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METHOD FOR EXTRACTING AND DISPLAYING HEALTHCARE HISTORY

机译:提取和显示卫生保健历史的方法

摘要

The present invention relates to a system and method for automated extraction and display of past health care use to aid in predicting future health status. A system and method that converts raw medical and pharmacy claims data into Hierarchical Major Clinical Condition (HMCC) and Place of Treatment (POT) time-series data to facilitate the health assessment of a member’s total clinical conditions and aid in predicting his or her future health status. The HMCC categories are organized in body systems and likely disease progression to permit both spatio-temporal digital signal processing and the development of a dynamical learning system. Each medical and pharmacy claim of the member is mapped onto one or more HMCC/POT-time cells. At the end of mapping, multiple entries in each HMCC-time cell are accumulated with the temporal resolution determined as a function of group size and temporal fidelity required for model building. Individual HMCC/POT-time maps can be rolled up to a group level to facilitate employer-by-employer or market-by-market comparison so that clinical strategies can be tailored to each employer or geographic region. Multiple nonlinear visualization mapping algorithms are provided to cope with highly nonlinear nature of claims cost data.
机译:本发明涉及一种用于自动提取和显示过去的医疗保健用途以帮助预测未来健康状况的系统和方法。一种将原始医疗和药学索赔数据转换为主要临床主要症状(HMCC)和治疗地点(POT)时间序列数据的系统和方法,以帮助对成员的总体临床状况进行健康评估,并有助于预测其未来健康状况。 HMCC类别在人体系统中进行组织,并可能在疾病中发展,以允许时空数字信号处理和动态学习系统的发展。成员的每个医学和药学要求都映射到一个或多个HMCC / POT时间单元上。在映射结束时,每个HMCC时间单元中的多个条目将被累积,其时间分辨率取决于模型构建所需的组大小和时间保真度。可以将单个HMCC / POT时间地图汇总到组级别,以方便按雇主或按市场进行比较,从而可以针对每个雇主或地理区域量身定制临床策略。提供了多种非线性可视化映射算法来应对索赔成本数据的高度非线性性质。

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