Telehealth’s Role Enabling Sustainable Innovation and Circular Economies in Health
DOI:
https://doi.org/10.30953/thmt.v8.409Keywords:
artificial intelligence (AI), circular economy, data asymmetry, digital health, health innovation ecosystems, telehealth, value-based careAbstract
Digital health interventions including telehealth support an increasingly broad range of improvement goals for prevention and treatment. Limitations obstructing the many digital benefits of telehealth from reaching their full potential include lack of robust usability and user centered design, regulatory policy paradigms, lack of adequate high-quality evidence and methodologies to evaluate the performance generalization and clinical robustness. Health innovation is explored in the context of different value systems and a solution is proposed to the fundamental limitations arising in the data value system, an approach to a new telehealth paradigm and incorporated intervention designs which combine clinical innovation with innovation in data resource development. Machine learning and artificial intelligence have the potential to enable circular economies for digital and health innovation, in which sustainable solutions can be offered within a data-enabled collaborative and shared digital ecosystem. Alignment of industry standards, adjustments to regulatory policies, and embracing new governance models for telehealth-based innovation are essential for this new approach to health innovation scaling, clinical adoption and social innovation. Given the trends in technological advances in the past decades, it is likely that healthcare reliance on telehealth will continue to grow.
Downloads
References
Wherton J, Shaw S, Papoutsi C, Seuren L, Greenhalgh T.
Guidance on the introduction and use of video consultations
during COVID-19: important lessons from qualitative
research. BMJ Leader 2020; 4(3): 120–3. doi: 10.1136/
leader-2020-000262
Tabacof L, Wood J, Mohammadi N, Link KE, Tosto-Mancuso,
Dewil S, et al. Remote patient monitoring identifies the need for
triage in patients with acute COVID-19 infection. Telemed J E
Health 2022; 28(4): 495–500. doi: 10.1089/tmj.2021.0101.
Catalyst N. What is telehealth? NEJM Catalyst 2018; 4(1).
Salisbury C, O’Cathain A, Edwards L, Thomas C, Gaunt D,
Hollinghurst S, et al. Effectiveness of an integrated telehealth
service for patients with depression: a pragmatic randomised
controlled trial of a complex intervention. Lancet Psychiatry
; 3(6): 515–25.
Davidson L, Boland MR. Towards deep phenotyping pregnancy:
a systematic review on artificial intelligence and machine
learning methods to improve pregnancy outcomes. Brief Bioinform.
; 22(5): bbaa369. doi: 10.1093/bib/bbaa369.
Verma A, Towfighi A, Brown A, Abhat A, Casillas A. Moving
towards equity with digital health innovations for stroke
care. Stroke 2022;53(3):689–97. doi: 10.1161/STROKEAHA.
035307
Martinez-Martin N, Insel TR, Dagum P, Greely HT, Cho MK.
Data mining for health: staking out the ethical territory of digital
phenotyping. npj Digital Med. 2018; 1: 68. doi: 10.1038/
s41746-018-0075-8
Milne R, Costa A, & Brenman N. Digital phenotyping and the
(data) shadow of Alzheimer’s disease. Big Data & Society. 2022;
(1). doi: 10.1177/20539517211070748
Bilal AM, Fransson E, Bränn E, Eriksson A, Zhong M, Gidén
K, et al. Predicting perinatal health outcomes using smartphone-
based digital phenotyping and machine learning in a prospective
Swedish cohort (Mom2B): study protocol. BMJ Open.
; 12(4): e059033. doi: 10.1136/bmjopen-2021-059033
Engelmann L. Digital epidemiology, deep phenotyping and the
enduring fantasy of pathological omniscience. Big Data & Society.
; 9(1). doi: 10.1177/20539517211066451
Tomičić A, Malešević A, Čartolovni A. Ethical, legal and social
issues of digital phenotyping as a future solution for present-day
challenges: A scoping review. Sci Eng Ethics. 2021; 28(1): 1. doi:
1007/s11948-021-00354-1
Huckvale K, Venkatesh S, Christensen H. Toward clinical digital
phenotyping: a timely opportunity to consider purpose,
quality, and safety. NPJ Digit Med. 2019; 2: 88. doi: 10.1038/
s41746-019-0166-1
Torous J, Kiang MV, Lorme J, Onnela JP. New tools for new
research in psychiatry: a scalable and customizable platform to
empower data driven smartphone research. JMIR Ment Health.
; 3(2): e16. doi: 10.2196/mental.5165
Jayakumar P, Lin E, Galea V, Mathew AJ, Panda N, Vetter I,
et al. Digital phenotyping and patient-generated health data for
outcome measurement in surgical care: a scoping review. J Pers
Med. 2020; 10(4): 282. doi: 10.3390/jpm10040282
[AQ6]
[AQ7]
[AQ8]
Citation: Telehealth and Medicine Today 2023, 8: 409 - http://dx.doi.org/10.30953/thmt.v8.409
(page number not for citation purpose)
Dimitrios Kalogeropoulos and Paul Barach
Nguyen B, Ivanov M, Bhat V, Krishnan S. Digital phenotyping
for classification of anxiety severity during COVID-19. Front
Digit Health. 2022; 4: 877762. doi: 10.3389/fdgth.2022.877762
Snoswell CL, Chelberg G, De Guzman KR, Haydon HH,
Thomas EE, Caffery LJ, et al. The clinical effectiveness of
telehealth: a systematic review of meta-analyses from 2010
to 2019. J Telemed Telecare. 2021: 1357633X211022907. doi:
1177/1357633x211022907
Snoswell CL, Taylor ML, Comans TA, Smith AC, Gray LC,
Caffery LJ. Determining if telehealth can reduce health system
costs: scoping review. J Med Internet Res. 2020; 22(10): e17298.
doi: 10.2196/17298
Snoswell CL, Stringer H, Taylor ML, Caffery LJ, Smith AC.
An overview of the effect of telehealth on mortality: a systematic
review of meta-analyses. J Telemed Telecare. 2021:
X211023700. doi: 10.1177/1357633x211023700
Van Niekerk L, Manderson L. & Balabanova D. The application
of social innovation in healthcare: a scoping review. Infect Dis
Poverty. 2021; 10: 26. doi: 10.1186/s40249-021-00794-8
Whyle E, Olivier J. Social values and health systems in health
policy and systems research: a mixed-method systematic review
and evidence map. Health Policy Plan. 2020; 35(6): 735–51. doi:
1093/heapol/czaa038
Haring M, Freigang F, Amelung V, Gersch M . What can healthcare
systems learn from looking at tensions in innovation processes?
A systematic literature review. BMC Health Serv Res.
; 22: 1299. doi: 10.1186/s12913-022-08626-7
Wang A, Ahmed R, Ray J, Hughes P, Eric McCoy E, Marc A, et
al. Supporting the quadruple aim using simulation and human
factors during COVID-19 care. Am J Med Qual. 2021; 36(2):
–83. doi: 10.1097/01.JMQ.0000735432.16289.d2
Verhulst S, Young A. Identifying and addressing data asymmetries
so as to enable (better) science. Front Big Data. 2022; 5:
doi: 10.3389/fdata.2022.888384
Final Terms of Reference of the Alliance for Transformative
Action on Climate and Health (ATACH), World Health Organization
Technical Document, 31 August 2022. Available from:
https://www.who.int/publications/m/item/atach-terms-of-reference
[cited 1 February 2023].
Communication from the commission to the European Parliament
and the council, 2022 strategic foresight report – Twinning
the green and digital transitions in the new geopolitical
context. COM/2022/289 final. Available from: https://eur-lex.
europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52022DC0289&
qid=1658824364827 [cited 1 February 2023].
Coalition for Digital Environmental Sustainability (CODES).
Action plan for a sustainable planet in the digital age (2022).
Available from: https://doi.org/10.5281/zenodo.6573509 [cited 1
February 2023].
Equity within digital health technology within the WHO European
Region: a scoping review. World Health Organization,
December 2022, WHO/EURO:2022-6810-46576-67595.
Available from: https://www.who.int/europe/publications/i/
item/WHO-EURO-2022-6810-46576-67595 [cited 1 February
.
Apweiler R, Beissbarth T, Berthold MR, Blüthgen N, Burmeister
Y, Dammann O, et al. Whither systems medicine? Exp Mol
Med. 2018; 50(3): e453. doi: 10.1038/emm.2017.290.
Schleidgen S, Fernau S, Fleischer H, Schickhardt C, Oßa
AK, Winkler EC. Applying systems biology to biomedical research
and health care: a précising definition of systems medicine.
BMC Health Serv Res. 2017; 17(1): 761. doi: 10.1186/
s12913-017-2688-z
Soenksen LR, Ma Y, Zeng C, Boussioux L, Carballo KV, Na
L, et al. Integrated multimodal artificial intelligence framework
for healthcare applications. npj Digit Med. 2022; 5: 149. doi:
1038/s41746-022-00689-4
Tian Q, Price ND, Hood L. Systems cancer medicine: towards
realization of predictive, preventive, personalized and participatory
(P4) medicine. J Intern Med. 2012; 271(2): 111–21. doi:
1111/j.1365-2796.2011.02498.x
Stahlberg EA, Abdel-Rahman M, Aguilar B, Asadpoure A,
Beckman RA, Borkon LL, et al. Exploring approaches for predictive
cancer patient digital twins: opportunities for collaboration
and innovation. Front Digit Health. 2022; 4: 1007784. doi:
3389/fdgth.2022.1007784
Seyhan AA, Carini C. Are innovation and new technologies in
precision medicine paving a new era in patients centric care?. J
Transl Med. 2019; 17(1): 114. doi: 10.1186/s12967-019-1864-9
Davidson L, Boland MR. Towards deep phenotyping pregnancy:
a systematic review on artificial intelligence and machine
learning methods to improve pregnancy outcomes. Brief Bioinform.
; 22(5): bbaa369. doi: 10.1093/bib/bbaa369
Weng C, Shah NH, Hripcsak G. Deep phenotyping: embracing
complexity and temporality-towards scalability, portability,
and interoperability. J Biomed Inform. 2020; 105: 103433. doi:
1016/j.jbi.2020.103433
Subbiah, V. The next generation of evidence-based medicine.
Nat Med. 2023; 29: 49–58. doi: 10.1038/s41591-022-02160-z
Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal
biomedical AI. Nat Med. 2022; 28(9): 1773–84. doi: 10.1038/
s41591-022-01981-2
Webster, K. A circular economy is about the economy. Circ
Econ Sust. 2021; 1: 115–26. doi: 10.1007/s43615-021-00034-z
Guo C, Ashrafian H, Ghafur S, Fontana G, Gardner C, Prime
M. Challenges for the evaluation of digital health solutions–A
call for innovative evidence generation approaches. npj Digit
Med. 2020; 3: 110. doi: 10.1038/s41746-020-00314-2
Gomes M, Murray E, Raftery J. Economic evaluation of digital
health interventions: Methodological issues and recommendations
for practice. Pharmacoeconomics. 2022; 40(4): 367–78.
doi: 10.1007/s40273-022-01130-0
Artificial Intelligence Index Report. Stanford Institute for human-
centered AI, Stanford University. 2022. Available from https://
aiindex.stanford.edu/wp-content/uploads/2022/03/2022-AI-Index-
Report_Master.pdf [cited 1 February 2023].
Brent Mittelstadt. The impact of artificial intelligence on the
doctor-patient relationship. Research at the Oxford Internet Institute,
University of Oxford, United Kingdom Commissioned
by the Council of Europe Steering Committee for Human rights
in the fields of Biomedicine and Health (CDBIO). 2021. Available
from https://rm.coe.int/inf-2022-5-report-impact-of-ai-ondoctor-
patient-relations-e/1680a68859 [cited 1 February 2023].
Day S, Shah V, Kaganoff S, Powelson S, Mathews SC. Assessing
the clinical robustness of digital health startups: cross-sectional
observational analysis. J Med Internet Res. 2022; 24(6): e37677.
doi: 10.2196/37677
Bringing the Benefits of Genome Sequencing to the World. Public
policy projects, Global Insights. 2021. Available from: https://
publicpolicyprojects.com/policy/ [cited 1 February 2023].
Koutsouleris N, Hauser TU, Skvortsova V, De Choudhury M.
From promise to practice: towards the realisation of AI-informed
mental health care. Lancet Digit Health. 2022; 4(11):
E829–40. doi: 10.1016/s2589-7500(22)00153-4
Lemmen C, Woopen C, Stock S. Systems medicine 2030: A
Delphi study on implementation in the German healthcare system. Health Policy. 2021; 125(1): 104–14. doi: 10.1016/j.
healthpol.2020.11.010
Abdelhalim H, Berber A, Lodi M, Jain R, Nair A, Pappu A,
et al. Artificial Intelligence, healthcare, clinical genomics, and
pharmacogenomics approaches in precision medicine. Front
Genet. 2022; 13: 929736. doi: 10.3389/fgene.2022.929736
Tape, TG. Coherence and correspondence in medicine. Judgm
Decis Mak. 2009; 4(2): 134–40. doi: 10.1017/S1930297500002564
Eliza Strickland. 6 reactions to the White House’s AI Bill of
Rights The nonbinding principles are being both celebrated and
vilified. IEEE spectrum 14 October 2022. Available from: https://
spectrum.ieee.org/white-house-ai [cited 1 February 2023].
Afolabi O, Parsekar K, Sibson L, Patel A, Fordham R. Cost
effectiveness analysis of the East of England stroke telemedicine
service. J Stroke Cerebrovasc Dis. 2023; 32(4): 106939. doi:
1016/j.jstrokecerebrovasdis.2022.106939
Wang SV, Sreedhara SK, Schneeweiss S. Reproducibility of real-
world evidence studies using clinical practice data to inform
regulatory and coverage decisions. Nat Commun. 2022; 13(1):
doi: 10.1038/s41467-022-32310-3
Essén A, Stern AD, Hase CB, Car J, Greaves F, Paparova D, et
al. Health app policy: international comparison of nine countries’
approaches. npj Digit Med. 2022; 5(1): 31. doi: 10.1038/
s41746-022-00573-1
Diao JA, Venkatesh KP, Raza MM, Kvedar JC. Multinational
landscape of health app policy: toward regulatory consensus
on digital health. npj Digit Med. 2022; 5(1): 61. doi: 10.1038/
s41746-022-00604-x
Neal D, Engelsma T, Tan J, Craven MP, Marcilly R, Peute L,
et al. Limitations of the new ISO standard for health and wellness
apps. Lancet Digit Health. 2022; 4(2): e80-2. doi: 10.1016/
s2589-7500(21)00273-9
Maliha G, Gerke S, Cohen IG, Parikh RB. Artificial Intelligence
and liability in medicine: balancing safety and innovation. Milbank
Q. 2021; 99(3): 629-47. doi: 10.1111/1468-0009.12504
Sharkey CM. & Fodouop KM. AI and the regulatory paradigm
shift at the FDA. 72 Duke Law J. 2022: 86–112. Available
from: https://scholarship.law.duke.edu/cgi/viewcontent.cgi?article=
&context=dlj_online [cited 1 February 2023].
Richardson S, Lawrence K, Schoenthaler AM, Mann D. A
framework for digital health equity. npj Digit Med. 2022; 5(1):
doi: 10.1038/s41746-022-00663-0
Kaihlanen AM, Virtanen L, Buchert U, Safarov N, Valkonen P,
Hietapakka L, et al. Towards digital health equity – A qualitative
study of the challenges experienced by vulnerable groups in
using digital health services in the COVID-19 era. BMC Health
Serv Res. 2022; 22: 188. doi: 10.1186/s12913-022-07584-4
Gonzales A, Guruswamy G, Smith SR. Synthetic data in health
care: a narrative review. PLOS Digital Health. 2023; 2(1):
e0000082. 10.1371/journal.pdig.0000082
Reiner Benaim A, Almog R, Gorelik Y, Hochberg I, Nassar L,
Mashiach T, et al. Analyzing medical research results based on
synthetic data and their relation to real data results: systematic
comparison from five observational studies. JMIR Med Inform.
; 8(2): e16492. doi: 10.2196/16492
Kokosi T, Harron K. Synthetic data in medical research. BMJ
Medicine. 2022; 1: e000167. doi: 10.1136/bmjmed-2022-000167
Ishii-Rousseau JE, Seino S, Ebner DK, Vareth M, Po MJ, Celi
LA. The ‘Ecosystem as a Service (EaaS)’ approach to advance
clinical artificial intelligence (cAI). PLOS Digit Health. 2022;
(2): e0000011. doi: 10.1371/journal.pdig.0000011
Wang SV, Sreedhara SK, Schneeweiss S. REPEAT initiative.
Reproducibility of real-world evidence studies using clinical
practice data to inform regulatory and coverage decisions. Nat
Commun. 2022; 13(1): 5126. doi: 10.1038/s41467-022-32310-3
Rudrapatna VA, Butte AJ. Opportunities and challenges in
using real-world data for health care. J Clin Invest. 2020; 130(2):
–74. doi: 10.1172/JCI129197
Regulation of the European Parliament and of the Council laying
down harmonised rules on Artificial Intelligence (Artificial
Intelligence Act) and amending certain Union Legislative Acts.
European Commission, Brussels, 21.4.2021, COM(2021) 206
final 2021/0106 (COD). Available from: https://eur-lex.europa.
eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206 [cited 1
February 2023]
Why health-care services are in chaos everywhere. The Economist,
January 2023. Available from: https://www.economist.
com/finance-and-economics/2023/01/15/why-health-care-services-
are-in-chaos-everywhere [cited 1 February 2023]
Siegler JE, Patel NN, Dine CJ. Prioritizing paperwork over patient
care: Why can’t we do both? J Grad Med Educ. 2015; 7(1):
–8. doi: 10.4300/JGME-D-14-00494.1
Doctors wasting over two-thirds of their time doing paperwork.
Forbes, Innovation in Healthcare. 2016. Available from: https://
www.forbes.com/sites/brucelee/2016/09/07/doctors-wastingover-
two-thirds-of-their-time-doing-paperwork/ [cited 1 February
Field RI. Why is health care regulation so complex? P T. 2008;
(10): 607-8.
Braithwaite J. Changing how we think about healthcare improvement.
BMJ. 2018; 361: k2014. doi: 10.1136/bmj.k2014
Hudson CC, Gauvin S, Tabanfar R, Poffenroth AM, Lee JS,
O’Riordan AL. Promotion of role clarification in the health
care team challenge. J Interprof Care. 2017;31(3):401–3. doi:
1080/13561820.2016.1258393
Ly O, Sibbald SL, Verma JY,Rocker GM. Exploring role clarity
in interorganizational spread and scale-up initiatives: the ‘INSPIRED’
COPD collaborative. BMC Health Serv Res. 2018; 18:
doi: 10.1186/s12913-018-3474-2
Patel RS, Bachu R, Adikey A, Malik M, Shah M. Factors related
to physician burnout and its consequences: a review. Behav
Sci (Basel). 2018; 8: 98. doi: 10.3390/bs8110098.30366419
Gardner RL, Cooper E, Haskell J. Physician stress and burnout:
the impact of health information technology. J Am Med Inform
Assoc. 2019; 26: 106–114. doi: 10.1093/jamia/ocy145.30517663
Berbís MA, McClintock DS, Bychkov A, Van der Laak J, Pantanowitz
L, Lennerz JK, et al. Computational pathology in 2030:
a Delphi study forecasting the role of AI in pathology within
the next decade. EBioMedicine. 2023; 88: 104427. doi: 10.1016/j.
ebiom.2022.104427
‘Practicing at the top of your license’ and the ‘Great’ American
healthcare labor arbitrage. Forbes, Innovation in Healthcare.
Available from: https://www.forbes.com/sites/sachinjain/
/04/04/the-great-american-healthcare-labor-arbitrage/
[cited 1 February 2023].
Subbe C, Barach P. Impact of electronic health records on
pre-defined safety outcomes in patients admitted to hospital. A
scoping review. BMJ Open. 2011; 11: e047446.
Califf RM. Now is the time to fix the evidence generation
system. Clinical Trials. 2023: 17407745221147689. doi:
1177/17407745221147689
Dlima S, Shevade S, Menezes S, Ganju A. Digital phenotyping
in health using machine learning approaches: scoping review.
JMIR Bioinform Biotech. 2022; 3(1): e39618. doi: 10.2196/39618
Kanazawa N, Iijima H, Fushimi K. In-hospital cardiac rehabilitation
and clinical outcomes in patients with acute myocardial infarction after percutaneous coronary intervention: a retrospective
cohort study. BMJ Open. 2020; 10(9): e039096. doi:
1136/bmjopen-2020-039096
Bruce CR, Harrison P, Nisar T, Giammattei C, Tan NM, Bliven
C, et al. Assessing the impact of patient-facing mobile health
technology on patient outcomes: retrospective observational
cohort study. JMIR Mhealth Uhealth. 2020; 8(6): e19333. doi:
2196/19333
Parretti C, Tartaglia R, La Regina M, Venneri F, Sbrana G,
Mandò M, et al. Improved FMEA methods for proactive
health care risk assessment of the effectiveness and efficiency
of COVID-19 remote patient telemonitoring. Am J Med Qual.
; 37(6): 535–44. doi: 10.1097/JMQ.0000000000000089
Hartl D, De Luca V, Kostikova A, Laramie J, Kennedy S,
Ferrero E, et al. Translational precision medicine: an industry
perspective. J Transl Med. 2021; 19: 245. doi: 10.1186/
s12967-021-02910-6
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Dimitrios Kalogeropoulos, PhD, Paul Barach, BSC, MD, MPH
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors retain copyright of their work, with first publication rights granted to Telehealth and Medicine Today (THMT).
THMT is published under a Creative Commons Attribution-NonCommercial 4.0 International License.
