Using routinely collected patient data to support clinical trials research in accountable care organizations

TL;DR

This study demonstrates that automating patient data extraction and de-identification from EHRs can efficiently support clinical trial planning within accountable care organizations, ensuring privacy and accelerating research processes.

Contribution

The paper introduces an automated method for extracting and de-identifying patient data from EHRs to facilitate clinical trial recruitment and planning in ACOs, with high accuracy.

Findings

Prevalence of RA was 4% in the study population.

De-identification achieved 99% accuracy, sensitivity, and specificity.

Clinical documentation was successfully extracted from three EHR systems.

Abstract

Background: More than half (57%) of pharma clinical research spend is in support of clinical trials. One reason is that Electronic Health Record (EHR) systems and HIPAA privacy rules often limit how broadly patient information can be shared, resulting in laborious human efforts to manually collect, de-identify, and summarize patient information for use in clinical studies. Purpose: Conduct feasibility study for a Rheumatoid Arthritis (RA) clinical trial in an Accountable Care Organization. Measure prevalence of RA and related conditions matching study criteria. Evaluate automation of patient de-identification and summarization to support patient cohort development for clinical studies. Methods: Collect original clinical documentation directly from the provider EHR system and extract clinical concepts necessary for matching study criteria. Automatically de-identify Protected Health Information (PHI) protect patient privacy and promote sharing. Leverage existing physician expert knowledge sources to enable analysis of patient populations. Results: Prevalence of RA was four percent (4%) in the study population (mean age 53 years, 52% female, 48% male). Clinical documentation for 3500 patient were extracted from three (3) EHR systems. Grouped diagnosis codes revealed high prevalence of diabetes and diseases of the circulatory system, as expected. De-identification accurately removed 99% of PHI identifiers with 99% sensitivity and 99% specificity. Conclusions: Results suggest the approach can improve automation and accelerate planning and construction of new clinical studies in the ACO setting. De-identification accuracy was better than previously approved requirements defined by four (4) hospital Institutional Review Boards.