SeNiors empOWered via Big Data to Joint-Manage Their Medication-Related Risk of Falling in Primary Care: The SNOWDROP Project

Authors Leonie Westerbeek , Noman Dormosh , André Blom, Martijn Heymans , Meefa Hogenes, Annemiek Linn , Stephanie Medlock, Martijn Schut, Nathalie van der Velde , Henk van Weert , Julia van Weert , Ameen Abu-Hanna



PDF
Thumbnail PDF

File

OASIcs.Commit2Data.4.pdf
  • Filesize: 0.81 MB
  • 12 pages

Document Identifiers

Author Details

Leonie Westerbeek
  • Amsterdam School of Communication Research/ASCoR, University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Noman Dormosh
  • Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
André Blom
  • Uw Zorg Online, Amsterdam, The Netherlands
Martijn Heymans
  • Department of Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit , Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Meefa Hogenes
  • Department of Medical Content, ExpertDoc, Rotterdam, The Netherlands
Annemiek Linn
  • Amsterdam School of Communication Research/ASCoR, University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Stephanie Medlock
  • Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Martijn Schut
  • Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, The Netherlands
  • Department of Laboratory Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Nathalie van der Velde
  • Department of Internal Medicine, Section of Geriatric Medicine, Amsterdam UMC location University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Henk van Weert
  • Department of General Practice, Amsterdam UMC location University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Julia van Weert
  • Amsterdam School of Communication Research/ASCoR, University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Ameen Abu-Hanna
  • Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands

Cite AsGet BibTex

Leonie Westerbeek, Noman Dormosh, André Blom, Martijn Heymans, Meefa Hogenes, Annemiek Linn, Stephanie Medlock, Martijn Schut, Nathalie van der Velde, Henk van Weert, Julia van Weert, and Ameen Abu-Hanna. SeNiors empOWered via Big Data to Joint-Manage Their Medication-Related Risk of Falling in Primary Care: The SNOWDROP Project. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 4:1-4:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/OASIcs.Commit2Data.4

Abstract

In older persons, falls are the leading cause of injuries, often resulting in emergency room visits, serious injuries, and possibly even death. Medications are a major risk factor for falls. Because we lack tools to assess individualized risks, general practitioners (GPs) struggle with fall related medication management for seniors, and senior patients are not properly equipped to engage in the joint management of their medications. Our aim in this project is to develop and evaluate a comprehensive data-driven science approach for valid prediction of personalized risk of falling that effectively supports joint medication management between seniors and GPs. The project has two objectives. First, we aim to develop and validate prediction models from electronic health records for assessing individualized risk of medication-related falls. Data science challenges include free text analysis; accounting for missing values; searching medication hierarchies; engineering new predictors, and understanding limitations of our approach. Second, we aim to develop and evaluate a joint medication management strategy for older patients and GPs, consisting of a clinical decision support system (CDSS) and a patient portal. We evaluate the effects of this strategy on changes in the quality of shared decision-making during a medication review consultation, medication management, and patient outcomes. The learnings from this project and the architecture underpinned by predictive modelling to support both GPs and patients can also be applied to other major health problems in the future.

Subject Classification

ACM Subject Classification
  • Applied computing → Life and medical sciences
Keywords
  • accidental falls
  • fall risk
  • medication
  • prediction model
  • clinical decision support
  • patient portal
  • shared decision-making

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. F.R. Bach. Bolasso: model consistent lasso estimation through the bootstrap. In Proceedings of the 25th international conference on Machine learning, pages 33-40, 2008. URL: https://doi.org/10.1145/1390156.1390161.
  2. D. Bosch-Lenders, D.W.H.A. Maessen, H.E.J.H. Stoffers, J.A. Knottnerus, B. Winkens, and M. Akker. Wat weten ouderen met polyfarmacie van hun pillen? Nederlands Tijdschrift Voor Geneeskunde, 160:736, 2016. URL: https://www.ntvg.nl/artikelen/wat-weten-ouderen-met-polyfarmacie-van-hun-pillen.
  3. J. Brunner, E. Chuang, C. Goldzweig, C.L. Cain, C. Sugar, and E.M. Yano. User-centered design to improve clinical decision support in primary care. International journal of medical informatics, 104:56-64, 2017. URL: https://doi.org/10.1016/j.ijmedinf.2017.05.004.
  4. C.I. Chen, C.T. Liu, CI Chen, Y.C. Li, and C.C. Chao. Medical errors in a hospital in taiwan: incidence, aetiology and proposed solutions. J Inf Technol Healthcare, 2:11-18, 2004. URL: https://doi.org/10.1145/2939672.2939785.
  5. K.R. Chowdhary. Natural language processing. Fundamentals of artificial intelligence, pages 603-649, 2020. URL: https://doi.org/10.1007/978-81-322-3972-7_19.
  6. R. Churchill and L. Singh. The evolution of topic modeling. ACM Computing Surveys, 54(10s):1-35, 2022. URL: https://doi.org/10.1145/3507900.
  7. K.K. de Wildt, B. van de Loo, A.J. Linn, S.K. Medlock, S.S. Groos, K.J. Ploegmakers, L.J. Seppala, J.E. Bosmans, A. Abu-Hanna, J.C.M. van Weert, et al. Effects of a clinical decision support system and patient portal for preventing medication-related falls in older fallers: Protocol of a cluster randomized controlled trial with embedded process and economic evaluations (ad f ice_it). PLoS one, 18(9):e0289385, 2023. URL: https://doi.org/10.1371/journal.pone.0289385.
  8. N. Dormosh, M.W. Heymans, N. Velde, J. Hugtenburg, O. Maarsingh, P. Slottje, A. Abu-Hanna, and M.C. Schut. External validation of a prediction model for falls in older people based on electronic health records in primary care. Journal of the American Medical Directors Association, 23(10):1691-1697 3, 2022. URL: https://doi.org/10.1016/j.jamda.2022.07.002.
  9. N. Dormosh, M.C. Schut, M.W. Heymans, O. Maarsingh, J. Bouman, N. Velde, and A. Abu-Hanna. Predicting future falls in older people using natural language processing of general practitioners’ clinical notes. Age and Ageing, 52(4):1-11, 2023. URL: https://doi.org/10.1093/AGEING/AFAD046.
  10. N. Dormosh, M.C. Schut, M.W. Heymans, N. Velde, and A. Abu-Hanna. Development and internal validation of a risk prediction model for falls among older people using primary care electronic health records. The Journals of Gerontology: Series A, 77(7):1438-1445, 2022. URL: https://doi.org/10.1093/GERONA/GLAB311.
  11. G. Elwyn, M.A. Durand, J. Song, J. Aarts, P.J. Barr, Z. Berger, N. Cochran, D. Frosch, D. Galasiński, P. Gulbrandsen, P.K.J. Han, M. Härter, P. Kinnersley, A. Lloyd, M. Mishra, L. Perestelo-Perez, I. Scholl, K. Tomori, L. Trevena, and T.Van der Weijden. A three-talk model for shared decision making: multistage consultation process. BMJ, 359:4891, 2017. URL: https://doi.org/10.1136/BMJ.J4891.
  12. A. Ham, M. Swart, A. Enneman, S. Dijk, S. Oliai Araghi, J. Wijngaarden, N. Zwaluw, E. Brouwer-Brolsma, R. Dhonukshe-Rutten, N. Schoor, T. Cammen, P. Lips, C. Groot, A. Uitterlinden, R. Witkamp, B. Stricker, and N. Velde. Medication-related fall incidents in an older, ambulant population: The b-proof study. Drugs Aging, 31:917-927, 2014. URL: https://doi.org/10.1007/s40266-014-0225-x.
  13. N. Masnoon, S. Shakib, L. Kalisch-Ellett, and G.E. Caughey. What is polypharmacy? a systematic review of definitions. BMC geriatrics, 17:1-10, 2017. URL: https://doi.org/10.1186/s12877-017-0621-2.
  14. J. Michalcova, K. Vasut, M. Airaksinen, and K. Bielakova. Inclusion of medication-related fall risk in fall risk assessment tool in geriatric care units. BMC Geriatrics, 20(1):454, 2020. URL: https://doi.org/10.1186/s12877-020-01845-9.
  15. K.G.M. Moons, D.G. Altman, J.B. Reitsma, J.P.A. Ioannidis, P. Macaskill, E.W. Steyerberg, A.J. Vickers, D.F. Ransohoff, and G.S. Collins. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (tripod): Explanation and elaboration. Https://Doi.Org/10.7326/M14-0698, 162(1):1-73, 2015. URL: https://doi.org/10.7326/M14-0698.
  16. K.M. Nazi, C.L. Turvey, D.M. Klein, and T.P. Hogan. A decade of veteran voices: examining patient portal enhancements through the lens of user-centered design. Journal of medical Internet research, 20(7):e10413, 2018. URL: https://doi.org/10.2196/10413.
  17. N. RiahiNia, F. Shadanpour, K. Borna, and G.A. Montazer. Automatic keyword extraction using latent dirichlet allocation topic modeling: Similarity with golden standard and users' evaluation. Human Information Interaction, 9(3):1-22, 2022. URL: https://doi.org/10.5555/944919.944937.
  18. K. Skivington, L. Matthews, S.A. Simpson, P. Craig, J. Baird, J.M. Blazeby, K.A. Boyd, N. Craig, D.P. French, E. McIntosh, M. Petticrew, J. Rycroft-Malone, M. White, and L. Moore. A new framework for developing and evaluating complex interventions: update of medical research council guidance. BMJ, 374, 2021. URL: https://doi.org/10.1136/BMJ.N2061.
  19. T.A. Soriano, L.V. DeCherrie, and D.C. Thomas. Falls in the community-dwelling older adult: a review for primary-care providers. Clinical interventions in aging, 2(4):545-553, 2007. URL: https://doi.org/10.2147/cia.s1080.
  20. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1):267-288, 1996. URL: https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
  21. VeiligheidNL. Infographic valongevallen 65-plussers 2022, 2022. URL: https://www.veiligheid.nl/kennisaanbod/infographic/infographic-valongevallen-65-plussers-2022.
  22. H.M. Vu, L.H. Nguyen, H.L.T. Nguyen, G.T. Vu, C.T. Nguyen, T.N. Hoang, T.H. Tran, K.T.H. Pham, A. Latkin, Xuan Tran C., S.H.Ho B., C., and R.C.M. Ho. Individual and environmental factors associated with recurrent falls in elderly patients hospitalized after falls. International Journal of Environmental Research and Public Health, 17(7):2441, 2020. URL: https://doi.org/10.3390/ijerph17072441.
  23. L. Westerbeek, G.J. Bruijn, H.C. Weert, A. Abu-Hanna, S. Medlock, and J.C.M. Weert. General practitioners’ needs and wishes for clinical decision support systems: A focus group study. International Journal of Medical Informatics, 168:104901, 2022. URL: https://doi.org/10.1016/J.IJMEDINF.2022.104901.
  24. L. Westerbeek, K.J. Ploegmakers, G.J. Bruijn, A.J. Linn, J.C.M. Weert, J.G. Daams, N. Velde, H.C. Weert, A. Abu-Hanna, and S. Medlock. Barriers and facilitators influencing medication-related cdss acceptance according to clinicians: A systematic review. International Journal of Medical Informatics, 152:104506, 2021. URL: https://doi.org/10.1016/J.IJMEDINF.2021.104506.
  25. C. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, and Y. Gao. A survey on federated learning. Knowledge-Based Systems, 216:106775, 2021. URL: https://doi.org/10.1016/j.knosys.2021.106775.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail