I would like to add some thoughts about the communication gap between the frontline collectors and the analysts regarding the main challenges in the article.
Missing Data: Data is often missing because workers are busy and sometimes don’t see the point of completing it. They should be shown how their data turns into better patient care or improves their workflow. If they see positive results, they would be less likely to skip the info. Automated error-checking can also be included in the process of checking for missing data and errors.
Selection Bias: Big data, for example, the national registries, should be checked for quality and coverage so they do not miss the full picture of representatives. Also, analysts can later use statistical techniques to adjust for bias in this observational data.
Training and applications: We can start by training the data lead in each department and then branch out. The key is making it practical by the collectors need to know how to keep data clean, while analysts need to master the stats/AI/tech tools to handle these big datasets.
Privacy and Ethics: The current system feels like every institution has its own rules and ethical committees, making data sharing difficult. We need more centralized regulation to streamline the process.