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Taylor Larsen

Why Health Systems Must Use Data Science to Improve Outcomes

In today’s improvement-driven healthcare environment, organizations must ensure that improvement measures help them reach desired outcomes and focus on the opportunities with optimal ROI. With data science-based analysis, health systems leverage machine learning to determine if improvement measures align with specific outcomes and avoid the risk and cost of carrying out interventions that are unlikely to support their goals.
There are four essential reasons that insights from data science help health systems implement and sustain improvement:

Measures aligned with desired outcomes drive improvement.
Improvement teams focus on processes they can impact.
Outcome-specific interventions might impact other outcomes.
Identifies opportunities with optimal ROI.

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Brian Crick, MBA
Holly Burke
Dr. Needham Ward

How to Apply Machine Learning in Healthcare to Reduce Heart Failure Readmissions

One large healthcare system in the Pacific Northwest is moving machine learning technology from theory to practice. MultiCare Health System is using machine learning to develop a predictive model for reducing heart failure readmissions. Starting with 88 predictive variables applied to data from 69,000 heart failure patient encounters, the machine learning team has been able to quickly develop and refine a predictive model.
The output from the model has guided resource allocation efforts and pre-discharge decision making to significantly improve patient care management activities. And the data has engendered trust among clinicians who rely on it the most for clinical decision making.
This inside look at the application of advanced technology offers lessons for any healthcare system planning to ramp up its machine learning and predictive analytics efforts.

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Katherine Foag
Taylor Larsen

Top Five Elements of an Effective Readmission Risk Score

Under value-based healthcare and the 2012 Hospital Readmission Reduction Program, healthcare organizations are more motivated than ever to reduce their incidence of preventable readmissions.
Health systems can reduce risk of hospital readmissions by developing readmission risk scores tailored specifically to their populations. A risk model that meets the following five requirements will have significant predictive value and is most likely to achieve systemwide adoption:

Identifies at-risk patients early.
Separates patients relevant to the disease-specific identification method and intervention strategy from all other in-hospital patients.
Uses organization-specific data to train a disease-specific model.
Exceeds performance of existing models.
Is developed in collaboration with domain experts.

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Bobbi Brown

Hospital Readmissions Reduction Program: Keys to Success

Avoidable readmissions are a major financial major problem for the healthcare industry, especially for government payers. To tackle this problem, CMS launched the Hospital Readmissions Reduction Program (HRRP). While some hospitals may be able to absorb the financial penalties under HRRP, they still need to track increasingly complex reporting metrics. Most tracking solutions are inadequate for today’s complicated reporting needs. A healthcare enterprise data warehouse and analytics applications, however, are designed to solve the numerous reporting burdens. When used together, they also deliver a robust solution that enables hospitals to track and drive real cost and quality improvement initiatives, all without the need for users to be technical experts.

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