Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-10T10:57:00.976Z Has data issue: false hasContentIssue false
  • English
  • Français

Technology assessment framework for precision health applications

Published online by Cambridge University Press:  26 May 2021

M. Sazzad Hussain ,
David Silvera-Tawil Open the ORCID record for David Silvera-Tawil [Opens in a new window]  and
Geremy Farr-Wharton
M. Sazzad Hussain
Affiliation:
The Australian e-Health Research Centre, CSIRO Health & Biosecurity, Sydney, Australia
David Silvera-Tawil*
Affiliation:
The Australian e-Health Research Centre, CSIRO Health & Biosecurity, Sydney, Australia
Geremy Farr-Wharton
Affiliation:
The Australian e-Health Research Centre, CSIRO Health & Biosecurity, Sydney, Australia
*
Author for correspondence: David Silvera-Tawil, E-mail: david.silvera-tawil@csiro.au
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective

Established and emerging technologies—such as wearable sensors, smartphones, mobile apps, and artificial intelligence—are shaping positive healthcare models and patient outcomes. These technologies have the potential to become precision health (PH) innovations. However, not all innovations meet regulatory standards or have the required scientific evidence to be used for health applications. In response, an assessment framework was developed to facilitate and standardize the assessment of innovations deemed suitable for PH.

Methods

A scoping literature review undertaken through PubMed and Google Scholar identified approximately 100 relevant articles. These were then shortlisted (n = 12) to those that included specific metrics, criteria, or frameworks for assessing technologies that could be applied to the PH context.

Results

The proposed framework identified nine core criteria with subcriteria and grouped them into four categories for assessment: technical, clinical, human factors, and implementation. Guiding statements with response options and recommendations were used as metrics against each criterion.

Conclusion

The proposed framework supports health services, health technology innovators, and researchers in leveraging current and emerging technologies for PH innovations. It covers a comprehensive set of criteria as part of the assessment process of these technologies.

Keywords

Emerging technologiesHealth technology assessmentPrecision health
Type
Assessment
Information
International Journal of Technology Assessment in Health Care , Volume 37 , Issue 1 , 2021 , e67
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Disabled People in the World in 2019: facts and figures [Internet]. Inclusive City Maker. Available from: https://www.inclusivecitymaker.com/disabled-people-in-the-world-in-2019-facts-and-figures/.Google Scholar
World Report on Disability [Internet]. 2011. Available from: http://documents1.worldbank.org/curated/en/665131468331271288/pdf/627830WP0World00PUBLIC00BOX361491B0.pdf.Google Scholar
Schüssler-Fiorenza Rose, SM, Contrepois, K, Moneghetti, KJ, Zhou, W, Mishra, T, Mataraso, S, et al. A longitudinal big data approach for precision health. Nat Med. 2019;25:792–804.CrossRefGoogle ScholarPubMed
Silvera-Tawil, D, Hussain, MS, Li, J. Emerging technologies for precision health: An insight into sensing technologies for health and wellbeing. Smart Health. 2020;15:1–19.CrossRefGoogle Scholar
Stangt, KC. The problem of fragmentation and the need for integrative solutions. Ann Fam Med. 2009;7:100–3.CrossRefGoogle Scholar
Spina, G, Huang, G, Vaes, A, Spruit, M, Amft, O. COPDTrainer: A smartphone-based motion rehabilitation training system with real-time acoustic feedback. Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013); 2013. p. 597–606.CrossRefGoogle Scholar
Health Technology Assessment (HTA) [Internet]. Australian Government Department of Health. Available from: https://www1.health.gov.au/internet/hta/publishing.nsf/Content/home-1.Google Scholar
Bhavnani, SP, Parakh, K, Atreja, A, Druz, R, Graham, GN, Hayek, SS, et al. 2017 roadmap for innovation—ACC health policy statement on healthcare transformation in the era of digital health, big data, and precision health: A report of the American college of cardiology task force on health policy statements and systems of care. J Am Coll Cardiol. 2017;70:2696–718.CrossRefGoogle Scholar
O'Rourke, B, Oortwijn, W, Schuller, T. The new definition of health technology assessment: A milestone in international collaboration. Int J Technol Assess Health Care. 2020;36(3):1–4.CrossRefGoogle ScholarPubMed
NSW Framework for New Health Technologies and Specialised Services [Internet]. NSW Health. Available from: http://www1.health.nsw.gov.au/pds/ActivePDSDocuments/GL2017_020.pdf.Google Scholar
Monitoring and Evaluating Digital Health Interventions: A practical guide to conducting research and assessment [Internet]. World Health Organization; 2016. Available from: http://apps.who.int/bookorders; https://www.who.int/reproductivehealth/publications/mhealth/digital-health-interventions/en/; http://www.wipo.int/amc/en/mediation/rules.Google Scholar
Rodriguez-Villa, E, Torous, J. Regulating digital health technologies with transparency: The case for dynamic and multi-stakeholder evaluation. BMC Med. 2019;17:1–5.CrossRefGoogle ScholarPubMed
Vis, C, Bührmann, L, Riper, H, Ossebaard, HC. Health technology assessment frameworks for eHealth: A systematic review. Int J Technol Assess Health Care. 2020;36(3):204–216.CrossRefGoogle ScholarPubMed
Angelis, A, Kanavos, P. Multiple criteria decision analysis (MCDA) for evaluating new medicines in health technology assessment and beyond: The advance value framework. Soc Sci Med. 2017;188:137–56.CrossRefGoogle ScholarPubMed
Sedrakyan, A, Campbell, B, Merino, JG, Kuntz, R, Hirst, A, McCulloch, P. IDEAL-D: A rational framework for evaluating and regulating the use of medical devices. BMJ. 2016;353:i2372.CrossRefGoogle ScholarPubMed
Love-Koh, J, Peel, A, Rejon-Parrilla, JC, Ennis, K, Lovett, R, Manca, A, et al. The future of precision medicine: Potential impacts for health technology assessment. Pharmacoeconomics. 2018;36:1439–51.CrossRefGoogle ScholarPubMed
Sebestyen, G, Hangan, A, Oniga, S, Gal, Z. eHealth solutions in the context of internet of things. Proceedings of 2014 IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR 2014; 2014.CrossRefGoogle Scholar
Beatty, AL, Fukuoka, Y, Whooley, MA. Using mobile technology for cardiac rehabilitation: A review and framework for development and evaluation. J Am Heart Assoc. 2013;2:1–8.CrossRefGoogle ScholarPubMed
Bashshur, RL. On the definition and evaluation of telemedicine. Telemed J. 1995;1:19–30.CrossRefGoogle ScholarPubMed
Gerdes, M, Smaradottir, B, Fensli, R. End-to-end infrastructure for usability evaluation of eHealth applications and services. Scandinavian Conference on Health Informatics; 2014. p. 53.Google Scholar
Eysenbach, G, CONSORT-EHEALTH Group. CONSORT-EHEALTH: Improving and standardizing evaluation reports of web-based and mobile health interventions. J Med Internet Res. 2011;13(4):e126.CrossRefGoogle ScholarPubMed
Savini, M, Ionas, A, Meier, A, Pop, C, Stormer, H. The eSana framework: Mobile services in eHealth using SOA. Proceedings of the Second European Conference on Mobile Government; 2006. p. 191–200.Google Scholar
Leister, W, Hamdi, M, Abie, H, Poslad, S, Torjusen, A. An evaluation framework for adaptive security for the IoT in eHealth. Int J Adv Secur. 2014;7:93–109.Google Scholar
Guyatt, GH, Tugwell, PX, Feeny, DH, Haynes, RB, Drummond, M. A framework for clinical evaluation of diagnostic technologies. Can Med Assoc J. 1986;134:587–94.Google ScholarPubMed
Johnson, AP, Sikich, NJ, Evans, G, Evans, W, Giacomini, M, Glendining, M, et al. Health technology assessment: A comprehensive framework for evidence-based recommendations in Ontario. Int J Technol Assess Health Care. 2009;25:141–50.CrossRefGoogle ScholarPubMed
Khalifa, M, Magrabi, F, Gallego, B. Developing a framework for evidence-based grading and assessment of predictive tools for clinical decision support. BMC Med Inform Decis Mak. 2019;19:1–17.CrossRefGoogle ScholarPubMed
Supplementary material: File

Hussain et al. supplementary material

Appendix 1

Download Hussain et al. supplementary material(File)
File 65.9 KB