Review ArticleCommunicative aspects of decision aids for localized prostate cancer treatment – A systematic review
Introduction
Men newly diagnosed with localized prostate cancer (LPC) are facing difficult decisions regarding treatment. They need to choose from a range of treatment options (e.g., surgery, external beam radiotherapy, brachytherapy, or active surveillance) [1], which have equivalent survival outcomes but differ in the risk of adverse outcomes [2], [3]. This scenario calls for shared decision-making (SDM), a 3-step process by which clinician and patient (1) discuss treatment options, (2) compare risks and benefits, and (3) make sure that the final decision is preference based [4], [5]. SDM may involve decision aids (DAs), which are tools (e.g., booklets or websites) that provide balanced information about options and the associated risks and benefits, and help patients to clarify values and preferences and how to communicate these with their clinician [6]. Today, there are hundreds of patient DAs in various health domains, ranging from cancer to heart disease [7]. Even though DAs have potential [7], systematic reviews have shown variability in the effects of DAs for LPC treatment on decisional outcomes (including decisional conflict and knowledge) and treatment choice [8], [9], [10].
An explanation for the inconsistent effects may be that DAs have been developed and implemented without taking into account the communicative process in which SDM occurs [11]. Classic models of this process assume that communication requires a sender and a receiver who are exchanging information through a certain channel [12]. In addition, this communication process can involve aspects such as feedback (i.e., the receiver's response to a message) or noise (i.e., anything not intended by the sender). Seen from this perspective, SDM is a similar 2-way communicative process in which both clinician and patient convey and receive messages through available channels in order to reach a decision regarding treatment [13]. Indeed, communication models of SDM also acknowledge the role of DAs in this communication process [14]. Therefore, it is important to look into communicative aspects (CAs) of DAs that could potentially influence elements of the communication process between clinician and patient.
These CAs include, first of all, the channels through which DAs communicate to patients, which can either be unimodal (e.g., using text or pictures alone) or multimodal (e.g., using text with pictures or audiovisual information) [15], [16], [17]. The latter is particularly important for complex topics such as explaining surgical procedures or statistical information [18], [19]. Another aspect is that DAs can signal information based on interactions with the patient, for instance, by clarifying values or preferences, or by providing personalized information for a specific receiver based on input of that receiver [20], [21]. Moreover, information provided by DAs may also be less suitable or accessible because of various forms of noise such as complex language use (e.g., jargon), or biased presentations of risks and benefits of treatments [22]. Despite the importance of communication characteristics of DAs, no research exists that has systematically reviewed such patient tools for LPC treatment from a communication point of view.
When reviewing the quality of DAs, researchers often make use of a standardized quality checklist developed by the International Patient Decision Aids Standards (IPDAS) Collaboration [6], [23]. Nevertheless, even though the IPDAS checklist is seen as the golden standard for developing and evaluating DAs [24], it is also important to consider other aspects of the communication process that are not covered by the IPDAS. Until now, only one systematic review by Adsul et al. has reviewed the quality of DAs for LPC treatment by using additional items related to implementation (e.g., health literacy) [25]. Although their results lead to a global understanding of the variability in characteristics and quality of DAs, more in-depth analyses of some CAs are still required to get a more complete understanding of DAs as a communicative tool in the context of SDM.
The objectives of this review are to (1) systematically identify currently available DAs for LPC treatment through both academic and online sources, (2) review these tools for IPDAS criteria and, crucially, (3) assess them on a range of aspects deemed to be important for the communication process. By doing so, this review will both update and extend previous work [25], and will also take a closer look at various CAs of DAs.
Section snippets
Materials and methods
This systematic review was reported in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines [26].
Search results and general characteristics
Fig. 1 illustrates the flow chart of this systematic review. A search through databases resulted in 8,501 records, and an additional 2 records through other sources. After removing 2,025 duplicates, 6,478 unique records were identified. Titles and abstracts were screened to identify 103 potentially eligible records. Initially, full text review of these records resulted in 25 articles that met eligibility criteria, including 17 unique DAs through published literature. However, given that full
Discussion
In this systematic review, we identified 19 DAs for LPC treatment decision-making, and reviewed them for IPDAS criteria and their usage of various CAs. Consistent with previous reviews [8], [9], [10], [25], adherence to the IPDAS checklist varied substantially across DAs. Many did not adhere to good practice guidance on the presentation of outcome probabilities associated with treatment options, and also lacked substantial information regarding the development process and readability levels of
Conclusions
The integration of DAs for LPC into daily clinical practice is becoming an important intervention to support patient participation in SDM [4], [5], [55]. Using insights from communication research and relying on technological advances in artificial intelligence research, we argue that patient DAs for LPC treatment could be further improved by taking CAs such as personalization of treatment information, interaction, and the possible channels to communicate information into account. Such
Acknowledgments
We would like to thank Kim Tenfelde for her help with assessing part of the decision aids, and Robin Vernooij for developing the search strategy.
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Funding statement: RV received funding from the Data Science Center Tilburg (DSC/t). EK would like to acknowledge The Netherlands Organisation for Scientific Research (NWO) for grant 628.001.030, “Helping cancer patients to choose the best treatment: Data-driven shared decision-making on cancer treatment for individual patients.”