Be scared of health economics models: Very scared!

The UK economy is tanking due to inflation and poor economic policy. The Bank of England is under fire because its models have not been fit for purpose. If the BoE can't model, should we be scared that Health Economics models are flawed?

Being worried about this situation, I gave ChatGPT these prompts and asked for a Linkedin article: "Act as an expert in economic modelling. Review performance of Bank of England economic models at predicting inflation. Why have the models failed? What does this failure tell us about economic modelling? Draw lessons for health economists who build economic models for the pharmaceutical industry. Should health economists be scared that their models are flawed? Should we be scared of health economics models failing to accurate predict the costs and benefits of new technologies?"

This is what ChatGPT wrote:

Title: Lessons from Economic Modeling: Addressing Limitations and Enhancing Decision-Making

Introduction:

Economic modeling plays a pivotal role in shaping policies and informing decision-making processes across various sectors. However, recent discussions surrounding the Bank of England's economic models and their performance in predicting inflation have raised questions about the limitations and challenges inherent in economic modeling. These lessons have valuable implications for health economists working in the pharmaceutical industry, who also build economic models to assess the costs and benefits of new technologies. In this article, we explore the lessons learned from economic modeling and their significance for health economists.

Lesson 1: Acknowledge Uncertainty:

Economic models are not crystal balls; they are subject to uncertainties and limitations. Health economists must recognize and communicate these limitations. It is imperative to convey that economic predictions are based on a range of assumptions and influenced by external factors. By acknowledging uncertainty, health economists can foster a more nuanced understanding of the models' predictions and encourage informed decision-making.

Lesson 2: Continuous Model Updating:

In the face of evolving economic landscapes, health economists should regularly update their models. By incorporating new data and insights, economists can improve the accuracy of their predictions. Staying abreast of the latest developments in the field and refining models accordingly ensures that decision-makers have access to the most up-to-date information.

Lesson 3: Account for Complexity:

Healthcare systems are complex entities with intricate dynamics involving various stakeholders. Economic models in the pharmaceutical industry should strive to capture this complexity. By accounting for the interactions between patients, healthcare providers, policymakers, and other key players, economists can enhance the accuracy and relevance of their models. Ignoring or oversimplifying these interactions can lead to flawed predictions.

Lesson 4: Robustness Testing:

To address the limitations of economic models, health economists should subject their models to rigorous testing and sensitivity analyses. This process helps identify the key drivers of outcomes and assesses the models' robustness to changes in assumptions or input parameters. By scrutinizing the models through various scenarios, economists can identify potential flaws and strengthen the reliability of their predictions.

Lesson 5: Transparency and Peer Review:

Openness and transparency are crucial aspects of economic modeling. Health economists should be forthcoming in sharing their methodologies, assumptions, and data sources. By subjecting their models to peer review and external validation, economists can identify potential flaws and enhance the credibility of their models. This collaborative approach fosters accountability and ensures that decision-makers have confidence in the economic predictions provided.

Lesson 6: Collaboration and Multidisciplinary Approaches:

Given the complexity of health economics, collaboration among economists, healthcare professionals, policymakers, and other stakeholders is paramount. By incorporating diverse perspectives and expertise, health economists can gain a comprehensive understanding of the challenges at hand. This multidisciplinary approach strengthens the models' robustness and applicability, facilitating more informed decision-making processes.

Conclusion:

Flawless economic predictions are an unrealistic expectation. Economic models, including those used by the Bank of England and health economists in the pharmaceutical industry, are valuable tools that provide insights and support decision-making. By acknowledging and addressing the limitations of economic modeling, health economists can enhance the accuracy and reliability of their predictions. It is essential to understand that economic models are just one piece of the puzzle, and decisions should be based on a holistic assessment that considers multiple inputs and perspectives. Through continuous learning, collaboration, and transparency, health economists can contribute to more informed and robust decision-making in the pharmaceutical industry.