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Avoiding and identifying errors in health technology assessment models: qualitative study and methodological review

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Summary

This article explores the understanding of errors in health technology assessment (HTA) modelling and the strategies used to avoid and identify errors in the development and debugging of models. Qualitative interviews were conducted with 12 HTA modellers from academic and commercial sectors. Results indicated that there was no common language in the discussion of modelling errors and different boundaries of what constituted an error. Additionally, there was a focus on risks rather than errors, and the concept of model validation should not be externalized from the decision-makers and the decision-making process. The article also makes recommendations for future research.

Q&As

What processes are currently employed by the health technology assessment (HTA) community to avoid and identify errors in modelling?
The HTA community currently employs engaging with clinical experts, clients and decision-makers to ensure mutual understanding, producing written documentation of the proposed model, explicit conceptual modelling, stepping through skeleton models with experts, ensuring transparency in reporting, adopting standard housekeeping techniques, and ensuring that those parties involved in the model development process have sufficient and relevant training to avoid and identify errors in modelling.

What types of errors occur in HTA models?
Errors in the description of the decision problem, in model structure, in use of evidence, in implementation of the model, in operation of the model, and in presentation and understanding of results occur in HTA models.

How can clarity and mutual understanding help to reduce the occurrence and identification of errors in models?
Clarity and mutual understanding can help to reduce the occurrence of errors in models by establishing the model's significance and its warranty. This highlights that model credibility is the central concern of decision-makers using models, so it is crucial that the concept of model validation should not be externalized from the decision-makers and the decision-making process.

What recommendations can be made to improve the process of model development and the identification of errors in models?
Recommendations for future research include studies of verification and validation, the model development process, and identification of modifications to the modelling process with the aim of preventing the occurrence of errors and improving the identification of errors in models.

How do the HTA error classifications compare to existing classifications of model errors in the literature?
The HTA error classifications are compared against existing classifications of model errors in the literature and are consistent with the views expressed by the HTA community.

AI Comments

👍 This article provides a comprehensive overview of the strategies and processes for avoiding and identifying errors in health technology assessment models. It is a great resource for those looking to increase the credibility of their models.

👎 This article lacks an in-depth exploration of the skills requirements for the development, operation, and use of HTA models. Additionally, there is not enough discussion of the philosophical position of verification and validation.

AI Discussion

Me: It's about avoiding and identifying errors in health technology assessment models. It discusses the need for better understanding of model errors, developing a taxonomy of model errors, and potential methods for reducing the occurrence of errors in models.

Friend: That's really interesting. What are the implications of this article?

Me: The article suggests that there is a need for improved understanding of the skills required for the development, operation, and use of HTA models. It also highlights the importance of clarity and mutual understanding between modellers and clients. Additionally, it stresses the need for better understanding of the cognitive basis of human error and suggests that discussions of modelling risks should reflect the complex network of cognitive breakdowns that lead to errors in models. It also recommends further research on verification and validation, the model development process, and modifications to the modelling process with the aim of preventing the occurrence of errors and improving the identification of errors in models.

Action items

Research and review existing literature on verification and validation of models.
Develop a better understanding of the skills requirements for the development, operation and use of HTA models.
Explore potential methods and procedures to reduce the occurrence of errors in models.

Technical terms

Data Interpretation: The process of making sense of data by analyzing it and drawing conclusions from it.
Statistical: Relating to the practice or science of collecting and analyzing numerical data in large quantities, especially for the purpose of inferring proportions in a whole from those in a representative sample.
Decision Support Techniques: Techniques used to help decision makers make better decisions.
Evidence-Based Medicine: A medical approach that uses the best available evidence from research to make decisions about diagnosis, treatment, and prevention.
Health Policy: A set of decisions, plans, and actions that are undertaken to achieve specific health care goals within a society.
Policy Making: The process of making decisions about how to achieve a desired outcome or goal.
Qualitative Research: A type of research that uses non-numerical data to gain an understanding of underlying reasons, opinions, and motivations.
Reproducibility of Results: The ability to reproduce the same results when the same methods are used.
Research Design: The process of planning and conducting a study to answer a research question.
Technology Assessment: The process of evaluating the potential of a technology to meet a specific need.
LinkOut: A service that provides links to external resources related to a particular article.