Creating API Calls in Large Clinical Datasets for Surgery Outcomes Research

BackgroundApplication Programming Interfaces (APIs) enable seamless communication and data exchange between different software systems. In clinical research, APIs provide a powerful and efficient way to access large datasets, allowing researchers to develop reproducible workflows for data extraction and analysis.Methods and ResultsAPI calls were constructed using Python to extract data from 2 publicly available sources: NIH RePORTER and the CMS Medicare datasets. The NIH API was queried with a JSON payload to retrieve project-level funding data by year and keyword, while the CMS API was accessed via URL parameters to filter Medicare enrollment by state and year. Scripts were written to scale queries across multiple years and automate data collection, with outputs saved in CSV format for further analysis.ConclusionAPI-based data extraction is a scalable and reproducible method for accessing large clinical and research datasets. By leveraging NIH and CMS APIs, researchers can automate queries, customize filters, and retrieve longitudinal data to support health care analysis. Broader use of API workflows may enhance data accessibility and research efficiency across clinical disciplines.