Patient-Patient Centered Outcomes Research最新文献

Preference information describes the relative desirability or acceptability of specified alternatives that differ across health states, interventions, or services. Studies that generate preference information are being designed to support patient-centered decision making across all stages of the medical product lifecycle, as well as in healthcare more generally. Ensuring high-quality preference research with the potential for impact requires transparent and thoughtful study design, a core aspect of which often includes the development of attributes. Good practices for attribute development in preference studies have started to emerge and demonstrate that developing attributes requires substantial time and effort. Resources to more easily and systematically identify potentially relevant attributes may support the accessibility, interoperability, and reusability of attributes, in turn improving the efficiency of preference study design and comparability of findings across studies. In this paper, we first describe the need for and potential benefit of tools that promote the purposeful re-use of attributes for preference studies. We next present a taxonomy for categorizing and describing attributes that could be applied to facilitate their identification. Finally, we apply this taxonomy to a prototype "attribute library," developed as a part of a Medical Device Innovation Consortium work group, to demonstrate the potential value of these resources to support the preference research community.

Background: Polygenic scores (PGS) capture a proportion of the genomic liability for cancer in unselected and high-risk cohorts, with meaningful application in improving risk-stratified screening and management. However, there are significant evidence gaps regarding future clinical implementation. Despite being key interest-holders, recipient views are underrepresented. The objective of this study was to explore recipients' views on the clinical implementation of PGS for hereditary cancer risk assessment in Australian cancer genetics clinics.

Methods: Three video-conferenced focus groups were conducted with recipients who had been given their breast and ovarian cancer PGS through the PRiMo trial. Nominal Group Technique was used to enable evaluation of implementation determinants and strategies, and priority setting. Descriptive and deductive content analyses were conducted utilising the Consolidated Framework for Implementation Research and the Expert Recommendations for Implementing Change compilation of facilitative strategies.

Results: Participants (N = 10) were female, with an average age of 36 years (range 18-70 years). Of these, 50% (N = 5) experienced a change in their hereditary cancer risk assessment due to their PGS. Participants prioritised the positive value and impact of PGS, and the behavioural characteristics of recipients, notably their knowledge and expectations of PGS and cancer genetics clinics, as major determinants of implementation success. Implementation strategies that prepared and supported recipients to access, engage, and use PGS were emphasised, with a focus on a clear results report, educational resources, in-clinic resources, and delivery of ongoing good clinical follow-up.

Conclusion: Evidence-based strategies should be deployed to address recipients' priority barriers to the clinical implementation of PGS for hereditary cancer risk assessment. Centralising recipient voices in implementation design will improve effectiveness and success.

Background and objective: Best-worst scaling (BWS) is a stated preference elicitation method used for prioritizing attributes of healthcare interventions. Best-worst scaling attribute development is commonly based on literature review, qualitative work, and methodological/clinical expert input. There is limited research incorporating BWS in focus groups as part of the attribute development process. We sought to explore how incorporating BWS questions using the list of potential attributes in focus groups could be used to improve understanding of patient preferences and refine the list of potential BWS attributes as part of the attribute development process.

Methods: We administered BWS questions on healthcare priorities for inflammatory bowel disease in five focus groups with Canadian patients with inflammatory bowel disease to (1) understand the "what," "how," and "why" of participant choices and (2) note how participants understand the attributes and the language they use to refine the list of potential BWS attributes. A list of 20 potential attributes was used to generate the BWS questions. We coded most/least important choices ("what") and used a thematic analysis to derive subthemes indicating "how" and "why" participants made their choices. We coded how participants understood the attributes/BWS questions and language used when discussing the attributes.

Results: Across the 36 participants, the most frequently chosen most/least important attributes were summarized. Three subthemes explaining the "how" and "why" of participant choices were identified: perceived gain; influence of individual experiences; current health state and personal circumstances. Participants identified challenges understanding specific attributes and BWS questions, and provided suggestions for modifications to attribute language/descriptions. Administering BWS questions in focus groups provided: (1) insight into the assumptions participants made when completing the BWS questions; (2) clarity in language and attribute descriptions, and challenges participants had when completing the BWS questions that can be used to refine the list of potential attributes as part of the attribute development process; and (3) understanding of which attributes were most/least important and why to identify potential attributes to remove during the next steps of the attribute development process.

Conclusions: Best-worst scaling questions conducted within focus groups can stimulate discussions around relative importance and prioritization of attributes. Through open dialogue, this method can unveil unforeseen responses or identify areas that are unclear and enable a transparent approach to refine the list of potential attributes as part of the attribute development process.

Background and objective: The COVID-19 pandemic has significantly influenced vaccination strategies and public health policies. Discrete choice experiments have emerged as a valuable tool for understanding preferences regarding vaccination. This study systematically reviews discrete choice experiments conducted on COVID-19 public vaccination preferences to identify key determinants influencing vaccine uptake and to assess methodological approaches used in these studies.

Methods: A systematic literature search was conducted across major databases, including PubMed, Scopus, and Web of Science, to identify discrete choice experiments focusing on COVID-19 vaccination preferences up to 31 December, 2024. Attribute categorization into five dimensions Outcome, Process, Cost, Trust, and Framing was performed and quality appraised according to the DIRECT checklist. Conditional relative importance as well as geographical differences were assessed.

Results: The review identified 58 studies employing discrete choice experiments that assessed public COVID-19 vaccine preferences. Among attribute categories, outcome-related factors were the most frequently used and had the highest relative importance. Other commonly evaluated attributes included cost, origin/brand, and required doses. A notable geographic disparity was observed, with studies being unevenly distributed across different regions. Methodological heterogeneity was observed in attribute selection and experimental design.

Conclusions: This review emphasizes the importance of considering individual preferences into vaccination strategies to enhance uptake, particularly in preparation for future pandemics. The findings reveal that vaccine effectiveness and safety are key concerns for individuals. Future research could focus on increasing representation of underexamined regions in preference studies to better inform local policymakers in developing effective vaccination programs for future health crises.

Clinical trial registration: This review was prospectively registered in PROSPERO (International Prospective Register of Systematic Reviews) with the ID CRD42025543234.

Background: Breast cancer screening is considered an effective early detection strategy. Artificial intelligence (AI) may both offer benefits and create risks for breast screening programmes. To use AI in health screening services, the views and expectations of consumers are critical. This study examined the preferences of Australian women regarding AI use in breast cancer screening and the impact of information on preferences using discrete choice experiments.

Methods: The experiment presented two alternative screening services based on seven attributes (reading method, screening sensitivity, screening specificity, time between screening and receiving results, supporting evidence, fair representation, and who should be held accountable) to 2063 women aged between 40 and 74 years recruited from an online panel. Participants were randomised into two arms. Both received standard information on AI use in breast screening, but one arm received additional information on its potential benefits. Preferences for hypothetical breast cancer screening services were modelled using a random parameter logit model. Relative attribute importance and uptake rates were estimated.

Results: Participants preferred mixed reading (radiologist + AI system) over the other two reading methods. They showed a strong preference for fewer missed cases with a high attribute relative importance. Fewer false positives and a shorter waiting time for results were also preferred. Strength of preferences for mixed reading was significantly higher compared to two radiologists when additional information on AI is provided, highlighting the impact of information.

Conclusions: This study revealed the preferences among Australian women for the use of AI-driven breast cancer screening services. Results generally suggest women are open to their mammograms being read by both a radiologist and an AI-based system under certain conditions.

Choice-based preference elicitation methods such as the discrete choice experiment (DCE) present hypothetical choices to respondents, with an expectation that these hypothetical choices accurately reflect a 'real world' health-related decision context and that consequently the choice data can be held to be a true representation of the respondent's health or treatment preferences. For this to be the case, careful consideration needs to be given to the format of the choice task in a choice experiment. The overarching aim of this paper is to highlight important aspects to consider when designing and 'setting up' the choice tasks to be presented to respondents in a DCE. This includes the importance of considering the potential impact of format (e.g. choice context, choice set presentation and size) as well as choice set content (e.g. labelled and unlabelled choice sets and inclusion of reference alternatives) and choice questions (stated choice versus additional questions designed to explore complete preference orders) on the preference estimates that are elicited from studies. We endeavoure to instil a holistic approach to choice task design that considers format alongside content, experimental design and analysis.

Partnerships between patients and the medical research community are strengthening. Patient involvement in research processes through collaborative workstreams provides authentic insights and perspectives, enhances trust between stakeholders and the patient community, brings balance to authorship groups and adds value and contextualisation to publications. Here, patient advocates, representatives from patient and caregiver communities and pharmaceutical and medical communications professionals propose seven actions to advance patient authorship and collaboration in peer-reviewed publications. Drawing on research, personal experience and professional insight, they call for a shift in conventional publication development practices-from seeking reasons to include patient authors to requiring justification for their exclusion-thereby facilitating greater inclusion and representation of the patient voice. The authors advocate moving beyond the concept of 'patient-centricity' towards 'patient partnership' to reflect a collaborative approach and more equitable balance of power and benefits among stakeholders. They also emphasise the importance of involving patients holistically in publication steering committees to ensure that the publication landscape includes patient perspectives and represents lived experiences. Continued facilitation and strengthening of partnerships between patient and non-patient authors is noted as essential for improving communication, understanding and equity within authorship groups. To support the visibility and recognition of patient authors, they recommend the use of the 'patient author' affiliation metatag to better identify, search, filter and standardise publications with patient involvement, identify patient authors and help build an evidence base from which best practice and guidance can be developed. Additionally, the authors highlight the need to consider and develop guidance around compensation of patient authors to acknowledge the contribution and time commitments across the research process and enable greater diversity, equity and inclusion. Finally, they stress the importance of extending the reach of publications to wider audiences through enhanced accessibility formats and open access.

Health preference research plays a critical role in shaping healthcare policy and decision-making; however the underrepresentation of underserved populations challenges the validity and reliability of preference estimates. Despite efforts to diversify recruitment, health preference studies often have limited demographic diversity and non-representative sampling, leading to potentially biased findings that overlook the preferences of underserved populations. We discuss the importance of engaging underserved populations in health preference research from both ethical and research perspectives. We identify key challenges to the inclusion of underserved groups and outline strategies to address them, illustrating these with examples where possible. By prioritising inclusive and flexible methodologies, health preference researchers can generate more representative data, ensuring that estimates reflect the diverse needs and values of all populations. Ultimately, these efforts will support the development of more equitable, evidence-based, and impactful healthcare policies.