TempoQL: A Readable, Precise, and Portable Query System for Electronic Health Record Data

TL;DR

TempoQL is a Python toolkit that simplifies querying electronic health record data with a human-readable language, supporting multiple standards and enhancing usability for machine learning applications.

Contribution

It introduces a novel, user-friendly query language and interface for EHR data that is expressive, portable, and compatible with various data standards, improving data extraction workflows.

Findings

TempoQL maintains high precision in cohort creation.

It improves speed and reproducibility of EHR data queries.

Supports multiple data standards including OMOP and MEDS.

Abstract

Electronic health record (EHR) data is an essential data source for machine learning for health, but researchers and clinicians face steep barriers in extracting and validating EHR data for modeling. Existing tools incur trade-offs between expressivity and usability and are typically specialized to a single data standard, making it difficult to write temporal queries that are ready for modern model-building pipelines and adaptable to new datasets. This paper introduces TempoQL, a Python-based toolkit designed to lower these barriers. TempoQL provides a simple, human-readable language for temporal queries; support for multiple EHR data standards, including OMOP, MEDS, and others; and an interactive notebook-based query interface with optional large language model (LLM) authoring assistance. Through a performance evaluation and two use cases on different datasets, we demonstrate that TempoQL simplifies the creation of cohorts for machine learning while maintaining precision, speed, and reproducibility.