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
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.
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
DATA Warehouse modernization
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
Legacy system’s large data are growing exponentially. Customer needed a mechanism to reduce the cost, discover business intelligence and discover new revenue streams
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.
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
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.
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.
Inventory process improvements
Inventory strategy by segment
Improved obsolescence risk management
Inventory flow and bottleneck visibility
Casual analysis to improve throughput rate
Aid in demand forecasting
Sub-optimal process integration, unexpected events such as shortage of raw materials and other inventories, 5 – 10% wastage due to obsolescence of inventory, delays in transportation, supply of low grade ore, loss of materials during transportation, reduction in the throughput rate of the processing plats which results in significant financial losses
Elogic has proposed a two-phased solution.
In the first phase, we embark on studying their processing points and inventory characteristics like resource utilization trends, excess and obsolescence trends, segmentation analysis, replenishment cycles, pilferage analysis.
Collect, enrich available historical data of equipment(s) health
In the second phase, Logi-Crunch, our flagship bigdata analytics streaming pipeline will be leveraged to enable –
Iteratively, refine and predict failures of critical devices in the supply-chain processes
Iteratively integrate and automate current manual processes across the supply chain lifecycle
Real-time threshold alerts around replenishments and potential obsolescence