HIV Treatment Error Reduction Using a Genotype Database

在发达国家，联合抗艾滋病毒药物（HAART）显著降低了艾滋病毒和艾滋病的发病率和死亡率。高效抗逆转录病毒疗法的一个主要缺点是产生耐药性。基因型耐药检测经常用于临床实践，产生临床有用但复杂的数据。误解或忽视这些数据可能导致不良后果。电子医疗记录的出现为更好地将复杂的基因型检测数据整合到艾滋病毒感染的管理中提供了机会，从而提高了HAART的安全性和有效性。该项目将创建一个原型，未来将药物基因组学信息整合到电子病历中，以提高患者的安全性和护理质量，使用HIV基因型作为模型系统。这项提议有两个目的。首先是设计和实施一个自动化系统，将HIV基因型检测结果与相应的患者用药数据整合到电子病历系统中，以减少抗逆转录病毒处方错误，改善抗逆转录病毒药物的选择。这将通过开发一个自动化过程来实现，将基因型检测数据存储在电子病历中，创建并实施“专家规则”，以识别基因型数据背景下不正确或次优抗逆转录病毒药物选择的实例，创建一个机制，当专家规则被触发时提醒护理提供者，并验证规则。第二个目标是评估该系统在社区的有效性和可用性，Ryan white资助的门诊设置主要服务于城市，少数民族和低收入人群。要测量和分析的数据是触发规则的频率、提供者对警报的反应以及通过HIV病毒载量测量的临床结果。临床结果将根据两种不同的成功定义进行统计分析：病毒载量<400和降低log（病毒载量）_ >.5。这两种分析都将查看成功或失败与警报是否存在以及提供者对警报的响应之间的相关性。分析将使用费雪精确检验。供应商的系统可用性也将进行定性评估。

Combination anti-HIV drugs (HAART) have dramatically reduced the morbidity and mortality due to HIV and AIDS in the developed world. A major shortcoming with HAART is the development of drug resistance. Genotypic resistance testing is regularly used in clinical practice, producing clinically useful but complex data. Misinterpreting or overlooking such data can lead to adverse outcomes. The advent of electronic medical records presents an opportunity to better integrate complex genotypic testing data into the management of HIV infection, increasing the safety and effectiveness of HAART. This project will create a prototype for future integration of pharmacogenomic information into the electronic medical record for the purpose of improving patient safety and quality of care using HIV genotype as a model system. This proposal has two aims. First is to design and implement an automated system that will integrate HIV genotypic testing results with corresponding patient medication data within an electronic medical record system in order to reduce antiretroviral prescribing errors and improving antiretroviral drug selection. This will be accomplished by developing an automated process to store genotypic testing data within the electronic medical record, creating and implementing "expert rules" to recognize instances of an incorrect or sub-optimal antiretroviral drug selection in the context of genotypic data, creating a mechanism to alert care providers when the expert rules are triggered, and validating the rules. The second aim is to assess the efficacy and usability of this system in a community-based, Ryan White-funded outpatient setting serving a predominantly urban, minority, and low-income population. Data to be measured and analyzed are the frequency of rules triggered, provider responses to alerts, and clinical outcomes measured by HIV viral load. Clinical outcomes will be statistically analyzed as success or failure by two different definitions of success: viral load of <400 and decrease in log (viral load) of _>0.5. Both analyses will look at correlation of success or failure with the presence or absence of an alert and the provider response to alerts. Analyses will be carried out using Fisher's Exact Test. System usabili b providers will also be assessed qualitatively.