CASE STUDY: Machine learning for activity prediction, as part of lead compound generation
The Challenge: The ability to quickly iterate multiple large feature sets with the flexibility to test models at scale is a challenge for any data scientist. Read More
USE CASE: Value Based Care
CLIENT: Major UK based Healthcare network in partnership with Intacare.
OVERVIEW: The annual cost of radiotherapy is escalating year-on-year with little visibility of root cause and control. Maintaining cost efficient healthcare for patients required an investigation of current code/claim and cost data.
GOAL: Identify and quantify potential cost savings of revising existing reimbursement mechanisms.
PROBLEM: Data was incongruous. Each healthcare provider used different systems, taxonomies, codes and cost basis for mapping radiotherapy procedures when submitting claims.
- 75,000+ claims
- 1725+ unique narratives
- 1000’s of individual codes and duplicate codes
- Data types: text, numeric
SOLUTION: Intacare used Augusta Pre-Processing workflows to quickly normalize procedure and cost data collected from multiple sources. The team also created an automatable workflow to streamline future analysis and report generation.
- Identified inefficiencies across 24,281 claims
- 39% of total claims
- Projected cost savings: $5Million
Pre-processing of data using Augusta workflows reduced the time to manage the data from multiple weeks to just hours.
Moreover, the workflows are now available within Augusta as standard packages, easily replicated for future projects or if additional data needs to be interrogated.
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Challenge: Combine Disparate Data Sets in PreProcessing for ML
Summary: Compelling results show that combining data sources generally allowed better diagnostic performance than with any data set alone (Figures 1&2) Read More
Report Title: Distributed Processing Frameworks for Machine Learning of Combined Biomedical Data Types
Whitepaper discusses the computing requirements of combined data types for which the Augusta™ platform was constructed to operate
This is a must read for understanding the compute power complexities of pre-processing various data types and identifying ideal scenarios when using/pricing Augusta™
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