Data environment requirements for a clinical toxicology learning health system: A mixed-methods survey

Clinical toxicology research depends on reusable observational data, but toxicology information is often fragmented across systems and difficult to extract reliably. Learning health systems (LHS) use routinely collected data to generate knowledge and improve care. This study examined the conditions needed to support an LHS for clinical toxicology through reliable, clinically meaningful and reusable data.

We conducted a cross-sectional mixed-methods survey of professionals with expertise in clinical toxicology, poisons information services, emergency care, digital health, informatics, data governance, data engineering or health service data use. Survey items assessed six areas: data quality and standardisation, data access and governance, technical infrastructure, clinical workflow fit, organisational readiness, and future use and scalability. Open-text responses were analysed using descriptive content analysis and integrated with quantitative findings using a joint display.

Thirty-four respondents initiated the survey, and 21 provided analysable Likert data. Section summaries showed strongest agreement for future use and scalability and technical infrastructure, and lowest agreement for data quality and standardisation. Strong support was observed for timely clinical access (19/19), co-designed governance, workflow and technical design (17/17), sustainability through standards and stewardship (16/16), automation with human validation (16/17), and a concise essential dataset (19/21). Agreement was lower for integrating clinical terminologies into extraction and reporting (5/21) and prioritising completeness and accuracy over real-time entry speed (10/21). Integrated findings identified clinically meaningful source data, automation with clinical validation, timely access, proportionate governance, reliable and adaptable infrastructure, workflow fit and sustained stewardship as conditions supporting reliable data extraction and use.

Establishing an LHS for clinical toxicology depends on source data quality, validation, governance, workflow fit and organisational capacity to sustain data use. These findings can guide data environment design for research, surveillance and quality improvement. Targeted, non-probability recruitment and the Australia-New Zealand sample may limit representation and transferability.