Computerized Triggers Of Big Data To Detect Delays In Follow-up Of Chest Imaging Results

Authors: Daniel R. Murphy, Ashley N.D. Meyer, Viraj Bhise, Elise Russo, Dean F. Sittig, Li Wei, Louis Wu, Hardened Singh

Abstract:

Background

We tested a “trigger” algorithm to identify delays in follow-up of abnormal chest imaging results in a large national clinical data warehouse of electronic health record (EHR) data.

Methods

We applied a trigger in a repository hosting EHR data from all US Veterans Affairs (VA) healthcare facilities and analyzed data from seven facilities. Using literature reviews and expert input, we refined previously-developed trigger criteria designed to identify patients potentially experiencing delays in diagnostic evaluation of chest imaging flagged as ‘suspicious for malignancy.’ The trigger then excluded patients where further evaluation was unnecessary (e.g., those with terminal illnesses or already completed biopsies). We programmed the criteria into a computerized algorithm. Reviewers examined a random sample of trigger-positive (i.e., patients with trigger-identified delay) and trigger negative (i.e. patients with an abnormal imaging result, but no delay) records and confirmed presence or absence of delay or need for additional tracking (e.g., repeat imaging in 6-months). We calculated trigger positive and negative predictive values, sensitivity, and specificity.

Key words

Electronic health records; health information technology; triggers; medical informatics; primary care; radiology; lung cancer

Citation: Daniel R. Murphy, Ashley N.D. Meyer, Viraj Bhise, Elise Russo, Dean F. Sittig, Li Wei, Louis Wu, Hardened Singh Computerized Triggers Of Big Data To Detect Delays In Follow-up Of Chest Imaging Results doi:10.1016/j.chest.2016.05.001

Received: 7 December 2015, Revised: 14 April 2016, Accepted: 2 May 2016, Available online: 10 May 2016

Copyright: © 2016 Published by Elsevier Inc. under license from the American College of Chest Physicians.

Conclusions

Application of triggers on ‘big’ EHR data may aid in identifying patients experiencing delays in diagnostic evaluation of chest imaging results suspicious for malignancy.

Funding

This project is funded by a Veteran Affairs Health Services Research and Development CREATE grant (CRE-12-033) and partially funded by the Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN 13–413). Dr. Murphy is additionally funded by an Agency for Healthcare Research & Quality Mentored Career Development Award (K08-HS022901) and Dr. Singh is additionally supported by the VA Health Services Research and Development Service (CRE 12-033; Presidential Early Career Award for Scientists and Engineers USA 14-274), the VA National Center for Patient Safety and the Agency for Health Care Research and Quality (R01HS022087). These funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. There are no conflicts of interest for any authors.