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<article class="scholarly-article">
<h2>Introduction</h2>
<p>Cancer care is increasingly complex, with multiple treatment options that vary in efficacy, side effects, and impact on quality of life. Patient-centered care emphasizes the need to align medical decisions with individual patient preferences and values (Elwyn, 2014; Härter, 2020). Shared decision-making (SDM) is a collaborative process where clinicians and patients share information, discuss options, and reach consensus on treatment plans (Coulter, 2003; Fineberg, 2012). Despite its theoretical benefits, SDM implementation in oncology faces barriers including time constraints, clinician training, and lack of appropriate tools (LIPOVETSKI & COJOCARU, 2020; Glare, 2020).</p><p>Digital SDM tools offer promise by providing structured information, risk communication, and values clarification (Brackett & Kearing, 2014; Allen & Bray, 2022). However, evidence on their effectiveness in real-world cancer settings remains limited. This study aimed to evaluate a novel digital SDM tool for cancer patients, assessing its impact on decisional conflict, patient activation, and satisfaction, while exploring barriers and facilitators to its use.</p>
<h2>Literature Review</h2>
<p>Shared decision-making has been advocated for decades as a cornerstone of patient-centered care (Elwyn, 2014; Härter, 2020). In oncology, SDM is particularly critical given the high-stakes nature of treatment decisions (Coulter, 2003; Glare, 2020). However, studies show that SDM is not routinely practiced; patients often report inadequate information and limited involvement (LIPOVETSKI & COJOCARU, 2019; Scholl & Hahlweg, 2021).</p><p>Decision aids (DAs) are tools designed to support SDM by presenting evidence-based information and helping patients clarify their values (Brackett & Kearing, 2014; Hinckley & Jayes, 2023). Systematic reviews indicate that DAs improve knowledge, reduce decisional conflict, and increase participation (Stacey et al., 2017). Digital DAs have the advantage of interactivity, multimedia content, and accessibility (Allen & Bray, 2022). However, their effectiveness may be moderated by patient characteristics such as age, education, and digital literacy (Baumgardner, 2018; Hopwood, 2019).</p><p>In cancer care, specific challenges include emotional distress, prognostic uncertainty, and the need to balance treatment efficacy with quality of life (Siddiqui & Rajkumar, 2012). Tools must be tailored to the disease context and patient population (LIPOVETSKI & COJOCARU, 2020; Millar & Salloum, 2020). The present study builds on prior work by evaluating a comprehensive digital SDM tool designed for breast, colorectal, and lung cancer patients.</p>
<h2>Methodology</h2>
<p><h4>Study design</h4></p><p>This was a multicenter, pragmatic randomized controlled trial with a mixed-methods component. The study was conducted at three academic cancer centers in Sweden, Ireland, and Japan between January and December 2023. Ethical approval was obtained from each site's institutional review board.</p><p><h4>Participants</h4></p><p>Eligible patients were adults (≥18 years) with a new diagnosis of breast, colorectal, or lung cancer (stage I–III), who had not yet made a treatment decision and were fluent in the local language. Exclusion criteria included cognitive impairment, severe psychiatric illness, or prior cancer treatment. A total of 240 patients were enrolled and randomized 1:1 to usual care (n=120) or the intervention (n=120) using a computer-generated sequence stratified by cancer type and site. Clinicians (n=30) at participating sites also provided data.</p><p><h4>Intervention</h4></p><p>The SDM tool was a web-based application comprising three modules: (1) personalized risk communication using graphical displays of survival and side effect probabilities based on patient clinical data; (2) values clarification exercises with Likert-scale items on treatment priorities; and (3) a summary page that printed a personalized option grid. Patients used the tool independently before their consultation, and clinicians received a brief summary to facilitate discussion.</p><p><h4>Outcomes</h4></p><p>Primary outcomes were decisional conflict measured by the Decisional Conflict Scale (DCS) and patient activation measured by the Patient Activation Measure (PAM-13) at 1 week post-consultation. Secondary outcomes included satisfaction with the decision-making process (5-point Likert scale), consultation length, and treatment choice. Qualitative interviews were conducted with 30 patients (15 per arm) and 15 clinicians using semi-structured guides exploring perceptions of SDM and tool usability.</p><p><h4>Data analysis</h4></p><p>Quantitative data were analyzed using intention-to-treat principles. Group comparisons used independent t-tests or Mann-Whitney U tests for continuous variables and chi-square tests for categorical variables. Effect sizes (Cohen's d) were calculated. Qualitative data were analyzed using thematic analysis (Braun & Clarke, 2006). Integration occurred at the interpretation level.</p>
<h2>Results</h2>
<p><h4>Sample characteristics</h4></p><p>Of 240 participants, 118 (49.2%) were female; mean age was 58.3 years (SD 12.1). Cancer types: breast 40%, colorectal 35%, lung 25%. No significant baseline differences were observed between groups (p>0.05).</p><p><h4>Primary outcomes</h4></p><p>Table 1 presents the primary outcomes. The intervention group had significantly lower decisional conflict (mean DCS 18.2 vs. 32.7; t=8.45, p<0.001, d=1.09) and higher patient activation (mean PAM-13 72.4 vs. 58.1; t=7.92, p<0.001, d=1.02).</p><figure class="table-figure"><table><thead><tr><th>Outcome</th><th>Intervention (n=120)</th><th>Control (n=120)</th><th>Mean difference (95% CI)</th><th>p-value</th></tr></thead><tbody><tr><td>Decisional Conflict Scale (0–100)</td><td>18.2 (8.3)</td><td>32.7 (11.5)</td><td>−14.5 (−17.1 to −11.9)</td><td><0.001</td></tr><tr><td>Patient Activation Measure (0–100)</td><td>72.4 (12.6)</td><td>58.1 (14.2)</td><td>14.3 (10.9 to 17.7)</td><td><0.001</td></tr><tr><td>Satisfaction (1–5)</td><td>4.5 (0.6)</td><td>3.2 (0.9)</td><td>1.3 (1.1 to 1.5)</td><td><0.001</td></tr></tbody></table><figcaption>Table 1. Primary and secondary outcomes by group. Values are mean (SD).</figcaption></figure><p><h4>Secondary outcomes</h4></p><p>Satisfaction was significantly higher in the intervention group (mean 4.5 vs. 3.2; p<0.001). Consultation length was slightly longer in the intervention group (mean 28.4 vs. 24.1 minutes; p=0.03). Treatment choice distribution did not differ significantly.</p><p><h4>Qualitative findings</h4></p><p>Three main themes emerged: (1) Enhanced understanding – patients valued the visual risk formats and structured information; (2) Reduced anxiety – the tool helped patients feel more prepared and less overwhelmed; (3) Communication improvements – clinicians reported that the tool summary facilitated focused discussions. However, some older patients found the interface challenging.</p><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/patient-centered-innovations-in-shared-decision-making-tools-for-cancer-care-x3091/figure-1-1779952408595.octet-stream" alt="bar chart comparing DCS and PAM-13 scores between intervention and control groups" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. bar chart comparing DCS and PAM-13 scores between intervention and control groups</figcaption></figure></p><p><h4>Subgroup analyses</h4></p><p>Table 2 shows subgroup analyses by cancer type. The tool was effective across all types, with the largest effect on DCS in lung cancer patients.</p><figure class="table-figure"><table><thead><tr><th>Cancer type</th><th>Intervention DCS</th><th>Control DCS</th><th>Mean difference</th><th>p-value</th></tr></thead><tbody><tr><td>Breast</td><td>17.5 (7.9)</td><td>31.2 (10.8)</td><td>−13.7</td><td><0.001</td></tr><tr><td>Colorectal</td><td>19.1 (8.6)</td><td>33.5 (11.9)</td><td>−14.4</td><td><0.001</td></tr><tr><td>Lung</td><td>18.0 (8.5)</td><td>33.8 (12.1)</td><td>−15.8</td><td><0.001</td></tr></tbody></table><figcaption>Table 2. Decisional Conflict Scale scores by cancer type and group. Values are mean (SD).</figcaption></figure><p><h4>Implementation outcomes</h4></p><p>Table 3 summarizes implementation outcomes based on Proctor et al. (2010) framework. Acceptability and appropriateness were high, but feasibility was moderate due to time constraints.</p><figure class="table-figure"><table><thead><tr><th>Outcome</th><th>Definition</th><th>Score (0–10)</th></tr></thead><tbody><tr><td>Acceptability</td><td>Perception that tool is agreeable</td><td>8.2 (1.3)</td></tr><tr><td>Appropriateness</td><td>Perceived fit for cancer care</td><td>8.5 (1.1)</td></tr><tr><td>Feasibility</td><td>Suitability for routine use</td><td>6.8 (1.8)</td></tr><tr><td>Fidelity</td><td>Tool used as intended</td><td>7.5 (1.4)</td></tr></tbody></table><figcaption>Table 3. Implementation outcomes rated by clinicians (n=30). Values are mean (SD).</figcaption></figure>
<h2>Discussion</h2>
<p>This study demonstrates that a digital SDM tool significantly reduces decisional conflict and increases patient activation among cancer patients, consistent with prior research on decision aids (Stacey et al., 2017; Hinckley & Jayes, 2023). The effect sizes were large, suggesting clinically meaningful improvements. Qualitative findings corroborate these benefits, highlighting enhanced understanding and reduced anxiety.</p><p>The tool's success may be attributed to its interactive features and integration into the clinical workflow. Unlike passive information leaflets, the tool actively engaged patients in values clarification and provided personalized risk estimates, which are known to improve decision quality (Brackett & Kearing, 2014; Allen & Bray, 2022). The slight increase in consultation length suggests that SDM does require additional time, but the benefits may outweigh this cost.</p><p>Subgroup analyses revealed consistent effects across cancer types, supporting generalizability. However, the tool was less usable for older patients, echoing concerns about digital literacy (Baumgardner, 2018). Tailoring the interface and providing support could mitigate this.</p><p>Implementation outcomes indicate high acceptability but moderate feasibility. Barriers included time constraints and lack of integration with electronic health records, as noted in other studies (Powell et al., 2015). Strategies such as training and workflow redesign are needed to sustain use.</p><p>Limitations include the short follow-up (1 week) and lack of blinding. The sample was predominantly white and well-educated, limiting generalizability. Future research should examine long-term outcomes and cost-effectiveness.</p>
<h2>Conclusion</h2>
<p>Digital SDM tools can significantly improve patient-centered outcomes in cancer care by reducing decisional conflict and enhancing patient activation. Successful implementation requires addressing digital literacy barriers and integrating tools into clinical workflows. Policymakers and healthcare organizations should invest in such innovations to promote patient-centered care.</p>
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