医生剖析
公司规模
Large Corporate
地区
- Europe
产品
- DSS
技术栈
- Machine Learning
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
技术
- 分析与建模 - 机器学习
适用行业
- 医疗保健和医院
适用功能
- 质量保证
用例
- 质量预测分析
服务
- 数据科学服务
关于客户
客户是位于西欧的一家大型医院。该医院拥有 2300 多名员工，致力于改善临床结果，同时提高成本竞争力。他们有意采用责任医疗组织 (ACO) 模型，但由于数据来源不协调且质量较差，他们面临着准确衡量医生和医疗机构绩效的挑战。医院估计，在错误的时间实施错误的护理每年会造成高达 160 万美元的损失。
挑战
客户是西欧的一家大型医院，由于数据来源不协调、数据不规范且质量低劣、结果风险调整不足以及医生分析流程缺乏自动化，在准确衡量医生和医疗机构的绩效方面面临挑战。他们正在寻求采用负责任的医疗组织 (ACO) 模式来改善临床结果并在成本上展开竞争。一些临床流程，如开出昂贵或不必要的药物或建议住院时间超过需要的时间，不仅成本高昂，而且对患者护理不利。客户估计，在错误的时间实施错误的护理每年造成高达 160 万美元的损失，他们认为这个问题可以通过准确的医生分析来解决。
解决方案
客户的质量经理团队使用 DSS 构建了一项数据服务，可自动清理和汇总各种数据集（索赔、患者、医生和处方数据）。汇总数据使他们能够准确识别哪些患者、治疗和结果与哪些医生处理相关。这项由 DSS 提供支持的数据服务的核心集成了一种机器学习算法，使他们能够分离出揭示对患者健康结果的具体影响的模式。当模型处理来自各个系统的新传入数据时，从药物处方到住院时间等实践都会根据它们在成本和特定患者健康方面的危害或益处进行评分。
运营影响
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