背景:电子健康记录(EHRs)在低收入和中等收入国家提供艾滋病毒护理方面发挥着越来越重要的作用。收集的数据用于直接临床护理,质量改进,程序监控,公共卫生干预措施,和研究。尽管在非洲国家广泛使用EHR进行艾滋病毒护理,挑战依然存在,特别是在收集高质量数据方面。
目的:我们旨在评估数据的完整性,准确度,与纸质记录相比,以及及时性,以及影响卢旺达大规模EHR部署数据质量的因素。
方法:我们使用OpenMRS随机选择了50个医疗机构(HFs),支持卢旺达艾滋病毒护理的EHR系统,并进行了数据质量评估。所有HFs都是一项更大的随机对照试验的一部分,25例HFs通过临床决策支持系统接受增强的EHR。训练有素的数据收集器访问了50个HF,使用OpenDataKit应用程序从纸质图表和EHR系统中收集28个变量。我们测量了数据的完整性,及时性、及时性以及纸质和EHR记录中数据的匹配程度,并计算出一致性分数。可能影响数据质量的因素来自先前对50个HF用户的调查。
结果:我们随机选择了3467份患者记录,审查纸质和EHR副本(总共194,152个数据项)。除病毒载量(VL)结果外,所有数据元素的数据完整性均>85%阈值,第二行,和三线药物方案。数据值的匹配分数接近或>85%阈值,除了日期,特别是药物拾取和VL。15个(68%)变量的平均数据一致性为10.2(SD1.28)。HF和用户因素(例如,多年的EHR使用,技术经验,EHR可用性和正常运行时间,和干预状态)与数据质量指标的相关性。EHR系统可用性和正常运行时间与一致性呈正相关,而用户对技术的体验与一致性呈负相关。在11个干预HFs实施的VL结果缺失警报显示,EHR和纸质记录中VL结果最初低匹配的及时性和完整性得到了改善(11.9%-26.7%;P<.001)。在药物拾取记录的完整性上观察到类似的效果(18.7%-32.6%;P<.001)。
结论:除VL结果外,50例HF中的EHR记录通常具有较高的完整性。非日期变量的匹配结果接近或>85%阈值。更高的EHR稳定性和正常运行时间,和进入VL的警报都大大提高了数据质量。大多数数据被认为符合目的,但是更定期的数据质量评估,培训,以及EHR表格的技术改进,数据报告,并建议发出警报。本研究中描述的质量改进技术的应用应有利于广泛的HF和数据用于临床护理,公共卫生,和疾病监测。
BACKGROUND: Electronic health records (EHRs) play an increasingly important role in delivering HIV care in low- and middle-income countries. The data collected are used for direct clinical care, quality improvement, program monitoring, public health interventions, and research. Despite widespread EHR use for HIV care in African countries, challenges remain, especially in collecting high-quality data.
OBJECTIVE: We aimed to assess data completeness, accuracy, and timeliness compared to paper-based records, and factors influencing data quality in a large-scale EHR deployment in Rwanda.
METHODS: We randomly selected 50 health facilities (HFs) using OpenMRS, an EHR system that supports HIV care in Rwanda, and performed a data quality evaluation. All HFs were part of a larger randomized controlled
trial, with 25 HFs receiving an enhanced EHR with clinical decision support systems. Trained data collectors visited the 50 HFs to collect 28 variables from the paper charts and the EHR system using the Open Data Kit app. We measured data completeness, timeliness, and the degree of matching of the data in paper and EHR records, and calculated concordance scores. Factors potentially affecting data quality were drawn from a previous survey of users in the 50 HFs.
RESULTS: We randomly selected 3467 patient records, reviewing both paper and EHR copies (194,152 total data items). Data completeness was >85% threshold for all data elements except viral load (VL) results, second-line, and third-line drug regimens. Matching scores for data values were close to or >85% threshold, except for dates, particularly for drug pickups and VL. The mean data concordance was 10.2 (SD 1.28) for 15 (68%) variables. HF and user factors (eg, years of EHR use, technology experience, EHR availability and uptime, and intervention status) were tested for correlation with data quality measures. EHR system availability and uptime was positively correlated with concordance, whereas users\' experience with technology was negatively correlated with concordance. The alerts for missing VL results implemented at 11 intervention HFs showed clear evidence of improving timeliness and completeness of initially low matching of VL results in the EHRs and paper records (11.9%-26.7%; P<.001). Similar effects were seen on the completeness of the recording of medication pickups (18.7%-32.6%; P<.001).
CONCLUSIONS: The EHR records in the 50 HFs generally had high levels of completeness except for VL results. Matching results were close to or >85% threshold for nondate variables. Higher EHR stability and uptime, and alerts for entering VL both strongly improved data quality. Most data were considered fit for purpose, but more regular data quality assessments, training, and technical improvements in EHR forms, data reports, and alerts are recommended. The application of quality improvement techniques described in this
study should benefit a wide range of HFs and data uses for clinical care, public health, and disease surveillance.