Promoting patient engagement in cancer genomics research programs: An environmental scan.

Background: A national priority in the United States is to promote patient engagement in cancer genomics research, especially among diverse and understudied populations. Several cancer genomics research programs have emerged to accomplish this priority, yet questions remain about the meaning and methods of patient engagement. This study explored how cancer genomics research programs define engagement and what strategies they use to engage patients across stages in the conduct of research. Methods: An environmental scan was conducted of cancer genomics research programs focused on patient engagement. Research programs were identified and characterized using materials identified from publicly available sources (e.g., websites), a targeted literature review, and interviews with key informants. Descriptive information about the programs and their definitions of engagement, were synthesized using thematic analysis. The engagement strategies were synthesized and mapped to different stages in the conduct of research, including recruitment, consent, data collection, sharing results, and retention. Results: Ten research programs were identified, examples of which include the Cancer Moonshot Biobank, the MyPART Network, NCI-CONNECT, and the Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network. All programs aimed to include understudied or underrepresented populations. Based on publicly available information, four programs explicitly defined engagement. These definitions similarly characterized engagement as being interpersonal, reciprocal, and continuous. Five general strategies of engagement were identified across the programs: 1) digital (such as websites) and 2) non-digital communications (such as radio broadcasts, or printed brochures); 3) partnering with community organizations; 4) providing incentives; and 5) affiliating with non-academic medical centers. Digital communications were the only strategy used across all stages of the conduct of research. Programs tailored these strategies to their study goals, including overcoming barriers to research participation among diverse populations. Conclusion: Programs studying cancer genomics are deeply committed to increasing research participation among diverse populations through patient engagement. Yet, the field needs to reach a consensus on the meaning of patient engagement, develop a taxonomy of patient engagement measures in cancer genomics research, and identify optimal strategies to engage patients in cancer genomics. Addressing these needs could enable patient engagement to fulfill its potential and accelerate the pace of cancer genomic discoveries.

Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks.

BACKGROUND: Artificial intelligence (AI) is rapidly expanding in medicine despite a lack of consensus on its application and evaluation. OBJECTIVE: We sought to identify current frameworks guiding the application and evaluation of AI for predictive analytics in medicine and to describe the content of these frameworks. We also assessed what stages along the AI translational spectrum (ie, AI development, reporting, evaluation, implementation, and surveillance) the content of each framework has been discussed. METHODS: We performed a literature review of frameworks regarding the oversight of AI in medicine. The search included key topics such as "artificial intelligence," "machine learning," "guidance as topic," and "translational science," and spanned the time period 2014-2022. Documents were included if they provided generalizable guidance regarding the use or evaluation of AI in medicine. Included frameworks are summarized descriptively and were subjected to content analysis. A novel evaluation matrix was developed and applied to appraise the frameworks' coverage of content areas across translational stages. RESULTS: Fourteen frameworks are featured in the review, including six frameworks that provide descriptive guidance and eight that provide reporting checklists for medical applications of AI. Content analysis revealed five considerations related to the oversight of AI in medicine across frameworks: transparency, reproducibility, ethics, effectiveness, and engagement. All frameworks include discussions regarding transparency, reproducibility, ethics, and effectiveness, while only half of the frameworks discuss engagement. The evaluation matrix revealed that frameworks were most likely to report AI considerations for the translational stage of development and were least likely to report considerations for the translational stage of surveillance. CONCLUSIONS: Existing frameworks for the application and evaluation of AI in medicine notably offer less input on the role of engagement in oversight and regarding the translational stage of surveillance. Identifying and optimizing strategies for engagement are essential to ensure that AI can meaningfully benefit patients and other end users.

Best-Worst Scaling and the Prioritization of Objects in Health: A Systematic Review.

BACKGROUND AND OBJECTIVE: Best-worst scaling is a theory-driven method that can be used to prioritize objects in health. We sought to characterize all studies of best-worst scaling to prioritize objects in health, to assess trends of using best-worst scaling in prioritization over time, and to assess the relationship between a legacy measure of quality (PREFS) and a novel assessment of subjective quality and policy relevance. METHODS: A systematic review identified studies published through to the end of 2021 that applied best-worst scaling to study priorities in health (PROSPERO CRD42020209745), updating a prior review published in 2016. The PubMed, EBSCOhost, Embase, Scopus, APA PsychInfo, Web of Science, and Google Scholar databases were used and were supplemented by a hand search. Data describing the application, development, design, administration/analysis, quality, and policy relevance were summarized and we tested for trends by comparing articles before and after 1 January, 2017. Multivariate statistics were then used to assess the relationships between PREFS, subjective quality, policy relevance, and other possible indicators. RESULTS: From a total of 2826 unique papers identified, 165 best-worst scaling studies were included in this review. Applications of best-worst scaling to study priorities in health have continued to grow (p < 0.01) and are now used in all regions of the world, most often to study the priorities of patients/consumers (67%). Several key trends can be observed over time: increased use of pretesting (p < 0.05); increased use of online administration (p < 0.01), and decreased use of paper self-administered surveys (p = 0.02); increased use of heterogeneity analysis (p = 0.02); an increase in having a clearly stated purpose (p < 0.01); and a decrease in comparing respondents to non-respondents (p = 0.01). The average sample size has more than doubled, from 228 to 472 respondents, but formal sample size justifications remain low (5.3%) and unchanged over time (p = 0.68). While the average PREFS score remained unchanged at 3.1/5, both subjective quality and policy relevance trended up, but changes were not statistically significant (p = 0.06 and p = 0.13). Most of the variation in subjective quality was driven by PREFS (R2 = 0.42), but it was also positively assosciated with policy relevance, heterogeneity analysis, and using a balanced incomplete block design, and was negatively associated with not using developmental methods and an increasing sample size. CONCLUSIONS: Using best-worst scaling to prioritize objects is now commonly used around the world to assess the priorities of patients and other stakeholders in health. Best practices are clearly emerging for best-worst scaling. Although legacy measures (PREFS) to measure study quality are reasonable, there may need to be new tools to assess both study quality and policy relevance.