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A Framework for Leveraging Business Intelligence to Manage Transactional Data Flows between Private Healthcare Providers and Medical Aid Administrators

机译:利用商业智能管理私人医疗保健提供商和医疗救助管理员之间的交易数据流的框架

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Leaders at company AB within different functional areas needed to effectively facilitate the integration of BI initiatives into business operations. Semi-structured interviews were used to extract key concepts and attributes relevant to business functional areas, from business leaders and these were related to BI techniques. Thematic analysis on collected data was used to identify critical success factors (CSFs). A conceptual framework was developed which comprises business CSFs that are related to opportunities for value derivation from BI activities. This framework can be used as a guideline by Company AB for opportunity assessment and BI implementation, thereby enabling Company AB to leverage the value of BI. A decision tree predictive analytics model whose business rules potentially assist in proactive churn management for companies that have customer transaction volumes as a feature, was developed. This analytics model shows that claims that are not submitted to a client's historically most frequently used medical aids and variances in transactional claim volumes of more than 20%, are good indicators of a client churn. Companies that provide value to the private healthcare industry via the facilitation and management of transactional data flows between healthcare providers and medical aid administrators will benefit from the insights derived from this model.
机译:AB公司不同职能领域的领导者需要有效地促进将BI计划整合到业务运营中。使用半结构化访谈从业务主管中提取与业务功能领域相关的关键概念和属性,这些概念和属性与BI技术有关。对收集到的数据进行主题分析,以确定成功的关键因素(CSF)。开发了一个概念框架,其中包括与商务智能活动产生价值的机会有关的业务CSF。 AB公司可以将此框架用作机会评估和BI实施的指南,从而使AB公司能够利用BI的价值。开发了决策树预测分析模型,该模型的业务规则可能有助于以客户交易量为特征的公司主动进行客户流失管理。此分析模型显示,未提交给客户历史上最常用的医疗帮助的索赔以及交易索赔额中超过20%的差异是客户流失的良好指标。通过促进和管理医疗保健提供者与医疗救助管理员之间的交易数据流为私营医疗保健行业提供价值的公司将受益于此模型。

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