Health risk assessment using real time clinical data

描述(由申请人提供)：卫生信息技术的进步显著提高了我们通过更准确、及时和临床相关的措施识别基于人群的健康状况和临床需求的能力。私人和公共保险发起人以及其他对调整财务或临床质量和业绩数据感兴趣的人现在最常使用的工具和工具依赖于健康状况的代理衡量标准，如诊断、药房数据或程序，通常从索赔或其他行政数据来源获取。这些工具及其衍生的风险评估被用来调整健康计划或提供者的付款和医生档案，以进行质量评估等用途。每个模型的基础是，根据索赔或管理数据生成的诊断或药房配药作为潜在健康状况的信号，并可用于解释当前或预测未来的医疗保健使用。之所以开发依赖于这些数据的模型，是因为直到最近，从人群的临床记录中获取实际临床状态的标记物一直是不切实际的，或者成本太高。然而，电子医疗记录的采用，包括以前对大量人口不可用的临床数据，可能会改变我们评估基于人口的风险的方式，并将这些评估应用于调整临床表现、健康计划和提供者支付。我们建议开发和测试一个风险评估模型，该模型使用来自电子病历的实时临床数据。我们将检验这一假设，即使用医疗需求的临床表达来评估人群风险的能力将比纯粹从诊断或药学数据得出的衡量标准更准确，并在指导临床实践方面更具相关性。

DESCRIPTION (provided by applicant): Advances in health information technology significantly improve our ability to identify population based health status and clinical need through more accurate, timely and clinically relevant measures. The tools and instruments that are now used most often by private and public insurance sponsors and others interested in adjusting financial or clinical quality and performance data rely on proxy measures of health status such as diagnoses, pharmacy data or procedures, usually captured from claims or other administrative data sources. These instruments and the risk assessments derived from them are used to adjust health plan or provider payments and physician profiles for quality assessments among other uses. The foundation of each model is that diagnoses or pharmacy dispenses generated from claims or administrative data serve as a signal of underlying health status and can be used to explain current or predict future health care use. Models that rely on these data were developed because until recently it has been impractical or too costly to capture markers of actual clinical status from clinical records on a population basis. However adoption of electronic medical records, which include clinical data previously not available for large populations has the potential to change the ways we assess population based risk and apply these assessment to adjusting clinical performance and health plan and provider payments. We propose to develop and test a risk assessment model that uses real time clinical data from an electronic medical record. We will test the hypothesis that the ability to assess population risk using clinical expressions of medical need will be more accurate than measures derived purely from diagnostic or pharmacy data and be more relevant in guiding clinical practice.