Medical Decision Making最新文献

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.

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.

Purpose: Overdiagnosis is a concept central to making informed breast cancer screening decisions, and yet some people may react to overdiagnosis with doubt and skepticism. The present research assessed 4 related reactions to overdiagnosis: reactance, self-exemption, disbelief, and source derogation (REDS). The degree to which the concept of overdiagnosis conflicts with participants' prior beliefs and health messages (information conflict) was also assessed as a potential antecedent of REDS. We developed a scale to assess these reactions, evaluated how those reactions are related, and identified their potential implications for screening decision making.

Methods: Female participants aged 39 to 49 years read information about overdiagnosis in mammography screening and completed survey questions assessing their reactions to that information. We used a multidimensional theoretical framework to assess dimensionality and overall domain-specific internal consistency of the REDS and Information Conflict questions. Exploratory and confirmatory factor analyses were performed using data randomly split into a training set and test set. Correlations between REDS, screening intentions, and other outcomes were evaluated.

Results: Five-hundred twenty-five participants completed an online survey. Exploratory and confirmatory factor analyses identified that Reactance, Self Exemption, Disbelief, Source Derogation, and Information Conflict represent unique constructs. A reduced 20-item scale was created by selecting 4 items per construct, which showed good model fit. Reactance, Disbelief, and Source Derogation were associated with lower intent to use information about overdiagnosis in decision making and the belief that informing people about overdiagnosis is unimportant.

Conclusions: REDS and Information Conflict are distinct but correlated constructs that are common reactions to overdiagnosis. Some of these reactions may have negative implications for making informed screening decisions.

Highlights: Overdiagnosis is a concept central to making informed breast cancer screening decisions, and yet when provided information about overdiagnosis, some people are skeptical.This research developed a measure that assessed different ways in which people might express skepticism about overdiagnosis (reactance, self-exemption, disbelief, source derogation) and also the perception that overdiagnosis conflicts with prior knowledge and health messages (information conflict).These different reactions are distinct but correlated and are common reactions when people learn about overdiagnosis.Reactance, disbelief, and source derogation are associated with lower intent to use information about overdiagnosis in decision making as well as the belief that informing people about overdiagnosis is unimportant.

Objectives: Physician preference items (PPIs) are high-cost medical devices for which clinicians express firm preferences with respect to a particular manufacturer or product. This study aims to identify the most important factors in the choice of new PPIs (hip or knee prosthesis) and infer about the existence of possible response biases in using 2 alternative stated preference techniques.

Methods: Six key attributes with 3 levels each were identified based on a literature review and clinical experts' opinions. An online survey was administered to Italian hospital orthopedists using type 1 best-worst scaling (BWS) and binary discrete choice experiment (DCE). BWS data were analyzed through descriptive statistics and conditional logit model. A mixed logit regression model was applied to DCE data, and willingness-to-pay (WTP) was estimated. All analyses were conducted using Stata 16.

Results: A sample of 108 orthopedists were enrolled. In BWS, the most important attribute was "clinical evidence," followed by "quality of products," while the least relevant items were "relationship with the sales representative" and "cost." DCE results suggested instead that orthopedists prefer high-quality products with robust clinical evidence, positive health technology assessment recommendation and affordable cost, and for which they have a consolidated experience of use and a good relationship with the sales representative.

Conclusions: The elicitation of preferences for PPIs using alternative methods can lead to different results. The BWS of type 1, which is similar to a ranking exercise, seems to be more affected by acquiescent responding and social desirability than the DCE, which introduces tradeoffs in the choice task and is likely to reveal more about true preferences.

Highlights: Physician preference items (PPIs) are medical devices particularly exposed to physicians' choice with regard to type of product and supplier.Some established techniques of collecting preferences can be affected by response biases such as acquiescent responding and social desirability.Discrete choice experiments, introducing more complex tradeoffs in the choice task, are likely to mitigate such biases and reveal true physicians' preferences for PPIs.

Highlights: This tutorial provides a user-friendly guide to mathematically formulating constrained optimization problems and implementing them using Python.Two examples are presented to illustrate how constrained optimization is used in health applications, with accompanying Python code provided.