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ObjectivesTo assess the communicative quality of colorectal cancer patient decision aids (DAs) about treatment options, the current systematic review was conducted.DesignSystematic review.Data sourcesDAs (published between 2006 and 2019) were identified through academic literature (MEDLINE, Embase, CINAHL, Cochrane Library and PsycINFO) and online sources.Eligibility criteriaDAs were only included if they supported the decision-making process of patients with colon, rectal or colorectal cancer in stages I–III.Data extraction and synthesisAfter the search strategy was adapted from similar systematic reviews and checked by a colorectal cancer surgeon, two independent reviewers screened and selected the articles. After initial screening, disagreements were resolved with a third reviewer. The review was conducted in concordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. DAs were assessed using the International Patient Decision Aid Standards (IPDAS) and Communicative Aspects (CA) checklist.ResultsIn total, 18 DAs were selected. Both the IPDAS and CA checklist revealed that there was a lot of variation in the (communicative) quality of DAs. The findings highlight that (1) personalisation of treatment information in DAs is lacking, (2) outcome probability information is mostly communicated verbally and (3) information in DAs is generally biased towards a specific treatment. Additionally, (4) DAs about colorectal cancer are lengthy and (5) many DAs are not written in plain language.ConclusionsBoth instruments (IPDAS and CA) revealed great variation in the (communicative) quality of colorectal cancer DAs. Developers of patient DAs should focus on personalisation techniques and could use both the IPDAS and CA checklist in the developmental process to ensure personalised health communication and facilitate shared decision making in clinical practice.

In this paper, we present a novel data-to-text system for cancer patients, providing information on quality of life implications after treatment, which can be embedded in the context of shared decision making. Currently, information on quality of life implications is often not discussed, partly because (until recently) data has been lacking. In our work, we rely on a newly developed prediction model, which assigns patients to scenarios. Furthermore, we use data-to-text techniques to explain these scenario-based predictions in personalized and understandable language. We highlight the possibilities of NLG for personalization, discuss ethical implications and also present the outcomes of a first evaluation with clinicians.

Background Statistical information (e.g., on long-term survival or side effects) may be valuable for healthcare providers to share with their patients to facilitate shared decision making on treatment options. In this pre-registered study, we assessed cancer survivors’ need for generic (population-based) versus personalized (tailored towards patient/tumor characteristics) statistical information after their diagnosis. We examined how information coping style, subjective numeracy, and anxiety levels of survivors relate to these needs and identified statistical need profiles. Additionally, we qualitatively explored survivors’ considerations for (not) wanting statistical information. Methods Cancer survivors’ need for statistics regarding incidence, survival, recurrence, side effects and quality of life were assessed with an online questionnaire. For each of these topics, survivors were asked to think back to their first cancer diagnosis and to indicate their need for generic and personalized statistics on a 4-point scale (‘not at all’- ‘very much’). Associations between information coping style, subjective numeracy, and anxiety with need for generic and personalized statistics were examined with Pearson’s correlations. Statistical need profiles were identified using latent class analysis. Considerations for (not) wanting statistics were analyzed qualitatively. Results Overall, cancer survivors (n = 174) had a higher need for personalized than for generic statistics (p < .001, d = 0.74). Need for personalized statistics was associated with higher subjective numeracy (r = .29) and an information-seeking coping style (r = .41). Three statistical need profiles were identified (1) a strong need for both generic and personalized statistics (34%), (2) a stronger need for personalized than for generic statistics (55%), and (3) a little need for both generic and personalized statistics (11%). Considerations for wanting personalized cancer statistics ranged from feelings of being in control to making better informed decisions about treatment. Considerations for not wanting statistics related to negative experience with statistics and to the unpredictability of future events for individual patients. Conclusions In light of the increased possibilities for using personalized statistics in clinical practice and decision aids, it appears that most cancer survivors want personalized statistical information during treatment decision-making. Subjective numeracy and information coping style seem important factors influencing this need. We encourage further development and implementation of data-driven personalized decision support technologies in oncological care to support patients in treatment decision making.