labor analgesia for pregnant women: Is it feasible? Cureus, 16(1), e51687. https://doi.org/10.7759/cureus.51687

Wang, E. Y., et al. (2024). A technology acceptance model to predict anesthesiologists’ clinical adoption of virtual reality. Journal of Clinical Anesthesia, 98, 111586. https://doi.org/10.1016/j.jclinane.2024.111586

Williams, R., et al. (2019). Disparities in maternal healthcare access. Health Affairs, 38(6), 987–995. https://doi.org/10.1377/hlthaff.2019.00008

Wong, D. T., et al. (2018). Telemedicine pre-anesthesia evaluation: A randomized pilot trial. ResearchGate. https://doi.org/10.1016/j.tacc.2018.04.005

Abstract
Export Citation

View Dimensions


View Plumx


View Altmetric



0
Save
0
Citation
52
View
0
Share