Intervention studies on shared decision-making with adult patients for treatment among cancer: A systematic review and meta-analysis

Abstract

Background:

Patients have the right to participate in the decision-making on their treatments. A shared decision-making (SDM) approach has been extensively studied and applied across a wide range of medical conditions beyond cancer, demonstrating various benefits. There is increasing support for involving cancer patients with SDM for complex treatments, but most previous studies on SDM in cancer patients were focused on children or the elderly. The purpose of this study was to synthesize findings from intervention studies on SDM regarding knowledge, decision satisfaction, decision conflict, and decision regret.

Methods:

Three electronic databases (PubMed, CINHAL, and Web of Science) were searched from their inception to June 2023. We included controlled trials on SDM in cancer patients over 18 years of age. Potential target studies were identified using a set of keywords with the aim of including all kinds of SDM interventions. Data were extracted using a standardized form. Quality appraisal was based on the Cochrane Risk of Bias Tool.

Results:

Of the 2759 citations retrieved, 8 studies with a total of 1505 participants met the inclusion criteria. The frequency of interventions ranged from 1 to 3 times per week. SDM methods included weekly assignments, live action videos, and brochures. Treatment preference congruence was higher for intervention groups. Synthesized outcome measurements that reached statistical significance included satisfaction (mean difference in satisfaction questionnaire = 0.18; 95% confidence interval [CI]: 0.07–0.29), decision regret (mean difference in decision regret scale = –0.27; 95% CI: –0.04 to –0.10), decision conflict (mean difference in decision regret scale = –0.49; 95% CI: –0.96 to –0.02), and knowledge (mean difference in knowledge score = 0.39; 95% CI: 0.01–0.76) after SDM interventions for adult cancer patients.

Conclusion:

Although SDM has been delivered to adult cancer patients in various forms, overall it promoted satisfaction and knowledge of the patients while reducing their decision regret and decision conflict. Still, the development of standard assessment tools for the outcomes of SDM interventions is needed to identify more effective formats.

1. Introduction

Cancer, a complex and devastating disease, remains one of the most significant health challenges worldwide.^[1,2] The diagnosis and treatment of cancer can profoundly impact patients’ lives and well-being and thus often involve decision-making of the patients or their family members.^[1] Navigating through the numerous treatment options, understanding potential risks and benefits, and shared decision-making (SDM) can be overwhelming and distressing for both patients and their families.^[1] Consequently, there is increasing recognition of the fact that enhancing SDM is needed for cancer patients to ensure that they receive appropriate and personalized care.^[2]

In recent decades, promotion of patients’ autonomy and active participation in their healthcare decisions has become a widely accepted objective.^[3] To achieve this goal, providing patients with sufficient information is paramount. Previous research has indicated a substantial demand among patients for comprehensive information about their SDM.^[3,4]

The concept of SDM, which aims to involve patients in the decision-making and jointly determine the most suitable treatment options, has gained increasing attention in healthcare.^[5–8] To this end, healthcare providers, researchers, and policymakers have developed various interventions to support cancer patients’ involvement in SDM, with the aims to empower patients and align treatment decisions with patients’ preferences and values.^[9,10]

This study aims to conduct a comprehensive systematic review and meta-analysis of interventions of SDM on adult patients with cancer. By synthesizing findings from existing studies, this research may provide a clear and evidence-based understanding of the effectiveness and overall impact of SDM on adult patients with cancer.

2. Methods

2.1. Literature search

This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.^[11] It consisted of a systematic review in which a literature search was conducted and articles were evaluated according to the Cochrane Handbook for Systematic Reviews of Interventions.^[12] This study was approved by the institutional review board of Chi Mei Medical Center, 11011-L04. The study is a review and the article does not contain clinical or patient data. We conducted a thorough search of 3 electronic databases, PubMed, Web of Science, and CINHAL, for studies published from their inception to July 2023. We employed a set of specific keywords outlined in Table 1 to target pertinent articles aligning with the objectives of our study. Reference lists of publications were also searched for potentially relevant articles. Each search strategy was combined with Boolean logic operators to obtain focused results (Table 1).

Table 1.

Strategies used for searching electronic databases.

Group	Search terms
1	“neoplasms” OR “cancer-” OR “oncolog-” OR “hematolog-”
2	“recruitment” OR “research participation” OR “research subject” OR “trial enrollment” OR “trial participation”
3	“decision making” OR “decision support techniques” OR “decision theory” OR “informed consent” OR “choice behavio-” OR “consensus” OR “consent” OR “co-operative behavio-” OR “decision-” OR “informed assent” OR “informed choice” OR “informed decision-” OR “shared decision-” OR “sharing decision-”
4	(“random- AND “trial-[tiab]) OR “randomized” [tiab] OR “randomly” [tiab] OR “randomized controlled trial” [publication type] OR “controlled clinical trial” [publication type] OR “randomized controlled trials as topic” [MeSH Terms] OR “placebos” [MeSH Terms] or “placebo” OR “intervention”
5	#1 AND #2 AND #3 AND #4

2.2. Study selection

Two authors screened abstracts to select studies. The included studies were reports of randomized controlled trials and other controlled studies, adults diagnosed with cancer that focus on treatment decision-making, interventions designed to empower patients in SDM (e.g., improving knowledge), and interventions intended for patient use only or clinician SDM training that aims to enable/empower patient involvement (Table 2). Studies were selected through a 3-step process: duplicate records were removed using reference management software; interventions that only support the adult patient’s family were removed; and eligibility of the remaining studies was independently assessed by 2 authors through examination of full-text articles. Disagreements were resolved through discussion or consultation with a third team member (Fig. 1). Evidence-based interventions for supporting patients with cancer through the decision-making process are critically important. The varied results across different studies highlight the need for tailored approaches that consider the specific context, cancer type, and individual patient needs (Table 3). The interventions designed to support decision-making in cancer care are diverse and complex, highlighting the importance of well-designed and carefully implemented interventions grounded in robust conceptual models and delivered by trained professionals to effectively meet patients’ informational and decisional needs (Table 3).

Table 2.

Inclusion and exclusion criteria for determining article eligibility.

	Inclusion criterion	Exclusion criterion
Participants	• Cancer diagnosis at 18 years of age or older. • Actively participated in SDM in the treatment.	• Decisions not directly pertaining to cancer treatment.
Interventions	Designed to empower patients in SDM.	Intervention that only supports the adult patient’s family.
Design	Randomized controlled trial and other controlled studies	• Qualitative studies.
Outcomes	Outcomes that affected the quality of SDM for the adult with cancer.
Language	English or Mandarin.

Figure 1.

Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart of study selection.

Table 3.

Characteristics of the studies included in the meta-analysis.

Study	Type/conceptual models	Participants	Settings	Sample size (intervention/control)	Intervention protocol	Interval	Provider/training	Measurement	Mechanism of action	Main findings (intervention/control)
Villarreal-Garza et al (2023)[13]	RCT/unclear	Recent breast cancer diagnosis	8 centers	82/83	F&T: 1 session, D: unclear, R: none, S: brochure	7–21 d, 30–51 d	Medical oncologist; a surgeon; 2 clinical researchers; 2 psychologists	Satisfaction questionnaire,	Evaluated participants’ understanding and communication of medical information. Used quality decision-making	No significant differences on SDM between intervention groups: mean (95% CI) 84.45 (77.8–91.7) versus 82.2 (79.7–91.1)
Durand et al (2021)[14]	RCT/community-based participatory research methods	Early-stage breast cancer	7 centers	276/271	F&T: 5 times via phone or email, D: unclear, R: 4 follow-up visits, S: posters and information sheets displayed on corkboard; educational pamphlets; educational flyers; video clip; handbook	Pre-consultation (T0); during surgical consultation (T1); post-visit (T2), 1-wk post-surgery (T3), 12-wk post-surgery (T4), 1-yr after surgery	Patients’ partners, clinical stakeholders, scientific consultants	Satisfaction questionnaire; decision regret scale, knowledge questionnaire	Measured the extent to which patients were informed about treatment options and received surgery aligned with their preferences.	Significant differences between 2 groups on knowledge and SDM (coefficient = 0.27; 95% CI, 0.01–0.53; P = .04); 24.7 (5.9–43.5); P = .01 Significant differences between 2 groups on decision regret and knowledge: 8.1 versus 12.9; 57.5 versus 57.3
Jalil et al (2022)[8]	RCT/iterative approach	Localized prostate cancer	6 tertiary referral hospitals	27/22	F&T: 1 session, D: unclear, R: none, S: DA booklet	1 mo	Nurses at each hospital	Decisional conflict scale; Satisfaction questionnaire; Self-administered questionnaire, which evaluated the knowledge of prostate cancer (23 items) and preparation for decision-making (10 items)	Measured the participant’s general knowledge of prostate cancer and its treatment options, focusing on information considered essential for decision-making	Significant differences between 2 groups on decision conflict, satisfaction, and knowledge: 12.41 versus 12.50; 76.02 versus 75.23; 11.26 versus 10.23
Lam et al (2013)[15]	RCT/develop and pilot-test a decision aid	Early-stage breast cancer	2 Hong Kong Government-funded breast centers	113/112	F&T: 1 time, D: unclear, R: none, S: DA booklet/	4 to 7 d after the consultation and 1, 4, and 10 mo after surgery	1 research assistant	Decisional conflict scale; knowledge questionnaire; decision regret	Enhanced decision-making and reduced post-decisional regret and distress	Significant differences between 2 groups on decisional conflict, knowledge, and decision regret: 15.8 versus 19.9; 6.1 versus 5.9; 21.4 versus 23.1
Sawka et al (2015)[16]	Parallel-RCT/grounded theory	Early-stage papillary thyroid cancer	Canadian tertiary	34/36	F&T: 1 time, D: up to 60 min, R: none, S: patient decision aid web site, audio record	15 to 23 mo after randomization	1 research assistant	Satisfaction questionnaire; decision regret scale; knowledge questionnaire; decisional conflict scale	Provided an explicit explanation of evidence uncertainty related to the potential therapeutic benefit of radioactive	Significant differences between 2 groups on satisfaction, decision regret, knowledge, and decisional conflict: 4.5 versus 4.2; 12.7 versus 18.5; 9.7 versus 7.8; 25.2 versus 52.1
Snowden et al (2023)[17]	RCT/holistic needs assessment model	Head and neck, skin, or colorectal cancer	Four out-patient oncology clinics in Scotland, UK	73/74	F&T: 1 time, D: unclear, R: none, S: in-person counseling/	Unclear	9 clinicians	Satisfaction questionnaire	Determined the effect on patient participation in the out-patient cancer consultation process and the patient’s perception of SDM and subsequent self-efficacy	Significant differences between 2 groups on decision-making: 25.43 versus 25.03
Whelan et al (2004)[18]	RCT/unclear	Stage I or II breast cancer	Canada	94/107	F&T: 1 time, D: unclear, R: unclear, S: decision board/	6 mo; 12 mo	Surgical staff, research assistant	Decisional conflict scale; satisfaction questionnaire; knowledge questionnaire	Provided information from clinical trials to patients regarding their treatment options; the acute and long-term adverse effects associated with treatment; and the effects of treatment on a patient’s breast, long-term survival, and quality of life.	Significant differences between 2 groups on decisional conflict, satisfaction, and knowledge: 1.40 versus 1.62; 4.50 versus 4.32; 66.9 versus 58.7
Wilkin et al (2006)[19]	RCT/unclear	Stage I or stage II breast cancer	US	52/49	F&T: 2 sessions in 1 wk, D: 60 min, R: 1 wk after initial treatment, S: SDM video	1 wk	Surgical, medical, and radiation oncologists; plastic surgeons; gynecologists; social workers; and nurses	Satisfaction questionnaire; knowledge questionnaire	Provided information on breast cancer and analysis of mastectomy and breast conservation as the primary treatment options. Provided the relative risks and benefits of the treatment choices.	No significant differences between 2 groups on satisfaction and knowledge: 26.83 versus 26.14; 77.23 versus 77.54

CI = confidence interval, RCT = randomized controlled trial, SDM = shared decision-making.

2.3. Quality appraisal

The quality of randomized controlled trials was conducted using the Risk of Bias Tool from the Cochrane Handbook.^[11] Two authors independently performed the assessment, and any disagreements were resolved through discussion. The risks of bias of each study selected were categorized as high, low, or unclear, as depicted in Figure 2.

Figure 2.

Risk of bias.

2.4. Measures

The main focus of interest was identification of interventions that support the decision-making process, including knowledge, decision satisfaction, decision conflict, and decision regret. Consistent with Cochrane reviews, when trials had more than 1 intervention group, we compared the findings that provided the strongest contrast between the intervention and control groups. When studies used similar outcome measures, we pooled results by meta-analysis using Review Manager 5, and data were analyzed using a random-effects model or fixed-effects model. The overall mean effect sizes were estimated using random effect models or fixed effect models according to statistical heterogeneity I² tests (for sizes of <40%, fixed-effects models were used).^[20] Outcome variables were reported as standardized mean differences and their 95% confidence intervals (CIs).

2.5. Data extraction

We used a data extraction sheet based on the “Template for Intervention Description and Replication” checklist and guide.^[21] The sheet incorporated the following key elements: author names, country of origin, study design, participant characteristics, sample size, detailed intervention descriptions (including components, processes, and mechanisms of action), measurement tools, and main findings. Data were extracted independently by 2 authors to ensure accuracy and consistency. Any disagreements were resolved through discussion or by involving a third-party arbiter. This process allowed us to critically evaluate the quality and rigor of the studies and provide a transparent appraisal of evidence.

2.6. Statistical analysis

We used funnel plots and the risk of bias tool from the Cochrane Handbook to evaluate publication bias, as outlined by Higgins and Green.^[12] If publication bias was observed across studies, we performed a sensitivity analysis in which outliers were excluded. Sensitivity analyses were also conducted by excluding studies that included patients with metastatic cancers. All statistical analyses were performed using RStudio (version 1.3.1093). The metafor package was used to conduct meta-analyses. Significance was defined as a 2-tailed P-value <.05.

3. Results

3.1. Characteristics of the studies included in the analysis

We conducted a comprehensive search of 3 databases: Pubmed (n = 3771,703), Web of Science (n = 2737), and CINAHL (n = 10,868,798). We identified 5782 studies including 9 studies found through the reference lists of publications retrieved. We used EndNote software to remove duplicate articles, which led to the exclusion of 14,637,720 duplicates. After this initial screening, we were left with 2759 unique studies. Next, we screened the titles and abstracts of these 2759 studies, leading to the exclusion of 2748 that did not meet the inclusion criteria. Subsequently, we conducted a full-text review of the remaining 11 studies to assess their eligibility. All included studies featured structured programs with defined frequencies, intensities, and durations of interventions. Three more studies were excluded. To understand the components of the interventions, the conceptual models, contents, and mechanisms of the interventions in each of the 8 included studies were illustrated (Fig. 1).^[8,13–19] Overall mean effect sizes were estimated using random-effects or fixed-effects models according to the heterogeneity among studies, with I² values of 25%, 25% to 50%, and >50% corresponding to small, moderate, and high heterogeneity, respectively. Fixed-effects models were used when I² was ≤50%.^[11]

3.2. Participants

The 8 studies included in the meta-analysis had a total of 1505 participants, we then divided them into an intervention group with 751 patients and a control group with 754 patients. The number of participants per study varied from 49 to 547.^[8,l6] Among the participants, the majority had breast cancer (n = 1074),^{[13–15,18,19]} followed by thyroid cancer (n = 70),^[16] prostate cancer (n = 49),^[8] and other cancers, such as head and neck, skin, or colorectal cancer (n = 147).^[17]

3.3. Outcomes

All 8 studies reported the quality of decision-making and used a questionnaire to measure participations’ knowledge of their cancer after a consultation period. Knowledge at the time of decision-making was assessed with questions scored for the accuracy of answers. A random-effects model showed that the difference in the scores between the 2 groups reached statistical significance (SMD = 0.39, 95% CI: [0.01–0.76]) (Fig. 3).

Figure 3.

Differences in the knowledge of SDM. SDM = shared decision-making.

The 8 studies used different tools to assess the quality of the decision-making process, including decision conflict scale (4 studies), decision regret scale (3 studies), and self-administered questionnaire (one study). Whereas the study on thyroid cancer did not assess decision conflict, an earlier study of the same group of patients did,^[16] which was a randomized controlled trial of a computerized decision aid on adjuvant radioactive iodine treatment for patients with early-stage papillary thyroid cancer, so we include data from the earlier study for this part of analysis. These measurement tools are widely recognized and accepted for evaluating SDM. They measure the degree to which patients perceive healthcare providers’ efforts to clarify their health concerns, actively listen to their worries, and integrate their viewpoints and health beliefs into the decision-making process.

Hence, the limited number of eligible studies and the heterogeneity of their designs, interventions, and outcome measures prevented the pooling of results for meta-analysis. Moreover, because of the risk of bias in the included studies, the findings of this review must be interpreted cautiously.

When the results were synthesized, the difference in the decision-making satisfaction score reached statistical significance (SMD = 0.18, 95% CI: [0.07–0.29]) (Fig. 4). Furthermore, the decision conflict score (SMD = −0.49, 95% CI: [−0.96–0.02]; Figure 5) and the difference in the decision regret score (SMD = −0.2714, 95% CI: [−0.0441 to −0.1015]) also reached statistical significance (Fig. 6).

Figure 4.

Decision-making satisfaction.

Figure 5.

Decision regret.

Figure 6.

Decision conflict.

Five studies aimed to enhance participants’ understanding and communication of medical information, using an effect size like Cohen d to quantify improvements in patient comprehension.^{[8,14,16,17,21]} Three studies used participatory research methods to inform patients about treatment options and measured effects as odds ratios.^[13,15,18]

4. Discussion

This systematic review and meta-analysis demonstrated major components and knowledge, decision satisfaction, decision conflict, and decision regret of SDM interventions in adults with cancer. SDM interventions can help adults with cancer improve their knowledge, decision satisfaction, decision conflict, and decision regret. Our meta-analysis, encompassing 8 studies involving 1505 participants, revealed significant insights into the impact of structured programs on the quality of decision-making in cancer care. The observed statistical significance in the scores for knowledge of cancer, decision-making satisfaction, and decision regret underscores the effectiveness of these interventions. Specifically, the improvement in knowledge at decision-making (SMD = 0.39) highlights the importance of providing patients with comprehensive information to facilitate informed decisions.

The difference in the scores on knowledge in SDM between the 2 groups reached statistical significance. The concept of SDM hinges on integrating clinical expertise with patient values and preferences to arrive at the best healthcare decisions. Our meta-analysis highlighted that a major challenge in achieving effective SDM is the knowledge disparity between healthcare professionals and patients. Healthcare professionals have extensive knowledge about disease mechanisms, treatment options, and their potential risks and benefits. In contrast, patients often have a profound understanding of their personal values, the lifestyle implications of treatments, and their preferences. This divergence in knowledge bases can lead to communication barriers, making it difficult for patients to fully engage in the decision-making process. As Charles et al^[22] highlighted in their seminal work, for SDM to be truly effective, both parties must be experts in their respective domains – healthcare professionals in clinical knowledge and patients in their personal values and life context. Bridging this knowledge gap is crucial, necessitating using decision aids, patient education programs, and consultations tailored to enhance patient understanding and involvement.

The significant difference in decision-making satisfaction (SMD = 0.18) and the reduction in decision regret scores (SMD = −0.2714) further illustrate the potential of structured interventions to enhance patient outcomes. These findings are crucial since they point towards the positive impact of incorporating patients’ viewpoints and health beliefs into the decision-making process, thereby improving their overall satisfaction with the care received. Our meta-analysis showed that SDM enhanced patients’ satisfaction with the decision, which might, in turn, increase their compliance with and adherence to cancer treatment. Theoretically, patients’ satisfaction is closely related to the treatment outcome. However, patients with cancer can rarely compare treatments with and without SDM for the same condition. One study showed that a high percentage (92.8%) of the patients felt respected by the physician throughout the consultation.^[23] They argued that this is especially important in building a good doctor-patient relationship. While a good doctor-patient relationship leads to better compliance and, thus, better clinical outcomes, when the patient cannot compare the outcomes, feeling respected may be an important factor contributing to their satisfaction. SDM facilitates communication between the doctor and the patient and is regarded as a model for effective communication.^[24] Better communication may be another factor contributing to satisfaction. In SDM, clinicians typically provide the patient with sufficient information on the available options so that they can be actively involved in the decision-making process. While it is a part of effective communication, obtaining sufficient information alone may contribute to patient satisfaction.

The evidence from our analysis suggests that healthcare providers should consider integrating structured programs that emphasize SDM into routine cancer care. These programs should be designed to address the specific needs of different cancer types, as our findings indicate a variation in the effectiveness of interventions across different patient groups. The significant impact on decision conflict scores (SMD = −0.49) also highlights the potential of these interventions to reduce patients’ uncertainty and indecision regarding their treatment options. SDM is 1 technique for reducing decision conflict in primary care.^[25] A progressively popular strategy for promoting patient engagement in decision-making is patient decision aids. These aids address patients’ informational requirements, explain the decision, and endorse the patients’ active involvement in the process. Recent research has revealed that patients with cancer primarily seek information related to treatment experiences, posttreatment quality of life, and the impact of side effects.^[18] In contrast, clinicians tend to prioritize clinical outcomes in their discussions.^[26]

The 8 intervention studies demonstrated that a variety of SDM approaches had been applied, which reflected the multi-factorial nature of what constitutes support for patient decision-making. Booklets, videos, and web-based decision aids are the prevailing interventions; while recently consultations with healthcare professionals have been incorporated to delve into patient preferences. These interventions predominantly hinge on the intrinsic motivation of patients to engage with the decision aid, which enhances their comprehension of the inherent merits and drawbacks associated with each treatment option. Consequently, patients are anticipated to reengage with their physicians armed with a heightened understanding of their condition, courtesy of the specific decision aid employed.

Moreover, these interventions actively encourage patients to reflect on their individual values and preferences. Nonetheless, the extent to which these personal values and preferences influence the subsequent consultation and ultimate decision-making process with the physician remains somewhat ambiguous. For example, in prostate cancer, Scherr et al (2017)^[27] reported that patient treatment choices have largely been influenced by recommendations from urologists, grounded predominantly in medical criteria such as age and Gleason score, rather than by the patients’ own nuanced assessments of the relative pros and cons of available treatment options.

A study on pregnant women with previous cesarean section showed that many barriers are potentially modifiable, and can be addressed by attitudinal changes at the levels of patient, clinician/healthcare team, and the organization, and improved patient knowledge in self-care.^[25] This might also be true in cancer patients. By fostering a more balanced knowledge exchange, SDM can more accurately reflect the best interests of the patient, making healthcare decisions more personalized and appropriate.

A decrease in the decision regret score was observed in our meta-analysis. Insufficient patient involvement in decision-making may lead to decision regret, and decision regret can potentially be modified through SDM.^[28] A study found that SDM can be used in prenatal care to reduce the rate of repeated cesarean section.^[25] Again, theoretically, decision regret should be closely related to the outcome of treatment, it is rare that a cancer patient can compare treatments with and without SDM for the same condition as in the case of cesarean section. Therefore, sufficient patient involvement in decision-making should be an important factor contribute to the reduction of decision regret by SDM. In addition, obtaining sufficient information, as a part of SDM process, may also contribute to the reduction.

We assessed the validity of the studies, including using the Critical Appraisal Skills Programme (2020) recommendation of the Harvard style that provides tools and checklists to help individuals critically appraise different types of research studies for their quality and relevance. Villarreal-Garza et al^[13] stated that bias could arise from blinding participants and personnel (performance bias) or if the outcome assessment was not blinded (detection bias). In addition, Durand et al^[14] stated that selection bias could arise from the nonrandom assignment of participants to intervention or control groups. Moreover, Jalil et al^[8] stated that attrition bias from dropouts not accounted for or unevenly distributed between groups could affect the study, and performance bias could occur if participants were aware of their group assignments, affecting their responses.^[19] Furthermore, Whelan et al^[18] stated that selection bias arising from nonrandom allocation or attrition bias arising from high dropout rates could affect the study.

Future studies should aim to address the limitations identified in this review by focusing on larger, more diverse populations and standardizing the outcome measures for SDM. Exploring the long-term effects of these interventions on patient outcomes and satisfaction would also be valuable. Additionally, research into the cost-effectiveness of integrating structured programs into routine care could provide essential insights for healthcare policymakers. Our meta-analysis demonstrates the potential benefits of structured interventions in improving knowledge and satisfaction and reducing decision regret among patients with cancer. These findings support integrating SDM practices into cancer care to enhance patient-centered outcomes. However, further research is needed to solidify these findings and explore their broader implications.

4.1. Limitations

This review benefits from a comprehensive search strategy and a rigorous screening process, leading to the inclusion of high-quality studies. However, the risk of bias in the included studies warrants a cautious interpretation of the results. In addition, the limited number of eligible studies further restricts our ability to draw definitive conclusions about the effectiveness of SDM interventions across all cancer types.

Author contributions

Conceptualization: Chi-Kun Hsieh, Wei-Li Lin, How-Ran Guo, Wen-Tsung Huang.

Data curation: Tsung-Mu Wu, Chien-Chieh Wang.

Formal analysis: Tsung-Mu Wu, Chien-Chieh Wang.

Investigation: Tsung-Mu Wu, Hsuan-Chih Liu.

Methodology: Tsung-Mu Wu, Hsuan-Chih Liu.

Supervision: Chi-Kun Hsieh, Wen-Li Lin, How-Ran Guo, Wen-Tsung Huang.

Validation: Tsung-Mu Wu, Hsuan-Chih Liu, Wen-Tsung Huang.

Writing – original draft: Hsuan-Chih Liu, Soon Cen Huang, How-Ran Guo.

Writing – review & editing: Chi-Kun Hsieh, Thi-Hoang-Yen Nguyen.