Saving Lives With Real-Time Analytics
By: Melanie Pasch
It seems like there are advanced analytics and AI embedded in all kinds of applications these days. These modern approaches help us find the fastest route to work, the perfect song to play in the moment, or help us do our jobs better. Needless to say, the value an application can provide us varies tremendously.
What makes the software we use every day provide the most value? Many would say data. But even the richest data in its raw form is far from providing applications with true value. In other words, data itself is meaningless to end users. It is the way an application can deliver timely value from that data that makes data today’s golden goose.
So, what makes an application provide the most value? The answer is the ability to ingest, combine, process, and deliver results at UX speeds while undertaking complex computations. An application that can digest rich, large scale data along with its context and analysis in real-time can deliver infinite value.
Live Data-Driven Diagnostics In Action
Doctors see an average of 20 patients every day. These 20 patients are unique individuals with diverse medical histories, conditions, and health risks. Doctors, nurses, and other medical professionals in hospitals and clinics make life or death decisions several times a day often running on very little sleep. The stakes couldn’t be higher. While they are brilliant human beings, a real-time decision support application with predictive capabilities could significantly improve patient outcomes and help them do their jobs even better.
This application presents doctors with instant predicted diagnoses, suggested treatments and warnings based on each unique patient’s medical records cross referenced with real-time streaming data from monitors, recent lab results, outcomes across hospitals, and population health data.
For example, data from one EKG on its own provides limited value. Combining that data with automated analysis and correlation of psychographic, pharmacodynamic, and epidemiologic data in real time however, enhances a doctor’s decision making, resulting in better patient outcomes.
Arming doctors with real-time data analysis from large scale, complex data sources can have limitless use cases. According to the Center for Disease Control (CDC), 1.7 million adults in America develop sepsis each year. Approximately 270,000 Americans die annually from sepsis. In fact, 1 in 3 patients who die in American hospitals have sepsis. Think about the lives that could be saved by an application that could detect sepsis earlier.
Another use case for this type of real-time application is predicting unexpected drug interactions and flagging them to doctors before it’s too late. “About 350,000 patients each year need to be hospitalized for further treatment after emergency visits for adverse drug events,” the CDC reports. I can’t help but wonder how many of these hospitalizations could be avoided if doctors were presented with so much more analyzed data when prescribing a patient new medications and providing instructions and warning for those medications.
Instead of making decisions, while sometimes literally on their feet for hours, with potentially incomprehensive medical histories and drug information, doctors could make decisions aided by a wealth of contextual knowledge only real-time analysis can provide. The results would be less doctor fatigue, happier, healthier patients, less avoidable hospitalizations, and lives saved.
Molecula’s novel approach to data access is game changing to your application.
- Reliable, scalable big data architecture to enable use cases never possible before. Simplify real-time data architecture use cases across massive amounts of distributed data sets by streamlining your data architecture and eliminating the need to pre-aggregate, federate, copy or move distributed data.
- Increased user loyalty and adoption and at least 1000x performance improvement. Delight your users and drive adoption by enabling continuous, personalized insights at UX speed.
- Reduce total cost of ownership by 10-100x. Control costs, infrastructure resources and risk of data access through a reduced data and infrastructure footprint and secure data format.
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