Objective: Quality improvement of health care requires robust measurable indicators to track performance. However identifying which indicators are supported by strong clinical evidence, typically from clinical trials, is often laborious. This study tests a novel method for automatically linking indicators to clinical trial registrations.
Design: A set of 522 quality of care indicators for 22 common conditions drawn from the CareTrack study were automatically mapped to outcome measures reported in 13 971 trials from ClinicalTrials.gov.
Intervention: Text mining methods extracted phrases mentioning indicators and outcome phrases, and these were compared using the Levenshtein edit distance ratio to measure similarity.
Main outcome measure: Number of care indicators that mapped to outcome measures in clinical trials.
Results: While only 13% of the 522 CareTrack indicators were thought to have Level I or II evidence behind them, 353 (68%) could be directly linked to randomized controlled trials. Within these 522, 50 of 70 (71%) Level I and II evidence-based indicators, and 268 of 370 (72%) Level V (consensus-based) indicators could be linked to evidence. Of the indicators known to have evidence behind them, only 5.7% (4 of 70) were mentioned in the trial reports but were missed by our method.
Conclusions: We automatically linked indicators to clinical trial registrations with high precision. Whilst the majority of quality indicators studied could be directly linked to research evidence, a small portion could not and these require closer scrutiny. It is feasible to support the process of indicator development using automated methods to identify research evidence.
Keywords: clinical trials; concept mapping; indicator; quality of health care; text mining.
© The Author 2017. Published by Oxford University Press in association with the International Society for Quality in Health Care.