1. Introduction
Shared decision making (SDM) is a practice where clinicians and patients collaborate to make informed decisions by sharing the present scientific available evidence [1,2]. This healthcare approach supports patients in considering their clinical options while ensuring their preferences are communicated to the clinicians [3] (e.g., if the patient prioritises maintaining her breast, the surgeon should be aware to consider treatments that align with this). It is essential for patients to acquire a comprehension of their health conditions, treatment options, and the associated risks and benefits. This understanding promotes patients to properly articulate their values and priorities when communicating with clinicians. The employment of decision-making tools into SDM can enhance communication skills to foster a more collaborative healthcare context.
In the case of SDM in oncology, patient decision aid (PtDA) tools are being developed and tested to support the SDM process, but it is uncertain whether these devices increase the use of SDM by providers effectively [4]. On one hand, a pre-pandemic study [5] showed a mere 36% of the patients and surgeons perceived their consultation as SDM-based. Interestingly, it was observed that surgeons perceived the SDM more frequently than patients did but with scarce objective measures (e.g., validated metrics). On the other hand, a post-pandemic study [6] examined the criteria for SDM in oncology, emphasising the involvement of decision makers (patients and clinicians), decision-specific criteria (e.g., IPDAS), and contextual factors (e.g., hospital). Leinweber et al. in 2019 [7] identified 107 PtDAs to help oncologic patients to make informed decisions, but only 39 were developed to assist patients in selecting treatments and 5 intended to aid in the selection between a major open surgical procedure and a less invasive option. Furthermore, the majority of tools employed were designed to enhance patient decision making in breast cancer (BC). As one of the most prevalent oncologic diseases worldwide, BC ranked as the second highest in incidence according to the World Health Organization [8]. These factors significantly informed the selection of BC as the focus of the present study.
To the best of the authors’ knowledge, only a few systematic reviews have targeted the role of PtDA in BC care. Zdenkowski et al. [9] identified 23 PtDA tools between 2011 and 2015 for early BC care, in which decisions addressed included the choice between breast-conserving surgery and mastectomy, the administration of chemotherapy and/or endocrine therapy, radiotherapy, and fertility preservation. The outcome measures were heterogeneous, with most studies reporting a reduction in decisional conflict and an increase in both knowledge and satisfaction among patients, with no changes in levels of anxiety or depression. Spronk et al. [10] identified seven oncologic PtDA tools between 2006 and 2021, of which three were designed for metastatic BC. These tools showed applicability across various stages of the healthcare process and were useful in facilitating decisions regarding care, independently of the chemotherapy.
Indeed, the International Patient Decision Aid Standards (IPDAS) Collaboration initiative was established in 2003 to enhance the quality of healthcare in SDM. The framework focuses on the components of the PtDA tools that support informed, values-based reasoning and engagement with healthcare professionals [11]. There are six IPDAS versions of the criteria: the original with 80 items within 12 broad criteria [12], the IPDAS Checklist (74 items [13]), the short version IPDAi Assessment (47 items [14]), the minimal criteria proposal IPDAS Checklist (44 items [15]), the Standards for Universal reporting of patient Decision Aid Evaluation studies (SUNDAE) Checklist (26 items [16]), and, lastly, the IPDAS 2.0. Checklist (11 core domains [17]).
This systematic review seeks to provide a comprehensive and updated analysis of clinical studies related to SDM within the context of BC medical treatment. It specifically focuses on the utilisation of the PtDA tools to evaluate their characteristics and effects in clinical practice. The specific aims are the four following: (1) to examine the type of studies conducted in this area of clinical healthcare practice; (2) to assess the PtDA tool main characteristics and the medical treatment options they present; (3) to evaluate the main and secondary outcomes achieved; and (4) to identify, where applicable, the version of the IPDAS utilised in the development of the PtDA tool used for selecting BC treatments through SDM.
2. Materials and Methods
Guidance from the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement [18] informed the methods for conducting and reporting the present comprehensive systematic review.
2.1. Search Strategy
The following scientific bibliographic databases were searched: PubMed, PsycINFO, and Google Scholar. The review process was undertaken with an equation as follows: (((“breast cancer”) AND (“treatment*” OR “therapy*”)) AND (“shared decision-making” OR “SDM”) OR (“decision aids” OR “patient decision aid” OR “PDA” OR “PtDA” OR “decision aid tool*” OR “decision aid app*”)) NOT “screening” NOT “diagnostic”. Additionally, the Google Scholar database, only titles were selected with the equation as follows: allintitle: (“shared decision making” OR “SDM” OR “app” OR “PtDA”) AND (“breast cancer”).
2.2. Eligibility Criteria
The systematic review was initiated in 2013 with the official launch of the minimum quality standards for the implementation of PtDA tools 2.0. by the IPDAS [15].
The studies included in the present review were selected based on predetermined inclusion criteria: (1) it assessed SDM concerning the selection of medical treatments following a BC diagnosis, using a PtDA tool in an SDM clinical context; (2) it constituted an empirical study (i.e., employing quantitative, qualitative, or mixed method approaches); (3) it was published during the period selected (2013–2024); (4) the articles were retrievable as a full text within a peer-reviewed journal; and (5) the language used was English, Spanish, or French. The exclusion criteria were as follows: (1) studies that assessed SDM in other oncological pathologies not targeted (e.g., prostate cancer), with no inclusion of the BC, or that addressed nonmedical treatment options (e.g., screening methods); (2) papers that were theoretical research papers (e.g., reviews and meta-analyses or protocols); (3) publications that were outside the selected period (e.g., prior to 2013 or 2025); (4) articles that could not be retrieved electronically or were not published in peer-reviewed journals; and (5) languages used were other than those specified.
A total of 128 papers were initially identified from the three selected databases. Following the removal of duplicate entries (n = 6), 122 publications were screened based on their titles and abstracts. From this group, 54 studies were excluded for not meeting the predefined criteria (e.g., systematic reviews and conference abstracts). This resulted in 68 reports that underwent further evaluation through a comprehensive review of the retrieved articles. Of these, 39 studies were excluded due to various reasons (e.g., the absence of a clear application of SDM and PtDA tools or nonmedical treatment options). Consequently, a total of 29 studies were identified for consideration in this evaluation. (see Figure 1).
2.3. Selection Process
The selection of studies was conducted through a rigorous three-stage process. Initially, three reviewers applied the search strategy by utilising the defined equation within a database to identify the initial set of publications. The second stage involved a comprehensive review to remove duplicates and ensure that each selected study adhered to the aims (C.P.A.C., O.L.-F.). This phase included a careful evaluation of the titles and abstracts from the primary pool of articles. Following discussions and consensus reached during meetings, another refined pool of papers was established. In the final stage, the full texts were meticulously screened by two reviewers, comparing their content against the inclusion criteria through two rounds of evaluation. The first round aimed to identify clear publications that corresponded with the designed aims (C.P.A.C., B.H.). The second round was undertaken in cases where the inclusion of an article was uncertain, in which it was reviewed by a secondary reviewer (O.L.-F.). A collaborative discussion was conducted to reach a consensus on whether to include or exclude the article from the final selection.
Figure 1 depicts the study selection process in accordance with PRISMA guidelines [18]. A total of 29 studies were included in the current review after the aforementioned criteria.
2.4. Quality Study, Data Extraction, and Management
The quality and risk of bias were assessed at a study level using a critical assessment tool selected from the Joanna Briggs Institute (JBI)—Critical appraisal tools—in concrete, the Checklist for analytical cross-sectional studies (Moola et al., 2015 [19,20]). This choice was made because all the research designs examined were cross-sectional in nature. The studies were analysed according to Moola et al.’s eight questions [20]. Differences in results were resolved through discussion (C.P.A.C., O.L.-F.).
All the papers that appeared to meet the inclusion criteria were qualitatively assessed using the full text according to a set of predefined themes. The key pieces of information were extracted, including authors and year of publication (with country), aims and available BC treatment options, sample characteristics, study design, and measures employed. Additionally, the PtDA tools’ characteristics applied within an SDM process were noted, along with main study results with SDM outcomes, as well as the IPDAS version applied, when appropriate. A thematic synthesis was conducted, with all authors actively participating in the data extraction. The BC surgeon led the extraction of medical treatment options and outcomes when relevant (M.L.S.-R.), which was supervised by the senior surgeon (H.G.), while health psychologists and a methodologist provided insights on other variables (C.P.A.C., B.H., O.L.-F.), including the versions of IPDAS identified (B.H., O.L.-F.).
3. Results
A total of 29 studies meeting the inclusion criteria have been summarised in Table 1. These studies employed clinical samples and examined medical treatment characteristics for BC with a focus on SDM facilitated by a PtDA tool. The analysis revealed three primary themes: (1) study characteristics, including countries, sample sizes, and methodologies; (2) the clinical characteristics and outcomes of the SDM processes and the implementation of PtDA tools for BC medical treatment selection; and (3) the various versions of the IPDAS utilised. The forthcoming results section will provide a brief detailed discussion of each of these themes following Table 1.
3.1. Study Quality Results
To evaluate the methodological quality of the studies (see Table 2), the Checklist for analytical cross-sectional studies [19] was undertaken. The eight questions of the assessment were addressed, with the exception concerning confounding factors in a few studies [37,38,39,40]. Additionally, as this Checklist was applied to the qualitative or mixed methods studies [47,48,49], a few questions were considered either nonapplicable or partially applicable, respectively (e.g., question 8 about statistical analysis), as in similar healthcare studies [50].
3.2. Key Methodological Features of the Selected Studies
Among the 29 studies, almost half of them (n = 13) were conducted in Europe, with contributions from the Netherlands [21,32,39,44,47,48], Germany [31,36], the United Kingdom [43,46], Denmark [38,40], and Switzerland [35], followed by a third from North America (n = 10) [23,24,25,26,27,28,29,33,34,45]. Lastly, a fifth of the articles (n = 6) were conducted in Asia, including Taiwan [37,41,42], China [22,49], and Japan [30].
The sample sizes across these studies ranged from 10 [47] to 1339 participants [46]. In 58,6% of the studies (n = 17; [22,23,24,25,27,29,30,33,34,35,36,40,42,43,46,48,49]), the samples comprised only patients, while 10,3% (n = 3; [21,39,44]) included both patients and clinicians (usually physicians). Additionally, 20.7% of the studies (n = 6; [28,31,38,41,45,47]) featured patients along with a variety of other healthcare professionals (e.g., nurses), while only one study [32] focused on physicians and other providers.
In the domain of research designs, a hierarchy of strategies is typically observed, organised from those exhibiting the greatest to the least internal control. The following outlines the designs: (1) Randomised Controlled Trials (RCTs) [22,24,30,33,34,35,36,43,45,46], (2) Factorial Experimental Designs [23], (3) Quasi-Experimental Designs [41,44], (4) Mixed Methods Designs [27,31,37], (5) Qualitative Designs [21,30,39,47,48,49], and (6) Observational Designs [29,38].
In the context of research techniques, questionnaires emerged as the principal instrument for data collection. At the baseline phase of the SDM study, several validated SDM tests were employed, such as the Decisional Conflict Scale (DCS [22,26,30,35,41,42,44]), particularly its subscale on decision making [22,24,30], the Decision Making Styles Inventory (DMI [25]), Preparation for SDM (PrepDM [27]), the nine-item Shared Decision-Making Questionnaire (SDM-Q-9 [44])), the Decision Satisfaction Scale (DSS [27]), and the Control Preferences Scale (CPS [44]). Following decision making, the post-test phase usually comprised the Decision Regret Scale (DRS [22,40,42,46]), the Observing Patient Involvement Scale (OPTION [42]), the Subjective Decision Quality scale (SDQ [33]), the Control Preferences Scale (CPS [34]), the SDM Process Scale (SDMPS [34]), and the collaboRATE tool [44,46].
Additionally, oncological measures were incorporated, including BC-specific questionnaires, such as the Body Image Scale (BIS [42]) and a range of quality-of-life questionnaires developed by the European Organisation for the Research and Treatment of Cancer (EORTC), such as the Cancer Breast, with a 23-item cancer-specific supplement (QLQ-BR-23 [46]).
Finally, selected studies integrated psychological assessments to evaluate the impact of the PtDA tools on the mental health of BC patients. These assessments included validated scales for pre-surgical and post-surgical anxiety, such as the Spielberger Short State-Trait Anxiety Inventory (STAI [30,46]), and the Hospital Anxiety and Depression Scale (HADS [22,26,45]), alongside coping strategies measured by the Brief COPE inventory [46].
3.3. Clinical Characteristics and Outcomes of Decision Support Tools for Breast Cancer Treatment
The majority of the studies identified primarily focus on early BC [22,23,24,25,26,27,28,30,32,40] apart from locoregional BC [33,46,47], including fertility considerations [21,35]. The medical treatment options identified (by frequency order) were:
(1). Breast-conserving therapy (with radiotherapy) or mastectomy (with or without radiotherapy) [23,24,30,32,39];
(2). Mastectomy or breast-conservation therapy [33,34,37,45];
(3). Surgery and decide whether to use adjuvant therapy or not (e.g., hemotherapy, human epidermal growth factor receptor 2 targeted treatment, endocrine treatment, zoledronic acid treatment, and/or adjuvant radiotherapy) [38,40,46,47];
(4). Breast reconstruction surgery options (e.g., implant-based breast reconstruction or autologous) [41,42,48];
(5). Surgery options, breast-conserving therapy and radiation, and mastectomy (with or without reconstruction) [25,49];
(6). Cryopreservation of embryos, ovarian tissue, or none [21,35];
(7). Surgery plus breast reconstruction, breast-conserving therapy, mastectomy, or mastectomy with breast reconstruction [22,29];
(8). Mastectomy (with reconstruction or not) or breast-conserving therapy with radiation [27,28];
(9). Breast-conserving therapy with radiation, mastectomy, watchful waiting, and breast-conserving surgery without radiation [31,36];
(10). Mastectomy or not [26];
(11). Primary endocrine therapy or surgery with adjuvant therapies or to have adjuvant chemotherapy after surgery or not [43];
(12). Radiotherapy or not [44].
In terms of the types of PtDA examined, these include a variety of formats, such as leaflets and paper-based booklets [16], multimedia resources (e.g., videos with animations [21], websites [34], option grids [27,28], visual tools that present various treatment options alongside their associated risks and benefits [23,34], mobile applications [42], patient narrative tools [23,25], and value clarification exercises [21,27,30]).
The clinical studies on BC treatment options yielded significant outcomes based on the classification by Légaré et al. [4]. The main outcomes identified were:
(1.1) Shared decision making [28,30,31,32,33,34,35,36,38,39,40,41,42,43,44,45,46,47,48,49];
(1.2) Costs and cost analysis [36,40,45];
(1.3) Decision making [24,25,26,27,29];
(1.4) Observer-reported outcome [28,34,38,39,43,47,48];
(1.5) Health-related quality of life [37,41];
(1.6) Patient-reported outcome [21,22,37,42,45].
The secondary outcomes identified were:
(2.1) Patient outcomes
-
Affective-cognitive outcomes [21,22,24,25,26,27,29,35,37,41,42,44,46,47];
-
Knowledge [21,22,24,26,27,28,29,30,41,44,46,47,48];
-
Satisfaction [24,27,28,34,38,41,42,45,48];
-
Decisional conflict [22,24,26,29,30,35,41,44];
-
Decision regret [22,24,30];
-
Patient–clinician communication [36,45];
-
Self-efficacy [29,47];
-
Empowerment [25,26].
(2.2) Behavioural outcomes
-
Match between preferred option and decision made [27,36,41,42,47].
(2.3) Health outcomes
-
Health-related quality of life [47];
-
Anxiety [29,37];
-
Depression [22,24,37];
-
Stress [29,37].
(2.4.) Process outcomes
-
Consultation length [23,36,45].
Regarding the usages of the PtDA tools, research indicates that these could play a significant role in mitigating conflicts in decision making among patients, particularly in the context of choosing between lumpectomy and mastectomy [26,30,34]. These tools are usually associated with improvements in patient knowledge, enhanced participation in the decision-making process, and increased satisfaction with the decision-making experience (e.g., [30]). Nonetheless, the effectiveness of PtDA tools in improving information perception is not consistently observed. A study evaluating a web-based PtDA for breast reconstruction following mastectomy [34] found that it did not enhance the patients’ perception of the conveyed information when compared to high-quality websites. However, this study did report an increase in satisfaction.
Additionally, patient narratives, whether presented in a text or video format, could substantially influence information-seeking behaviours in PtDA tools. A study discovered that patients who viewed video narratives engaged more with information concerning mastectomy, while those who read text narratives were more inclined to focus on lumpectomy information [23]. Furthermore, the inclusion of patient narratives in PtDA tools may reduce reliance on anecdotal reasoning and facilitate a clearer understanding of the risk–benefit ratios associated with various treatment options, thereby enhancing SDM [24,25]. Illustrative materials and comics may also serve as effective tools within PtDA, particularly for patients with low health literacy [28,37,45]. Indeed, providing training for physicians in SDM practices may alleviate anxiety and depression in cancer patients [22].
Therefore, the implementation of PtDA tools within clinical practice presents a variety of challenges in the BC treatment [43]. Risk factors include practical barriers, insufficient professional commitment, resistance to altering established practices, and constraints related to time. Conversely, protective factors may include support from management, effective integration with electronic health records, and thorough training for staff [28,36]. The creation of PtDA tools in collaboration between patients and clinicians has the potential to enhance their relevance and acceptance among users [47].
The PtDA tools ought to be developed and employed in an ethical manner. It seems essential to address disparities in health literacy and numeracy to ensure that PtDA tools are accessible and comprehensible for all patients [25,26,28]. Numerous articles [21,37] discuss critical considerations for the design and content of PtDA tools: the simplification of medical jargon, the enhancement of navigational features, the provision of clear explanations, and the incorporation of visual aids, such as comics, can significantly increase the accessibility and understandability of PtDA tools.
3.4. IPDAS Applied in the Decision Support Tools for Breast Cancer Treatment
Regarding the use of the different versions of the IPDAS criteria, almost half of the papers (n = 14) informed about using a specific version for the PtDA tool. Interestingly, the SUNDAE Checklist was not indicated by any article, and the last IPDAS 2.0. only was cited in an article. Additionally, a few papers cited the IPDAS versions without explicitly mentioning if a version was used [22,30].
-
2005 IPDAS (80 criteria/items in 12 broad criteria) [29,30];
-
2006 IPDAS Checklist (74/64 items) [21,27,30,32,44];
-
2009 IPDAi Assessment (47 items) [28,31,36,38,48];
-
2013 IPDAS Checklist (44 items) [40,49];
-
2021 IPDAS 2.0. Checklist (11 core domains) [47].
4. Discussion
This systematic review study was designed with four specific aims: (1) to examine research related to SDM in BC using PtDA tools; (2) to evaluate the influence of PtDA tools on decision makers in BC treatment selection; (3) to assess the healthcare outcomes achieved through their use; and (4) to identify how the IPDAS versions are utilised.
In examining the studies published in this specific clinical practice between 2013 and 2024, it appears that SDM is more prevalent in Western cultures, leading in the European continent (central and north Europe), followed by the USA and Asia. Similar systematic reviews [9,10] have identified comparable regions, noting that the USA [9] and the Netherlands [10] are the countries with higher prevalence rates of SDM in early and metastatic BC, respectively. Another systematic review found the primary global regions publishing clinical practice guidelines (CPGs) and consensus statements (CSs) about SDM in BC are precisely Europe and North America [51]. The studies included in the present review were assessed to be of high-quality level according to the JBI Checklist for the Cross-sectional Studies, similar to the findings of other comparable reviews [9,10].
In terms of research methods, the RCTs and other manipulative designs are the more common strategies. Nevertheless, nonmanipulative methods, such as mixed methods, qualitative, and observational studies are also present. Previous similar reviews identified a balance of methods between RCT and other designs [9,10], which highlights that the field is evolving towards more controlled and comprehensive SDM in BC studies. This development also probably explains why standardised and validated tests are typically applied to collect evidence on the changes produced through the PtDA use in relation to SDM styles, perceptions regarding BC, and the psychological issues associated with the diagnosis. Indeed, contrary to the findings of de Mik et al., 2018 [5], a significant number of objective measures were applied in the quantitative studies analysed. The SDM tests commonly utilised include a set applied in pretest measures (e.g., DCS and DMI) and another set employed in post-test measures (e.g., DRS and Option). Recent studies have begun to incorporate tests focused on concerns related to BC (e.g., EORTC scales), as well as anxiety tests before and after applying the SDM process (e.g., HADS and STAI).
Regarding the role of the decision makers, the present findings indicate that only a minority of studies include both patients and clinicians as participants, while a larger number of publications involve only patients. Other systematic reviews focusing on SDM in BC have shown that PtDA tools do not effectively promote their actual use in clinical practice (e.g., tools should be integrated in the clinical workflow [27]). Concerning the options provided, most SDM interventions have been designed to reflect the specific medical treatment options (e.g., whether undergoing mastectomy or pursuing adjuvant therapies), compared to patients’ attributes (e.g., like age and literacy levels). This highlights the imbalance between the clinical factors and personal attributes considered in the PtDA tools and decision-supported interventions studied [52]. The limited inclusion of physicians (e.g., surgeons and oncologists) may indirectly contribute to biased perception of the need for SDM [10]. There is also a need for SDM training that is tailored to various clinical roles, both within and outside consultations (e.g., coaching and on-the-job instructions for healthcare providers [36,39]). Furthermore, existing PtDA tools need clear instructions on how and when to use them, with strategies to enhance communication between clinicians and patients to determine the best treatment option based on the clinical and personal factors.
The present review identified fewer PtDA tools for BC compared to the review undertaken by Leinweber et al. in 2019 [7]. However, those identified (e.g., BRAID, iCanDecide, and Pink) were developed in various formats to assist patients in selecting BC treatments, including invasive surgical procedures and less invasive options. As noted by Zdenkowski et al. [9], the decisions evaluated included the choice between breast-conserving therapy and mastectomy, whether to undergo surgery and apply adjuvant therapy, radiotherapy options, and considerations for fertility preservation. In the present findings, before discussing radiation options, breast reconstruction emerged as a crucial shared decision to make. Other treatment options included watchful waiting (especially in frail patients) or breast-conserving surgery, among others. Additionally, a recent review emphasised surgical decision making in BC treatment, covering mastectomy, breast-conserving therapy, unilateral mastectomy, contralateral prophylactic mastectomy, and breast reconstruction decision [53]. This indicates that our review, along with that recent one, encompasses a broader range of BC treatment options than similar previous reviews [9,10].
The main outcome measures [4] highlighted in this systematic review indicate that SDM and observed patient-reported outcomes are particularly prevalent in the present findings. This suggests a promising link between the artificial intelligence and the patient-reported outcomes regarding the BC treatment options. It is worth noting that some relevant results were excluded from this review because they were based on protocols from ongoing RCT (e.g., Cinderella project founded by the European Union [53]). In terms of secondary outcomes, improvements were observed in both patient and health-related areas, cognitive-emotional aspects (i.e., anxiety and depression) and decisional conflict decreased, while knowledge and satisfaction increased [9]. From a behavioural perspective, the alignment between the patient’s personal preference and treatment options emerged as the most significant factor; however, the time spent in consultation has been identified as a key obstacle. SDM in BC seems to face challenges at various levels. At the system level, while decisions rights and autonomy are generally upheld in modern societies, the implementation of personalised healthcare remains in its infancy [54]. At the individual level, patient-related factors (e.g., psychology and education), clinician-related factors (e.g., attitudes and consultation styles), and the interaction between the two (e.g., doctor–patient trust and communication) influence SDM. Additionally, decision-making behaviours (e.g., encompassing information, patient preferences, and use of the PtDA) could facilitate SDM or lead to passive decision making, the latter being more prevalent at BC healthcare [54,55].
Concerning the existing versions of the IPDAS, the most commonly used criteria include the IPDA Checklist with 74 items, combined through 64 items [13], and the IPDAi Assessment [14], each representing 17% of the total. However, half of the studies reviewed assessed the quality of the PtDA tools. A systematic review focused on evaluating the IPDAS for early-stage BC treatment from 2006 to 2018 [56] found a wide variation in the quality levels of the PtDA tools, which showed limited adherence to the IPDAS Checklists. Notably, the communicative aspects were identified as the weakest. It could be inferred that utilising available multilanguage resources and providing online training on IPDAS Checklists, among other options (e.g., CPG and CS), could improve adherence to best practices when applying IPDAS criteria to PtDA tools for BC decision making.
5. Conclusions
The open debate on SDM in the clinical field [57] still requires further investment in the systemic factors. A number of well-designed studies utilising a diverse range of methods have been reviewed, revealing a set of heterogenous PtDA tools for SDM in BC treatment options. These medical interventions expand the available options from the past, including nearly all available treatments across surgical and nonsurgical options with an evolution of the technologies used. Positive general and specific outcomes show beneficial results with this healthcare approach. However, only half of the studies included tools assessed with an IPDAS criteria, and explicit information about the number of criteria or scores achieved is often lacking. It is noteworthy that, despite the promising results of these tools, there are challenges in integrating them into routine clinical practice. The implementation of educational technologies and personally reported outcomes, combined with the clinical outcomes, appears essential to effectively engage decision makers during and outside the clinical encounters and within the workflow of the oncologic treatment process, which could involve multiple stages.
Conceptualisation, H.G. and O.L.-F.; methodology, O.L.-F.; validation, O.L.-F., M.L.S.d.M.R. and H.G.; formal analysis, C.P.A.C., B.H., M.L.S.d.M.R. and O.L.-F.; investigation, O.L.-F.; resources, O.L.-F. and M.L.S.d.M.R.; data curation, O.L.-F.; writing—original draft preparation, C.P.A.C. and O.L.-F.; writing—review and editing, C.P.A.C., B.H., M.L.S.d.M.R., H.G. and O.L.-F.; visualisation, C.P.A.C., B.H. and O.L.-F.; supervision, O.L.-F.; project administration, O.L.-F. and M.L.S.d.M.R.; funding acquisition, H.G. and O.L.-F. All authors have read and agreed to the published version of the manuscript.
Jonathan McFarland initially supported the project and Damián Garcia-Olmo supported the development of the research project. We utilised the GenAI feature in Grammarly for rewriting. The authors have reviewed and edited the output and take full responsibility for the content of this publication.
The authors declare no conflicts of interest.
The following abbreviations are used in this manuscript:
| SDM | Shared decision making |
| PtDA | Patient decision aid |
| BC | Breast cancer |
| IPDAS | International Patient Decision Aid Standards |
| PRISMA | Preferred Reporting Items for Systematic reviews and Meta-Analyses |
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
![View Image - Figure 1. Flow diagram following PRISMA guidelines [18].](https://media.proquest.com/media/hms/THUM/2/l8yEc?_a=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%3D%3D&_s=lYmV8v%2Fuc0HcJZXR2hp7l%2Bgbob0%3D "View Image - Figure 1. Flow diagram following PRISMA guidelines [18].")
Clinical studies reviewed.
| Author, Year [Reference] (Country) | Aim and Treatment Options | Sample | Design | Measures | PtDA Characteristics | Results and | IPDAS |
|---|---|---|---|---|---|---|---|
| Garverlink et al., 2013 [ | To improve information about fertility preservation for BC | N = 185 Participants | Development in 4 stages: | S2: structured interviews. | Web-based PtDA with | PtDA regarded as a relevant source of information | 2006 IPDAS Checklist (64 items). |
| Lam et al., 2013 [ | To explore the effectiveness of a PtDA beyond consultations | N = 276 | Randomised controlled trial. | Interview-based questionnaires at 4T after consultation: | After a pilot study, the PtDA results in a booklet with content based on current clinical guidelines for surgical management of early-stage BC. | Primary outcomes: | Based on IPDAS with no score. |
| Shaffer et al., 2013 [ | To examine the impact of video and text-based narratives on information search in a Web-based patient PtDA for early-stage BC. | N = 56 Women | Multilevel modelling. | Two text versions of the Web PtDA by replacing the patient and physician interviews with text transcripts of the videos | Participants had access to video controls that allowed them to play, pause, and move to different positions in the video timeline. | Participants viewing PtDA with patient narratives spent more time searching for information than those with PtDA without it. | |
| Shaffer et al., 2013 [ | To examine the effect of patient narratives that discuss decision processes versus patient experiences on decisions about treatments for early-stage BC | N = 302 Women | 2 (content: process versus experience) × 2 (evaluative valence: positive only versus mixed) factorial design. | Information. Search task | MouselabWEB uses an | Participants viewing process narratives spent more time searching for info. Participants viewing experience narratives reported a greater ability to imagine treatment’s experience; they also evaluated their decision more positively on several dimensions. | |
| Shaffer et al., 2014 [ | To evaluate the effect of narratives used in a popular, public PtDA on hypothetical treatment decisions and attitudes toward the PtDA and explore the moderating effects of participant numeracy, electronic health literacy, and decision-making style. | N = 200 Women | Randomised controlled trial. | eHEALS electronic health literacy. | Narrative video PtDA made with large video about surgical options for early-stage BC and short section of another video about breast reconstruction. | Narratives affected | |
| Manne et al., 2015 [ | To test the acceptability and preliminary efficacy of a novel interactive web-based breast reconstruction decision support aid (BRAID) for newly diagnosed BC patients | N = 55 Women | Participants completed measures of breast reconstruction knowledge, preparation to decide, DCS, anxiety, and BR intentions. | Before randomisation and 2 weeks later. | BRAID is a menu-driven program organised into 10 modules. | BRAID participants returned less surveys. Both interventions increased breast reconstruction knowledge. Both had a significant reduction in DCS. There were no differences. | |
| Hawley et al., 2016 [ | To develop and evaluate a web-based PtDA to determine if this tool could improve the quality of decisions focused on locoregional BC treatment. | N = 101 Newly diagnosed BC patients. | Pilot study | Knowledge about: Surgical treatment and breast reconstruction. Patient’s appraisal: Decision Satisfaction Scale and perceived values concordance | Web-based PtDA. | PtDA improves the quality of decisions raising patients’knowledge about treatments, improving appraisal of the process of decision making. | 2006 IPDAS Checklist (64 items) |
| Durand et al., 2016 [ | To develop and test the usability, acceptability, | N = 71 | Qualitative study with a community-based participatory research approach with 3 phases: | P1: feedback about prototypes | The pictorial encounter PtDA was derived from an evidence-based table comparing treatment options for BC | P1: Researchers and clinicians preferred the black and white prototype. | 2009 IPDAi Assessment (47 items) |
| Serpico et al., 2016 [ | To prove if providing accurate info. about BC with a BC Video before initial consultation will decrease distress and increase self-reported knowledge | N = 156 Patients | Prospective observational study composed of two groups. | G1: self-reported of BC knowledge. Perceived distress related to diagnosis. | The BC Video consists of a standardised overview of BC and general BC surgical treatments | G1 demonstrated an | 2005 IPDAS (80 criteria/items in 12 broad criteria) |
| Osaka et al., 2017 [ | To develop a PtDA with patient narratives and determine | N = 210 | Single-centre three-arm parallel randomised controlled trial. | DCS and anxiety (STAI) at: T1 (baseline), T2 (post intervention), T3 (1 month after). Satisfaction with DM (effective DM (subscale of the DCS); at: T2, T3. M Demographic and clinical variables. | PtDA comprises | PtDA with and without patient narratives are equivalently effective at reducing postoperative DCS in Japanese women with early-stage BC. | 2005 IPDAS (80 criteria/items in 12 broad criteria) and |
| Berger-Höger et al., 2017 [ | To develop and pilot a new approach: an inter-professional Informed SDM programme for specialised nurses and physicians to enable them to provide Informed SDM in BC centres. | N = 34 | Mixed methods pilot study: focus groups, individual interviews, and observations. | The acceptance of the intervention by patients and professionals, the applicability to the BC centres’ procedures, | Patients attained adequate knowledge (answers: 9–11 of 11). | SDM coaching is feasible. | 2009 IPDAi Assessment (47 items) |
| Savelberg et al., 2017 [ | To develop, alpha test, and improve a patient PtDA for early-stage BC. | N = 26 Professionals | Qualitative descriptive study. | Face-to-face think-aloud interviews, a focus group, and semi-structured telephone interviews. Alpha testing: comprehensibility (patients). Usability (patients and professionals). Acceptability (professionals) | Website with interactive elements to tailor information. | PtDA developed in four iterative test rounds. | 2006 IPDAS Checklist (64 items) |
| Hawley et al., 2018 [ | To determine the effect of a PtDA (iCanDecide,) regarding locoregional BC treatment and on patient appraisal of SDM. | N = 537 Women | Randomised controlled trial of newly diagnosed patients with early-stage BC. | T1: Baseline survey | Website which included: | IG: ↑ odds of making a high-quality decision and higher decision preparation | Based on IPDAS with no score. |
| Stankowski-Drengler et al., 2019 [ | To examine the impact of a web-based PtDA vs. high-quality websites on patients’ perceptions of information conveyed during the BC surgical consultation, and satisfaction with the DM process.Options:Breast-conserving therapy, mastectomy. | Pre-Survey | Randomised controlled trial. | Demographic info. | The PtDA consisted of didactic information about cancers, as well as reconstruction options. Also included were video clinical vignettes to encourage incorporation of personal values and preferences in DM. | There was no association between randomisation arm and perceptions of information conveyed, being asked surgical preference, or satisfaction with the decision process. | |
| Ehrbar et al., 2019 [ | Main aim: | N = 79 Patients. | Randomised controlled trial (with a block randomisation) include Ehrbar 2019 BC female patients who were referred by their treating oncologist to fertility preservation counselling | (T1: after counselling, T2: 1 month, T3: 12 months) | Specific information on cancer treatment, impact on fertility, fertility preservation procedures. | All participants showed low DCS scores. | URL: clinicaltrials.gov (no. NCT02404883) |
| Berger-Höger et al., 2019 [ | To investigate if an informed SDM intervention for women with BC ductal carcinoma in situ comprising an evidence-based PtDA with nurse-led decision coaching enhances the extent of the SDM behaviour of patients and professionals | N = 192 Women | Cluster randomised controlled trial with accompanying process evaluation. | The acceptance of the intervention by women and professionals, the applicability to the breast care centres’ procedures, women’s knowledge, patient involvement in treatment decision making assessed with the MAPPIN’ SDM-observer | Treatment Decision Making assessed with the MAPPIN’ SDM observer | Patients attained adequate knowledge (range of correct answers: 9–11 of 11). A basic level of patient involvement in TDM was observed for nurses and patient–nurse dyads (Mindicator(MAPPIN-Odyad): 2.15 and M(MAPPIN-Onurse): 1.90). Relevant barriers were identified, physicians barely tolerated women’s preferences that were not in line with the medical recommendation. Classifying women as | 2009 IPDAi Assessment (47 items) |
| Lee et al., 2019 [ | To describe the developmental process of creating an animated comic as a web-based surgery patient PtDA for patients with BC | Action phase | Mixed methods | Planning phase: | Web-based animated comic with audio explanations. | Comic acts as an information resource and is aimed at | |
| Olling et al., 2019 | To explore if a PtDA improved SDM and supported a patient-centred approach in BC and lung cancerOptions: | N = 54 | Nonexperimental, observational study. | Real-life observations using OPTION 12. A nurse made and rated the observation. Another nurse listened and rated the recording. All the nurses took turns at the different tasks. | Using a PtDA increased the OPTION score. | PtDA improved SDM behaviour and promoted a patient-centred approach. | 2009 IPDAi Assessment (47 items) |
| Savelberg et al., 2019 | To explore the experiences, issues, and concerns of early-adopter professionals with regards to SDM in BC | N = 27 Clinicians’ recruitment: | Qualitative descriptive study. Face-to-face interview. | Topics: SDM attitude and behaviour. Knowledge. patient PtDA use | Patients access with login code. | Most clinicians focused only on the first steps of SDM. | |
| Søndergaard et al., 2020 [ | To evaluate the impact on BC consultation | N = 261 BC patients | Prospective cohort study. | Time registration. OPTION 12. DRS (6 months after). | PtDA design supports a 4-step approach to SDM: choice talk, preference talk, option talk, and decision talk. | The introduction of SDM and an in-consult patient PtDA did not increase the consultation length. SDM led to more conservative | 2013 IPDAS Checklist (44 items). |
| Lin et al., 2019 | To develop an app as a PtDA and examine the feasibility and usability of it | P1: Development | 2 phases. | Sociodemographic info. | Pink Journey contains | Less decisional conflict in I on each subscale of the DCS Most women felt the app was both helpful and user-friendly. | |
| Fang et al., 2021 [ | To examine the effects of a decision support app on SDM quality and psychological morbidity for women considering breast reconstruction surgery due to BC. | N = 96 | Randomised controlled trial with permuted block randomisation. | T0: baseline data collection (demographic and clinical). DCS, subscale involvement in breast reconstruction SDM, process scale, DRS, BIS, HADS at 4 T after surgery: T1: 1 week after. T2: 1 month after. T3: 8 months after. T4: 12 months after. | Participants watch a video compatible with the pamphlet information. | Body image distress declined in IG and increased in CG. | |
| Burton et al., 2021 [ | To improve treatment through SDM for older women with BC (which is a high-risk population group) by developing and testing two decision support interventions, each one supporting one option/decision. | N = 82 Women (>70 years) | Multi-centre, parallel group, pragmatic, cluster randomised controlled trial, DCS nested within a larger cohort study of older women with early BC. | Primary outcome: improvement in QoL. | The decision support interventions were developed to ensure that the information | Reach: The online tool was | |
| Raphael et al., 2021 [ | Main aim: | N = 403 BC patients | Multi-centre pre- and post- intervention study. | Tests, DCS, SDM—Q9, CollaboRATE: | Online patient PtDA starts with an introduction on SDM. Explanations about how radiation treatment is performed (in text and in animation film). | Corrections in age and educational level. | 2006 IPDAS Checklist (64 items |
| Schubbe et al., 2021 [ | To explore strategies that promote the BC conversation aids sustained use and dissemination. | N = 43 | Multi-site randomised controlled trial. | Normalisation Process Theory: coherence. Cognitive participation. Collective action. Reflexive monitoring. | Option Grid: evidence-based information on | Patients and surgeons felt the conversation aids should be used | |
| Wyld et al., 2021 [ | Main aim: | 46 BC units participate. | Multicentre, parallel group. | Baseline, 6 weeks, 6 months. | Decision support intervention; online decision algorithm, booklets, brief PtDA to inform choices between treatment options. Decision support intervention adjusted for co-morbidities and frailty. The tool produces personalised survival outcomes according to fitness, frailty, stage, treatment choice, disease biology. Tool developed and used the preferred informational content, format, terminology, and media for this population and were piloted extensively in this age group. | No significant difference in global QoL at 6 months. | URL: Age Gap Decision ToolVC; |
| van Strien-Knippenberg et al., 2021 [ | To design | P1(3 sessions): N = 10, N = 8 and N = 7 patients | Qualitive approach in 2 phases: (1) cocreation, (2) user testing. | Demographics and test for educational level. P1: Important decision timeline, relevant info. DM, health literacy, self-reported questionnaire about nausea and fatigue. P2: questions about the PtDA, questions about the visualisation. | PtDA with personalised estimates information: summary table about benefit/harm treatment options. Survival rates. Side effects. | Important needs: | 2021 IPDAS 2.0. Checklist (11 core domains) |
| Ter Stege et al., 2021 [ | To develop a patient PtDA that could support patients with BC in making an informed decision | N = 86 | Development in 4 stages: 1. Multidisciplinary team, 2. Needs assessment, 3. Creation, 4. Acceptability and usability. | Semi-structured interviews (patients) and survey (healthcare professionals). S4: think-aloud (patients) and interviews (healthcare professionals and representatives). | 1. A consultation sheet, | The PtDA was perceived to be informative, helpful, and easy to use by patients and healthcare professionals. | 2009 IPDAi Assessment (47 items) |
| Pan et al., 2024 [ | Explore the perceptions and needs of BC patients regarding the utilisation of web-based surgical decision aids. | N = 16 BC patients. | Descriptive qualitative study. The study used a thematic analysis to explore the perceptions and needs of BC patients regarding the use of surgical PtDA. | Semi-structured interviews with purposive sampling that were audio-recorded and transcribed verbatim. A thematic analysis was conducted using NVivo 12 software. | Criteria for Reporting Qualitative Research (COREQ) checklist. | Themes with corresponding sub-themes: (1) informative and useful content (need to know as much information as possible, easy to understand, and presented in multiple ways and highly credible from a reliable resource); (2) user-friendly on design (easy to operate, simple function, and man–machine interaction); (3) suggested timing of use. | 2013 IPDAS Checklist (44 items) |
Note: ADL: activities of daily living; BC: breast cancer; BIPQ: Brief Illness Perceptions Questionnaire; BIS; The Body Image Scale; Brief COPE: the short version of the Coping Orientation to Problems Experienced Inventory; CG: Control Group; CollaboRATE: a short scale for Shared Decision Making to patients, parents, or their representatives; CPS: Control Preferences Scale; DCS: Decisional Conflict Scale; DM: Decision Making (subscale of the DCS); DRS: Decision Regret Scale; eHEALS: e-Health Literacy Scale; EORTC: European Organisation for the Research and Treatment of Cancer; EORTC QLQ-BR-23: European Organisation for Research and Treatment of Cancer Breast with 23 items of cancer-specific supplement information; EORTC QLQ BR-45: European Organisation for Research and Treatment of Cancer of Cancer Breast—Breast-Cancer-Specific Questionnaire; EORTC QLQ-C-30: European Organisation for Research and Treatment of Cancer Breast—Cancer-Specific Quality of Life Questionnaire—Core Questionnaire; EORTC QLQ-ELD-14: European Organisation for the Research and Treatment of Cancer Quality of Life Elderly Cancer Patients; IG: Intervention Group; G: Group; HADS: The Hospital Anxiety and Depression Scale; IPDAS: International Patient Decision Aid Standards; MAPPIN’SDM: extent of informed shared decision making assessed by the observer-based instrument MAPPIN-Odyad of the validated inventory Multifocal Approach to the sharing‘ IN Shared Decision-Making; N: sample size; OPTION: the observing patient involvement scale; P: phase; PtDA: Patient Decision Aid; QoL: quality of life; SDM: shared decision making; S: sample; SDQ: Subjective Decision Quality scale; SDMPS: Satisfaction with the Decision-Making Process Scale; STAI: Spielberger Short State-Trait Anxiety Inventory; R: round; T: time point.
Risk of bias according to JBI, the critical assessment tool to cross-sectional studies.
| Study | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 |
|---|---|---|---|---|---|---|---|---|
| Garverlink et al., 2013 [ | | | | | | | | |
| Lam et al., 2013 [ | | | | | | | | |
| Shaffer et al., 2013 [ | | | | | | | | |
| Shaffer et al., 2013 [ | | | | | | | | |
| Shaffer et al., 2014 [ | | | | | | | | |
| Manne et al., 2015 [ | | | | | | | | |
| Hawley et al., 2016 [ | | | | | | | | |
| Durand et al., 2016 [ | | | | | | | | ∕ |
| Serpico et al., 2016 [ | | | | | | | | |
| Osaka et al., 2017 [ | | | | | | | | |
| Berger-Höger et al., 2017 [ | | | | | | | | |
| Savelberg et al., 2017 [ | | | | | | | | ∕ |
| Hawley et al., 2018 [ | | | | | | | | |
| Stankowski-Drengler et al., 2019 [ | | | | | | | | |
| Ehrbar et al., 2019 [ | | | | | | | | |
| Berger-Höger et al. [ | | | | | | | | |
| Lee et al., 2019 [ | | | | | | | | ∕ |
| Olling et al., 2019 [ | | | | | | | | |
| Savelberg et al., 2019 [ | | | | | | | | ∕ |
| Søndergaard et al., 2020 [ | | | | | | | | |
| Lin et al., 2019 [ | | | | | | | | |
| Fang et al., 2021 [ | | | | | | | | |
| Burton et al., 2021 [ | | | | | | | | |
| Raphael et al., 2021 [ | | | | | | | | |
| Schubbe et al., 2021 [ | | | | | | | | |
| Wyld et al., 2021 [ | | | | | | | | |
| van Strien-Knippenberg et al., 2021 [ | | | ∕ | ∕ | | ∕ | | ∕ |
| Ter Stege et al., 2021 [ | | | ∕ | ∕ | | ∕ | | ∕ |
| Pan et al., 2024 [ | | | ∕ | ∕ | | | | ∕ |
Notes:
References
1. Duke, D.L.; Showers, B.K.; Imber, M. Teachers and shared decision making: The costs and benefits of involvement. Educ. Adm. Q; 1980; 16, pp. 93-106. [DOI: https://dx.doi.org/10.1177/0013161X8001600108]
2. Elwyn, G. Shared decision making: What is the work?. Patient Educ. Couns.; 2021; 104, pp. 1591-1595. [DOI: https://dx.doi.org/10.1016/j.pec.2020.11.032]
3. Elwyn, G.; Laitner, S.; Coulter, A.; Walker, E.; Watson, P.; Thomson, R. Implementing shared decision making in the NHS. BMJ; 2010; 341, c5146. [DOI: https://dx.doi.org/10.1136/bmj.c5146] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20947577]
4. Légaré, F.; Adekpedjou, R.; Stacey, D.; Turcotte, S.; Kryworuchko, J.; Graham, I.D.; Lyddiatt, A.; Politi, M.C.; Thomson, R.; Elwyn, G. et al. Interventions for increasing the use of shared decision making by healthcare professionals. Cochrane Database Syst. Rev.; 2018; 7, CD006732. [DOI: https://dx.doi.org/10.1002/14651858.CD006732.pub4] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30025154]
5. de Mik, S.M.L.; Stubenrouch, F.E.; Balm, R.; Ubbink, D.T. Systematic review of shared decision-making in surgery. Br. J. Surg.; 2018; 105, pp. 1721-1730. [DOI: https://dx.doi.org/10.1002/bjs.11009]
6. Glatzer, M.; Panje, C.M.; Sirén, C.; Cihoric, N.; Putora, P.M. Decision Making Criteria in Oncology. Oncol; 2020; 98, pp. 370-378. [DOI: https://dx.doi.org/10.1159/000492272]
7. Leinweber, K.A.; Columbo, J.A.; Kang, R.; Trooboff, S.W.; Goodney, P.P. A Review of Decision Aids for Patients Considering More Than One Type of Invasive Treatment. J. Surg. Res.; 2019; 235, pp. 350-366. [DOI: https://dx.doi.org/10.1016/j.jss.2018.09.017]
8. Ferlay, J.; Ervik, M.; Lam, F.; Laversanne, M.; Colombet, M.; Mery, L.; Piñeros, M.; Znaor, A.; Soerjomataram, I.; Bray, F. Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer. Available online: https://gco.iarc.who.int/today (accessed on 9 March 2025).
9. Zdenkowski, N.; Butow, P.; Tesson, S.; Boyle, F. A systematic review of decision aids for patients making a decision about treatment for early breast cancer. Breast; 2016; 26, pp. 31-45. [DOI: https://dx.doi.org/10.1016/j.breast.2015.12.007]
10. Spronk, I.; Burgers, J.S.; Schellevis, F.G.; Liesbeth, M.V.; Korevaar, J.C. The availability and effectiveness of tools supporting shared decision making in metastatic breast cancer care: A review. Healthcare; 2018; 17, 74. [DOI: https://dx.doi.org/10.1186/s12904-018-0330-4]
11. The International Patient Decision Aid Standards (IPDAS) Collaboration. Available online: https://decisionaid.ohri.ca/IPDAS/news.html (accessed on 24 January 2025).
12. O’Connor, A.; Elwyn, G.; Stacey, D.; Volk, R.; Thomson, R.; Barratt, A.; Barry, M.; Coulter, A.; Holmes-Rovner, M.; Llewellyn-Thomas, H. et al. International Patient Decision Aid Standards [IPDAS] Collaboration reaches consensus on indicators for judging the quality of patient decision aids. Proceedings of the 27th Annual Meeting of the Society for Medical Decision Making; San Francisco, CA, USA, 21–24 October 2005.
13. Elwyn, G.; O’Connor, A.; Stacey, D.; Volk, R.; Edwards, A.; Coulter, A.; Thomson, R.; Barratt, A.; Barry, M.; Bernstein, S. et al. Developing a quality criteria framework for patient decision aids: Online international Delphi consensus process. BMJ; 2006; 333, 417. [DOI: https://dx.doi.org/10.1136/bmj.38926.629329.AE]
14. Elwyn, G.; O’Connor, A.M.; Bennett, C.; Newcombe, R.G.; Politi, M.; Durand, M.A.; Drake, E.; Joseph-Williams, N.; Khangura, S.; Saarimaki, A. et al. Assessing the quality of decision support technologies using the International Patient Decision Ais Standards instrument (IPDASi). PLoS ONE; 2009; 4, e4705. [DOI: https://dx.doi.org/10.1371/journal.pone.0004705]
15. Joseph-Williams, N.; Newcombe, R.; Politi, M.; Durand, M.A.; Sivell, S.; Stacey, D.; O’Connor, A.; Volk, R.J.; Edwards, A.; Bennett, C. et al. Toward Minimum Standards for Certifying Patient Decision Aids: A Modified Delphi Consensus Process. Med. Decis. Mak.; 2013; 34, pp. 699-710. [DOI: https://dx.doi.org/10.1177/0272989X13501721]
16. Hoffman, A.S.; Sepucha, K.R.; Abhyankar, P.; Sheridan, S.; Bekker, H.; LeBlanc, A.; Levin, C.; Ropka, M.; Shaffer, V.; Stacey, D. et al. Explanation and elaboration of the Standards for UNiversal reporting of patient Decision Aid Evaluations (SUNDAE) guidelines: Examples of reporting SUNDAE items from patient decision aid evaluation literature. BMJ Qual. Saf.; 2018; 27, pp. 389-412. [DOI: https://dx.doi.org/10.1136/bmjqs-2017-006985] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29467235]
17. Stacey, D.; Volk, R.J. The International Patient Decision Aid Standards (IPDAS) Collaboration: Evidence Update 2.0. Med. Decis. Mak.; 2021; 41, pp. 729-733. [DOI: https://dx.doi.org/10.1177/0272989X211035681]
18. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; FBrennan, S.E. et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. J. Clin. Epidemiol.; 2021; 134, pp. 178-189. [DOI: https://dx.doi.org/10.1016/j.jclinepi.2021.03.001] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33789819]
19. Moola, S.; Munn, Z.; Sears, K.; Sfetcu, R.; Currie, M.; Lisy, K.; Tufanaru, C.; Qureshi, R.; Mattis, P.; Mu, P. Conducting systematic reviews of association (etiology): The Joanna Briggs Institute’s approach. Int. J. Evid. Based Healthc.; 2015; 13, pp. 163-169. [DOI: https://dx.doi.org/10.1097/XEB.0000000000000064]
20. Moola, S.; Munn, Z.; Tufanaru, C.; Aromataris, E.; Sears, K.; Sfetcu, R.; Currie, M.; Qureshi, R.; Mattis, P.; Lisy, K. et al. JBI Manual for Evidence; Aromataris, E.; Munn, Z. JBI: Adelaide, Australia, 2020.
21. Garvelink, M.M.; ter Kuile, M.M.; Fischer, M.J.; Louwé, L.A.; Hilders, C.G.; Kroep, J.R.; Stiggelbout, A.M. Development of a Decision Aid about fertility preservation for women with breast cancer in The Netherlands. J. Psychosom Obstet Gynaecol.; 2013; 34, pp. 170-178. [DOI: https://dx.doi.org/10.3109/0167482X.2013.851663] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24188788]
22. Lam, W.W.; Chan, M.; Or, A.; Kwong, A.; Suen, D.; Fielding, R. Reducing treatment decision conflict difficulties in breast cancer surgery: A randomized controlled trial. J. Clin. Oncol.; 2013; 31, pp. 2879-2885. [DOI: https://dx.doi.org/10.1200/JCO.2012.45.1856]
23. Shaffer, V.A.; Owens, J.; Zikmund-Fisher, B.J. The effect of patient narratives on information search in a web-based breast cancer decision aid: An eye-tracking study. J. Med. Internet Res.; 2013; 15, e273. [DOI: https://dx.doi.org/10.2196/jmir.2784]
24. Shaffer, V.A.; Hulsey, L.; Zikmund-Fisher, B.J. The effects of process-focused versus experience-focused narratives in a breast cancer treatment decision task. Patient Educ. Couns.; 2013; 93, pp. 255-264. [DOI: https://dx.doi.org/10.1016/j.pec.2013.07.013]
25. Shaffer, V.A.; Tomek, S.; Hulsey, L. The effect of narrative information in a publicly available patient decision aid for early-stage breast cancer. Health Commun; 2014; 29, pp. 64-73. [DOI: https://dx.doi.org/10.1080/10410236.2012.717341] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23384155]
26. Manne, S.L.; Topham, N.; D’Agostino, T.A.; Virtue, S.M.; Kirstein, L.; Brill, K.; Manning, C.; Grana, G.; Schwartz, M.D.; Ohman-Strickland, P. Acceptability and pilot efficacy trial of a web-based breast reconstruction decision support aid for women considering mastectomy. Psychooncology; 2016; 25, pp. 1424-1433. [DOI: https://dx.doi.org/10.1002/pon.3984]
27. Hawley, S.T.; Newman, L.; Griggs, J.J.; Kosir, M.A.; Katz, S.J. Evaluating a Decision Aid for Improving Decision Making in Patients with Early-stage Breast Cancer. Patient; 2016; 9, pp. 161-169. [DOI: https://dx.doi.org/10.1007/s40271-015-0135-y]
28. Durand, M.A.; Alam, S.; Grande, S.W.; Elwyn, G. ’Much clearer with pictures’: Using community-based participatory research to design and test a Picture Option Grid for underserved patients with breast cancer. BMJ Open; 2016; 6, e010008. [DOI: https://dx.doi.org/10.1136/bmjopen-2015-010008]
29. Serpico, V.; Liepert, A.E.; Boucher, K.; Fouts, D.L.; Anderson, L.; Pell, J.; Neumayer, L. The Effect of Previsit Education in Breast Cancer Patients: A Study of a Shared-decision-making Tool. Am. Surg.; 2016; 82, pp. 259-265. [DOI: https://dx.doi.org/10.1177/000313481608200320] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27099063]
30. Osaka, W.; Nakayama, K. Effect of a decision aid with patient narratives in reducing decisional conflict in choice for surgery among early-stage breast cancer patients: A three-arm randomized controlled trial. Patient Educ. Couns.; 2017; 100, pp. 550-562. [DOI: https://dx.doi.org/10.1016/j.pec.2016.09.011]
31. Berger-Höger, B.; Liethmann, K.; Mühlhauser, I.; Steckelberg, A. Implementation of shared decision-making in oncology: Development and pilot study of a nurse-led decision-coaching programme for women with ductal carcinoma in situ. BMC Med Inform Decis. Mak.; 2017; 17, 160. [DOI: https://dx.doi.org/10.1186/s12911-017-0548-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29212475]
32. Savelberg, W.; van der Weijden, T.; Boersma, L.; Smidt, M.; Willekens, C.; Moser, A. Developing a patient decision aid for the treatment of women with early stage breast cancer: The struggle between simplicity and complexity. BMC Med. Inform Decis. Mak.; 2017; 17, 112. [DOI: https://dx.doi.org/10.1186/s12911-017-0505-6]
33. Hawley, S.T.; Li, Y.; An, L.C.; Resnicow, K.; Janz, N.K.; Sabel, M.S.; Ward, K.C.; Fagerlin, A.; Morrow, M.; Jagsi, R. et al. Improving Breast Cancer Surgical Treatment Decision Making: The iCanDecide Randomized Clinical Trial. J. Clin. Oncol.; 2018; 36, pp. 659-666. [DOI: https://dx.doi.org/10.1200/JCO.2017.74.8442]
34. Stankowski-Drengler, T.J.; Tucholka, J.L.; Bruce, J.G.; Steffens, N.M.; Schumacher, J.R.; Greenberg, C.C.; Wilke, L.G.; Hanlon, B.; Steiman, J.; Neuman, H.B. A Randomized Controlled Trial Evaluating the Impact of Pre-Consultation Information on Patients’ Perception of Information Conveyed and Satisfaction with the Decision-Making Process. Ann. Surg. Oncol.; 2019; 26, pp. 3275-3281. [DOI: https://dx.doi.org/10.1245/s10434-019-07535-0]
35. Ehrbar, V.; Urech, C.; Rochlitz, C.; Dällenbach, R.Z.; Moffat, R.; Stiller, R.; Germeyer, A.; Nawroth, F.; Dangel, A.; Findeklee, S. et al. Randomized controlled trial on the effect of an online decision aid for young female cancer patients regarding fertility preservation. Hum. Reprod.; 2019; 34, pp. 1726-1734. [DOI: https://dx.doi.org/10.1093/humrep/dez136] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31398258]
36. Berger-Höger, B.; Liethmann, K.; Mühlhauser, I.; Haastert, B.; Steckelberg, A. Nurse-led coaching of shared decision-making for women with ductal carcinoma in situ in breast care centers: A cluster randomized controlled trial. Int. J. Nurs. Stud.; 2019; 93, pp. 141-152. [DOI: https://dx.doi.org/10.1016/j.ijnurstu.2019.01.013] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30925280]
37. Lee, T.I.; Sheu, S.J.; Chang, H.C.; Hung, Y.T.; Tseng, L.M.; Chou, S.S.; Liang, T.H.; Liu, H.J.; Lu, H.L.; Chen, M.C. et al. Developing a Web-Based Comic for Newly Diagnosed Women with Breast Cancer: An Action Research Approach. J. Med. Internet Res.; 2019; 21, e10716. [DOI: https://dx.doi.org/10.2196/10716] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30714947]
38. Olling, K.; Stie, M.; Winther, B.; Steffensen, K.D. The impact of a patient decision aid on shared decision-making behaviour in oncology care and pulmonary medicine-A field study based on real-life observations. J. Eval. Clin. Pract.; 2019; 25, pp. 1121-1130. [DOI: https://dx.doi.org/10.1111/jep.13196]
39. Savelberg, W.; Boersma, L.J.; Smidt, M.; Goossens, M.F.J.; Hermanns, R.; van der Weijden, T. Does lack of deeper understanding of shared decision making explains the suboptimal performance on crucial parts of it? An example from breast cancer care. Eur. J. Oncol. Nurs.; 2019; 38, pp. 92-97. [DOI: https://dx.doi.org/10.1016/j.ejon.2018.12.004]
40. Søndergaard, S.R.; Madsen, P.H.; Hilberg, O.; Bechmann, T.; Jakobsen, E.; Jensen, K.M.; Olling, K.; Steffensen, K.D. The impact of shared decision making on time consumption and clinical decisions. A prospective cohort study. Patient Educ. Couns.; 2021; 104, pp. 1560-1567. [DOI: https://dx.doi.org/10.1016/j.pec.2020.12.014]
41. Lin, P.J.; Fang, S.Y.; Kuo, Y.L. Development and Usability Testing of a Decision Support App for Women Considering Breast Reconstruction Surgery. J. Cancer Educ.; 2021; 36, pp. 160-167. [DOI: https://dx.doi.org/10.1007/s13187-019-01612-4]
42. Fang, S.Y.; Lin, P.J.; Kuo, Y.L. Long-Term Effectiveness of a Decision Support App (Pink Journey) for Women Considering Breast Reconstruction Surgery: Pilot Randomized Controlled Trial. JMIR Mhealth Uhealth; 2021; 9, e31092. [DOI: https://dx.doi.org/10.2196/31092]
43. Burton, M.; Lifford, K.J.; Wyld, L.; Armitage, F.; Ring, A.; Nettleship, A.; Collins, K.; Morgan, J.; Reed, M.W.R.; Holmes, G.R. et al. Process evaluation of the Bridging the Age Gap in Breast Cancer decision support intervention cluster randomised trial. Trials; 2021; 22, 447. [DOI: https://dx.doi.org/10.1186/s13063-021-05360-z]
44. Raphael, D.B.; Russell, N.S.; Winkens, B.; Immink, J.M.; Westhoff, P.G.; Stenfert Kroese, M.C.; Stam, M.R.; Bijker, N.; van Gestel, C.M.J.; van der Weijden, T. et al. A patient decision aid for breast cancer patients deciding on their radiation treatment, no change in decisional conflict but better informed choices. Tech. Innov. Patient Support Radiat. Oncol.; 2021; 20, pp. 1-9. [DOI: https://dx.doi.org/10.1016/j.tipsro.2021.08.002]
45. Schubbe, D.; Yen, R.W.; Saunders, C.H.; Elwyn, G.; Forcino, R.C.; O’Malley, A.J.; Politi, M.C.; Margenthaler, J.; Volk, R.J.; Sepucha, K. et al. Implementation and sustainability factors of two early-stage breast cancer conversation aids in diverse practices. Implement Sci.; 2021; 16, 51. [DOI: https://dx.doi.org/10.1186/s13012-021-01115-1]
46. Wyld, L.; Reed, M.W.R.; Collins, K.; Burton, M.; Lifford, K.; Edwards, A.; Ward, S.; Holmes, G.; Morgan, J.; Bradburn, M. et al. Bridging the age gap in breast cancer: Cluster randomized trial of two decision support interventions for older women with operable breast cancer on quality of life, survival, decision quality, and treatment choices. Br. J. Surg.; 2021; 108, pp. 499-510. [DOI: https://dx.doi.org/10.1093/bjs/znab005] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33760077]
47. van Strien-Knippenberg, I.S.; Boshuizen, M.C.S.; Determann, D.; de Boer, J.H.; Damman, O.C. Cocreation with Dutch patients of decision-relevant information to support shared decision-making about adjuvant treatment in breast cancer care. Health Expect; 2022; 25, pp. 1664-1677. [DOI: https://dx.doi.org/10.1111/hex.13510]
48. Ter Stege, J.A.; Raphael, D.B.; Oldenburg, H.S.A.; van Huizum, M.A.; van Duijnhoven, F.H.; Hahn, D.E.E.; The Ir, R.; Karssen Ir, K.; Corten, E.M.L.; Krabbe-Timmerman, I.S. et al. Development of a patient decision aid for patients with breast cancer who consider immediate breast reconstruction after mastectomy. Health Expect.; 2022; 25, pp. 232-244. [DOI: https://dx.doi.org/10.1111/hex.13368] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34708487]
49. Pan, C.; Yin, H.; Xu, J.; Hu, Y.; Li, Y.; Yang, Y. Breast cancer patients’ perspectives and needs about wed-based surgical decision aid: A qualitative study. Eur. J. Oncol. Nurs.; 2024; 72, 102689. [DOI: https://dx.doi.org/10.1016/j.ejon.2024.102689] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39305739]
50. Rodrigues, B.V.; Lopes, P.C.; Mello-Moura, A.C.; Flores-Fraile, J.; Veiga, N. Literacy in the Scope of Radiation Protection for Healthcare Professionals Exposed to Ionizing Radiation: A Systematic Review. Healthcare; 2024; 12, 2033. [DOI: https://dx.doi.org/10.3390/healthcare12202033]
51. Maes-Carballo, M.; Muñoz-Núñez, I.; Martín-Díaz, M.; Mignini, L.; Bueno-Cavanillas, A.; Khan, K.S. Shared decision making in breast cancer treatment guidelines: Development of a quality assessment tool and a systematic review. Health Expect.; 2020; 23, pp. 1045-1064. [DOI: https://dx.doi.org/10.1111/hex.13112]
52. Oprea, N.; Ardito, V.; Ciani, O. Implementing shared decision-making interventions in breast cancer clinical practice: A scoping review. BMC Med. Inform Decis. Mak.; 2023; 23, 164. [DOI: https://dx.doi.org/10.1186/s12911-023-02263-8]
53. Kaidar-Person, O.; Antunes, M.; Cardoso, J.S.; Ciani, O.; Cruz, H.; Di Micco, R.; Gentilini, O.D.; Gonçalves, T.; Gouveia, P.; Heil, J. et al. Evaluating the ability of an artificial-intelligence cloud-based platform designed to provide information prior to locoregional therapy for breast cancer in improving patient’s satisfaction with therapy: The CINDERELLA trial. PLoS ONE; 2023; 18, e0289365. [DOI: https://dx.doi.org/10.1371/journal.pone.0289365]
54. Zheng, H.; Yang, L.; Hu, J.; Yang, Y. Behaviour, barriers and facilitators of shared decision making in breast cancer surgical treatment: A qualitative systematic review using a ’Best Fit’ framework approach. Health Expect.; 2024; 27, e14019. [DOI: https://dx.doi.org/10.1111/hex.14019]
55. Zheng, H.; Yang, L.; Hu, J.; Yang, Y. Frequency and Influencing Factors of Shared Decision Making Among Breast Cancer Patients Receiving Surgery: A Systematic Review and Meta-Analysis. Clin. Breast Cancer; 2023; 23, pp. e20-e31. [DOI: https://dx.doi.org/10.1016/j.clbc.2022.10.007] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36357267]
56. Vromans, R.; Tenfelde, K.; Pauws, S.; Van Eenbergen, M.; Mares-Engelberts, I.; Velikova, G.; van de Poll-Franse, L.; Krahmer, E. Assessing the quality and communicative aspects of patient decision aids for early-stage breast cancer treatment: A systematic review. Breast Cancer Res. Treat.; 2019; 178, pp. 1-15. [DOI: https://dx.doi.org/10.1007/s10549-019-05351-4] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31342311]
57. Guadalajara, H.; Lopez-Fernandez, O.; León Arellano, M.; Domínguez-Prieto, V.; Caramés, C.; Garcia-Olmo, D. The Role of Shared Decision-Making in Personalised Medicine: Opening the Debate. Pharmaceuticals; 2022; 15, 215. [DOI: https://dx.doi.org/10.3390/ph15020215] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35215327]
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Aguilar Castillo, Carmen P 2 ; Horrillo, Bárbara 3
; María Luisa Sánchez de Molina Ramperez 4 ; Guadalajara, Héctor 5
1 Department of Behavioural Sciences Methodology, Faculty of Psychology, Universidad Nacional de Educación a Distancia, Moncloa-Aravaca, 28040 Madrid, Spain
2 Instituto de Investigación Sanitaria Fundación Jiménez Díaz, 28040 Madrid, Spain;
3 Faculty of Health Sciences, Universidad Villanueva, 28034 Madrid, Spain;
4 Department of General and Digestive Surgery, Hospital Universitario Fundación Jiménez Díaz, 28040 Madrid, Spain;
5 Department of General and Digestive Surgery, Hospital Universitario Fundación Jiménez Díaz, 28040 Madrid, Spain;