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Supporting Quality Data Systems: Lessons Learned from Early Implementation of Routine Viral Load Monitoring at a Large Clinic in Lilongwe Malawi

机译:支持质量数据系统:马拉维利隆圭一家大型诊所早期实施常规病毒载量监测的经验教训

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摘要

Successful viral load programs rely on the presence of data systems and high quality of patient data. Using a cohort of 49 patients at Partners in Hope, a large, urban HIV clinic in Malawi, we performed a quality improvement assessment of a new viral load program with a focus on accuracy of data collected from patients as well as adherence to Malawi HIV Guidelines in regard to response to elevated viral loads (≥1,000 copies/mL). Data were obtained from three parallel medical record systems to investigate the proportion of patients with a repeat viral load and whether the three data systems agreed in regard to sociodemographic and clinical data. Fewer than 30% of patients had a repeat viral load within six months, as recommended in the Malawi HIV Guidelines. There were significant problems with data agreement across the three parallel databases used for care. Date of birth was consistent for 55.1% (N=27) of patients, while a different date of birth was noted in all three sources for 10.2% of pateints (N=1). For 65.3% (N=32), the viral load from the laboratory did not match the recorded viral load in the electronic or paper record. Scale-up of viral load monitoring must be accompanied by the development of data systems that support workflow from sample collection to lab and back to provider. Education of providers and strategies for data collection with minimal errors can facilitate scale-up of high quality programs.
机译:成功的病毒载量程序取决于数据系统的存在和高质量的患者数据。我们在马拉维一家大型城市HIV诊所的Hope Partners中使用了49名患者,对新的病毒载量计划进行了质量改进评估,重点是从患者收集的数据的准确性以及对马拉维HIV指南的遵守情况关于对病毒载量升高的反应(≥1,000拷贝/ mL)。从三个平行的医疗记录系统中获取数据,以调查具有重复病毒载量的患者比例,以及这三个数据系统是否在社会人口统计学和临床​​数据方面达成一致。根据《马拉维HIV指南》的建议,少于30%的患者在六个月内有重复病毒载量。在用于护理的三个并行数据库中,数据一致性存在重大问题。 55.1%(N = 27)的患者的出生日期是一致的,而在所有三种来源中,10.2%的患者(N = 1)的出生日期都不同。对于65.3%(N = 32),来自实验室的病毒载量与电子或纸质记录中记录的病毒载量不匹配。病毒载量监测的扩大规模必须伴随数据系统的发展,以支持从样品采集到实验室再到提供者的工作流程。提供者的教育和数据收集策略的错误最少,可以促进高质量程序的扩展。

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