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Case Studies
Predict patient critical condition in Real-Time
Problems
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
Healthcare UCI,SJHS
- 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
DATA Warehouse modernization
Problems
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
Healthcare Meharry
- 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
Patient De-duplication
Problems
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
- Enables cohort analysis
- Lowers probability of repeat tests and treatment delays
- Aids in precision medicine
Our Valued Partners
Testimonials
Charles Boicey
Co-Founder, CIO,Clearsense
Team ElogicSquare is highly knowledgeable and well experienced in the field of Data Streaming and Analytics. The professionals at ElogicSquare took the time to understand Clearsense vision and technology needs to provide the most efficient solutions and highest quality products. They have been instrumental in shaping our portfolio of products, intensifying an analytics culture within and a key stakeholder in decision making. With Team ElogicSquare by our side, we are confident in growing Clearsense market presence in the competitive Healthcare Analytics domain.
Lisa Dahm, PhD
Director, Enterprise Data & Analytics, UC Irvine Health Information Services
ELogic has been a technology partner of the University of California, Irvine Health since 2011, initially building out the first Hadoop based technology solution in healthcare as well as developing the first Hadoop based system to retire healthcare information systems. Additionally, the team developed a surveillance system that detects the subtle changes in a patient’s physiology and predicts the potential for the patient to deteriorate in the near future. The eLogic team has been attentive to our needs and has provided innovative long term solutions for our complex healthcare data problems
Amy M. Andrade, MS, PMP
Senior Advisor to the President – Technology & Innovation,MMC,Nashville
In collaboration with Clearsense the eLogic team has been an integral component to the success of the development of the Data Science Institute at Meharry Medical College in Nashville, Tennessee, USA. The team developed the infrastructure to support the educational and the research needs of the institute. Our future work with eLogic centers on combining omic data sets with healthcare, exposome & social determinants data sets for clinical and research use cases. eLogic has been of great service in our quest to bring affordable solutions to serve the underserved.
