data science is a tool that has been applied to many problems in the modern workplace. Thanks to faster computing and cheaper storage we have been able to predict and calculate outcomes that would have taken several times more human hours to process. Insurance claims analysts can now utilize algorithms to help detect fraudulent behavior, retail salespeople can better tailor your experience both online and in store all thanks to data science. We have combined a few examples of real life projects we have worked on as well as a few other ideas we know other teams are working on to help inspire your team. Let us know if you need help figuring out your next data science project!
One of the true factors of business success is “Location, Location, Location”. You have probably seen this to be true when you see a spot that always has a new restaurant or store. For some reason, it just will never succeed. This forces businesses to think long and hard about where is the best location for their business. The answer is where your customers are when they think about your product. But where is that?
This example is actually being taken on by a few companies. One example is Buxtonco. Buxtonco is answering where should you open your next business with data! Their site exclaims:
“That any retailer can achieve greater success and growth by understanding their customer and that there is a science behind identifying who that customer is, where potential customers live, and which customers are the most valuable”
The concept is brilliant. Think Facebook geo-fencing in real life. By looking for where your customers may spend their time, and what they might be doing in certain locations the technology can help determine where it would be best to open your next business. Whether that be a coffee shop or a dress store. Data science and machine learning can occasionally seem limited to the internet. However, information provides power both online and in real life.
Predicting why patients are being readmitted
Being able to predict patient readmission can help hospitals reduce their costs as well as increase population health. Knowing who is likely to be readmitted can also help data scientist find the “why” behind specific populations being readmitted. This is not just important because of public health but also because the affordable care act reduces the amount of medicaid for claims when readmission occur prior to 30 days.
Hospitals around the country aremelding multiple data sources beyond just typical claims data to get insight into what is causing readmission. One of the common approaches is researching ties between readmission and socioeconomic data points like income, addresses, crime rates, and air pollution.
Similar to the way marketers are targeting customers using machine learning and product recommendation systems that factor socioeconomic data points to tell how to sell to a customer. Hospitals are trying to better tailor their care to help their patients based off of how other similar patients have responded in the past.
Even a phone call at the right time after an operation has been shown toreduce the amount of readmission that occurs. Sometimes the reason patients are readmitted can have nothing to do with how the doctors treated them in the hospital but instead it could be that the patient didn’t understand how to take their medication, or they didn’t have anyone at their house to help take care of them. Thus, being able to figure out the why behind the readmission can in turn fix it. Once policy makers understand the why, it is much easier to develop better practices to approach each patient.
Insurancefraud costs companies and the consumers (who are subjected to higher rates) tens of billions of dollars a year. To add to the problem, attempting to prove claims are fraudulent can in turn costs the companies more than the original cost of the claim itself.
