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Background Healthcare spending has grown over the last decades in all developed countries. Making hard choices for investments in a rational, evidence-informed, systematic, transparent and legitimate manner constitutes an important objective. Yet, most scientific work in this area has focused on developing/improving prescriptive approaches for decision making and presenting case studies. The present work aimed to describe existing practices of priority setting and resource allocation (PSRA) within the context of publicly funded health care systems of high-income countries and inform areas for further improvement and research. Methods An online qualitative survey, developed from a theoretical framework, was administered with decision-makers and academics from 18 countries. 450 individuals were invited and 58 participated (13% of response rate). Results We found evidence that resource allocation is still largely carried out based on historical patterns and through ad hoc decisions, despite the widely held understanding that decisions should be based on multiple explicit criteria. Health technology assessment (HTA) was the tool most commonly indicated by respondents as a formal priority setting strategy. Several approaches were reported to have been used, with special emphasis on Program Budgeting and Marginal Analysis (PBMA), but limited evidence exists on their evaluation and routine use. Disinvestment frameworks are still very rare. There is increasing convergence on the use of multiple types of evidence to judge the value of investment options. Conclusions Efforts to establish formal and explicit processes and rationales for decision-making in priority setting and resource allocation have been still rare outside the HTA realm. Our work indicates the need of development/improvement of decision-making frameworks in PSRA that: 1) have well-defined steps; 2) are based on multiple criteria; 3) are capable of assessing the opportunity costs involved; 4) focus on achieving higher value and not just on adoption; 5) engage involved stakeholders and the general public; 6) make good use and appraisal of all evidence available; and 6) emphasize transparency, legitimacy, and fairness.

Background Longitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a machine learning approach that automates balancing of covariate histories. We examine performance by comparing the proposed extension against baseline propensity score matching and time-dependent propensity score matching. Methods To evaluate comparative performance, we developed a Monte Carlo simulation framework that reflects a static treatment assigned at multiple time points. Data generation considers a treatment assignment model, a continuous outcome model, and underlying covariates. In simulation, we generated 1,000 datasets, each consisting of 1,000 subjects, and applied: (1) nearest neighbour matching on time-invariant, baseline propensity scores; (2) sequential risk set matching on time-dependent propensity scores; and (3) longitudinal genetic matching on time-dependent covariates. To measure comparative performance, we estimated covariate balance, efficiency, bias, and root mean squared error (RMSE) of treatment effect estimates. In scenario analysis, we varied underlying assumptions for assumed covariate distributions, correlations, treatment assignment models, and outcome models. Results In all scenarios, baseline propensity score matching resulted in biased effect estimation in the presence of time-dependent confounding, with mean bias ranging from 29.7% to 37.2%. In contrast, time-dependent propensity score matching and longitudinal genetic matching achieved stronger covariate balance and yielded less biased estimation, with mean bias ranging from 0.7% to 13.7%. Across scenarios, longitudinal genetic matching achieved similar or better performance than time-dependent propensity score matching without requiring manual re-specifications or normality of covariates. Conclusions While the most appropriate longitudinal method will depend on research questions and underlying data patterns, our study can help guide these decisions. Simulation results demonstrate the validity of our longitudinal genetic matching approach for supporting future real-world assessments of treatments accessible at multiple time points.

Evaluating precision oncology outcomes requires access to real-world and clinical trial data. Access is based on consent, and consent is based on patients' informed preferences when deciding to share their data. Decision-making is often modeled using utility theory, but a complex decision context calls for a consideration of how heuristic, intuitive thought processes interact with rational utility maximization. Data-sharing decision-making has been studied using heuristic theory, but almost no heuristic research exists in the health data context. This study explores this evidence gap, applying a qualitative approach to probe for evidence of heuristic mechanisms behind the health data-sharing preferences of those who have experienced cancer. Exploring qualitative decision-making reveals the types of heuristics used and how they are related to the process of decision-making to better understand whether consent mechanisms should consider nonrational processes to better serve patient decision-making. This study aimed to explore how patients with cancer use heuristics when deciding whether to share their data for research. The researchers conducted a focus group study of Canadians who have experienced cancer. We recruited participants through an online advertisement, screening individuals based on their ability to increase demographic diversity in the sample. We reviewed the literature on data-sharing platforms to develop a semistructured topic guide on concerns about data sharing, incentives to share, and consent and control. Focus group facilitators led the open-ended discussions about data-sharing preferences that revealed underlying heuristics. Two qualitative analysts coded transcripts using a heuristic framework developed from a review of the literature. Transcripts were analyzed for heuristic instances which were grouped according to sociocultural categories. Using thematic analysis, the analysts generated reflexive themes through norming sessions and consultations. A total of 3 focus groups were held with 19 participants in total. The analysis identified 12 heuristics underlying intentions to share data. From the thematic analysis, we identified how the heuristics of social norms and community building were expressed through altruism; the recognition, reputation, and authority heuristics led to (dis)trust in certain institutions; the need for security prompted the illusion of control and transparency heuristics; and the availability and affect heuristics influenced attitudes around risk and benefit. These thematic relationships all had impacts on the participants' intentions to share their health data. The findings provide a novel qualitative understanding of how health data-sharing decisions and preferences may be based on heuristic processing. As patients consider the extent of risks and benefits, heuristic processes influence their assessment of anticipated outcomes, which may not result in rational, truly informed consent. This study shows how considering heuristic processing when designing current consent mechanisms opens up the opportunity for more meaningful and realistic interactions with the complex decision-making context.

Costly targeted cancer treatments challenge publicly-funded healthcare systems seeking to align expected benefit with value for money. In 2021, The Canadian Agency for Drugs and Technologies in Health (CADTH) published a provisional funding algorithm for risk-based treatment of chronic lymphocytic leukemia (CLL). We estimate the cost-effectiveness of this algorithm against current standard of care. We constructed a probabilistic Markov model comparing next generation sequencing (NGS) assay-guided front-line treatment of acalabrutinib versus venetoclax with obinutuzumab to a comparator wherein patients initiate acalabrutinib. The primary outcome was the incremental cost-effectiveness ratio (ICER) per quality-adjusted life-year (QALY) gained. Analyses were conducted from the British Columbia healthcare system perspective, with outcomes discounted at 1.5%. Assay informed treatment for patients with CLL resulted in an incremental cost effectiveness ratio of $18,040 (95% CI $16,491–$19,501) per quality adjusted life-year (QALY) gained. The probability of the NGS guided treatment algorithm being cost effective was 80% at a willingness to pay threshold of $50,000 and a corresponding ICER of $18,040. Assay-guided treatment sequencing adds additional costs to healthcare but may be a cost-effective intervention for adult patients with CLL. Integration of real-world evidence would improve the validity and reliability of model estimated for decision-makers.

Background Multi‐gene panel testing is replacing single‐gene testing for patients with suspected hereditary cancer syndromes. The detection of a hereditary cancer syndrome allows tested individuals to initiate enhanced primary and secondary prevention efforts—where available—with a view to reduce disease burden. Current policy prevents testing programmes from communicating genetic test results with potentially affected family members, yet it is well documented that tested individuals face multiple challenges in initiating such discussions with relatives. Objective In response to this challenge, we sought patient recommendations about how to improve genetic risk communication to enhance interfamilial discussions about primary and secondary disease prevention. Design We conducted 25 semi‐structured interviews with individuals who received genetic testing through British Columbia’s Hereditary Cancer Program between 2017 and 2018. Interviews were professionally transcribed and analysed using a constant comparative approach. Results Participants described difficulty engaging in conversations with relatives who were resistant to receiving genetic risk information, when communicating with younger relatives and where participants reported strained familial relationships. Participants recommended that testing facilities provide a summary of results and implications and that resources be made available to prepare patients for challenging discussions with family members. Discussion Our study demonstrates that individuals undergoing genetic testing for suspected hereditary cancer syndromes would benefit from additional supportive resources alongside genetic counselling. Providing this on‐going support will enhance the accurate and transparent communication of risk to facilitate the uptake of cascade testing and enhanced prevention strategies.

Health technology assessment (HTA) can be used to make healthcare systems more equitable and efficient. Advances in precision oncology are challenging conventional thinking about HTA. Precision oncology advances are rapid, involve small patient groups, and are frequently evaluated without a randomized comparison group. In light of these challenges, mechanisms to manage precision oncology uncertainties are critical. We propose a life-cycle HTA framework and outline supporting criteria to manage uncertainties based on real world data collected from learning healthcare systems. If appropriately designed, we argue that life-cycle HTA is the driver of real world evidence generation and furthers our understanding of comparative effectiveness and value. We conclude that life-cycle HTA deliberation processes must be embedded into healthcare systems for an agile response to the constantly changing landscape of precision oncology innovation. We encourage further research outlining the core requirements, infrastructure, and checklists needed to achieve the goal of learning healthcare supporting life-cycle HTA.

Precision oncology uses omics-based diagnostic technologies to inform histology-agnostic cancer treatment. To date, health system implementation remains limited owing to high uncertainty in regulatory and reimbursement evidence submissions. In this perspective, we describe a life-cycle approach to the evaluation of precision oncology technologies that addresses evidentiary uncertainty and is grounded in real-world evidence (RWE) derived using data routinely collected by healthcare systems. We consider the role for RWE in international regulatory and reimbursement decision-making, review common biases for observational precision oncology evaluations, make specific recommendations for RWE study design and analysis, and specify healthcare system requirements for data collection. We then explore how decision-grade real-world data can support the generation of decision-grade RWE, ultimately enabling real-world life-cycle assessment for precision oncology.

For new drugs or indications, substantial evidence of clinical effectiveness is required for market authorization. In most jurisdictions, substantial evidence is not explicitly defined. Health regulators exercise discretion and are increasingly tolerant of earlier or less mature evidence. To align with flexible evidentiary standards, we argue for the adoption of a principle and, context-based approach to the evidence threshold. Our approach aims to balance the potential benefits and harms of accelerated authorization, low therapeutic value, and safety, based on a value of information (VoI) framework. In our VoI framework, substantial evidence exists when the expected net health value of further research is less than or equal to zero. We operationalize this approach through two case examples that mirror real-time decision factors such as uncertainty, risk preferences and time inputs. As the evidentiary assessment landscape shifts towards flexibility, iterative and clearly defined approaches to risk assessment are warranted. Clarity will stimulate transparency and accountability for both stakeholders and regulators.

Introduction Genetic testing for hereditary cancer syndromes (HCSs) can improve health outcomes through cancer risk mitigation strategies. Effective communication between tested individuals and their family members is key to reducing the hereditary cancer burden. Our objective was to develop a patient portal to improve familial communication for patients undergoing HCS genetic testing, followed by an early‐phase evaluation. Methods The portal was developed following the completion of 25 semistructured interviews with individuals having undergone HCS susceptibility testing at BC Cancer. Following initial development, we recruited patients and healthcare providers to provide critical feedback informing portal refinement. Quantitative feedback was summarized using descriptive statistics, and qualitative feedback was synthesized by two reviewers who engaged in iterative discussion within the research team to prioritize recommendations for integration. Results The patient portal includes four key components consisting of (a) targeted educational information about hereditary cancer and HBOC syndrome associated risks and testing process overview, (b) a general frequently asked questions ‘FAQ’ page informed by the qualitative interviews, patient partner feedback, and consultation with the HCP, (c) guidance to support familial communication including a video developed with a patient partner describing their lived experience navigating the communication process and (d) a series of lay summaries of genetic test findings to support information transfer among family members. Thirteen healthcare providers and seven patients participated in user testing. Domains within which participant recommendations were provided included presentation, educational content and process clarification. Conclusions This investigation demonstrates the value of continual integration of patient and provider preferences through the development of tools endeavouring to assist with complex genomics‐informed decision‐making. Our work aims to broaden the population‐wide impact of HCS testing programs by improving communication processes between probands and their potentially affected family members. Patient or Public Contribution This work involved a patient partner who was actively engaged in all aspects of the research investigation including protocol development, review and editing of all study documentation (including that of the previously published qualitative investigation), interpretation of results, as well as reviewing and editing the manuscript. Patient partners and healthcare professionals were recruited as research participants to provide critical feedback on the patient portal.

IntroductionIndigenous peoples and perspectives are under-represented within genomic research. Qualitative methods can help redress this under-representation by informing the development of inclusive genomic resources aligned with Indigenous rights and interests. The difficult history of genomic research with Indigenous peoples requires that research be conducted responsibly and collaboratively. Research guidelines offer structuring principles, yet little guidance exists on how principles translate into practical, community-led methods. We identified the scope and nature of participatory practice described in published qualitative genomic research studies with Indigenous peoples.MethodsWe performed a search of PubMed, CINAHL, Embase, Scopus and the Bibliography of Indigenous Peoples in North America. Eligible studies reported qualitative methods investigating genomics-related topics with Indigenous populations in Canada, the USA, Australia or New Zealand. Abstracted participatory practices were defined through a literature review and mapped to a published ethical genomic research framework.ResultsWe identified 17 articles. Published articles described a breadth of methods across a diversity of Indigenous peoples and settings. Reported practices frequently promoted Indigenous-partnered research regulation, community engagement and co-creation of research methods. The extent of participatory and community-led practice appeared to decrease as studies progressed.ConclusionApplying non-prescriptive Indigenous genomic research guidelines to qualitative inquiry can be achieved through varied methodological approaches. Our findings affirm the adaptive nature of this process in real-world settings and identify opportunities for participatory practice and improved reporting across the research lifecycle. These findings and the breadth of characterised applied research practices are instructive for researchers seeking to develop much-needed qualitative genomic research partnerships.