CC BY 4.0 · Yearb Med Inform 2024; 33(01): 058-063
DOI: 10.1055/s-0044-1800719
Special Section: Digital Health for Precision in Prevention
Working Group Contributions

Evaluating Information Technology-enabled Precision Prevention Initiatives in Health and Care

Kathrin Cresswell
1   The University of Edinburgh, Usher Institute, Edinburgh, United Kingdom
,
Michael Rigby
2   Keele University, School of Social, Political and Global Studies and School of Primary, Community and Social Care, Keele, United Kingdom
,
Stephanie Medlock
3   Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, Netherlands
4   Amsterdam Public Health research institute, Digital Health and Quality of Care Amsterdam, The Netherlands
,
Mirela Prgomet
5   Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Australia
,
Elske Ammenwerth
6   UMIT TIROL, Private University for Health Sciences, Medical Informatics and Technology, Institute of Medical Informatics, Hall in Tirol, Austria
› Institutsangaben

Summary

Information technology-enabled precision prevention is a relatively new approach designed to improve population health. It forms an organic development linking principles of optimizing added value from health-related information technology and data systems with clinical aspirations to add longer-term problem prevention to immediate illness treatment. It includes drawing on information technology to identify persons at risk for developing certain conditions and then developing targeted behavioral and psychosocial approaches to modifying the behaviors of individuals or specific groups. We here discuss evaluation challenges associated with information technology-enabled precision prevention approaches to facilitate the development of an empirical evidence base. Challenges associated with measuring the impact of information technology-enabled precision prevention initiatives include considerations surrounding the relevance and fit of external data sources, the accuracy of prediction models, establishing added benefits of preventative activities, measuring pre-post outcomes at individual and population levels, and considerations surrounding cost-benefit analysis. Challenges associated with assessing processes of information technology-enabled precision prevention initiatives include the quality of data used to create underlying data models, exploring processes not necessarily related to each other, evolving social and environmental determinants of health and individual circumstances, the evolving nature of needs and interventions over time, and ethical considerations. If these challenges are attended to in evaluation activities, this will help to ensure that information technology-enabled approaches to precision prevention will have a positive impact on individual and population health.



Publikationsverlauf

Artikel online veröffentlicht:
08. April 2025

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 Jusot F, Or Z, Sirven N. Variations in preventive care utilisation in Europe. European Journal of Ageing. 2012 Mar;9:15-25.
  • 2 Eggert RW, Parkinson MD. Preventive medicine and health system reform: improving physician education, training, and practice. JAMA. 1994 Sep 7;272(9):688-93.
  • 3 Hey SP, Gerlach CV, Dunlap G, Prasad V, Kesselheim AS. The evidence landscape in precision medicine. Science Translational Medicine. 2020 Apr 22;12(540):eaaw7745.
  • 4 Kosorok MR, Laber EB. Precision medicine. Annual review of statistics and its application. 2019 Mar 7;6:263-86.
  • 5 Gillman MW, Hammond RA. Precision treatment and precision prevention: integrating “below and above the skin”. JAMA pediatrics. 2016 Jan 1;170(1):9-10.
  • 6 Barlow JH, Sturt J, Hearnshaw H. Self-management interventions for people with chronic conditions in primary care: examples from arthritis, asthma and diabetes. Health Education Journal. 2002 Dec;61(4):365-78.
  • 7 Bíró K, Dombrádi V, Jani A, Boruzs K, Gray M. Creating a common language: defining individualized, personalized and precision prevention in public health. Journal of Public Health. 2018 Dec 1;40(4):e552-9.
  • 8 Precision Prevention and Public Health. Available from: https://repository.upenn.edu/entities/publication/9ce0f712-f4ca-4e5d-916d-c2e70cb0a568
  • 9 Ramos KS, Bowers EC, Tavera-Garcia MA, Ramos IN. Precision prevention: A focused response to shifting paradigms in healthcare. Exp Biol Med (Maywood). 2019 Mar;244(3):207-212.
  • 10 Vineis P, Wild CP. The science of precision prevention of cancer. The Lancet Oncology. 2017 Aug 1;18(8):997-8.
  • 11 Environmental Protection Agency (Ireland). Air Quality Forecast. [Available at: https://www.epa.ie, accessed 9 November 2023].
  • 12 Organisation for Economic Cooperation and Development. ICTs and the Health Sector Towards Smarter Health and Wellness Models; OECD, Paris, 2013 [available at: www.oecd.org/sti/ieconomy/48915787.pdf, accessed 7 November 2023].
  • 13 M. Rigby, E. Ronchi (editors). OECD-NSF Workshop: Building a Smarter Health and Wellness Future - Summary of Key Messages; 15-16 February 2011; OECD, Paris, [available at: https://www.oecd.org/sti/ieconomy/48915787.pdf, accessed 7 November 2023].
  • 14 Bodhini D, Morton RW, Santhakumar V, Nakabuye M, Pomares-Millan H, Clemmensen C, Fitzpatrick SL, Guasch-Ferre M, Pankow J, Ried-Larsen M, Franks PW. Role of sociodemographic, clinical, behavioral, and molecular factors in precision prevention of type 2 diabetes: a systematic review. medRxiv. 2023:2023-05.
  • 15 Ingalls A, Rebman P, Martin L, Kushman E, Leonard A, Cisler A, Gschwind I, Brayak A, Amsler AM, Haroz EE. Towards precision home visiting: results at six months postpartum from a randomized pilot implementation trial to assess the feasibility of a precision approach to Family Spirit. BMC pregnancy and childbirth. 2022 Dec;22(1):1-6.
  • 16 Ammenwerth E, Rigby M, editors. Evidence-based health informatics: Promoting safety and efficiency through scientific methods and ethical policy. IOS press; 2016 May 20.
  • 17 Clark AM. What are the components of complex interventions in healthcare? Theorizing approaches to parts, powers and the whole intervention. Social science & medicine. 2013 Sep 1;93:185-93.
  • 18 Wang Y, Zhu M, Ma H, Shen H. Polygenic risk scores: the future of cancer risk prediction, screening, and precision prevention. Medical Review. 2021 Dec 20;1(2):129-49.
  • 19 Olesen JB, Lip GY, Hansen ML, Hansen PR, Tolstrup JS, Lindhardsen J, Selmer C, Ahlehoff O, Olsen AM, Gislason GH, Torp-Pedersen C. Validation of risk stratification schemes for predicting stroke and thromboembolism in patients with atrial fibrillation: nationwide cohort study. Bmj. 2011 Jan 31;342.
  • 20 Chatterjee N, Shi J, García-Closas M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nature Reviews Genetics. 2016 Jul;17(7):392-406.
  • 21 Gavan SP, Thompson AJ, Payne K. The economic case for precision medicine. Expert review of precision medicine and drug development. 2018 Jan 2;3(1):1-9.
  • 22 Centers for Disease Control and Prevention (CDC). Introduction to Public Health. In: Public Health 101 Series. Atlanta, GA: U.S. Department of Health and Human Services, CDC; 2014. [Available at: /training/publichealth101/prevention-effectiveness.html]
  • 23 Kirkwood BR, Cousens SN, Victora CG, De Zoysa I. Issues in the design and interpretation of studies to evaluate the impact of community-based interventions. Tropical Medicine & International Health. 1997 Nov;2(11):1022-9.
  • 24 Loomans-Kropp HA, Umar A. Cancer prevention and screening: the next step in the era of precision medicine. NPJ precision oncology. 2019 Jan 28;3(1):3.
  • 25 Liu Y, Wu M. Deep learning in precision medicine and focus on glioma. Bioengineering & Translational Medicine. 2023:e10553.
  • 26 Mirnezami R, Nicholson J, Darzi A. Preparing for precision medicine. N Engl J Med. 2012 Feb 9;366(6):489-91.
  • 27 Nastasi BK, Hitchcock J. Challenges of evaluating multilevel interventions. American Journal of Community Psychology. 2009 Jun;43(3-4):360-76.
  • 28 Standaert B, Sauboin C, DeAntonio R, Marijam A, Gomez J, Varghese L, Zhang S. How to assess for the full economic value of vaccines? From past to present, drawing lessons for the future. Journal of market access & health policy. 2020 Jan 1;8(1):1719588.
  • 29 Fine P, Eames K, Heymann DL. “Herd immunity”: a rough guide. Clinical infectious diseases. 2011 Apr 1;52(7):911-6.
  • 30 McGrath C, Palmarella G, Solomon S, Dupuis R. Precision prevention and public health. [Available at: https://repository.upenn.edu/entities/publication/9ce0f712-f4ca-4e5d-916d-c2e70cb0a568].
  • 31 Cresswell K, Rigby M, Magrabi F, Scott P, Brender J, Craven CK, Wong ZS, Kukhareva P, Ammenwerth E, Georgiou A, Medlock S. The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision. Health policy. 2023 Oct 1;136:104889.
  • 32 Sathish T. Precision prevention of type 2 diabetes: An approach to revitalize current lifestyle interventions. Diabetes Research and Clinical Practice. 2023 Jun 1;200.
  • 33 Strömberg U. How can precision prevention be approached from a general population perspective within the field of cancer epidemiology?. Acta Oncologica. 2021 Oct 3;60(10):1272-4.
  • 34 Hekler E, Tiro JA, Hunter CM, Nebeker C. Precision health: the role of the social and behavioral sciences in advancing the vision. Annals of Behavioral Medicine. 2020 Nov;54(11):805-26.
  • 35 M. Marmot, S.G. Wilkinson. Social Determinants of Health; Oxford University Press, 2005.
  • 36 Rigby M, Koch S, Keeling D, Hill P. Developing a New Understanding of Enabling Health and Wellbeing in Europe – Harmonising Health and Social Care Delivery and Informatics Support to Ensure Holistic Care; European Science Foundation, Strasbourg, 2013, ISBN: 978-2-918428-92-3; 52pp.; [available at: http://archives.esf.org/fileadmin/Public_documents/Publications/Health_Wellbeing_Europe.pdf].
  • 37 Rigby M, Hill P, Koch S, Keeling D. Social care informatics as an essential part of holistic health care: a call for action. International Journal of Medical Informatics. 2011 Aug 1;80(8):544-54.
  • 38 Rigby M. A Patient Care Electronic Diary to Empower the Patient and their Virtual Care Team; in P. Cunningham and M. Cunningham (eds) Collaboration and the Knowledge Economy: Issues, Applications, Case Studies; Amsterdam, IOS Press, 2008, 57-63.
  • 39 Frisoni GB, Ritchie C, Carrera E, et al. Re-aligning scientific and lay narratives of Alzheimer's disease. Lancet Neurol. 2019; 18(10): 918-919.
  • 40 Showell C, Turner P. The PLU problem: are we designing personal ehealth for people like us? Stud Health Technol Inform. (2013) 183:276–80. doi: 10.3233/978-1-61499-203-5-276
  • 41 Cresswell K, Anderson S, Montgomery C, Weir CJ, Atter M, Williams R. Evaluation of Digitalisation in Healthcare and the Quantification of the “Unmeasurable”. Journal of General Internal Medicine. 2023 Sep 15:1-6.
  • 42 Manrai AK, Funke BH, Rehm HL, Olesen MS, Maron BA, Szolovits P, Margulies DM, Loscalzo J, Kohane IS. Genetic misdiagnoses and the potential for health disparities. New England Journal of Medicine. 2016 Aug 18;375(7):655-65.
  • 43 Maeckelberghe E, Zdunek K, Marceglia S, Farsides B and Rigby M (2023) The ethical challenges of personalized digital health. Front. Med. 10:1123863. doi: 10.3389/fmed.2023.1123863
  • 44 Chanfreau-Coffinier C, Peredo J, Russell MM, Yano EM, Hamilton AB, Lerner B, Provenzale D, Knight SJ, Voils CI, Scheuner MT. A logic model for precision medicine implementation informed by stakeholder views and implementation science. Genetics in Medicine. 2019 May 1;21(5):1139-54.
  • 45 CDC Division for Heart Disease and Stroke Prevention. Developing and Using a Logic Model. [Available at: https://www.cdc.gov/dhdsp/docs/logic_model.pdf