Simulated dataset for analysis via Large language models, via the analytical framework Analysis of Individual Heterogeneity and Discriminatory Accuracy (AIHDA).

This dataset consists of 10 000 simulated observations. It is utilized to explore and apply Large language models for data analysis, via the analytical framework Analysis of Individual Heterogeneity and Discriminatory Accuracy (AIHDA). The dataset is based on a previous study's aggregated results (Öberg J, Khalaf K, Perez Vicente R, Johnell K, Fastbom J, J. M. Geographic and socioeconomic differences in potentially inappropriate medication among older adults – Applying a simplified analysis of individual heterogeneity and discriminatory accuracy (AIHDA) for basic comparisons of healthcare quality. BMC Health Services Research. 2024 (Under peer-review). Empirical patient data must be analyzed within a secure IT environment to ensure confidentiality. By utilizing simulated patient data, we can apply a cloud-based GPT to our analysis, thereby gaining access to computational power and LLM capabilities that would otherwise be inaccessible to us via local LLMs. For the purposes of our study, a simulated database is a suitable solution. The simulated database was created by ChatGPT 4o based on the previous publication already referenced. By doing so, we can illustrate the application of GPT-based analysis in a real-world example of a healthcare quality indicator. The quality indicator, known as potentially inappropriate medication among older adults, is managed by the Swedish National Board of Health and Welfare (NBHW).<br>