Kalsy Megha, Lin Jau-Huei, Bray Bruce E, Sward Katherine A
Author Affiliations: IDEAS Center SLC VA Healthcare System, Salt Lake City, UT (Drs Kalsy and Bray); Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City (Drs Kalsy, Lin, Bray, and Sward); GE Healthcare, Medical Informatics, Portland, OR (Dr Lin); and College of Nursing, University of Utah, Salt Lake City (Drs Bray and Sward).
Comput Inform Nurs. 2018 Oct;36(10):475-483. doi: 10.1097/CIN.0000000000000451.
Core measures are standard metrics to reflect the processes of care provided by hospitals. Hospitals in the United States are expected to extract data from electronic health records, automated computation of core measures, and electronic submission of the quality measures data. Traditional manual calculation processes are time intensive and susceptible to error. Automated calculation has the potential to provide timely, accurate information, which could guide quality-of-care decisions, but this vision has yet to be achieved. In this study, nursing informaticists and data analysts implemented a method to automatically extract data elements from electronic health records to calculate a core measure. We analyzed the sensitivity, specificity, and accuracy of core measure data elements extracted via SQL query and compared the results to manually extracted data elements. This method achieved excellent performance for the structured data elements but was less efficient for semistructured and unstructured elements. We analyzed challenges in automating the calculation of quality measures and proposed a rule-based (hybrid) approach for semistructured and unstructured data elements.
核心指标是反映医院所提供医疗过程的标准指标。美国的医院需要从电子健康记录中提取数据、自动计算核心指标,并以电子方式提交质量指标数据。传统的手工计算过程耗时且容易出错。自动计算有可能提供及时、准确的信息,从而指导医疗质量决策,但这一设想尚未实现。在本研究中,护理信息专家和数据分析师实施了一种从电子健康记录中自动提取数据元素以计算核心指标的方法。我们分析了通过SQL查询提取的核心指标数据元素的敏感性、特异性和准确性,并将结果与手工提取的数据元素进行了比较。该方法在结构化数据元素方面表现出色,但对半结构化和非结构化元素的效率较低。我们分析了质量指标自动化计算中的挑战,并针对半结构化和非结构化数据元素提出了一种基于规则的(混合)方法。