Machine Learning to Save Lives
公司规模
1,000+
地区
- America
国家
- United States
产品
- Advanced Alert Monitoring System
技术栈
- Machine Learning
- Data Analytics
- H2O
实施规模
- Enterprise-wide Deployment
影响指标
- Customer Satisfaction
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 医疗保健和医院
适用功能
- 质量保证
用例
- 预测性维护
- 远程病人监护
服务
- 数据科学服务
关于客户
Kaiser Permanente is an integrated healthcare system, where patients receive a variety of services and treatments under one roof. With 10 million members, Kaiser Permanente has vast amounts of data, and is using innovative approaches to make this data work for the benefit of its patients and clinicians. Kaiser has always been on the forefront of using technology – as early as 1970s, the company began keeping electronic medical records. Today, Kaiser members have a variety of methods available to them to interact with their doctors and nurses – using the web, e-mail and mobile apps to get the answers they need faster. All this data is captured and can be used to solve specific problems.
挑战
Kaiser Permanente, an integrated healthcare system, was facing a challenge with patients who undergo an unplanned transfer to the Intensive Care Unit (ICU). These patients experienced significantly worse outcomes than those directly admitted to the ICU. They represented about a fourth of all Kaiser ICU admissions, a fifth of all deaths in the hospital, and about an eighth of all of the hospital days. The patients who experienced an unplanned transfer to the ICU experienced two to five times the mortality of patients who are directly admitted to the ICU, and they would stay in the hospital an average of 8 to 12 days more than patients who are directly admitted to the ICU. The challenge was to identify these patients ahead of time who are likely to crash and be rushed to the ICU, and intervene before the deterioration.
解决方案
Kaiser decided to use available data about a patient and build a mechanism that could identify when deterioration might occur. They developed the Advanced Alert Monitoring (AAM) System, which helps identify patients who are likely to crash, and they recognize them 12 hours before they experience the deterioration. The AAM has four components: risk analysis and the creation of the score, monitoring and warning, dissemination and communication of the scope, and finally – the intervention. They used bed history data, chemistry, vital signs, comorbidities, and demographic information to estimate either the probability of a late transferring to the ICU that is a sudden crash or mortality in the hospital. Once the data is collected and cleaned, the team worked on feature engineering and then applied a variety of training and testing datasets. The team explored each parameter and its linkage, got the clinicians involved to look at every case, and then performed a characteristics review and threshold analysis to make sure that a threshold made sense.
运营影响
数量效益
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