Improving shared-decision making in plastic surgery: a systematic review and meta-analysis of patient-facing decision aids

Introduction

Plastic surgery encompasses a wide range of reconstructive and aesthetic procedures. In the United States, reconstructive procedures increased 2% from 2023 to 2024, and breast reconstruction reached 162,579 cases in 2024, a 3% year over year increase (1). These operations are often highly preference-sensitive, with options that differ in risks, benefits, and long-term consequences. Such complexity can lead to decisional conflict, anxiety, and, in some cases, postoperative regret when choices are not aligned with patients’ values (2-5).

Shared decision-making (SDM) has emerged as a key strategy to address these challenges by ensuring that clinical recommendations are balanced with individual patient goals and preferences. Patient-facing decision aids (PDAs) are evidence-based tools that support SDM by presenting treatment options, benefits, and risks in a structured and accessible way. The International Patient Decision Aid Standards (IPDAS) and the Ottawa Decision Support Framework provide widely recognized guidelines for their design and evaluation (6,7). Across healthcare settings, PDAs have been shown to reduce decisional conflict, improve knowledge, and increase the likelihood of value-concordant decisions (7).

Within plastic surgery, evidence for PDAs is most developed in oncologic and reconstructive settings, where randomized trials and systematic reviews demonstrate reductions in decisional conflict and regret broadly, though effects on anxiety and depression are mixed (2-5,8). These studies also often reported improved knowledge acquisition and greater patient satisfaction with decision-making, though measurement instruments were heterogeneous and the number of studies with sufficient data reporting of aforementioned metrics were not as frequent. Delivery format also may influence effectiveness; a recent network meta-analysis found web-based PDAs often outperform booklets for knowledge and satisfaction (9). Beyond these contexts, research on PDAs remains limited across other plastic surgery domains—such as gender-affirming, aesthetic, craniofacial, and hand surgery—despite similarly complex, values-driven decisions and multidisciplinary settings. Implementation barriers include workflow integration, clinician engagement, health literacy, and digital access. These gaps underscore the potential need to expand SDM tools across the specialty. Thus, we aimed to conduct a systematic review to provide a comprehensive, reproducible assessment of PDAs in PRS with predefined eligibility criteria, robust appraisal of patient outcomes, and to enable meta-analysis of decision-related outcomes where sufficiently comparable data were available. This systematic review synthesizes contemporary evidence on preoperative PDAs in plastic and reconstructive surgery (PRS), evaluating their impact on decision quality and patient-reported outcomes while identifying priorities for clinical adoption and future study. We present this article in accordance with the PRISMA reporting checklist (available at https://abs.amegroups.com/article/view/10.21037/abs-2025-1-66/rc).

Methods

Search strategy

PubMed, Cochrane Database of Systematic Reviews, and Google Scholar were strategically searched in July 8^th, 2025 using the combined keywords and phrases: “Breast” OR “Mammoplasty” OR “Mastectomy” OR “Mammaplasty” OR “Breast Implants” OR “Breast Reconstruction” OR “Breast Augmentation” OR “Nipple Reconstruction” OR “Nipple” OR “Mastopexy” OR “Oncoplastic Surgery” OR “Acellular Dermis” OR “Acellular Dermal Matrix” OR “Gender Affirming Surgery” OR “Facial Feminization” OR “Facial Masculinization” OR “Gender Affirming Mastectomy” OR “Phalloplasty” OR “Vaginoplasty” OR “Cosmetic Techniques” OR “Esthetics” OR “Abdominoplasty” OR “Blepharoplasty” OR “Facelift” OR “Facial” OR “Face” OR “Body Contouring” OR “Hair Transplantation” OR “Injectables” OR “Laser Therapy” OR “Liposuction” OR “Rhinoplasty” OR “Oculoplastic Surgery” OR “Hand Surgery” OR “Thumb Carpometacarpal Joint” OR “Thumb CMC Arthroplasty” OR “Distal Radius Fractures” OR “Metacarpal Fractures” OR “Dupuytren Contracture” OR “Peripheral Nerve Injuries” OR “Nerve Grafting” OR “Cleft Lip” OR “Cleft Palate” OR “Cleft Lip and Palate” OR “Brachial Plexus” OR “Brachial Plexus Injury” OR “Craniofacial Abnormalities” OR “Craniofacial”) AND (“Decision Support” OR “Shared Decision Making” OR “Decision Aid” OR “Decision Aids” OR “Decision Tool” OR “Shared Decision Tools”). A detailed search strategy for each database employed is compiled in Appendix 1.

Eligibility criteria/study selection

Two authors (D.M.L. and T.M.) independently screened and reviewed titles and abstracts against predefined inclusion and exclusion criteria as listed below. All disagreements were resolved by discussion with the senior author (K.G.E.) until consensus was achieved. The authors included unique full-text studies that studied PDAs intended to support pediatric and adult patients in making informed, values-concordant decisions about undergoing plastic or reconstructive surgery, including procedures such as breast reconstruction, cleft or craniofacial repair, hand and upper extremity surgery, gender-affirming surgery, and cosmetic surgery. When the full text of a potentially eligible study could not be obtained through the initial database, we pursued additional retrieval steps via alternative sources. This included institutional library holdings and other online repositories; specifically requesting the article through our institution’s interlibrary loan/document delivery services. If full texts remained unavailable, the study was excluded from full-text eligibility assessment and was documented in the PRISMA flow diagram. Eligible studies were evaluated for the use of PDAs such as printed materials (paper surveys, booklets, pamphlets), videos, digital tools (computer and/or phone-based apps, algorithms or modules hosted on websites), or any other interactive platforms designed to support shared decision making, preoperative counseling, and procedure selection. Studies employing formats such as group educational classes were evaluated and only included if there was sufficient demonstration of interactive components, elicitation of values, and incorporated individual patient engagement.

In order to assess the effect of decision aids upon patient reported outcomes in PRS, studies were filtered and included for further analysis if their study sufficiently reported at least one outcome related to shared decision making, patient satisfaction, decisional conflict, decisional regret, or decisional anxiety. Such outcomes were presented in the contexts of tools such as Decisional Conflict Scale, Decision Regret Scale, focus group findings, anxiety or depression [State-Trait Anxiety Inventory (STAI) state anxiety tool, Hospital Anxiety and Depression Scale (HADS)], knowledge or understanding of options (Knowledge Decision Quality Index), treatment choice alignment with patient values, clinical or functional outcomes, or validated patient reported outcome measures (BREAST-Q, GENDER-Q) (8-14).

We systematically extracted these key variables from each included study alongside information regarding: the author, year of publication, origin country of study, type of surgical procedure, study design, participant characteristics, name of the PDA if available, modality/method of delivery, and the study’s primary findings related to decision-making outcomes.

Several studies reported outcomes at multiple follow-up time points. To ensure each study contributed only one effect estimate per outcome, we extracted a single follow-up time point from each study for the primary meta-analysis. When multiple eligible time points were reported, we prespecified the use of the latest available follow-up and the closest available time point to the latest assessment.

Eligible study designs included randomized controlled trials, prospective or retrospective comparative cohort studies, mixed methods research, and qualitative studies. Systematic reviews and meta-analyses were screened for reference-mining and were not treated as eligible primary studies for quantitative synthesis. Case studies/reports, opinion pieces, and editorials were not included. Only English language publications were included. References of selected papers were also surveyed to discover other pertinent studies and to supplement the initial electronic search. These manual searches specifically screened the reference lists of included full-text articles and relevant systematic reviews/meta-analyses. Any newly identified records underwent the same iterations of title/abstract and full-text screening process using the prespecified eligibility criteria, with any discrepancies resolved by the senior author. We excluded publications that were not based on original research data or performed on non-human subjects. Studies were evaluated for methodology quality. Although some studies did not match criteria for analysis, their contributions to PDA development within the scope of PRS warranted review and discussion.

Study quality and bias assessment

Both study quality and presence of bias was assessed by two researchers (D.M.L. and T.M.) who consulted third researcher (K.G.E.) in cases of disagreement. Study quality was assessed for any bias arising from randomization, deviations from intended intervention, missing or unreported outcome data, differing outcome measurement, and selection of reported results. Randomized control trials were assessed using the Cochrane Risk of Bias tool, whilst other studies such as Quasi-randomized trials or qualitative research were analyzed using the ROBINS-I tool and CASP, respectively. Resulting plots were visualized with the robvis tool (15-17). The methodological quality of included qualitative studies was appraised using the Critical Appraisal Skills Programme (CASP) Qualitative Checklist. The researchers independently assessed each study across the 10 CASP domains (clear aims, appropriateness of qualitative methodology and design, recruitment strategy, data collection, researcher–participant relationship/reflexivity, ethical considerations, rigor of analysis, clarity of findings, and value of the research). CASP assessments were used to characterize overall study quality and transparency of reporting; studies were not excluded solely on the basis of appraisal results.

Statistical and meta analysis

All statistical analyses were performed in jamovi (Version 2.6) using the MAJOR module for meta-analysis. P value threshold for significance was established at <0.05. Pooled effects were estimated using an inverse-variance random-effects model, with between-study heterogeneity (τ²) estimated via restricted maximum likelihood (REML). Statistical heterogeneity was evaluated using Cochran’s Q and quantified with I². Heterogeneity was interpreted conservatively using Cochrane guidance for I² with thresholds signifying: may not be important (0–40%), potentially moderate (30–60%), potentially substantial (50–90%), and potentially considerable (75–100%) heterogeneity. When heterogeneity was present (τ²>0), 95% prediction intervals were reported to reflect the expected range of true effects across future settings. Potential outliers and influential studies were examined using studentized residuals and Cook’s distance, respectively, applying Bonferroni-adjusted thresholds to flag possible outliers and an interquartile range–based rule to identify influential observations. Studies were considered influential if their Cook’s distance exceeded the median plus six times the interquartile range. Testing was examined separately for each pooled outcome.

To assess the effect of PDAs on metrics of interest such as decisional conflict, decisional regret, decisional anxiety, we utilized an inverse variance-weighted mean differences random-effects model for meta-analysis. For quantitative synthesis, we restricted studies such that they reported outcomes of interest, such as DCS or decisional regret outcomes, in a parallel-group comparison (PDA vs. usual care/control) with extractable post-intervention data. Studies employing single-arm pre–post designs or non-parallel designs were not pooled with controlled trials because their effect estimates were not directly comparable without additional assumptions, and thus these studies were therefore summarized narratively. Between-study heterogeneity was anticipated across studies, particularly in the diverse patient populations, surgical contexts, and PDA characteristics and formats. Heterogeneity was assessed using Cochran’s Q and quantified with I².

Anticipated heterogeneity was accounted for via random-effects approach, which assumes true effects vary across studies due to differences in patient populations, procedures, and PDA formats. To allow for the direct interpretation of pooled effects in the instruments’ units and calculation of mean differences (MD), studies were grouped to ensure uniform reporting using the same instrument and scale. Additionally, pooled estimates were recalculated using alternative statistical models, including both fixed-effects and random-effects approaches, to assess the robustness of findings to assumptions regarding between-study heterogeneity.

Bias assessment

Small-study effects and potential publication bias were assessed by visual inspection of funnel plots and formally evaluated using rank correlation and Egger’s regression test for funnel plot asymmetry. Given the limited number of studies for some outcomes, these assessments were interpreted cautiously. Risk of bias due to missing results was additionally evaluated through funnel plot inspection. Certainty in the overall body of evidence was appraised by aggregate consideration of study design, methodological quality, consistency of effect estimates, precision of pooled estimates, and risk of reporting bias across studies.

Results

Search results

Our initial query yielded 5,480 citations. After filtering for duplicates and applying our eligibility criteria, a total of 23 stand-alone studies were initially considered eligible. After reviewing the selected papers and their references as well as 11 systematic reviews (2-5,18-24) that resulted from our initial search, an additional 8 relevant studies were identified, filtered for eligibility, and subsequently included. Thus, a final total of 31 studies reported sufficient data for further metanalysis (Figure 1). A total of 4,300 patients were studied, with a total of 11 named, unique plastic surgery relevant PDAs reported. Table 1 summarizes the various unique PDAs reported as well as the associated studies and their characteristics.

Figure 1 PRISMA search flow diagram. **, no automation tools were used in this search and screening process, and all records were excluded manually by human investigators. ***, no automation tools were used in this citations review and retrieval process, and all records were manually reviewed and included by human investigators.

Study and patient characteristics

Study characteristics are summarized in Table 1. The 31 studies were published across the timespan of 2008 to 2024. Eligible studies focused on breast reconstruction [28 of 31 (90.3%)], with only two (6.5%) addressing hand or upper extremity surgery and one (3.2%) evaluating gender-affirming surgery. No peer-reviewed PDA studies in craniofacial or aesthetic surgery met our eligibility criteria. A total of 12 studies (38.7%) were conducted in the United States. Breast reconstruction PDA studies featured new patients diagnosed with breast cancer and were candidates for breast reconstruction secondarily to their oncologic management. For gender-affirming PDAs study populations were only representative of transgender men. Hand and upper limb reconstruction PDAs featured older adults (range of mean ages of 57 to 65 years). All studies sufficiently studied their PDA reporting appropriate outcomes of interest such as: preparation for consultation, decisional confidence, decisional regret, decisional anxiety, knowledge, and satisfaction. However, key outcomes of interest such as knowledge and satisfaction were not robustly reported nor consistent in their scales. Meta-analysis was performed only when ≥2 studies reported an outcome using sufficiently comparable measures and provided extractable summary statistics. Outcomes such as decision making and knowledge were conservatively and narratively summarized narratively; as too few studies reported these outcomes with compatible instruments and adequate quantitative data for pooling. While standardized MDs could been employed in the setting of different continuous scales, the available studies operationalized decision making and satisfaction inconsistently and frequently lacked variance estimates; therefore, SMD pooling was not considered methodologically appropriate.

Eight studies (25.8%) utilized BREAST-Q within their study design (27,37,42-45,50,52). No study used GENDER-Q, likely due to the fact that it was only validated recently at the time of this work. The method of delivery for these PDAs favored digital and audio-visual formats (n=21, 67.7%), with the remainder as a mix of booklets, pamphlets, other paper-based materials (n=9, 29%), and a minority of in-person interactive educational sessions prior to consultation (n=2, 6%).

Several PDAs at various stages of development (n=7) did not meet our eligibility criteria for metanalyses. Reasons for exclusion included premature stage of development, insufficient objective reporting of outcomes of interest, lack of patient interaction, and/or inadequate elucidation of patients’ values regarding surgical approach and decision-making. However, such studies did merit some discussion due to their novel nature (Table 2) (56-62). Though ineligible by our criteria, these PDAs were reported to be in development for domains such as gender-affirming, brachial plexus, craniofacial, and distal radius fracture surgeries. Such PDAs were developed in the form of digital, web-based, and interactive tools for breast procedures (56,57) and gender-affirming surgery (60) and reported increased decisional confidence and informed consent quality. Other PDAs remain in early development or pilot stages, including promising aids for brachial plexus (59), craniofacial surgery (62), and distal radius fractures (61), all showing promise for future clinical integration. Overall, these tools are advancing patient-centered care across a broad surgical spectrum.

Meta-analysis

Across studies evaluating PDAs in PRS, aggregate analysis via random effects approach demonstrated a significant reduction in decisional conflict as well as decisional regret favoring intervention groups: decisional conflict [MD, –5.37 (of 100 points); 95% confidence interval (CI): –7.94 to –2.80; 16 studies; P<0.001] and decisional regret [MD, –4.93 (of 100 points); 95% CI: –8.73 to –1.12; 9 studies; P=0.01] (Figures 2,3). The negative MD indicated that participants who used PDAs reported lower decisional conflict and decisional regret scores (approximate decrease of 5 points on both instruments scales) compared with those receiving standard counseling or educational materials. Studies reported decisional anxiety via HADS or STAI outcome measures and were analyzed separately as [HADS, MD, −0.77 (of 21 points); 95% CI: –1.77 to 0.23; 3 studies, P=0.13)] and [STAI, MD =0.61 (of 80 points); 95% CI: –3.98 to 5.20; 4 studies, P=0.99] (Figure 4). When pooled together and remodeled to calculate a standardized mean difference (SMD), the model still did not reach significance (SMD =−0.08; 95% CI: –0.29 to 0.14; 7 studies, P=0.49) (Figure 5). Satisfaction of knowledge via BREAST-Q was also analyzed, showing directionality towards improved satisfaction in favor of PDA use, though it did not show significant differences [MD, −1.73 (of 100 points); 95% CI: –7.51 to 4.06; 4 studies; P=0.56] (Figure 6).

Figure 2 Forest plot of decisional conflict via Decisional Conflict Scale, random effects model, mean difference outcome measure. Majority of DA were focused on breast reconstruction unless otherwise denoted. *, Gender Affirming DA; **, Distal Radius Fracture DA; ***, Trapeziometacarpal Arthritis DA. CI, confidence interval; DA, decision aid; MD, mean difference; RE, random effect.

Figure 3 Forest plot of decisional regret via Decision Regret Scale, random effects model, mean difference outcome measure. Majority of DAs were focused on breast reconstruction unless otherwise denoted. ***, Trapeziometacarpal Arthritis DA. CI, confidence interval; DA, decision aid; MD, mean difference; RE, random effect.

Figure 4 Forest plot of anxiety via HADS, random effects model, mean difference outcome measure (top), forest plot of Anxiety via STAI, random effects model, mean difference outcome measure (bottom). CI, confidence interval; HADS, Hospital Anxiety and Depression Scale; MD, mean difference; RE, random effect; STAI, State-Trait Anxiety Inventory.

Figure 5 Forest plot of anxiety via both HADS and STAI, random effects model, standardized mean difference outcome measure. CI, confidence interval; HADS, Hospital Anxiety and Depression Scale; MD, mean difference; RE, random effect; STAI, State-Trait Anxiety Inventory.

Figure 6 Knowledge satisfaction via BREAST-Q, random effects model, mean difference outcome measure. CI, confidence interval; MD, mean difference; RE, random effect.

To confirm the consistency of our models, we re-evaluated these same studies through a fixed effects approach (Figures 7-10). The fixed effects model yielded the following: decisional conflict [MD, −5.38 (of 100 points); 95% CI: –6.81 to –3.95; 16 studies; P<0.001] and decisional regret [MD, –6.15 (of 100 points); 95% CI: −8.04 to –4.26; 9 studies; P<0.001] (Figures 7,8). Decisional anxiety via HADS or STAI outcome measures were analyzed separately through the fixed effects approach and yielded the following: [HADS, MD, −0.69 (of 21 points); 95% CI: −1.41 to 0.03; 3 studies, P=0.06] and [STAI, MD, 0.61 (of 80 points); 95% CI: −1.72 to 2.94; 4 studies, P=0.61] (Figure 9). Satisfaction of knowledge via BREAST-Q through fixed effects approach confirmed directionality, yielding the following: [MD, −2.92 (of 100 points); 95% CI: –5.92 to 0.08; 4 studies; P=0.056] (Figure 10).

Figure 7 Forest plot of decisional conflict via Decisional Conflict Scale, fixed effects model, mean difference outcome measure. Majority of DA were focused on breast reconstruction unless otherwise denoted. *, Gender Affirming DA; **, Distal Radius Fracture DA; ***, Trapeziometacarpal Arthritis DA. CI, confidence interval; DA, decision aid; FE, fixed effect; MD, mean difference.

Figure 8 Forest plot of decisional conflict via Decisional Regret Scale, fixed effects model, mean difference outcome measure. Majority of DA were focused on breast reconstruction unless otherwise denoted. ***, Trapeziometacarpal Arthritis DA. CI, confidence interval; DA, decision aid; FE, fixed effect; MD, mean difference.

Figure 9 Forest plot of decisional regret via Decision Regret Scale, fixed effects model, mean difference outcome measure. Majority of DA were focused on breast reconstruction unless otherwise denoted. CI, confidence interval; DA, decision aid; FE, fixed effect; MD, mean difference.

Figure 10 Knowledge satisfaction via BREAST-Q, fixed effects model, mean difference outcome measure. CI, confidence interval; DA, decision aid; FE, fixed effect.

Therefore, sensitivity analyses evaluating alternative pooling assumptions demonstrated consistent findings across models. Using both random- and fixed-effects approaches, PDA use was associated with significantly lower decisional conflict and decisional regret, with near-identical effect estimates for decisional conflict across models and a modestly larger magnitude for decisional regret under fixed effects. In contrast, decisional anxiety and knowledge satisfaction outcomes remained non-significant regardless of analytic specification, including instrument-specific pooling for HADS and STAI and a harmonized standardized MD model combining anxiety measures.

Due to the predominance of breast reconstruction PDAs, we conducted sub-analysis of these studies and re-assessed decisional conflict and decisional regret. We did not repeat the models for anxiety nor knowledge satisfaction as the studies included for previous inclusion and analysis already exclusively featured breast reconstruction PDAs. Breast reconstruction PDAs analysis was primarily analyzed via random effects approach: decisional conflict [MD, −4.39 (of 100 points); 95% CI: –7.11 to –1.66; 13 studies; P=0.002] and decisional regret [MD, −4.97 (of 100 points); 95% CI: −9.12 to –0.82; 8 studies; P=0.02] (Figures 11,12). It was then re-analyzed through a fixed effects approach: decisional conflict [MD, −4.37 (of 100 points); 95% CI: –5.94 to –2.80; 13 studies; P <0.001] and decisional regret [MD, –6.25 (of 100 points); 95% CI: −8.19 to –4.32; 8 studies; P <0.001] (Figures 13,14).

Figure 11 Forest plot of decisional conflict in breast reconstruction decision aids via Decisional Conflict Scale, random effects model, mean difference outcome measure. CI, confidence interval; DA, decision aid; RE, random effect.

Figure 12 Forest plot of decisional regret in breast reconstruction decision aids via Decision Regret Scale, random effects model, mean difference outcome measure. CI, confidence interval; DA, decision aid; RE, random effect.

Figure 13 Forest plot of decisional conflict in breast reconstruction decision aids via Decisional Conflict Scale, fixed effects model, mean difference outcome measure. CI, confidence interval; DA, decision aid; FE, fixed effect.

Figure 14 Forest plot of decisional regret in breast reconstruction decision aids via Decision Regret Scale, fixed effects model, mean difference outcome measure. CI, confidence interval; DA, decision aid; FE, fixed effect.

Q-test analysis confirmed substantial I² and significant heterogeneity (P<0.05) across models for decisional conflict, decisional regret, and knowledge satisfaction: decisional conflict (I²=63.95%), with Q(df=14)=39.930, P<0.001 (τ²=16.5059; τ=4.063); decisional regret (I²=74.32%), with Q(df=8)=34.895, P<0.001 (τ²=27.9826; τ=5.290); knowledge satisfaction (I²=69.91%), with Q(df=3)=10.041, P=0.02 (τ²=23.5918; τ=4.857). Our models for anxiety via HADS and STAI were assessed to have moderate to substantial heterogeneity: HADS (I²=45.4%), with Q(df=2)=3.641, P=0.16 (τ²=0.3563; τ=0.597); STAI (I²=69.83%), with Q(df=2)=6.917, P=0.03 (τ²=27.471; τ=5.241). Q-test analysis for the breast reconstruction PDA subgroup was consistent with these results: decisional conflict (I²=60.49%), with Q(df=12)=30.213, P=0.003 (τ²=13.4089; τ=3.662); decisional regret (I²=76.98%), with Q(df=7)=32.869, P<0.001 (τ²=26.8212; τ=5.179).

Influence diagnostics were examined for each pooled outcome, using studentized residuals with a Bonferroni-adjusted threshold and Cook’s distance to identify potential outliers.

In the decisional conflict random effects model, there were no studies flagged by studentized residuals for potential outliers and no study met criteria for undue influence according to Cook’s distance. In the decisional regret random effects model, Luan et al. 2016 was flagged by studentized residuals indicating it may be a potential outlier within the model’s context (37). However, no study, including Luan et al., met criteria for undue influence according to Cook’s distance. In the decisional anxiety random effects models by both HADS and STAI, there were no studies flagged by studentized residuals for potential outliers and no study met criteria for undue influence according to Cook’s distance. In the knowledge satisfaction random effects model, Varelas et al. [2020] was flagged by studentized residuals indicating it may be a potential outlier within the model’s context (52). However, no study, including Varelas et al., met criteria for undue influence according to Cook’s distance.

Literature quality assessment

A full assessment of bias was performed for each included study, completed with the RoB2 and Robins-I tool for assessing risk-of-bias as described within the methods section. Resultant plots for risk of bias for RCTs and quasi-/non-randomized trials are depicted in Figures 15,16, respectively. Biases assessed included random sequence generation, allocation concealment, performance, detection, attrition, and reporting. The evaluation of the risk of bias across the eligible and included studies indicated a low-to-moderate risk of bias across domains of biases. Failure to blind interventions to participants and implementers were common across RCTs and other studies studying PDAs thus contributing to potential performance bias. Methodological quality of included qualitative studies was assessed using the CASP checklist. Variable details regarding analytic procedures and rigor were observed. Ethical considerations and clarity of findings were generally well described. Full CASP ratings by domain are provided in Table S1.

Figure 15 Risk of bias traffic light and summary plots for included randomized control trial studies.

Figure 16 Risk of bias traffic light and summary plots for non-randomized interventional studies.

Funnel plot analysis was performed for decisional conflict analysis to test for publication bias, and other patient outcomes eligible for further analysis were assessed using Egger’s regression analysis (Figure S1). Additional funnel plot-based assessments were not performed for decisional regret, anxiety, and knowledge satisfaction, as fewer than ten studies were available for those outcomes of interest, rendering visual interpretation unreliable. Egger’s regression analysis revealed the following with regard to publication bias: decisional conflict (0.649, P=0.52), decisional regret (1.866, P=0.06), HADS (−0.985, P=0.33), STAI (−0.359, P=0.72), knowledge satisfaction (1.438, P=0.15). For breast reconstruction PDA sub-analysis, Egger’s regression for decisional conflict was 0.685 (P=0.49) and 2.170 (P=0.03) was for decisional regret (Figure S2).

For the decisional conflict model, visual inspection of the funnel plot suggested asymmetry. However, Egger’s regression test did not reach statistical significance, providing no formal evidence of small-study effects. For decisional regret (n=9 studies), Egger’s regression approached but did not reach significance (intercept=1.866, P=0.06). For HADS, STAI, and knowledge satisfaction (n=3, 4, and 4 studies respectively), Egger’s regression is not informative and is underpowered. Accordingly, the non-significant findings for these outcomes were not interpreted as evidence for the absence of small-study effects or publication bias. Overall, asymmetry-related inferences, particularly for decisional regret and the anxiety outcomes, were made conservatively given the limited number of studies and the potential for heterogeneity and chance to influence funnel plot-based metrics.

For our breast reconstruction PDA sub-analysis, Egger’s regression test for the decisional conflict model did not reach statistical significance, providing no formal evidence of small-study effects. However, in our decisional regret model, Egger’s regression was determined to be a value of 2.170 and reached significance (P=0.03) which may be consistent with small-study effects. As the number of studies (n=8) was below the commonly recommended threshold for asymmetry testing, this result was interpreted cautiously and not considered definitive evidence of publication bias. Again, we did not repeat publication bias analysis of models for anxiety nor knowledge satisfaction, as the studies included for previous inclusion and analysis already exclusively featured breast reconstruction PDAs.

Discussion

This systematic review demonstrates that patient-facing PDAs meaningfully enhance SDM in PRS, with pooled analyses showing a significant reduction in both decisional conflict and decisional regret. However, despite the broad scope of this review, most identified studies that met our eligibility criteria were concentrated in breast reconstruction (28 of 31, 90.3%), with only two addressing hand/upper extremity surgery and one evaluating a gender-affirming surgical procedure. No eligible studies evaluated SDM in the plastic surgery subspecialties of craniofacial surgery or aesthetic surgery. Most available PDAs utilized web or app-based platforms, with a minority utilizing printed materials. Eligible studies significantly reduced patient-reported decisional conflict and decisional regret, while PDA effects on decisional anxiety did not reach significance. Studies also often reported improved patient satisfaction with information and perceived involvement in decision-making, as well as improved patient knowledge scores, albeit via disparate and varied assessment of both domains. There are several important implications of these findings, with the most notable being that a validated evidence base for PDAs in PRS remains limited, and there remains a need for PDAs across other sub-specialties of PRS. While decision-support materials may exist in clinical practice or are already accessible publicly, many appear to lack standardized outcome evaluation, limiting our ability to determine effectiveness and generalizability with the presently available and eligible studies.

As PRS options expand, surgical decision-making has become more complex, increasing the importance of SDM when multiple appropriate approaches carry distinct risks, benefits, aesthetic outcomes, and long-term quality-of-life implications (63-67). PDAs are designed to facilitate SDM by presenting options and tradeoffs in an accessible format while eliciting patient values to support value-concordant decisions (6,65,68,69). Breast reconstruction after mastectomy is a classic setting for PDA use given the breadth of reconstructive choices and downstream functional and psychosocial implications, whereas PDAs may offer less incremental value when decisions are more binary or pathways are highly standardized (70).

Despite established development frameworks, our review highlights a persistent gap in the availability and procedural diversity of validated PDAs across PRS (6,7). The predominance of breast reconstruction PDAs likely reflects earlier oncologic and oncoplastic work where decision aids were first studied to support breast cancer treatment decisions and subsequently expanded to include reconstructive pathways (68,69). Within breast reconstruction, PDAs were associated with improved decisional conflict and decisional regret, with studies also reporting improvements in knowledge and satisfaction; however, these latter outcomes were inconsistently measured and often insufficiently reported, limiting synthesis an issue similarly observed across non-breast PRS PDAs in the included literature.

Validated PDAs for other subspecialties such as gender-affirming surgery, hand and upper limb reconstruction, craniofacial procedures, and aesthetic surgery were notably scarce. Mokken et al. evaluated one such novel PDA for genital gender-affirming surgery in transmen to promote informed, value-based surgical choices and SDM (41). The tool reduced decisional conflict and improved preparedness and confidence, consistent with prior evidence supporting PDAs in preference-sensitive care. Two validated PDAs were designed for hand and upper limb reconstruction. Both demonstrated measurable benefits in improving patient understanding and reducing decisional conflict. Kim et al. demonstrated that providing audiovisual information to patients with distal radius fractures significantly lowered decisional conflict compared to standard counseling, particularly among younger patients, highlighting the utility of multimedia education in acute trauma settings (33). Similarly, Wilkens et al. found that a web-based PDA for trapeziometacarpal arthritis decreased decisional conflict during initial consultations, emphasizing improved preparedness and comfort with decision-making (53).

The relative scarcity of validated PDAs outside of breast reconstruction may reflect a potential reluctance to invest significant resources into SDM tools. Standards upheld by International Patient Decision Aid Standards (IPDAS) and Ottawa Decision Support Framework (ODSF) are rigorous, and though published frameworks exist to assist in PDA development, the initial barriers may influence the rate at which associated research proceeds (71). With considerations to user acceptability and accessibility, most PDAs in our analysis were delivered digitally either through apps, websites, or other audio-visual means. These mediums require significant expertise as well as time investment and resources to create, record, organize, and/or produce.

Studies that did not meet inclusion criteria for meta-analysis nonetheless reflect growing interest in developing PDAs across PRS, and suggest that many may soon address current gaps and become available for future analyses. Broadly, those PDAs were designed for procedures within the domains of breast reconstruction, breast reduction/mammoplasty, cosmetic breast augmentation, abdominoplasty, distal radius fracture, brachial plexus birth injury, craniofacial/cleft lip and palate (46,48-52,54). Though the work surrounding PDA implementation in PRS has been promising, work remains to be done to validate these prototypes with metrics of interest and patient reported outcomes.

Collectively, our results support a broader implementation and testing of PDAs throughout the spectrum of PRS. Expanding PDA development could enhance communication, align treatment plans with patient values, and mitigate decisional regret. Moreover, the integration of evidence-based, user-centered decision tools into preoperative counseling may standardize how complex surgical information is delivered, ultimately fostering more equitable and informed SDM across diverse patient populations.

This study has several limitations to consider. This is a retrospective systematic review of validated PDAs. Thus, our eligibility criteria may not have captured the full scope of commercially available or marketed decision aids. We prioritized our efforts in analyzing studies with objective reporting of data that were peer-reviewed within a full-text English language scientific journals. Several studies met eligibility criteria but were unable to undergo further analysis due to concerns with study design and/or limited outcomes reporting. The lack of available studies overall limited further analysis and bias assessments.

Accurate meta-analysis of PDAs effects upon patient comprehension and knowledge outcomes was directly impacted. Knowledge assessments via recall surveys and quizzes regarding surgical approaches, risks and benefits, and outcomes were disparate and unstandardized. Despite this marked variability in outcome measures precluding further analysis, our review noted that most studies that reported knowledge outcomes often suggested improved patient comprehension and recall of PDA content.

Individuals with lower socioeconomic status may be less likely to engage in SDM, report lower satisfaction, and exhibit higher decisional regret (72). Few PDAs that met our inclusion criteria were explicitly designed for socially or educationally disadvantaged groups through accounting for literacy barriers and core social determinants of health (22). As new PDAs continue to emerge for the use in plastic and reconstructive procedures, we encourage future studies to reassess their impact upon patient-reported outcomes, review efforts to improve access to diverse patient populations.

Conclusions

In conclusion, PDAs can facilitate and elucidate value-based decision making in complex and high stakes procedures within PRS. With continued investment from surgical teams and institutions, PDAs have the potential to reduce decisional conflict and regret while enhancing shared decision making and patient satisfaction across PRS.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://abs.amegroups.com/article/view/10.21037/abs-2025-1-66/rc

Peer Review File: Available at https://abs.amegroups.com/article/view/10.21037/abs-2025-1-66/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://abs.amegroups.com/article/view/10.21037/abs-2025-1-66/coif). C.K. serves as an unpaid editorial board member of Annals of Breast Surgery from December 2025 to November 2027. D.W.M reports consulting fees from MTF Biologics and TELA Bio. C.K. reports consulting fees from BD, Bimini Health Tech, and TELA Bio; and honoraria from BD and TELA Bio for lectures. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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