Healthcare Intelligence and Analytic Services
Sownder Decision Support System is a research-based, data-driven AI application that primarily focuses on cost-efficient resource allocation for surgical case scheduling. The decision support system is based on mathematical modelling and advanced data analytics that can optimise schedules and improve surgical productivity by 30%. The scheduling services will increase cost-efficiency in such ways that either I) increases productivity by 20- 30% or II) introducing 6-hour working days instead of 8, while sustaining the same productivity.
Sownder Surgery Scheduling is a part of Sownder’s Healthcare portfolio in which Prediction, Performance- and Investment Analysis are two additional parts of the Healthcare portfolio.
Energy Optimisation for Commercial Buildings
The energy efficiency suite consists of analytic services for data streams and a Big Data solution as storage.
Sownder’s solution uses centralized information and the possibility to visualise energy usage in a generic way for households as well as commercial buildings.
Key functionalities are:
• Real-time visualization of energy usage –total and per individual appliance
• Historical visualization of energy usage –total and per individual appliance
• Connection to social media, e.g. Facebook
Operational functionalities are:
• Data storage – databases for storing the data
• Business logic – rules engine to apply specific processing to the data
• Data API – web service interface allowing for import and export of energy consumption data into external systems or interfaces
• User authentication API – web service interface to authenticate users into the system
• Provisioning API – web service interface to deploy, activate, de-activate and restore users
• Billing information – detailed billing information to allow further billing to both sub-customers and end-users
• Support (2nd line support) – SLA based 2nd line support into customer’s support organization
The Sownder energy analysis creates a clearer and proactive approach to develop the municipality’s adaptation and innovation capacity to meet residents’ expectations of increased service levels and delivery efficiency.
Data analysis for water and electricity consumption is well suited to acquiring knowledge in Internet of Things (IoT) and data analysis.
In Sownder’s pilot study, water and electricity consumption in selected apartments is recorded through sensors. By collecting data and conducting data analysis, a normal consumption pattern can be calculated per apartment. Deviations from the consumption pattern should be able to initiate appropriate action in order to ensure the residents safety.
It is also interesting to identify other applications that benefits from access to real-time electricity and water consumption data.