Business Process Optimization for Healthcare Services
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- Alpine Chorus
技术栈
- Predictive Analytics
- Data Blending
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Digital Expertise
技术
- 分析与建模 - 预测分析
适用行业
- 医疗保健和医院
适用功能
- 商业运营
用例
- 预测性维护
服务
- 数据科学服务
关于客户
The customer is a leading provider of Specialty Benefits Management solutions with a net worth of $1.5B. They provide services to payers and other risk-bearing entities, such as Accountable Care Organizations (ACOs). The company serves over 70 million insured members and processes over 2.5 million patient transactions every month. As a benefits organization, they deal with an extraordinary amount of data from medical procedures, insurance claims, to patient data.
挑战
The Company, a leading provider of Specialty Benefits Management solutions, was struggling with managing the extraordinary amount of data associated with benefits management in healthcare. As the number of insured members increased, it became crucial for them to leverage the constant influx of data to achieve operational efficiency. Their previous approach involved multi-month cycles with IT normalizing the data, and inconsistent ways of implementing their models into production. In order to scale their data efforts and achieve a more streamlined process, the company needed an end-to-end advanced analytics solution, from data blending to modeling.
解决方案
The Company adopted the Alpine Chorus platform, an end-to-end advanced analytics solution. This platform allowed the company to deploy advanced analytics solutions across business units easily and efficiently. The process of preparing and modeling the data for a particular unit is centralized within the Alpine Chorus platform, and allows business users to maintain governance over the entire process without putting stress on their data science team. Business analysts at The Company are able to access the power of predictive analytics through the Alpine platform, and can make determinations for authorizing medical procedures based on the new data. For the first time, The Company’s analysts can receive the input and approvals of various stakeholders in the analytics process from one collaborative platform, allowing them to deploy predictive models into production without wasted cycles.
运营影响
数量效益
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