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Data is Garbage. Here's How Healthcare Analytics Companies Make it Valuable.

Posted by Danielle on March 04, 2015

big dataWith big data all the rage, many are under the impression that data is a treasure trove. It’s not. It’s garbage.

Whereas a ruby, diamond and sapphire-filled treasure box is valuable upon discovery, a cardboard, candy wrapper and banana peel-filled dumpster must be sorted, processed and distributed to generate value.

Healthcare data has been overlooked, just like garbage, for a very long time. Not until recently have regulations emphasized the importance of taking the time to parse through the unorganized, disjointed and disparate records to discover health risk factors or the reusable cardboards in the garbage pile.

By comparing the recycling process to our healthcare analytics technology’s method, we can gain better understanding of what the valuable parts of data are and how successful healthcare analytics tools, and the data scientists behind them, make those valuable insights actionable. Much like recycling, effective healthcare analytics tools must identify and act to improve.


healthcare data analyticsIdentify. Healthcare analytics technologies begin with data—typically patient data like health history, vitals and medications. Not all of this information is pertinent though. That’s the data scientists’ first job, sifting through the garbage in order to determine what is pertinent.

To do this, he or she must understand the desired improved outcome that generates value. In recycling, the desired improved outcome may be resalable cardboard, plastic or rubber. So, in the garbage pile, finding these materials is pertinent.

In healthcare technology, the desired improved outcome, at least for some of our clients, is reducing hospital readmissions. Our data science team must pinpoint the pertinent data points that are indicative of readmission risk and then isolate them from the general mass of health data.

After the model is created for an organization’s historical data pile, the model is then applied to that organization’s patient population in an effort to pinpoint patients who are at risk of readmission.

Act. Isolating the appropriate recyclables from the mass of discarded materials is a good first step, but the remnants of cardboard, plastic and rubber are still trash to most. Only once you extract the valuable pieces, treat and process the material and then package and distribute it, does the rubber become valuable.

Similarly, identifying what factors contribute to a patient’s readmission risk is a good first step, but if the technology stops there, a patient’s outcome has not been improved. A useful technology will pinpoint those at risk patients and then help clinicians manage the at-risk population of patients by incorporating clear next steps like to a particular patient’s care plan like “schedule a call with this patient.” Only once patients are identified and appropriate interventions are applied can a patient’s risk of readmission be reduced.

Improve. When we identify and act upon trash by sorting, processing and distributing it, we drastically improve the environment and create value. For instance, Americans recycle 105,784 aluminum cans every minute. For each pound recovered, enough resources are saved to energize a city the size of Pittsburg for 6 years. To put some dollars and cents to this equation, Americans earn $16 billion a year recycling aluminum cans. The trash has become a treasure.

When our predictive analytics technology reuses discarded data to analyze, model and help clinicians effectively intervene with patients in need, the healthcare landscape is drastically improved and care providers are more effective and efficient. When one of our Alabama-based clients leveraged our readmission reduction analytics technology (Medalogix touch), they avoided more than 100 transfers to a higher acuity facility over a seven-month period.


This once cast-away patient data has become a treasure trove for the healthcare community as we strive to do more with less and improve care for all patients in the system.

Topics: Data and Analytics Review, Healthcare Industry


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