We developed a novel web-based DA, uniquely designed to allow patients with prostate cancer to evaluate different treatment modalities, outcomes and side effects based on their personal preferences and pre-treatment health state. While other DAs exist, there are few and they do not relate risk of toxicities to the specific patient. The IPDAS criteria for a quality DA recommends methods for “clarifying and expressing patients’ values” , which this DA specifically focuses on. As every patient has a different pre-treatment health state, discussion of toxicities in the context of the risk to that specific patient is imperative. For example, if patient A already has erectile dysfunction, they likely will not be concerned about further risk of ED and thus not attribute much value to this uncertainty, whereas patient B may have perfect erectile function and place more value on preservation of sexual function compared to even treatment success.
This DA provides a missing piece to the good decision model. In the world of decision analysis, experts classically describe the quality of one’s decision based upon balance of the three-legged stool, representing three pillars of information the decision maker needs for a quality decision; information, alternatives and personal preferences . Physicians are well versed at providing patients with information about their diagnosis, alternative treatment options, probability of a certain outcome (for example cancer cure) and side effect profiles. These skills are entrenched in the training of physicians practicing evidence-based medicine, providing data behind each decision and recommendation. It is the third pillar, patient preferences, which physicians struggle with most because the physician has a hard time providing personalized probabilities and of course, physicians cannot provide patients with information about their personal preferences. In order to elicit patient preferences, physicians must change their mindset to “diagnosing [patient] preferences”  in order to strengthen the third pillar. They must enable patients to participate in their own decision making by helping them define these preferences, thus balancing the three pillars of decision analysis, with the result being a quality decision.
This DA will fill a gap, as most DAs for SDM in prostate cancer exist as booklets or online education sites , with general information about treatment options and side effects, focused on usability . Even though physicians have been encouraged to participate in SDM and “patient-centered-care” since the 1980s , patients still report that physicians make treatment related decisions without involving them . The American Urological Association, American Society for Radiation Oncology and Society for Urologic Oncology (AUA/ASTRO/SUO) recognize the importance of SDM, recommending clinicians utilize SDM for patients diagnosed with prostate cancer; however, these organizations provide no recommendations on how exactly to implement SDM . Importantly, the Centers for Medicare & Medicaid Services (CMS) requires SDM in certain situations, for example patients undergoing low dose CT screening for lung cancer must undergo counseling and SDM, including the use of a decision aid . The CMS Oncology Care Model also discusses SDM , and perhaps decision aids will permeate oncologic care reimbursement in the future. As DAs become more prevalent in oncologic care models, we need to ensure quality and that they are in accordance with the IPDAS .
This personalized DA makes a problem with potentially multiple outcomes a binary one, thus reducing the burden on the decision-maker, the patient. Due to the robust PROs data published and utilized in this DA for radiation treatment modalities, we can individualize the results based on pre-treatment health state and personal preferences to post-treatment side effects or preferences regarding alternative treatments. This ultimately allows for improved SDM as patients can define their thresholds, evaluate treatment outcomes and toxicities based on their health state and preference thresholds. Importantly, this DA also allows patients to change or adjust their preferences and thresholds, visualizing how that may change the treatment recommendation in real time. Those who may not be sure about their preferences have time to ponder, adjust, and digest the information with a provider or in private. This DA is not meant to replace consultation with a specialist, rather enhance that discussion, facilitate SDM, and empower patients with knowledge.
Furthermore, this DA, or the methodology employed, could be embedded in existing DAs, customized on an institutional basis, or scaled to be more comprehensive. A limitation of this DA is its lack of surgical and brachytherapy monotherapy data. We are working to add more PROs data from other collaborators, which will make the data in the current DA more robust and allow for additional treatment alternatives to be included. In addition, in this model, no side effects were attributed to active surveillance, however a more accurate depiction of that treatment option would be to include PROs for men managed this way as they also can have urinary and erectile side effects with aging . We call for collaboration within the prostate cancer community, as more data will make this DA an even more powerful tool for all our patients.
Future directions include implementation of this DA in our radiation oncology consultation appointments. Through an IRB approved project, we will allow patients to use the DA and elicit feedback via a survey as well as one-on-one interviews, allowing for adjustments to this DA as we understand what is most important to patients and as recommended by the IPDAS . Other groups have reported challenges with implementation of DAs, at both the clinician and system level , however we are dedicated to bringing this DA to life in order to strengthen the quality of the decisions prostate cancer patients make about their treatment.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.