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Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment Options

Authors Emiel Krahmer , Felix Clouth , Saar Hommes , Ruben Vromans , Steffen Pauws , Jeroen Vermunt , Lonneke van de Poll-Franse , Xander Verbeek

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Author Details

Emiel Krahmer
  • Department of Communication and Cognition, Tilburg University, The Netherlands
Felix Clouth
  • Department of Methodology and Statistics, Tilburg University, The Netherlands
  • Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands
Saar Hommes
  • Department of Communication and Cognition, Tilburg University, The Netherlands
  • Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands
Ruben Vromans
  • Department of Communication and Cognition, Tilburg University, The Netherlands
  • Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands
Steffen Pauws
  • Department of Communication and Cognition, Tilburg University, The Netherlands
  • Innovation Excellence, Innovation & Strategy, Philips, Eindhoven, The Netherlands
Jeroen Vermunt
  • Department of Methodology and Statistics, Tilburg University, The Netherlands
Lonneke van de Poll-Franse
  • Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands
  • Department of Medical and Clinical Psychology, Tilburg University, The Netherlands
  • Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
Xander Verbeek
  • Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands

Funding

This research was funded by the commit2data program from the Netherlands Organisation for Scientific Research (NWO) [Grant No. 628.001.030], by the Data Science Center Tilburg (DSC/t), by the "We Care" program from Tilburg University and Elisabeth-TweeSteden hospital [Grant No. 42001155], and by the Netherlands Organization for Health Research and Development (ZonMW), "Kennisvragen Uitkomstgerichte Zorg" [Project No. 80-83900-98-3003], which are all gratefully acknowledged.

Cite AsGet BibTex

Emiel Krahmer, Felix Clouth, Saar Hommes, Ruben Vromans, Steffen Pauws, Jeroen Vermunt, Lonneke van de Poll-Franse, and Xander Verbeek. Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment Options. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 3:1-3:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/OASIcs.Commit2Data.3

Abstract

When a person is diagnosed with cancer, difficult decisions about treatments need to be made. In this chapter, we describe an interdisciplinary research project which aims to automatically generate personalized descriptions of treatment options for patients. We relied on two large databases provided by the Netherlands Comprehensive Cancer Organisation (IKNL): The Netherlands Cancer Registry and the PROFILES dataset. Combining these datasets allowed us to extract personalized information about treatment options for different types of cancer. In a next step we provided personalized context to these numbers, both in verbal statements and in narratives, with the aim to facilitate shared decision making about treatments. We discuss strengths and limitations of our approach, illustrate how it generalizes to other health domains, and reflect on the overall research project.

Subject Classification

ACM Subject Classification
  • Human-centered computing → Human computer interaction (HCI)
  • Computing methodologies → Artificial intelligence
  • Applied computing → Life and medical sciences
Keywords
  • Oncology
  • Data-driven shared decision making
  • Latent class analysis
  • Risk communication
  • Narratives
  • Personalization

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References

  1. A. Arora and A. Arora. The promise of large language models in health care. The Lancet, 401:641, 2023. Google Scholar
  2. L. A. Baezconde-Garbanati, J. S. Chatterjee, L. B. Frank, S. T. Murphy, M. B. Moran, L. N. Zhao N. Werth, P. Amezola de Herrera, D. Mayer, J. Kagan, and D. O'Brien. Tamale lesson: A case study of a narrative health communication intervention. Journal of Communication in Healthcare, 7(2):82-92, 2014. Google Scholar
  3. E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pages 610-623, 2021. Google Scholar
  4. N. Bol, E. Smit, and M. Lustria. Tailored health communication: Opportunities and challenges in the digital era. Digital Health, 6:1-3, 2020. Google Scholar
  5. C. Bonner, L. J. Trevena, W. Gaissmaier, P. K. J. Han, Y. Okan, E. Ozanne, E. Peters, D. Timmermans, and B. J. Zikmund-Fisher. Current best practice for presenting probabilities in patient decision aids: fundamental principles. Medical Decision Making, 41(7):821-833, 2021. Google Scholar
  6. E. Boomstra, S. Hommes, R. D. Vromans, A. M. van den Burg, A. M. Schrijver, M. W. J. M. Wouters, I. M. C. van der Ploeg, M. W. van de Kamp, E. J. Krahmer, L. V. van de Poll-Franse, and K. de Ligt. Numbers call for action, personalized narratives provide support and recognition”: A qualitative assessment of cancer patients’ perspectives on patient reported outcome measures (proms) feedback with narratives. Journal of Cancer Survivorship, 2024. Google Scholar
  7. C. Charles, A. Gafni, T. Whelan, and M. A. O'Brien. Treatment decision aids: conceptual issues and future directions. Health Expectations, 8(2):114-125, 2005. Google Scholar
  8. S. S. Chauhan, J. Kaur, M. Kumar, A. Matta, G. Srivastava, A. Alyass, J. Assi, I. Leong, C. MacMillan, I. Witterick, T. J. Colgan, N. K. Shukla, A. Thakar, M. C. Sharma, K. W. M. Siu, P. G. Walfish, and R. Ralhan. Prediction of recurrence-free survival using a protein expression-based risk classifier for head and neck cancer. Oncogenesis, 4(4):e147, 2015. Google Scholar
  9. P. C. Chen, Y. M. Yeh, B. W. Lin, R. H. Chan, P. F. Su, Y. C. Liu, C. T. Lee, S. H. Chen, and P. C. Lin. A prediction model for tumor recurrence in stage ii-iii colorectal cancer patients: From a machine learning model to genomic profiling. Biomedicines, 10(2), 2022. Google Scholar
  10. F. J. Clouth, M. J. Bijlsma, S. Pauws, and J. K. Vermunt. The parametric g-formula for latent Markov models, 2024. PsyArXiv preprint. Google Scholar
  11. F. J. Clouth, A. Moncada-Torres, G. Geleijnse, F. Mols, F. N. van Erning, I. H. J. T. de Hingh, S. C. Pauws, L. V. van de Poll-Franse, and J. K. Vermunt. Heterogeneity in quality of life of long-term colon cancer survivors: A latent class analysis of the population-based profiles registry. The Oncologist, 26(3):e492-e499, 2021. Google Scholar
  12. F. J. Clouth, S. Pauws, F. Mols, and J. K. Vermunt. A new three-step method for using inverse propensity weighting with latent class analysis. Advances in Data Analysis and Classification, 16(2):351-371, 2022. URL: https://doi.org/10.1007/S11634-021-00456-5.
  13. F. J. Clouth, S. Pauws, and J. K. Vermunt. Three-step latent class analysis with inverse propensity weighting in the presence of differential item functioning. Structural Equation Modeling: A Multidisciplinary Journal, 30(5):737-748, 2023. URL: https://doi.org/10.1080/10705511.2022.2161384.
  14. A. De Graaf, J. Sanders, and H. Hoeken. Characteristics of narrative interventions and health effects: A review of the content, form, and context of narratives in health-related narrative persuasion research. Review of Communication Research, 4:88-131, 2016. Google Scholar
  15. B. H. de Rooij, I. Ramsey, F. J. Clouth, N. Corsini, J. S. Heyworth, B. M. Lynch, J. K. Vallance, and T. Boyle. The association of circadian parameters and the clustering of fatigue, depression, and sleep problems in breast cancer survivors: a latent class analysis. Journal of Cancer Survivorship, 17(5):1405-1415, 2022. Google Scholar
  16. C. Dijkstra, E. Krahmer, and M. Swerts. Manipulating uncertainty: The contribution of different audiovisual prosodic cues to the perception of confidence. In Proceedings of the Speech Prosody Conference, pages 1-4, 2006. Google Scholar
  17. J. L. Donovan, F. C. Hamdy, J. A. Lane, M. Mason, C. Metcalfe, E. Walsh, ..., and ProtecT Study Group. Patient-reported outcomes after monitoring, surgery, or radiotherapy for prostate cancer. New England Journal of Medicine, 375(15):1425-1437, 2016. Google Scholar
  18. P. Dunn and E. Hazard. Technology approaches to digital health literacy. International Journal of Cardiology, 293:294-296, 2019. Google Scholar
  19. G. Elwyn, D. Frosch, R. Thomson, N. Joseph-Williams, A. Lloyd, P. Kinnersley, Cording E, Tomson D, Dodd C, Rollnick S, Edwards A, and M. Barry. Shared decision making: a model for clinical practice. Journal of General Internal Medicine, 27:1361-1367, 2012. Google Scholar
  20. E. G. Engelhardt, A. H. Pieterse, N. van Duijn-Bakker, J. R. Kroep, H. C. de Haes, E. M. Smets, and A. M. Stiggelbout. Breast cancer specialists' views on and use of risk prediction models in clinical practice: a mixed methods approach. Acta Oncologica, 54(3):361-367, 2015. Google Scholar
  21. A. Fagerlin, B. J. Zikmund-Fisher, and P. A. Ubel. If i'm better than average, then i'm ok? comparative information influences beliefs about risk and benefits. Patient Education and Counseling, 69(1-3):140-144, 2007. Google Scholar
  22. J. G. Ford, M. W. Howerton, G. Y. Lai, T. L. Gary, S. Bolen, M. C. Gibbons, J. Tilburt, C. Baffi, T. P. Tanpitukpongse, R. F. Wilson, N. R. Powe, and E. B. Bass. Barriers to recruiting underrepresented populations to cancer clinical trials: A systematic review. Cancer, 112:228-242, 2008. Google Scholar
  23. S. J. Franssen, S. M. Lagarde, J. R. van Werven, E. M. Smets, K. T. Tran, J. T. M. Plukker, J. J. van Lanschot, and H. C. de Haes. Psychological factors and preferences for communicating prognosis in esophageal cancer patients. Psycho-Oncology: Journal of the Psychological, Social and Behavioral Dimensions of Cancer, 18(11):1199-1207, 2009. Google Scholar
  24. A. Gatt and E. Krahmer. Survey of the state of the art in natural language generation: Core tasks, applications and evaluation. Journal of Artificial Intelligence Research, 61:65-170, 2018. URL: https://doi.org/10.1613/JAIR.5477.
  25. J. Geipel, C. Hadjichristidis, L. Savadori, and B. Keysar. Language modality influences risk perception: Innovations read well but sound even better. Risk Analysis, 43:558-570, 2022. Google Scholar
  26. G. Gigerenzer, W. Gaissmaier, E. Kurz-Milcke, L. M. Schwartz, and S. Woloshin. Helping doctors and patients make sense of health statistics. Psychological Science in the Public Interest, 8(2):53-96, 2008. Google Scholar
  27. N. Haase, C. Betsch, and F. Renkewitz. Source credibility and the biasing effect of narrative information on the perception of vaccination risks. Journal of Health Communication, 20(8):920-929, 2015. Google Scholar
  28. F. C. Hamdy, J. L. Donovan, J. A. Lane, M. Mason, C. Metcalfe, P. Holding, ..., and ProtecT Study Group. 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. New England Journal of Medicine, 375(15):1415-1424, 2016. Google Scholar
  29. R. J. M. Havermans, F. J. Clouth, K. W. W. Lansink, J. K. Vermunt, M. A. C. de Jongh, and L. de Munter. Prediction of recovery in trauma patients using latent Markov models. European Journal of Trauma and Emergency Surgery, 48(3):2059-2080, 2022. Google Scholar
  30. B. He, W. Chen, L. Liu, Z. Hou, H. Zhu, H. Cheng, Y. Zhang, S. Zhan, and S. Wang. Prediction models for prognosis of cervical cancer: Systematic review and critical appraisal. Frontiers in Public Health, 9:654454, 2021. Google Scholar
  31. L. Heilmann. Health and numeracy: the role of numeracy skills in health satisfaction and health-related behaviour. ZDM Mathematics Education, 52:407-418, 2020. Google Scholar
  32. M. A. Hernan and J. M. Robins. Estimating causal effects from epidemiological data. Journal of Epidemiology and Community Health, 60:578-586, 2006. Google Scholar
  33. R. Hernandez-Ramos, A. Aguilera, F. Garcia, J. Miramontes-Gomez, L. E. Pathak, C. A. Figueroa, and C. R. Lyles. Conducting internet-based visits for onboarding populations with limited digital literacy to an mhealth intervention: Development of a patient-centered approach. JMIR Formative Research, 5(4):25299, 2021. Google Scholar
  34. C. Hitchens. Mortality. Hachette, UK, 2012. Google Scholar
  35. S. Hommes, C. van der Lee, F. J. Clouth, X. Verbeek, and E. J. Krahmer. A personalized data-to-text support tool for cancer patients. In Proceedings of the 12th International Conference on Natural Language Generation, Tokio, Japan, pages 443-452, 2019. Google Scholar
  36. S. Hommes, R. Vromans, M. de Jongh, T. Houwen, N. Bol, E. de Groot, M. Antheunis, and E. Krahmer. Knowing what life has in store for me is better than being in the dark: Trauma patients' perspectives on receiving personalized predictions for life after injury, 2023. Google Scholar
  37. S. Hommes, R. D. Vromans, J. Bloemen, S. van Gent, G. Schipper, X. Verbeek, and E. J. Krahmer. Personalized predictions about quality of life after chemotherapy: Development of a data-driven support tool and initial evaluation of colorectal cancer patients and clinicians, 2023. Google Scholar
  38. S. Hommes, R. D. Vromans, F. J. Clouth, X. Verbeek, I. de Hingh, and E. J. Krahmer. Communication in decision aids for stage i-iii colorectal cancer patients: A systematic review. BMJ Open, 11:e044472, 2021. Google Scholar
  39. A. Kalch, C. Küchler, A. Albani, H. Bilandzic, S. Fischer, and I. Kirchberger. On the need for narratives in patient information: Differentiating types and functions of narratives from pulmonary embolism patients' point of view. Frontiers in Communication, 7:990819, 2022. Google Scholar
  40. K. Kenzik, M. Y. Martin, M. N. Fouad, and M. Pisu. Health-related quality of life in lung cancer survivors: Latent class and latent transition analysis. Cancer, 121(9):1520-1528, 2015. Google Scholar
  41. M. W. Kreuter, M. Green, J. Cappella, M. Slater, M. Wise, D. Storey, E. M. Clark, D. J. O'Keefe, D. O. Erwin, K. Holmes, L. J. Hinyard, T. Houston, and S. Woolley. Narrative communication in cancer prevention and control: A framework to guide research and application. Annals of Behavioral Medicine, 33(3):221-235, 2007. Google Scholar
  42. L. E. Kunst, M. A. L. M. van Assen, F. J. Clouth, C. Hunt, M. Abbott, J. Maas, and M. H. J. Bekker. Who cares what other people think? a longitudinal investigation of long-term self-esteem stability and its associations with autonomy-connectedness, 2024. Manuscript submitted for publication. Google Scholar
  43. Z. S. Morris, S. Wooding, and J. Grant. The answer is 17 years, what is the question: understanding time lags in translational research. Journal of the Royal Society of Medicine, 104(12):510-520, 2011. Google Scholar
  44. Netherlands Cancer Registry (ncr) and Netherlands Comprehensive Cancer Organisation. (iknl), derived via www.iknl.nl/en/ncr/ncr-data-figures [03/31/2023]. Google Scholar
  45. S. M. Noar, C. N. Benac, and M. S. Harris. Does tailoring matter? meta-analytic review of tailored print health behavior change interventions. Psychological Bulletin, 133(4):673-693, 2007. Google Scholar
  46. R. Parker and S. C. Ratzan. Health literacy: A second decade of distinction for americans. Journal of Health Communication, 15(2):20-33, 2010. Google Scholar
  47. S. Pauws, A. Gatt, E. Krahmer, and E. Reiter. Making effective use of healthcare data using data-to-text technology. In Data Science for Healthcare, pages 119-145. Springer, 2019. URL: https://doi.org/10.1007/978-3-030-05249-2_4.
  48. E. Peters. Innumeracy in the wild: Misunderstanding and misusing numbers. Oxford University Press, 2020. Google Scholar
  49. PROFILES. derived via. URL: https://www.dataarchive.profilesregistry.nl/publications.
  50. E. Reiter and R. Dale. Building Natural Language Generation Systems. Cambridge University Press, 2000. Google Scholar
  51. S. Salem, T. Weskott, and A. Holler. Does narrative perspective influence readers' perspective-taking? an empirical study on free indirect discourse, psycho-narration and first-person narration. Glossa: A Journal of General Linguistics, 2(1):61, 2017. Google Scholar
  52. V. A. Shaffer, E. S. Focella, A. Hathaway, L. D. Scherer, and B. J. Zikmund-Fisher. On the usefulness of narratives: An interdisciplinary review and theoretical model. Annals of Behavioral Medicine, 52(5):429-442, 2018. Google Scholar
  53. V. A. Shaffer and B. Zikmund-Fisher. All stories are not alike: A purpose-, content- and valance-based taxonomy of patient narratives in decision aids. Medical Decision Making, 33(1):4-13, 2013. Google Scholar
  54. F. Shen, V. C. Sheer, and R. Li. Impact of narratives on persuasion in health communication: A meta-analysis. Journal of Advertising, 44(2):105-113, 2015. Google Scholar
  55. R. L. Siegel, K. D. Miller, and A. Jemal. Cancer statistics. 2016. CA: A Cancer Journal for Clinicians, 66(1):7-30, 2016. Google Scholar
  56. L. V. van de Poll-Franse, N. Horevoorts, M. van Eenbergen, J. Denollet, J. A. Roukema, N. K. Aaronson, A. Vingerhoets, J. W. Coebergh, J. de Vries, M. L. Essink-Bot, and F. Mols. The patient reported outcomes following initial treatment and long term evaluation of survivorship registry: scope, rationale and design of an infrastructure for the study of physical and psychosocial outcomes in cancer survivorship cohorts. European Journal of Cancer, 47(14):2188-2194, 2011. Google Scholar
  57. H. G. van den Boorn, E. G. Engelhardt, J. van Kleef, M. A. G. Sprangers, M. G. H. van Oijen, A. Abu-Hanna, A. H. Zwinderman, V. M. H. Coupe, and H. W. M. van Laarhoven. Prediction models for patients with esophageal or gastric cancer: A systematic review and meta-analysis. PLoS One, 13(2):e0192310, 2018. Google Scholar
  58. N. C. van der Velden, H. W. van Laarhoven, S. A. Burgers, L. E. Hendriks, F. Y. de Vos, A. M. C. Dingemans, J. Jansen, J. W. van Haarst, J. Dits, E. M. Smets, and I. Henselmans. Characteristics of patients with advanced cancer preferring not to know prognosis: A multicenter survey study. BMC Cancer, 22(1):1-12, 2022. Google Scholar
  59. J. E. van Gemert-Pijnen, N. Nijland, M. van Limburg, H. C. Ossebaard, S. M. Kelders, G. Eysenbach, and E. R. Seydel. A holistic framework to improve the uptake and impact of ehealth technologies. Journal of Medical Internet Research, 13(4):e111, 2011. Google Scholar
  60. R. Vromans, K. Tenfelde, S. Pauws, M. Van Eenbergen, I. Mares-Engelberts, G. Velikova, L. van de Poll-Franse, and E. Krahmer. Assessing the quality and communicative aspects of patient decision aids for early-stage breast cancer treatment: a systematic review. Breast Cancer Research and Treatment, 178:1-15, 2019. Google Scholar
  61. R. D. Vromans, S. Hommes, F. J. Clouth, D. N. N. Lo-Fo-Wong, X. A. A. M. Verbeek, L. V. van de Poll-Franse, S. C. Pauws, and E. J. Krahmer. Need for numbers: Assessing cancer survivors' needs for personalized and generic statistical information. BMC Medical Informatics and Decision Making, 22(1):1-14, 2022. Google Scholar
  62. R. D. Vromans, S. C. Pauws, N. Bol, L. V. van de Poll-Franse, and E. J. Krahmer. Communicating tailored risk information of cancer treatment side effects: Only words or also numbers? BMC Medical Informatics and Decision Making, 20:1-12, 2020. Google Scholar
  63. R. D. Vromans, S. C. Pauws, L. V. van de Poll-Franse, and E. J. Krahmer. Effects of comparative information when communicating personalized risks of treatment outcomes: An experimental study. Journal of Risk Research, 26(3):324-343, 2022. Google Scholar
  64. R. D. Vromans, C. N. Tillier, S. C. Pauws, H. G. van der Poel, L. V. van de Poll-Franse, and E. J. Krahmer. Communication, perception, and use of personalized side-effect risks in prostate cancer treatment decision-making: An observational and interview study. Patient Education and Counseling, 105(8):2731-2739, 2022. Google Scholar
  65. R. D. Vromans, C. C. M. van de Ven, S. J. W. Willems, E. J. Krahmer, and M. Swerts. It is, uh, very likely? the impact of prosodic uncertainty cues on the perception and interpretation of spoken verbal probability phrases. Risk Analysis, pages 1-20, 2024. Google Scholar
  66. R. D. Vromans, M. C. van Eenbergen, G. Geleijnse, S. C. Pauws, L. V. van de Poll-Franse, and E. J. Krahmer. Exploring cancer survivor needs and preferences for communicating personalized cancer statistics from registry data: Qualitative multimethod study. JMIR Cancer, 7(4):e25659, 2021. Google Scholar
  67. R. D. Vromans, M. C. van Eenbergen, S. C. Pauws, G. Geleijnse, H. G. van der Poel, L. V. van de Poll-Franse, and E. J. Krahmer. Communicative aspects of decision aids for localized prostate cancer treatment-a systematic review. Urologic Oncology: Seminars and Original Investigations, 37(7):409-429, 2019. Google Scholar
  68. G. C. Wishart, E. M. Azzato, D. C. Greenberg, J. Rashbass, O. Kearins, G. Lawrence, C. Caldas, and P. D. P. Pharoah. Predict: a new uk prognostic model that predicts survival following surgery for invasive breast cancer. Breast Cancer Research, 12(1):1-10, 2010. Google Scholar
  69. BigScience Workshop:, T. L. Scao, A. Fan, C. Akiki, E. Pavlick, S. Ilić, D. Hesslow, ..., and P. Villegas. Bloom: A 176b-parameter open-access multilingual language model, 2022. arXiv preprint. URL: https://arxiv.org/abs/2211.05100.
  70. M. Zeguers, H. C. de Haes, L. C. Zandbelt, C. L. Ter Hoeven, S. J. Franssen, D. D. Geijsen, C. C. Koning, and E. M. Smets. The information needs of new radiotherapy patients: how to measure? do they want to know everything? and if not, why? International Journal of Radiation Oncology, Biology, Physics, 82(1):418-424, 2012. Google Scholar
  71. B. J. Zikmund-Fisher. The right tool is what they need, not what we have: a taxonomy of appropriate levels of precision in patient risk communication. Medical Care Research and Review, 70(1), 2013. Google Scholar
  72. B. J. Zikmund-Fisher. Helping people know whether measurements have good or bad implications: increasing the evaluability of health and science data communications. Policy Insights from the Behavioral and Brain Sciences, 6(1):29-37, 2019. Google Scholar
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