CORR Insights®: Patient Perceptions Correlate Weakly With Observed Patient Involvement in Decision-making in Orthopaedic Surgery
Bibliographic record
Abstract
Where Are We Now? David Sackett’s often-quoted definition of evidence-based medicine includes three key aspects: Patient values, clinical expertise, and best evidence [10]. Shared decision-making—the process of empowering patients to play an active role in their care—may be the epitome of evidence-based medicine, as it involves taking the best-available information and communicating it to the patient through the lens of the clinician’s best judgement, while also considering the patient’s values and preferences in the decision-making process. A Cochrane review of 115 trials in all specialties found that using decision aids to facilitate shared decision-making resulted in better doctor-patient communication, improved patient knowledge, and better patient comfort with the decision [12]. In the current study, Mertz and colleagues [9] asked 117 orthopaedic patients to rate their level of involvement in the decision-making process and they compared the patients’ perceptions with an objective measure of patient involvement. They found that patient-perceived involvement and observed involvement were weakly correlated and that patients perceived that they were more involved than they were, according to objective observations. These findings are in line with findings in other specialties [4] and they highlight some of the complexities surrounding shared decision-making in orthopaedics. This study opens the door for orthopaedic surgeons to have a conversation about patient involvement in decision-making and conducting further research and education on the topic. Where Do We Need To Go? After reading the study by Mertz and colleagues [9], a few questions remain about shared decision-making in orthopaedics. First, given the discordance between patient-perceived involvement and objective measures of involvement in decision-making, is objective involvement important, or is it the patient’s perceptions that matter more? One could argue that the most-important outcome of a shared decision-making encounter is that the patient is satisfied with the encounter and the decision. This line of thinking arises from the concept that patients are partners in the medical treatment process and not just passive agents who have particular treatments done to them. Additionally, Mertz and colleagues point out that patient satisfaction with decision-making can be associated with improved health outcomes. Perhaps we should focus our attention on patient-perceived involvement rather than objective involvement. Next, how can we improve and promote patient involvement (or perceptions of involvement)? There are several barriers to implementing shared decision-making strategies, including both patient-related factors and physician factors. A major barrier to shared decision-making is the physician’s perception that some patients don’t want to be involved in decision-making [7]. However, some research shows that most patients would prefer to be more involved in the decision-making process, so this belief is more of a myth than reality [5]. Other barriers such as negative surgeon perceptions, as well as time constraints, can be identified and targeted with interventions to break down those barriers. Finally, how does patient involvement in decision-making impact patient-important health outcomes and healthcare costs? A recent Cochrane in CORR® article [8] noted that there is a gap in knowledge when it comes to whether shared decision-making affects health outcomes, and there is limited cost-effectiveness information regarding shared decision-making programs. Some studies have shown that shared decision-making can improve health outcomes such as general physical function [1], but a Cochrane review showed no difference in health outcomes in most included studies [12]. The Cochrane review also demonstrated that patients who were exposed to shared decision-making through decision aids were less likely to choose elective surgical treatment, and that consults took longer when physicians used shared decision-making strategies [12]. This may have implications for healthcare costs and cost-effectiveness measures. How Do We Get There? Shared decision-making can thrive when patients and surgeons have the best-available information, meaning that high-quality research is needed in all areas where shared decision-making is used. For example, there are high-quality recommendations to avoid arthroscopic surgery for degenerative knee disorders [11], so patients facing the decision to undergo arthroscopic surgery will have high-quality information on which to base their decisions. Other treatment options, for example reverse total shoulder arthroplasty, may not have as much high-quality evidence to inform decisions. But it should be noted that the fact that there are guidelines does not guarantee that they are used in clinical practice [6]. The research process itself should also involve patients to ensure that patient values are being considered and the research will be useful to patients and surgeons to enhance uptake and use in clinical practice [2]. Future studies should focus on developing and evaluating surgeon-education programs that address actual and perceived barriers to implementing shared decision-making strategies in everyday practice. For example, Légaré and colleagues [5] presented 12 myths regarding shared decision-making and they dispel those myths with evidence, and in their study, Jo and An educated nurses on shared decision-making which improved nurses’ perceptions [3], which could be a good starting point on which to build future research in this area. Finally, as there is limited information on orthopaedic-specific health outcomes when implementing shared decision-making, future longitudinal studies should be conducted assessing outcomes that are important to the orthopaedic treatment of interest (such as post-surgical pain, joint-specific function, and reoperations). This can be better achieved by including orthopaedic patients in research protocol development, for example using strategies suggested by the Strategy for Patient-Oriented Research in Canada or Patient-Centered Outcomes Research Institute in the United States. Cost-effectiveness should also be assessed in conjunction with any future research by involving health technology assessment or health economics experts in protocol development. For efficiency, many cost-effectiveness designs can be integrated into other designs such as randomized trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.102 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".