Role of Artificial Intelligence within the Telehealth Domain

Summary

Objectives: This paper provides a discussion about the potential scope of applicability of Artificial Intelligence methods within the telehealth domain. These methods are focussed on clinical needs and provide some insight to current directions, based on reports of recent advances.

Methods: Examples of telehealth innovations involving Artificial Intelligence to support or supplement remote health care delivery were identified from recent literature by the authors, on the basis of expert knowledge. Observations from the examples were synthesized to yield an overview of contemporary directions for the perceived role of Artificial Intelligence in telehealth.

Results: Two major focus areas for related contemporary directions were established. These were first, quality improvement for existing clinical practice and service delivery, and second, the development and support of new models of care. Case studies from each focus area have been chosen for illustration purposes.

Conclusion: Examples of the role of Artificial Intelligence in delivery of health care remotely include use of tele-assessment, tele-diagnosis, tele-interactions, and tele-monitoring. Further developments of underlying algorithms and validation of methods will be required for wider adoption. Certain key social and ethical considerations also need consideration more generally in the health system, as Artificial-Intelligence-enabled-telehealth becomes more commonplace.

Keywords

Telemedicine - Artificial Intelligence - quality of health care - delivery of health care

• References

• 1 World Health Organisation A health telematics policy in support of WHO’s Health-For-All strategy for global health development: report of the WHO group consultation on health telematics, 11–16 December, Geneva, 1997. Geneva: World Health Organization 1998
• 2 Ajami S, Lamoochi P. Use of telemedicine in disaster and remote places. J Educ Health Promot 2014; 3: 26
• 3 McLean S, Protti D, Sheikh A. Telehealthcare for long term conditions. BMJ 2011; Feb 3 342: d120
• 4 World Health Organization Telemedicine: opportunities and developments in member states. Report on the second global survey on eHealth. Geneva: World Health Organization 2010
• 5 Wilson LS, Maeder AJ. Recent directions in telemedicine: review of trends in research and practice. Healthc Inform Res 2015; Oct 1 21 (04) 213-22
• 6 Russell S, Norvig P. Artificial intelligence - a modern approach. Prentice-Hall 1995
• 7 Pacis DM, Subido Jr ED, Bugtai NT. Trends in telemedicine utilizing artificial intelligence. In: AIP Conference Proceedings 2018; Feb 13 1933 (01) 040009
• 8 Peterson MC, Holbrook JH, Von Hales D, Smith NL, Staker LV. Contributions of the history, physical examination, and laboratory investigation in making medical diagnoses. West J Med 1992; Feb; 156 (02) 163-5
• 9 Roshan M, Rao A. A study on relative contributions of the history, physical examination and investigations in making medical diagnosis. J Assoc Physicians India 2000; 48 (08) 771-5
• 10 Liang CW. et al. Using Machine Learning to Predict Cancer Risk by Clinical Diagnosis History - A Case Study of Liver Cancer. In preparation
• 11 Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A. et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 2018; May 28 29 (08) 1836-42
• 12 Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM. Blau HMet al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; Feb; 542 (7639) 115
• 13 Goldstein BA, Navar AM, Pencina MJ, Ioannidis JP. Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. J Am Med Inform Assoc 2017; 24 (01) 198-208
• 14 Hendy J, Chrysanthaki T, Barlow J, Knapp M, Rogers A, Sanders C. et al. An organisational analysis of the implementation of telecare and telehealth: the whole systems demonstrator. BMC Health Serv Res 2012; Dec; 12 (01) 403
• 15 Bara A, Klein B, Proudfoot JG. Defining internet-supported therapeutic interventions. Ann Behav Med 2009; Aug 1 38 (01) 4-17
• 16 Hoermann S, McCabe KL, Milne DN, Calvo RA. Application of synchronous text-based dialogue systems in mental health interventions: systematic review. J Med Internet Res 2017 Aug; 19. (8)
• 17 Laranjo L, Dunn AG, Tong HL, Kocaballi AB, Chen J, Bashir R. et al. Conversational agents in healthcare: a systematic review. J Am Med Inform Assoc 2018; Jul 11 25 (09) 1248-58
• 18 Schumaker RP, Ginsburg M, Chen H, Liu Y. An evaluation of the chat and knowledge delivery components of a low-level dialog system: The az-alice experiment. Decis Support Syst 2007; January; 42: 2236-46
• 19 Weizenbaum J. ELIZA – A computer program for the study of natural language communication between man and machine. Communications of the ACM 1966; 9 (01) 36-45
• 20 Klopfenstein LC, Delpriori S, Malatini S, Bogliolo A. The rise of bots: a survey of conversational interfaces, patterns, and paradigms. In: Proceedings of the 2017 Conference on Designing Interactive Systems 2017; Jun 10 555-65
• 21 Lisetti C, Amini R, Yasavur U. Now all together: overview of virtual health assistants emulating face-to-face health interview experience. KI-Künstliche Intelligenz 2015; Jun 1 29 (02) 161-72
• 22 Yaghoubzadeh R, Kramer M, Pitsch K, Kopp S. Virtual agents as daily assistants for elderly or cognitively impaired people. In: International Workshop on Intelligent Virtual Agents 2013 Aug 29 Berlin, Heidelberg: Springer; 2013. p. 79-91
• 23 Bickmore TW, Utami D, Matsuyama R, Paasche-Orlow MK. Improving access to online health information with conversational agents: a randomized controlled experiment. J Med Internet Res 2016 Jan; 18. (1)
• 24 Shaked NA. Avatars and virtual agents–relationship interfaces for the elderly. Healthc Technol Lett 2017; Jun 28 4 (03) 83
• 25 Riccardi G. Towards healthcare personal agents. In: Proceedings of the 2014 Workshop on Roadmapping the Future of Multimodal Interaction Research including Business Opportunities and Challenges 2014 Nov 16. ACM 2014; p. 53-6
• 26 Nangalia V, Prytherch DR, Smith GB. Health technology assessment review: Remote monitoring of vital signs-current status and future challenges. Crit Care 2010; Oct; 14 (05) 233
• 27 Inglis SC, Clark RA, McAlister FA, Stewart S, Cleland JG. Which components of heart failure programmes are effective? A systematic review and meta-analysis of the outcomes of structured telephone support or telemonitoring as the primary component of chronic heart failure management in 8323 patients: abridged Cochrane Review. Eur J Heart Fail 2011; Sep; 13 (09) 1028-40
• 28 Bolton CE, Waters CS, Peirce S, Elwyn G. Insufficient evidence of benefit: a systematic review of home telemonitoring for COPD. J Eval Clin Pract. 2011; Dec; 17 (06) 1216-22
• 29 Polisena J, Tran K, Cimon K, Hutton B, McGill S, Palmer K. Home telehealth for diabetes management: a systematic review and meta analysis. Diabetes Obes Metab 2009; Oct; 11 (010) 913-30
• 30 Mohktar MS, Redmond SJ, Antoniades NC, Rochford PD, Pretto JJ, Basilakis J. et al. Predicting the risk of exacerbation in patients with chronic obstructive pulmonary disease using home telehealth measurement data. Artif Intell Med 2015; Jan 1 63 (01) 51-9
• 31 Thawer HA, Houghton PE, Woodbury MG, Keast D, Campbell K. Computer-assisted and manual wound size measurement. Ostomy Wound Manage 2002; 48 (010) 46-53
• 32 Loh E. Medicine and the rise of the robots: a qualitative review of recent advances of artificial intelligence in health. BMJ Leader 2018; 2: 59-63
• 33 Coiera E, Ash J, Berg M. The Unintended Consequences of Health Information Technology Revisited. Yearb Med Inform 2016; 163-9
• 34 Kuziemsky CE, Randell R, Borycki EM. Understanding Unintended Consequences and Health Information Technology: Contribution from the IMIA Organizational and Social Issues Working Group. Yearb Med Inform 2016; (01) 53-60
• 35 Gogia SB, Maeder A, Mars M, Hartvigsen G, Basu A, Abbott P. Unintended Consequences of Tele Health and Approaches for their Solutions. Yearb Med Inform 2016; (01) 41-6
• 36 Veinot TC, Mitchell H, Ancker JS. Good intentions are not enough: how informatics interventions can worsen inequality. J Am Med Inform Assoc 2018; 25: 1080-8
• 37 Ohno-Machado L. Understanding and mitigating the digital divide in health care. J Am Med Inform Assoc 2017; 24 (05) 881
• 38 McDonald CJ, Overhage JM, Mamlin BW, Dexter PD, Tierney WM. Physicians, information technology, and health care systems: a journey, not a destination. J Am Med Inform Assoc 2004; 11: 121-4
• 39 Friedman C, Rubin J, Brown J, Buntin M, Corn M, Etheredge L. et al. Toward a science of learning systems: a research agenda for the high-functioning Learning Health System. J Am Med Inform Assoc 2015; 22: 43-50
• 40 Epstein RM, Fiscella K, Lesser CS, Stange KC. Why the nation needs a policy push on patient-centered health care. Health Aff (Millwood) 2010; Aug 1 29 (08) 1489-95