Medical Decision Making最新文献

BackgroundResearch suggests that the method used to determine voluntary consent (i.e., opt-in versus opt-out policies) greatly affects the number of registered organ donors in various countries. Although the concept of organ transplantation is broadly supported, the relatively low percentage of registered donors in opt-in countries is puzzling. We suggest that deviating from the status quo (such as signing an organ donor card in opt-in countries or removing oneself from the list of registered donors in opt-out countries) heightens one's sense of vulnerability.DesignWe examined our prediction in 2 online experiments involving participants from the United States (studies 1 and 2), which has an opt-in organ-donation policy, and from the United Kingdom (study 2), a country that has recently changed its policy to opt out.ResultsIn study 1, registered organ donors perceived their vulnerability as greater after being reminded of their decision, but vulnerability perceptions were not affected by such a reminder among nondonors who upheld the status quo. In study 2, imagining oneself making an organ donation decision that deviates from the status quo (signing a commitment under an opt-in policy or removing oneself from the registered donors list under an opt-out policy) increased participants' perceived personal vulnerability.ConclusionsThe decision to become an organ donor may affect individuals' sense of physical vulnerability, depending on their country's donation policy. Potentially, deviating from the status quo may curtail willingness for organ donation. Understanding the psychological barriers to organ donation may help overcome them by presenting the issue in a manner that takes such perceptions into account. We recommend future research to explore whether this heightened sense of vulnerability potentially deters organ donation in opt-in countries.HighlightsThe decision to become an organ donor may affect individuals' sense of physical vulnerability, depending on their country's donation policy (opt in versus opt out).Registered organ donors perceived their vulnerability as greater after being reminded of their decision, but vulnerability perceptions were not affected by such a reminder among nondonors who upheld the status quo.Imagining oneself making an organ donation decision that deviates from the status quo (signing a commitment under an opt-in policy or removing oneself from the registered donors list under an opt-out policy) increased participants' perceived personal vulnerability.Future research is needed to examine whether this heightened sense of vulnerability affects actual organ donation decisions.

Decision aids (DA) are more likely to be adopted if co-developed with stakeholders and culturally adapted. Using the DEVELOPTOOLS Reporting Checklist, we describe a process for rapid co-development of a culturally adapted DA prototype for population-wide cancer-screening programs. Our systematic, collaborative, and iterative methodology had 7 phases: 1) set up the process by adopting best governance practices (e.g., identify and engage stakeholders, adapt our collaborative DA design process, validate development process), with governance comprising 20 individuals from a wide range of sectors including at least 2 citizens; 2) identify and analyze existing DAs relevant to the cancerscreening of interest by conducting a systematic review; 3) share results with stakeholders and make recommendations; 4) formulate Quebec-specific DA content and consult stakeholders including users by conducting e-Delphi surveys; 5) co-design a prototype with stakeholders, including users, following international DA standards; 6) translate the DA using translation-back translation approaches and deploy; and 7) knowledge mobilization (KMb) using end-of-grant and integrated KMb activities. Using the User-Centred Design 11-Item Measure (UCD-11), our proposed process scored 10 of 11 on the UCD-11. Overall, we expect this new co-developed process to ensure that good-quality, user-centered, and culturally adapted DAs for cancer screening are produced within reasonable timeframes. We also expect it to foster the adoption of the DAs.HighlightsWe report on a 7-step process for collaborating with various stakeholders to create a culturally adapted decision aid (DA) prototype for deciding about cancer screening in Quebec, Canada.The process includes: ○ Making sure the DA prototype design includes users and other interested parties and reflects their needs, perceptions, values, and preferences.○ Finding and analyzing existing DAs on cancer screening to decide what ours should include○ Respecting international standards and criteria for DA design○ Repeated rounds of expert consensus about the exact content, with revisions between each roundThis method could help the rapid creation of DAs shaped by users' interests and will ultimately encourage shared decision making.

IntroductionSocietal preferences over different health states are used to guide service planning, but there has been little investigation of treatment preferences at the end of life. This study aimed to examine population preferences for active treatment or palliation for cancer patients when life expectancy is limited and the relative importance of time spent in hospital or with functional limitation.MethodsWe used a discrete choice experiment that presented respondents with a series of hypothetical patients who had died, describing their last few months of life. Respondents selected the end-of-life alternative they thought best. Data were collected from 1,502 Australian adults participating in an online survey panel. Latent class analysis was used to identify groups with different preference patterns.ResultsFour preference groups were identified along with an additional group that we termed inattentive, as they appeared to respond at random. Among the 1,070 respondents assigned to 1 of the 4 preference groups, 33.5% favored longer overall survival regardless of how that time was spent; 26.1% were willing to accept a shorter survival time for less time in the hospital or completely incapacitated at home, and they had a stronger preference for palliative care in older patients; 22.5% strongly supported the use of palliative care regardless of the age of the patients, preferring less time in the hospital or time at home with any functional limitations; and 17.9% had a strong preference to not use palliative care.ConclusionsOur results show distinct heterogeneity in population preferences for end-of-life care. Policy goals and service planning should acknowledge this heterogeneity and provide end-of-life support services that offer the flexibility to enhance patient choice. Many current funding approaches are not consistent with the philosophy of patient-centered care. Policy makers can and should be exploring innovative approaches to improve efficiency and equity.HighlightsSocial preferences, based on a general population survey, vary across palliative and active care approaches.Preferences for palliative care and willingness to tolerate time in hospital and time at home with activity limitations varied within the groups willing to trade quality and quantity of life.Policy, resource allocation, and funding methods should accommodate this variability.

IntroductionHospitals are interested in improving the quality of data disaggregation and collection to advance diversity, equity, and inclusion (DEI) goals. We evaluated the extent to which hospitals are adopting DEI disaggregated data to inform organizational decisions and the characteristics associated with this adoption.MethodsWe analyzed data from the 2022 American Hospital Association Annual Survey, which included the final iteration of a new survey item about hospital DEI disaggregated data adoption for decision making. Descriptive statistics, logistic regression, and negative binomial regression were used to evaluate this survey item.ResultsAmong hospitals adopting DEI disaggregated data (n = 2,596, 41.9%), two-thirds used these data to inform decisions about patient outcomes, half about training or professional development, and one-third about supply chain or procurement. Larger, tax-exempt, Veteran Affairs, or metropolitan hospitals are significantly more likely to adopt DEI disaggregated data for decision making.LimitationsOur work is limited by the reporting of 1-y cross-sectional results.ConclusionsMost hospitals adopt DEI disaggregated data to inform decisions about patient outcomes. Future research should explore whether hospital decisions or disaggregated data adoption have advanced DEI and health equity for underserved communities.ImplicationsAnalysis of disaggregated data adoption could reveal how hospitals make decisions and funding allocations to advance DEI goals and health equity.HighlightsThere is a limited understanding of the extent to which hospitals adopt diversity, equity, and inclusion (DEI) disaggregated data to inform organizational decision making, highlighting a knowledge gap at the intersection of data equity and health care management.Among hospitals that adopt DEI disaggregated data, two-thirds use them to inform organizational decisions about patient outcomes, and half about professional development.Larger, tax-exempt, Veteran Affairs, or metropolitan hospitals are more likely to adopt DEI disaggregated data for organizational decision making.Future research is needed to explore whether hospital adoption of DEI disaggregated data has advanced DEI organizational goals and health equity for underserved populations.

BackgroundResearch with lay audiences (e.g., patients, the public) can inform the communication of health-related numerical information. However, a recent systematic review (Making Numbers Meaningful) highlighted several common issues in the literature that impair readers' ability to evaluate and replicate these studies.PurposeTo create a set of guidelines for reporting research regarding the research on communicating numbers to lay audiences for health-related purposes.Reporting RecommendationsWe present 6 common reporting issues from research on communicating numbers that pertain to the background motivating the study, experimental design and analysis reporting, description of the outcomes, and reporting of the data presentation formats. To address these issues, we propose a set of 7 reporting guidelines including 1) specifying how study objectives address a gap in evidence on research on communicating numbers, 2) clearly reporting all combinations of data presentation formats (experimental conditions) compared, 3) providing verbatim examples of the data that were presented to the audience, 4) describing whether or not participants had access to the data presentation formats while outcomes were assessed, 5) reporting the wording of all outcome measures, 6) using standardized terms for both outcomes and data presentation formats, and 7) ensuring that broad outcome concepts such as gist, comprehension, or knowledge are concretely defined.ConclusionsFuture studies involving research on communicating health-related numbers should use these guidelines to improve the quality of reporting and ease of evidence synthesis in future efforts.HighlightsOur systematic review allowed us to exhaustively identify and enumerate several common reporting issues from research on communicating numbers that make it challenging to synthesize evidence.Reporting issues involved not including the background motivating the gap the study addresses, insufficiently describing experimental designs and analyses, and failing to report information regarding the outcomes measured.We propose 7 reporting guidelines for future research on communicating numbers to address the issues detected:1. Specification of how study objectives address a gap in evidence on research communicating numbers2. Clearly reporting all combinations of data presentation format elements compared3. Providing verbatim examples of the data presentation formats4. Describing whether participants had access to the data presentation formats while outcomes were assessed5. Reporting the wording of all outcome measures6. Using standardized terms for both outcomes and data presentation formats7. Ensuring that broad outcome concepts such as gist, comprehension, or knowledge are concretely definedImplementation of these guidelines will facilitate knowledge synthesis of research on communicating numbers and support creating evidence-based guidelines of best practices for communicating health-related numbers to lay

PurposeTo summarize the degree to which evidence from our recent Making Numbers Meaningful (MNM) systematic review of the effects of data presentation format on communication of health numbers supports recommendations from the 2021 International Patient Decision Aids Standards (IPDAS) Collaboration papers on presenting probabilities.MethodsThe MNM review generated 1,119 distinct findings (derived from 316 papers) related to communication of probabilities to patients or other lay audiences, classifying each finding by its relation to audience task, type of stimulus (data and data presentation format), and up to 10 distinct sets of outcomes: identification and/or recall, contrast, categorization, computation, probability perceptions and/or feelings, effectiveness perceptions and/or feelings, behavioral intentions or behavior, trust, preference, and discrimination. Here, we summarize the findings related to each of the 35 IPDAS paper recommendations.ResultsStrong evidence exists to support several IPDAS recommendations, including those related to the use of part-to-whole graphical formats (e.g., icon arrays) and avoidance of verbal probability terms, 1-in-X formats, and relative risk formats to prevent amplification of probability perceptions, effectiveness perceptions, and/or behavioral intentions as well as the use of consistent denominators to improve computation outcomes. However, the evidence base appears weaker and less complete for other IPDAS recommendations (e.g., recommendations regarding numerical estimates in context and evaluative labels). The IPDAS papers and the MNM review agree that both communication of uncertainty and use of interactive formats need further research.ConclusionsThe idea that no one visual or numerical format is optimal for every probability communication situation is both an IPDAS panel recommendation and foundational to the MNM project's design. Although no MNM evidence contradicts IPDAS recommendations, the evidence base needed to support many common probability communication recommendations remains incomplete.HighlightsThe Making Numbers Meaningful (MNM) systematic review of the literature on communicating health numbers provides mixed support for the recommendations of the 2021 International Patient Decision Aids Standards (IPDAS) evidence papers on presenting probabilities in patient decision aids.Both the IPDAS papers and the MNM project agree that no single visual or numerical format is optimal for every probability communication situation.The MNM review provides strong evidentiary support for IPDAS recommendations in favor of using part-to-whole graphical formats (e.g., icon arrays) and consistent denominators.The MNM review also supports the IPDAS cautions against verbal probability terms and 1-in-X formats as well as its concerns about the potential biasing effects of relative risk formats and framing.MNM evidence is weaker related to IPDAS recommendations about placing numerical estimates in context

Artificial intelligence models display human-like cognitive biases when generating medical recommendations. We tested whether an explicit forewarning, "Please keep in mind cognitive biases and other pitfalls of reasoning," might mitigate biases in OpenAI's generative pretrained transformer large language model. We used 10 clinically nuanced cases to test specific biases with and without a forewarning. Responses from the forewarning group were 50% longer and discussed cognitive biases more than 100 times more frequently compared with responses from the control group. Despite these differences, the forewarning decreased overall bias by only 6.9%, and no bias was extinguished completely. These findings highlight the need for clinician vigilance when interpreting generated responses that might appear seemingly thoughtful and deliberate.HighlightsArtificial intelligence models can be warned to avoid racial and gender bias.Forewarning artificial intelligence models to avoid cognitive biases does not adequately mitigate multiple pitfalls of reasoning.Critical reasoning remains an important clinical skill for practicing physicians.

Machine-learning (ML) models have the potential to transform health care by enabling more personalized and data-driven clinical decision making. However, their successful implementation in clinical practice requires careful consideration of factors beyond predictive accuracy. We provide an overview of essential considerations for developing clinically applicable ML models, including methods for assessing and improving calibration, selecting appropriate decision thresholds, enhancing model explainability, identifying and mitigating bias, as well as methods for robust validation. We also discuss strategies for improving accessibility to ML models and performing real-world testing.HighlightsThis tutorial provides clinicians with a comprehensive guide to implementing machine-learning classification models in clinical practice.Key areas covered include model calibration, threshold selection, explainability, bias mitigation, validation, and real-world testing, all of which are essential for the clinical deployment of machine-learning models.Following these guidance can help clinicians bridge the gap between machine-learning model development and real-world application and enhance patient care outcomes.

IntroductionEconomic evaluations identify the best course of action by a decision maker with respect to the level of health within the overall population. Traditionally, they identify 1 optimal treatment choice. In many jurisdictions, multiple technologies can be covered for the same heterogeneous patient population, which limits the applicability of this framework for directly determining whether a new technology should be covered. This article explores the impact of different decision frameworks within this context.MethodsThree alternate decision frameworks were considered: the traditional normative framework in which only the optimal technology will be covered (normative); a commonly adopted framework in which the new technology is recommended for reimbursement only if it is optimal, with coverage of other technologies remaining as before (current); and a framework that assesses specifically whether coverage of the new technology is optimal, incorporating previous reimbursement decisions and the market share of current technologies (positivist). The implications of the frameworks were assessed using a simulated probabilistic Markov model for a chronic progressive condition.ResultsResults illustrate how the different frameworks can lead to different reimbursement recommendations. This in turn produces differences in population health effects and the resultant price reductions required for covering the new technology.ConclusionBy covering only the optimal treatment option, decision makers can maximize the level of health across a population. If decision makers are unwilling to defund technologies, however, the second best option of adopting the positivist framework has the greatest relevance with respect to deciding whether a new technology should be covered.HighlightsTraditionally, economic evaluations focus on identifying the optimal treatment choice.This paper considers three alternative decision frameworks, within the context of multiple technologies being covered for the same heterogeneous patient population.This paper highlight that if decision makers are unwilling to defund therapies, current approaches to assessing cost effectiveness may be non-optimal.