Efficacy of a Preference-Based Decision Tool on Treatment Decisions for a First-Time Anterior Shoulder Dislocation: A Randomized Controlled Trial of At-Risk Patients

Hutyra, Carolyn A.; Smiley, Stephen; Taylor, Dean C.; Orlando, Lori A.; Mather, Richard C.

doi:10.1177/0272989x19832915

Cited by 13 publications

(16 citation statements)

References 65 publications

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“…This technology can help patients review the latest evidence-based recommendations, limit surgeon bias, and serve as a starting point for discussion regarding benefits and risks of operative and nonoperative treatment. 5,9,12 Our study suggests that application of tools to measure and deliver patient preferences combined with evidence-based risk of recurrence could benefit both providers and patients in optimizing individualized treatment. Overall, CDS tools can serve as a method to improve communication and enhance outcomes.…”

Section: Discussionmentioning

confidence: 90%

“…Overall, CDS tools can serve as a method to improve communication and enhance outcomes. 5,9,12 There were several limitations of this study. For example, limited information was contained within each case study.…”

Section: Discussionmentioning

confidence: 95%

“…CDS tools, such as predictive models or decision aids, could be used to help mitigate biases and accurately disseminate evidence-based rates of recurrence while incorporating patient preferences. 5,12 For example, after a history and physical examination which indicates a diagnosis of FTASD then before a discussion of treatment options, the patient may be asked to complete a survey about his or her characteristics and treatment preferences. The results from this survey could then be used to estimate the recurrence risk while displaying patient preferences in a graphical format (see Table 2) for the surgeon to review with the patient.…”

Section: Discussionmentioning

confidence: 99%

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Surgeon Applications of Patient Preferences in Treatment Decision Making for First-Time Anterior Shoulder Dislocation

Lau

Hutyra

Streufert

et al. 2020

Orthopaedic Journal of Sports Medicine

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Background: Treatment of a first-time anterior shoulder dislocation (FTASD) is sensitive to patient preferences. The operative or nonoperative management debate provides an excellent opportunity to learn how surgeons apply patient preferences in treatment decisions. Purpose: To determine how patient preferences (repeat dislocation risk, recovery difficulties, fear of surgery, treatment costs) and surgeon factors influence a surgeon’s treatment plan for FTASD. Study Design: Cross-sectional study. Methods: Eight clinical vignettes of hypothetical patients with FTASD (including age, sex, and activity level) were presented to members of the Magellan Society. A second set of matched vignettes with patient preferences and clinical variables were also presented. The vignettes represented scenarios in which evidence does not favor one treatment over another. Respondents were asked how they would manage each hypothetical case. Respondents also estimated the risk of redislocation for the nonoperative cases for comparison with the published rates. Finally, respondents completed a Likert-scale questionnaire to determine their perceptions on factors influencing their decisions. Results: A total of 103 orthopaedic surgeons completed the survey; 48% practiced in an academic hospital; 79% were in practice for 10 years or longer; and 75% had completed a sports medicine fellowship. Patient preferences were the single most important factor influencing treatment recommendation, with activity type and age also important. Just 62% of the surgeon estimates of the risk of redislocation were consistent with the published rates. The inclusion of patient preferences to clinical variables changed treatment recommendations in 62.5% of our hypothetical cases. Respondents rated patient treatment preference as the leading factor in their treatment decision making. Conclusion: Patient preferences were important when deciding the appropriate treatment for FTASD. Respondents were inconsistent when applying evidence in their decision making and estimates of recurrent instability. Decision support tools that deliver patient preferences and personalized evidence-based outcome estimates improve the quality of decision making at the point of care.

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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

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Surgeon Applications of Patient Preferences in Treatment Decision Making for First-Time Anterior Shoulder Dislocation

Lau

Hutyra

Streufert

et al. 2020

Orthopaedic Journal of Sports Medicine

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“…The importance plot shows the relative influence that each attribute had on the treatment choice in the FTASD study using profile-based normalization. 13 That is, the utility difference between the most and least desirable profiles that can be generated with the attribute levels is normalized to 1.…”

Section: Resultsmentioning

confidence: 99%

“…To evaluate the approach described above, we relied on data from a recent study on treatment of FTASD that elicited patient preferences for various treatment features. 13 FTASD management involves either nonoperative treatment with immobilization, operative treatment with arthroscopic stabilization, or open repair. 27,28 In the previous study, individual-level preferences were estimated from an adaptive conjoint analysis survey ( N = 200) with 8 graded-pair questions on 4 attributes: limitations in arm motion after surgery, need for avoidance of contact sports and lifting overhead, chance of another dislocation, and out-of-pocket cost of treatment.…”

Section: Introductionmentioning

confidence: 99%

Patient-Preference Diagnostics: Adapting Stated-Preference Methods to Inform Effective Shared Decision Making

et al. 2022

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Background While clinical practice guidelines underscore the need to incorporate patient preferences in clinical decision making, incorporating meaningful assessment of patient preferences in clinical encounters is challenging. Structured approaches that combine quantitative patient preferences and clinical evidence could facilitate effective patient-provider communication and more patient-centric health care decisions. Adaptive conjoint or stated-preference approaches can identify individual preference parameters, but they can require a relatively large number of choice questions or simplifying assumptions about the error with which preferences are elicited. Method We propose an approach to efficiently diagnose preferences of patients for outcomes of treatment alternatives by leveraging prior information on patient preferences to generate adaptive choice questions to identify a patient’s proximity to known preference phenotypes. This information can be used for measuring sensitivity and specificity, much like any other diagnostic procedure. We simulated responses with varying levels of choice errors for hypothetical patients with specific preference profiles to measure sensitivity and specificity of a 2-question preference diagnostic. Results We identified 4 classes representing distinct preference profiles for patients who participated in a previous first-time anterior shoulder dislocation (FTASD) survey. Posterior probabilities of class membership at the end of a 2-question sequence ranged from 87% to 89%. We found that specificity and sensitivity of the 2-question sequences were robust to respondent errors. The questions appeared to have better specificity than sensitivity. Conclusions Our results suggest that this approach could help diagnose patient preferences for treatments for a condition such as FTASD with acceptable precision using as few as 2 choice questions. Such preference-diagnostic tools could be used to improve and document alignment of treatment choices and patient preferences. Highlights Approaches that combine patient preferences and clinical evidence can facilitate effective patient-provider communication and more patient-centric healthcare decisions. However, diagnosing individual-level preferences is challenging, and no formal diagnostic tools exist. We propose a structured approach to efficiently diagnose patient preferences based on prior information on the distribution of patient preferences in a population. We generated a 2-question test of preferences for the outcomes associated with the treatment of first-time anterior shoulder dislocation. The diagnosis of preferences can help physicians discuss relevant aspects of the treatment options and proactively address patient concerns during the clinical encounter.

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Decision aids for people facing health treatment or screening decisions

Stacey,

Lewis,

Smith

et al. 2024

Cochrane Database of Systematic Reviews

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Efficacy of a Preference-Based Decision Tool on Treatment Decisions for a First-Time Anterior Shoulder Dislocation: A Randomized Controlled Trial of At-Risk Patients

Cited by 13 publications

References 65 publications

Surgeon Applications of Patient Preferences in Treatment Decision Making for First-Time Anterior Shoulder Dislocation

Surgeon Applications of Patient Preferences in Treatment Decision Making for First-Time Anterior Shoulder Dislocation

Patient-Preference Diagnostics: Adapting Stated-Preference Methods to Inform Effective Shared Decision Making

Decision aids for people facing health treatment or screening decisions

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