Predictive Analytics Can Change the Way You View Your Business

AAEAAQAAAAAAAAN8AAAAJDVmNGQwOTY0LWIxMmEtNGUyMS04MGJiLWYwZjdjMTQ4MzJhNAPredictive Analytics is trending as the “next big thing” and business professionals are slowly beginning to understand the impact “it” can have on the decisions that they make. Consumer marketing is the obvious and most publicized area where Predictive Analytics is having an impact.  As an example, major online retailers and other frequent “consumer touch points” on the internet are gathering and analyzing massive amounts of data regarding Internet sites visits and buying habits and correlating them with other available information in an attempt to provide “their” customers with more accurate choices.

At a very simple level, that’s why we are bombarded with products from the Google searches that we complete and offered car rental and hotel deals from Priceline.com transactions we didn’t complete.  At a more sophisticated level, some of us are probably receiving email and actual mail from retailers that offer products that fit our demographics and buying habits.

This is all interesting but not particularly novel.  I remember working with a major cable provider back in the 1990’s on a project to match up consumer demographics with commercials.  The technology existed, it tested out but was never launched.

On a slightly more “creepy” level, most technology vendors today employ marketing software that gathers and analyzes how potential customers interact with their various internet based marketing vehicles such as websites, blogs, social media, etc. and then provides this information to their sales teams to reach out to those potential customers that are exhibiting potential buying habits.  I have been using this technology for years and have always wanted to have an automated email that stated, “Since you just visited out website and stayed on our product page for 36 seconds, we would like to offer you….”

Like I said, “creepy”!

One of the other areas that we are starting to see the use of Predictive Analytics is in sales forecasting.  Although I have been using additional information beyond the “credibility of the sales executive” for years to judge sales forecasts, it is now fairly easy to gather and analyze an appropriate amount of information about resellers, partners and even clients to know that they are going to purchase $1M worth of hardware with on $5 worth of credit or a track record of buying more than $10 worth of hardware in any previous period.  Sales executives hate this trend as it forces them to actually submit realistic forecasts.

An even more impressive use of Predictive Analytics is within the healthcare market where healthcare providers are able to more accurately predict patient behavior and therefore reduce potential issues and ultimately increase the patient experience and reduce costs.

In an article by Stephanie Baum is the Digital Health Editor for MedCityNews.com, published November 27, 2015 on the MedCityNew Site, titled, “Can predictive analytics change how hospitals interact with patients?“, Michael Cousins, the president and Chief Analytics Officer of Predictive Analytics startup Forecast Health, talks about some interesting use cases for healthcare.

Forecast Health uses roughly 100 consumer-oriented, socio-economic datasets that can point to different types of risks for hospitals such as medication non adherence, readmission, and other things. Cousins noted that several companies tend to focus on a zip code or other census data to gauge risk but he finds that insufficient.

For example, a patient could experience financial stress even if they live in a zipcode associated with wealthy households.

If patients don’t have access to a car and lack reliable public transportation, that can reduce the likelihood of making an appointment. Patients with financial distress are more likely to be readmitted because they may have difficulty affording medication. If they live alone, that can significantly ramp up the chances of readmission because when they are discharged from the hospital, they’re in a fragile state and may lack caregiver support. But it’s critical to do those kinds of assessments before patients are discharged when hospitals can intervene easily, Cousins said.

The full text of Stephanie Baum’s article is as follows:

The Department of Health and Human Services is pushing for value-based reimbursement models to account for half of Medicare payments by 2018, per an announcement earlier this year. The move underscores the need for hospitals and accountable care organizations to have the tools to mitigate risk from readmissions to medication non adherence to reducing ER visits.

Although some hospitals and ACOs have developed this infrastructure internally, the health IT vendors developing the software to address this challenge reflect a broad range of startups and established companies. They differ not only in their strategy but the kind of datasets they tap to develop the predictive analytics tools to help institutions identify which patients need more attention when they’re discharged.

In a phone interview with Michael Cousins, the president and Chief Analytics Officer of predictive analytics startup Forecast Health, he talked about its approach.

Cousins previously worked for Evolent Health and served as vice president of analytics at Cigna. He has also worked in health informatics at Wellpoint. Referring to ACOs, Cousins said: “Providers are becoming more like payers and need to better understand and execute managed risk. Essentially, we are trying to take a lot of the learning we have had with health plan analytics and construct those analytics to maximize and optimize what providers need today.”
Forecast Health uses roughly 100 consumer-oriented, socio-economic datasets that can point to different types of risks for hospitals such as medication non adherence, readmission, and other things. Cousins noted that several companies tend to focus on a zip code or other census data to gauge risk but he finds that insufficient.

For example, a patient could experience financial stress even if they live in a zipcode associated with wealthy households.

If patients don’t have access to a car and lack reliable public transportation, that can reduce the likelihood of making an appointment. Patients with financial distress are more likely to be readmitted because they may have difficulty affording medication. If they live alone, that can significantly ramp up the chances of readmission because when they are discharged from the hospital, they’re in a fragile state and may lack caregiver support. But it’s critical to do those kinds of assessments before patients are discharged when hospitals can intervene easily, Cousins said.

Its predictive analytics tools are embedded in electronic medical records. The ability to access this information when it’s needed can open up the way for a talk with patients about free medication or getting them into a discount program.

Admittedly, Cousins said the company is still working on pilots and was only willing disclose one client — University of North Carolina Health Care. It recently raised $1.45 million from a super angel to grow its national sales team. The investor backed the company because he believed in what the company is doing and thinks its approach could save lives.

He views many of the players in the predictive analytics market as being in a quandry. It is divided into two types of businesses with shortcomings in each camp. There are the established companies such as Optum, Verisk and Milliman which have extremely well-developed analytics tools, but as Cousins sees it, their analytics are as old as their respective companies. On the other hand, younger companies are more nimble and have a fresher approach to using predictive analytics, but they often lack a nuanced understanding of clinical workflows.

“Talking about predictive analytics to reduce readmissions is easy, but few are actually doing it,” he said. “What matters is not just having a big pot of data on a supercharged technology platform; you need to be careful about constructing the variables to predict impactable patient readmissions.”

 

About Charles Skamser
Charles Skamser is an internationally recognized technology sales, marketing and product management leader with over 25 years of experience in Information Governance, eDiscovery, Machine Learning, Computer Assisted Analytics, Cloud Computing, Big Data Analytics, IT Automation and ITOA. Charles is the founder and Senior Analyst for eDiscovery Solutions Group, a global provider of information management consulting, market intelligence and advisory services specializing in information governance, eDiscovery, Big Data analytics and cloud computing solutions. Previously, Charles served in various executive roles with disruptive technology start ups and well known industry technology providers. Charles is a prolific author and a regular speaker on the technology that the Global 2000 require to manage the accelerating increase in Electronically Stored Information (ESI). Charles holds a BA in Political Science and Economics from Macalester College.