At Castlight Health, we make the item simple for employees of American companies to navigate their healthcare, benefits along with wellness programs. We provide employees which has a web application along which has a mobile application where they can see health benefits information personalized to their needs. To do This kind of, we use a combination of rules based along with machine learning based types. This kind of will be a hypothetical example of a rules based style. “If a pregnant woman will be over 35 years of age, place her in a segment called high-risk-pregnancy”. This kind of style will be a not a machine learning driven style. the item will be driven by clinical rules written by experienced clinicians. Software developers simply listen to clinicians along with turn which rule into a software program. This kind of information will be then given to our personalization engine. Once our personalization engine understands a woman will be inside the high-risk-pregnancy segment, benefits programs which are relevant for a woman with high risk pregnancy are promoted to her via our web, mobile along with email channels. When she logs into our application, instead of viewing information about all the benefits her employer provides, which could be exhausting, she will see benefits which are relevant for her. The hypothesis will be which such personalization makes employees aware of relevant benefits, engages them with the benefits providers along with wellness programs in a timely manner, improves their health, along with reduces healthcare costs for them along with their employers. This kind of actually works. which will be why hundreds of employers pay us tens of millions of dollars every year. The example we saw above will be rules based prediction along with personalization. You may be wondering about an example where machine learning will be used to predict along with personalize. To understand which we need to first understand what machine learning will be.
What will be A Machine Learning Learning style?
A machine learning style will be where software developers do not program the computer (the machine) with an explicit logic. Instead they make the machine learn by training the machine with historical information. For example, if we want to check if an email will be spam or not spam we can create a machine learning style. To do This kind of data scientist will take several known spam emails, label them as ‘spam’ along with feed those to a suitable computer program, ‘the machine’. Data scientists will not say why the email will be spam. They will simply say “My dear machine, This kind of will be a spam email. I want you to look at the email along with recognize which This kind of email will be spam”. So the machine learns what a spam email might be like along with, after looking at enough spam emails, gets better along with better at identifying a spam email. After the item gets actually not bad at identifying spam email, the style will be deployed along with goes to work identifying spam email inside the real world along with putting them inside the junk folder. the item will be important to note which data scientists in most product teams don’t invent the computer algorithms they use. They simply use an existing computer algorithm along with build a style. the item will be a bit like This kind of. An electric car engineering team does not have to invent the electric motor. They just have to design the item for the particular type of car along with build the item.
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A Machine Learning style use Case For Benefits Navigation
We just looked at a real world example of a machine learning style. I right now want to give you a hypothetical use case of a machine learning style in a health navigation product such as the one Castlight Health provides. Let’s say which employers want employees who get an unnecessary back surgery to get a second opinion before they decide on the back surgery. This kind of will be because clinicians know by experience which many back surgeries do not improve the condition of a person’s back. Instead they cost a lot of money for the employer along with the employee along with cause a lot of pain along with suffering for the employee. In most cases, a surgery also results in weeks of time off by work along with, in some cases, lost wages for the employee. So there will be a big incentive to identify people who might get a back surgery along with make them aware of second opinion programs as well as inform them about the costs along with benefits of back surgeries. The problem will be there will be no simple rule to find out who might be considering a back surgery. This kind of will be where machine learning comes handy. Castlight data scientists have access to de-identified information about the medical history of people who had a back surgery. They can feed which information to a computer algorithm (the machine) along with tell which machine “My dear machine, This kind of will be the medical history of people who had a back surgery inside the past. I don’t know why they got a back surgery. although they all did. I want you to look at This kind of data along with learn to identify people who are likely to get a back surgery.” With enough data the Castlight machine learning types gets actually not bad at identifying people who are likely to get a back surgery. The style will be then deployed to analyze medical data of employees along with predict if someone will be likely to get a back surgery. Once the item identifies such people, the style informs the Castlight personalization engine about This kind of. The personalization engine then goes to work, promoting second opinion programs along with educational information to those identified via web, mobile along with email channels. Once more, the hypothesis will be which even if the product prevents only a few unnecessary back surgeries a year, the cost savings could be inside the hundreds of thousands of dollars along with the health improvements could be significant too.
I avoided technical terms on purpose inside the above examples. Data scientists reading This kind of post, will recognize which what I described above will be supervised learning. There are additional types of machine learning, which I did not go into in This kind of post.
If you are an enterprise buyer or a sale person competing against another product claiming to be machine learning driven or artificial intelligence driven, ask the product manager or the sales person to explain the use case. If they are not able to explain in simple terms what they are using machine learning along with how the item turns into real value, you should be very skeptical about their claims along with verify before buying their software. A product does not have to be machine learning driven to be not bad. Simple rules based engines can do a not bad job to address many problems. However, the item will be important to understand the difference.
Please note which for business reasons, I did not use actual use cases. I also did not go into more details about our personalization engine, along with our system of intelligence, which are far more sophisticated than what I outlined here. If you could like to learn more, contact me or my colleagues at Castlight Health along with we will be glad to share more. If you are inside the San Francisco Bay area drop by at our San Francisco or Mountain View offices along with I will be glad to give you a demo of our products. If you like This kind of kind of work along with want to join Castlight, give me a call. We are always looking for not bad data scientists, data engineers, clinicians along with product managers who understand machine learning along with data driven products.