Medical Decision Making最新文献

Purpose: To examine the effects of training general practitioners and nurses in shared decision-making (SDM) support as perceived by cancer patients and survivors.

Design: An innovative, experimental design was adopted that included analogue patients (APs), that is, people who have or have had cancer and who imagine themselves in the position of the actor-patient presented in a video. Each AP assessed a video-recorded simulated consultation of a health care professional (HCP) conducted before or after an SDM support training program. The primary outcome was the APs' perceived SDM support with 13 self-developed items reflecting the perceived patient benefit of SDM support as well as the perceived HCP support behavior. Secondary outcomes included an overall rating of SDM support, AP-reported extent of SDM (CollaboRATE), satisfaction with the communication (Patient Satisfaction Questionnaire), conversation appreciation and helpfulness, as well as decision-making satisfaction and confidence (visual analog scale, 0-100). In addition, patient and HCP characteristics associated with AP-perceived SDM support were examined.

Results: APs (n = 131) did not significantly differentiate trained from untrained HCPs in their perceptions of SDM support nor in secondary outcomes. Agreement between APs' perceptions was poor. The higher the perceived comparability of the consultation with APs' previous personal experiences, the higher their rating of SDM support.

Limitations: We used a nonvalidated primary outcome and an innovative study design that should be tested in future work.

Conclusions: Despite the limitations of the study design, the training seemed to not affect cancer patients' and survivors' perceived SDM support.

Implications: The clinical relevance of the training on SDM support needs to be established. The variation in APs' assessments suggests patients differ in their perception of SDM support, stressing the importance of patient-tailored SDM support.

Highlights: Cancer patients and survivors did not significantly differentiate trained from untrained HCPs when evaluating SDM support, and agreement between their perceptions was poor.The clinical relevance of training GPs and nurses in SDM support needs to be established.Patient-tailored SDM support may be recommended, given the variation in APs' assessments and their possible diverging perceptions of SDM support.This innovative study design (having patients watch and assess videos of simulated consultations made in the context of training evaluation) needs to be further developed.

Background: Health economic evaluations using common health-related quality of life measures may fall short in adequately measuring and valuing the benefits of mental health care interventions. The Mental Health Quality of Life questionnaire (MHQoL) is a standardized, self-administered mental health-related quality of life instrument covering 7 dimensions known to be relevant across and valued highly by people with mental health problems. The aim of this study was to derive a Dutch value set for the MHQoL to facilitate its use in cost-utility analyses.

Methods: The value set was estimated using a discrete choice experiment (DCE) with duration that accommodated nonlinear time preferences. The DCE was embedded in a web-based self-complete survey and administered to a representative sample (N = 1,308) of the Dutch adult population. The matched pairwise choice tasks were created using a Bayesian heterogeneous D-efficient design. The overall DCE design comprised 10 different subdesigns, with each subdesign containing 15 matched pairwise choice tasks. Each participant was asked to complete 1 of the subdesigns to which they were randomly assigned.

Results: The obtained coefficients indicated that "physical health,""mood," and "relationships" were the most important dimensions. All coefficients were in the expected direction and reflected the monotonic structure of the MHQoL, except for level 2 of the dimension "future." The predicted values for the MHQoL ranged from -0.741 for the worst state to 1 for the best state.

Conclusions: This study derived a Dutch value set for the recently introduced MHQoL. This value set allows for the generation of an index value for all MHQoL states on a QALY scale and may hence be used in Dutch cost-utility analyses of mental healthcare interventions.

Highlights: A discrete choice experiment was used to derive a Dutch value set for the MHQoL.This allows the use of the MHQoL in Dutch cost-utility analyses.The dimensions physical health, mood, and relationships were the most important.The utility values range from -0.741 for the worst state to 1 for the best state.

Background: While shared decision making (SDM) is advocated for ethical reasons and beneficial outcomes, SDM might also negatively affect patients with incurable cancer. The current study explored whether SDM, and an oncologist training in SDM, are associated with adverse outcomes (i.e., patient anxiety, tension, helplessness/hopelessness, decisional uncertainty, and reduced fighting spirit).

Design: A secondary analysis of a randomized clinical trial investigating the effects of SDM interventions in the context of advanced cancer. The relations between observed SDM (OPTION12), specific SDM elements (4SDM), oncologist SDM training, and adverse outcomes were analyzed. We modeled adverse outcomes as a multivariate phenomenon, followed by univariate regressions if significant.

Results: In total, 194 patients consulted by 31 oncologists were included. In a multivariate analysis, observed SDM and adverse outcomes were significantly related. More specifically, more observed SDM in the consultation was related to patients reporting more tension (P = 0.002) and more decisional uncertainty (P = 0.004) at 1 wk after the consultation. The SDM element "informing about the options" was especially found to be related to adverse outcomes, specifically to more helplessness/hopelessness (P = 0.002) and more tension (P = 0.016) at 1 wk after the consultation. Whether the patient consulted an oncologist who had received SDM training or not was not significantly related to adverse outcomes. No relations with long-term adverse outcomes were found.

Conclusions: It is important for oncologists to realize that for some patients, SDM may temporarily be associated with negative emotions. Further research is needed to untangle which, when, and how adverse outcomes might occur and whether and how burden may be minimized for patients.

Highlights: Observed shared decision making was related to more tension and uncertainty postconsultation in advanced cancer patientsHowever, training oncologists in SDM did not affect adverse outcomes.Further research is needed to untangle which, when, and how adverse outcomes might occur and how burden may be minimized.

Background: In orthopedics, the use of patient decision aids (ptDAs) is limited. With a mixed-method process evaluation, we investigated patient factors associated with accepting versus declining the use of the ptDA, patients' reasons for declining the ptDA, and clinicians' perceived barriers and facilitators for its use.

Methods: Patients with an indication for joint replacement surgery (N = 153) completed questionnaires measuring demographics, physical functioning, quality of life (EQ-5D-3L), and a visual analog scale (VAS) pain score at 1 time point. Subsequently, their clinician offered them the relevant ptDA. Using a retrospective design, we compared patients who used the ptDA (59%) with patients who declined (41%) on all these measures as well as the chosen treatment. If the use of the ptDA was declined, patients' reasons were recorded by their clinician and analysed (n = 46). To evaluate the experiences of clinicians (n = 5), semistructured interviews were conducted and thematically analyzed. Clinicians who did not use the ptDA substantially (<10 times) were also interviewed (n = 3).

Results: Compared with patients who used the ptDA, patients who declined use had higher VAS pain scores (7.2 v. 6.2, P < .001), reported significantly worse quality of life (on 4 of 6 EQ-5D-3L subscales), and were less likely to receive nonsurgical treatment (4% v. 28%, P < .001). Of the patients who declined to use the ptDA, 46% said they had enough information and felt ready to make a decision without the ptDA. The interviews revealed that clinicians considered the ptDAs most useful for newly diagnosed patients who had not received previous treatment.

Conclusion: These results suggest that the uptake of a ptDA may be improved if it is introduced in the early disease stages of hip and knee osteoarthritis.

Highlights: Patients who declined the use of a patient decision aid (ptDA) for hip and knee osteoarthritis reported more pain and worse quality of life.Most patients who declined to use a ptDA felt sufficiently well informed to make a treatment decision.Patients who declined the ptDA were more likely to have received prior treatment in primary care.Clinicians found the ptDA to be a helpful addition to the consultation, particularly for newly diagnosed patients.

Background: The test tradeoff curve helps investigators decide if collecting data for risk prediction is worthwhile when risk prediction is used for treatment decisions. At a given benefit-cost ratio (the number of false-positive predictions one would trade for a true positive prediction) or risk threshold (the probability of developing disease at indifference between treatment and no treatment), the test tradeoff is the minimum number of data collections per true positive to yield a positive maximum expected utility of risk prediction. For example, a test tradeoff of 3,000 invasive tests per true-positive prediction of cancer may suggest that risk prediction is not worthwhile. A test tradeoff curve plots test tradeoff versus benefit-cost ratio or risk threshold. The test tradeoff curve evaluates risk prediction at the optimal risk score cutpoint for treatment, which is the cutpoint of the risk score (the estimated risk of developing disease) that maximizes the expected utility of risk prediction when the receiver-operating characteristic (ROC) curve is concave.

Methods: Previous methods for estimating the test tradeoff required grouping risk scores. Using individual risk scores, the new method estimates a concave ROC curve by constructing a concave envelope of ROC points, taking a slope-based moving average, minimizing a sum of squared errors, and connecting successive ROC points with line segments.

Results: The estimated concave ROC curve yields an estimated test tradeoff curve. Analyses of 2 synthetic data sets illustrate the method.

Conclusion: Estimating the test tradeoff curve based on individual risk scores is straightforward to implement and more appealing than previous estimation methods that required grouping risk scores.

Highlights: The test tradeoff curve helps investigators decide if collecting data for risk prediction is worthwhile when risk prediction is used for treatment decisions.At a given benefit-cost ratio or risk threshold, the test tradeoff is the minimum number of data collections per true positive to yield a positive maximum expected utility of risk prediction.Unlike previous estimation methods that grouped risk scores, the method uses individual risk scores to estimate a concave ROC curve, which yields an estimated test tradeoff curve.

Objectives: In the context of validating a measure of patient report specific to diagnostic accuracy in emergency department or urgent care, this study investigates patients' and care partners' perceptions of diagnoses as accurate and explores variations in how they reason while they assess accuracy.

Methods: In February 2022, we surveyed a national panel of adults who had an emergency department or urgent care visit in the past month to test a patient-reported measure. As part of the survey validation, we asked for free-text responses about why the respondents indicated their (dis)agreement with 2 statements comprising patient-reported diagnostic accuracy: 1) the explanation they received of the health problem was true and 2) the explanation described what to expect of the health problem. Those paired free-text responses were qualitatively analyzed according to themes created inductively.

Results: A total of 1,116 patients and care partners provided 982 responses coded into 10 themes, which were further grouped into 3 reasoning types. Almost one-third (32%) of respondents used only corroborative reasoning in assessing the accuracy of the health problem explanation (alignment of the explanation with either test results, patients' subsequent health trajectory, their medical knowledge, symptoms, or another doctor's opinion), 26% used only perception-based reasoning (perceptions of diagnostic process, uncertainty around the explanation received, or clinical team's attitudes), and 27% used both types of reasoning. The remaining 15% used general beliefs or nonexplicated logic (used only about accurate diagnoses) and combinations of general reasoning with perception-based and corroborative.

Conclusions: Patients and care partners used multifaceted reasoning in their assessment of diagnostic accuracy.

Implications: As health care shifts toward meaningful diagnostic co-production and shared decision making, in-depth understanding of variations in patient reasoning and mental models informs use in clinical practice.

Highlights: An analysis of 982 responses examined how patients and care partners reason about the accuracy of diagnoses they received in emergency or urgent care.In reasoning, people used their perception of the process and whether the diagnosis matched other factual information they have.We introduce "patient reasoning" in the diagnostic measurement context as an area of further research to inform diagnostic shared decision making and co-production of health.