Coiera Enrico, Choong Miew Keen, Tsafnat Guy, Hibbert Peter, Runciman William B
Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine and Health Science, Macquarie University, Sydney, Australia.
Centre for Population Health Research, University of South Australia, Adelaide, South Australia.
Int J Qual Health Care. 2017 Aug 1;29(4):571-578. doi: 10.1093/intqhc/mzx076.
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.
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.
Text mining methods extracted phrases mentioning indicators and outcome phrases, and these were compared using the Levenshtein edit distance ratio to measure similarity.
Number of care indicators that mapped to outcome measures in clinical trials.
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.
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.
医疗保健质量的提升需要强有力的可衡量指标来追踪绩效。然而，确定哪些指标有强有力的临床证据支持，通常来自临床试验，往往很费力。本研究测试了一种将指标自动与临床试验注册相链接的新方法。
从CareTrack研究中提取的针对22种常见病症的522项护理质量指标被自动映射到ClinicalTrials.gov上13971项试验中报告的结局指标。
文本挖掘方法提取提及指标的短语和结局短语，并使用莱文斯坦编辑距离比率对其进行比较以衡量相似性。
映射到临床试验结局指标的护理指标数量。
虽然522项CareTrack指标中只有13%被认为有I级或II级证据支持，但353项（68%）可以直接与随机对照试验相链接。在这522项指标中，70项I级和II级循证指标中的50项（71%），以及370项V级（基于共识）指标中的268项（72%）可以与证据相链接。在已知有证据支持的指标中，只有5.7%（70项中的4项）在试验报告中被提及，但被我们的方法遗漏了。
我们以高精度自动将指标与临床试验注册相链接。虽然所研究的大多数质量指标可以直接与研究证据相链接，但一小部分不能，这些需要更仔细的审查。使用自动化方法识别研究证据来支持指标开发过程是可行的。
