This paper seemed quite intriguing and I fed it to Chat GPT for discussion.
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Whole genome sequencing in oncology:
using scenario drafting to explore future developments
https://bmccancer.biomedcentral.com/counter/pdf/10.1186/s12885-021-08214-8.pdf
Abstract
Background: In oncology, Whole Genome Sequencing (WGS) is not yet widely implemented due to uncertainties such as the required infrastructure and expertise, costs and reimbursements, and unknown pan-cancer clinical utility. Therefore, this study aimed to investigate possible future developments facilitating or impeding the use of WGS as a molecular diagnostic in oncology through scenario drafting.
Methods: A four-step process was adopted for scenario drafting. First, the literature was searched for barriers and facilitators related to the implementation of WGS. Second, they were prioritized by international experts, and third, combined into coherent scenarios. Fourth, the scenarios were implemented in an online survey and their likelihood of taking place within 5 years was elicited from another group of experts. Based on the minimum, maximum, and most likely (mode) parameters, individual Program Evaluation and Review Technique (PERT) probability density functions were determined. Subsequently, individual opinions were aggregated by performing unweighted linear pooling, from which summary statistics were extracted and reported.
Results: Sixty-two unique barriers and facilitators were extracted from 70 articles. Price, clinical utility, and turnaround time of WGS were ranked as the most important aspects. Nine scenarios were developed and scored on likelihood by 18 experts. The scenario about introducing WGS as a clinical diagnostic with a lower price, shorter turnaround time, and improved degree of actionability, scored the highest likelihood (median: 68.3%). Scenarios with low likelihoods and strong consensus were about better treatment responses to more actionable targets (26.1%), and the effect of centralizing WGS (24.1%).
Conclusions: Based on current expert opinions, the implementation of WGS as a clinical diagnostic in oncology is heavily dependent on the price, clinical utility (both in terms of identifying actionable targets as in adding sufficient value in subsequent treatment), and turnaround time. These aspects and the optimal way of service provision are the main drivers for the implementation of WGS and should be focused on in further research. More knowledge regarding these factors is needed to inform strategic decision making regarding the implementation of WGS, which warrants support from all relevant stakeholders. Keywords: Whole genome sequencing, Implementation, Scenario drafting, Uncertainty, Oncology
I have done a plain cut paste of the CHAT GPT below.
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The paper you've provided, titled "Scenario Drafting for Anticipating Future Developments in Whole Genome Sequencing (WGS) Implementation in Clinical Oncology," explores the potential future developments and challenges in the integration of Whole Genome Sequencing (WGS) into clinical practice in the field of oncology. This study is indeed intriguing and has several novel and interesting aspects:
Futuristic Perspective: The authors take a futuristic approach by using scenario drafting as a methodology. Scenario drafting is a technique borrowed from futures studies and strategic planning. It allows researchers and stakeholders to envision multiple possible futures based on a range of factors and uncertainties.
Anticipating Challenges: The primary goal of the study is to anticipate and understand the challenges and opportunities that may arise during the implementation of WGS in clinical oncology. This forward-looking approach is crucial for developing strategies to overcome obstacles and maximize the benefits of WGS technology.
Interdisciplinary Collaboration: The research involves a diverse group of international experts from various fields, including biotechnology, medicine, and health economics. This interdisciplinary collaboration ensures a comprehensive assessment of the factors affecting WGS implementation.
Identification of Barriers and Facilitators: The study identifies and categorizes 62 unique barriers and facilitators for WGS implementation based on a literature review. This comprehensive list provides a foundation for understanding the complexities involved in adopting WGS in clinical settings.
Scenario Development: The authors create nine distinct scenarios that cover different pathways for the implementation of WGS in clinical oncology. These scenarios incorporate various aspects, such as cost, clinical utility, and turnaround time. By exploring these scenarios, the authors offer insights into potential futures for WGS adoption.
Quantifying Expert Opinions: Experts were asked to provide quantitative estimates of the likelihood of each scenario occurring. This approach allows for a more systematic assessment of expert opinions and consensus levels, providing a clearer understanding of future possibilities.
Key Factors: The study highlights the importance of factors like price, clinical utility, and turnaround time in determining the likelihood of WGS implementation. These insights are valuable for policymakers and stakeholders in making informed decisions.
Role of Clinical Utility: The authors emphasize the role of clinical utility as a significant driver for WGS adoption. They discuss how demonstrating the added value of WGS technology is essential for its reimbursement and widespread use.
Policy and Research Implications: The findings have practical implications for policy and future research directions in genomics and oncology. They suggest that more research is needed to understand the clinical benefits of WGS and reduce uncertainty.
In summary, this paper takes a forward-thinking approach to anticipate the challenges and opportunities associated with the implementation of WGS in clinical oncology. Its interdisciplinary nature, comprehensive analysis of barriers and facilitators, scenario development, and quantification of expert opinions make it a valuable contribution to the field. This study could guide future strategies for the responsible and effective integration of WGS technology, ultimately benefiting patient care and precision medicine in oncology.
User
What do the authors predict or conclude?
ChatGPT
The authors of the paper "Scenario Drafting for Anticipating Future Developments in Whole Genome Sequencing (WGS) Implementation in Clinical Oncology" offer several predictions and conclusions based on their scenario analysis and expert opinions. Here are the key predictions and conclusions:
Likelihood of WGS Implementation Scenarios: The study predicts the likelihood of various scenarios related to the implementation of WGS in clinical oncology. The scenario in which WGS would be introduced as a clinical diagnostic (scenario 4) had the highest likelihood of taking place within the next 5 years, albeit with relatively weak consensus (68.3%). On the other hand, scenarios involving a better treatment response to actionable targets identified by WGS (scenario 9) and the centralization of organizing WGS (scenario 3) had the lowest likelihoods, with relatively strong consensus (26.1% and 24.1%, respectively).
Importance of Price and Clinical Utility: The authors emphasize the critical role of price, clinical utility, and turnaround time in determining the likelihood of WGS implementation. The reduction in costs of WGS to 3000 euros per patient and a decrease in turnaround time to 14 days were seen as potential catalysts for adoption.
Challenges in Demonstrating Clinical Utility: While clinical utility is identified as a significant driver for WGS adoption, the study notes that experts found it relatively unlikely that WGS would lead to a better treatment response to actionable targets within the foreseeable future. This suggests that demonstrating the clinical benefits of WGS, especially in comparison to other sequencing techniques, remains a challenge.
Complexity of Barriers and Facilitators: The research identifies 62 unique barriers and facilitators for WGS implementation, spanning various domains. This complexity underscores the need for a comprehensive approach that considers multiple factors and domains when introducing WGS into clinical practice.
Uncertainty in Expert Opinions: The study acknowledges a degree of uncertainty in expert opinions regarding the likelihood of different scenarios. This uncertainty can be attributed to various factors, including the challenges of quantifying scenarios, the inherent unpredictability of future technological developments, and the sample size of experts.
Future Research Directions: The authors suggest that future research should focus on investigating the clinical benefits of WGS, including its potential to improve treatment response and discover new biomarkers for targeted therapies. Additionally, efforts to make WGS more cost-effective and faster may contribute to its implementation in clinical practice.
Support from Stakeholders: The study emphasizes the importance of support from all relevant stakeholders in the successful implementation of WGS in clinical oncology. This includes policymakers, healthcare providers, researchers, and industry partners.
In summary, the authors predict that the implementation of WGS in clinical oncology will depend on factors such as price, clinical utility, and turnaround time. While certain scenarios show promise, there is uncertainty surrounding the timeline of adoption, particularly regarding the clinical benefits of WGS. The study highlights the need for further research and strategic decision-making to navigate the complexities of WGS integration into clinical practice.
User
Are there any weak points? Are they just crazy to try this technique in genomics? Are there situations where this technique has proven itself?
ChatGPT
While scenario drafting and expert elicitation techniques offer valuable insights, there are some potential weak points and challenges associated with this approach. It's essential to consider these limitations in the context of using this technique in genomics and other fields:
1. Uncertainty: The primary limitation of scenario drafting and expert elicitation is the inherent uncertainty in predicting future developments. While experts provide valuable insights, the future is unpredictable, especially in rapidly evolving fields like genomics. Thus, the scenarios generated may not always align with the actual future outcomes.
2. Expert Selection: The accuracy of scenario drafting relies heavily on the selection of experts and their expertise. If experts' views are biased or if certain critical perspectives are missing from the expert panel, it can affect the quality of scenarios and predictions.
3. Subjectivity: Scenario drafting involves subjective judgments and opinions of experts, which can introduce bias. Experts may have personal biases or different interpretations of the available evidence, leading to variations in predictions.
4. Limited Historical Data: In emerging fields like genomics, historical data on which to base scenarios may be limited. This can make it challenging to assess past performance and use it as a basis for future predictions.
5. Simplification: To create scenarios, some level of simplification is necessary, which can lead to oversights or missing critical factors. Real-world situations are often more complex than the scenarios constructed.
However, it's important to note that scenario drafting and expert elicitation techniques are not inherently flawed. They can provide valuable insights and help decision-makers anticipate and prepare for possible future developments. The weaknesses mentioned above are common to many prediction and forecasting methods.
As for the application of scenario drafting and expert elicitation in genomics:
1. Relevance: These techniques can be highly relevant in genomics, especially in the context of personalized medicine and the integration of advanced technologies like whole genome sequencing (WGS). Genomic medicine is still evolving, and policymakers, researchers, and healthcare providers need to anticipate future developments and challenges.
2. Previous Success: Scenario drafting and expert elicitation have been successfully applied in various fields, including technology assessment, healthcare policy, and environmental planning. They have helped identify potential obstacles and opportunities, allowing stakeholders to make informed decisions.
3. Strategic Planning: In genomics, these techniques can aid in strategic planning for the implementation of new technologies, ensuring that resources are allocated efficiently and potential barriers are addressed proactively.
In conclusion, while scenario drafting and expert elicitation have limitations and uncertainties, they are valuable tools for anticipating future developments in genomics and other fields. When used judiciously and in conjunction with other forecasting methods, they can provide valuable insights for informed decision-making and strategic planning.
