Healthcare

Problem: Managing the Volumes of System Sensor Data across their Hospital Chain

In a typical hospital setting, nurses do rounds and manually monitor patient vital signs. They may visit each bed every few hours to measure and record vital signs but the patient’s condition may decline between the time of scheduled visits.

This means that caregivers often respond to problems reactively, in situations where arriving earlier may have made a huge difference in the patient’s wellbeing.

Solutions:

Logi-Crunch a multi-tenant, scalable healthcare analytics platform that transforms and enriches these sensor data into a manageable dataset

Predicts code-blue pathway, septic pathway, CART rule-based-scores in real-time

Platform is built on micro-service principles with a plug and play model. Easily extensible to accommodate other predictive models of interest and rules evaluation in real-time

Onboarding a new facility, from inception to production, on an average takes about 2 months

One of the first few in the world, to build the scalable streaming analytics capability on Apache Nifi

Benefits:

• Proactively Predict events rather than reactively
• Real-time Alerts
• Capture & Transmit Patient Vitals at Much Higher Frequencies
• Improve Patient Satisfaction
• Improve Operational Efficiency
• Improved Response Times
• Reduce adverse Drug Response Times
• Scope to create real-time and offline models of interest

Problem: Legacy system’s large data are growing exponentially. Customer needed a mechanism to reduce the cost, discover business intelligence and discover new revenue streams.

Solutions: Our solution resulted in migrating the compete legacy dataset into the Hadoop ecosystem. Process
engineered to migrate the data in full-dumps, as well as incrementally. Validation framework, to validate migrated data, metadata and other workloads. Reload the transformed data back to the traditional EDW for cases for specific reporting and to enable phased migration.

Benefits:

• Fosters data-driven decisions
• Enables ‘schema-on-read’ strategy
• Low cost on storage and processing
• Eliminates vendor licensing cost
• Scope for advanced analytics powered by NoSQL variants

Problem:EMR systems ranges between 5-20 percent of duplicate patients record which increases the operational cost. Rate increases to 40% for those hospitals that have merged with other facilities

Solutions: Logi-MPI is an EMPI engine, powered by a probabilistic patient record matching algorithm. Engine is configurable and the attributes weights could be throttled based on their sensitivity. Engine was run over four of the Hospitals facilities and resulted in 27% match between the patient records across the facilities. Engine also flags probable matches that would need a stewards’ feedback.

Benefits:

• Improves quality of care
• 360º view of patient information across facilities
• Enables cohort analysis
• Lowers probability of repeat tests and treatment delays
• Aids in precision medicine