Welcome!

Linux Containers Authors: Yeshim Deniz, Liz McMillan, Zakia Bouachraoui, Elizabeth White, Pat Romanski

Related Topics: Linux Containers, Microservices Expo, @CloudExpo, @DXWorldExpo

Linux Containers: Blog Post

The Taming of the Skew | @CloudExpo #Cloud #BigData #Analytics

Two types of skewness: the statistical skew impacts data analysis, and the operational skew impacts operational processes

The Taming of the Skew
By Dr. Laura Gardner, VP, Products, CLARA Analytics

In the famous comedy by William Shakespeare, "The Taming of the Shrew," the main plot depicts the courtship of Petruchio and Katherina, the headstrong, uncooperative shrew. Initially, Katherina is an unwilling participant in the relationship, but Petruchio breaks down her resistance with various psychological torments, which make up the "taming" - until she finally becomes agreeable.

An analogous challenge exists when using predictive analytics with healthcare data. Healthcare data can often seem quite stubborn, like Katherina. One of the main features of healthcare data that needs to be "tamed" is the "skew" of the data. In this article, we describe two types of skewness: the statistical skew, which impacts data analysis, and the operational skew, which impacts operational processes.

The Statistical Skew
Because the distribution of healthcare costs is bounded on the lower end - that is, the cost of healthcare services is never less than zero but ranges widely on the upper end, sometimes into the millions of dollars - the frequency distribution of costs is a skewed distribution. More specifically, in the following plot of frequency by cost, the distribution of healthcare costs is right-skewed because the long tail is on the right (and the coefficient of skewness is positive):

This skewness is present whether we are looking at total claim expense in the workers' compensation sector or annual expenses in the group health sector. Why is this a problem? Simply because the most common methods for analyzing data depend on the ability to assume that there is a normal distribution, and a right-skewed distribution is clearly not normal. It fails to conform to the assumption of normality. To produce reliable and accurate predictions and generalizable results from analyses of healthcare costs, the data need to be "tamed" (i.e., various sophisticated analytic techniques must be utilized to deal with the right-skewness of the data). Among these techniques are logarithmic transformation of the dependent variable, random forest regression, machine learning, topical analysis and others.

It's essential to keep this in mind in any analytic effort with healthcare data, especially in workers' compensation. To get the required level of accuracy, we need to think "non-normal" and get comfortable with the "skewed" behavior of the data.

Operational Skew
There is an equally pervasive operational skew in workers' compensation that calls out for a radical change in business models. The operational skew is exemplified by:

  • The 80/20 split between simple, straightforward claims that can be auto-adjudicated and more complex claims that have the potential to escalate or incur attorney involvement (i.e., 80 percent of the costs come from 20 percent of the claims).
  • The even more extreme 90/10 split between good providers delivering state-of-the-art care and the "bad apples" whose care is less effective, less often compliant with evidence-based guidelines or more expensive for a similar or worse result. (i.e., 90 percent of the costs come from 10 percent of the providers).

How can we deal with operational skew? The first step is to be aware of it and be prepared to use different tactics depending on which end of the skew you're dealing with. In the two examples just given, we have observed that by using the proper statistical approaches:

  • Claims can be categorized as early as Day 1 into low vs. high risk with respect to potential for cost escalation or attorney involvement. This enables payers to apply the appropriate amount of oversight, intervention and cost containment resources based on the risk of the claim.
  • Provider outcomes can be evaluated, summarized and scored, thus empowering network managers to fine-tune their networks and claims adjusters to recommend the best doctors to each injured worker.

Both of these examples show that what used to be a single business process -managing every claim by the high-touch, "throw a nurse or a doctor at every claim" approach, as noble as that sounds - now requires the discipline to enact two entirely different business models in order to be operationally successful. Let me explain.

The difference between low- and high-risk claims is not a subtle distinction. Low-risk claims should receive a minimum amount of intervention, just enough oversight to ensure that they are going well and staying within expected parameters. Good technology can help provide this oversight. Added expense, such as nurse case management, is generally unnecessary. Conversely, high-risk claims might need nurse and/or physician involvement, weekly or even daily updates, multiple points of contact and a keen eye for opportunities to do a better job navigating this difficult journey with the recovering worker.

The same is true for managing your network. It would be nice if all providers could be treated alike, but in fact, a small percentage of providers drives the bulk of the opioid prescribing, attorney involvement, liens and independent medial review (IMR) requests. These "bad apples" are difficult to reform and are best avoided, using a sophisticated provider scoring system that focuses on multiple aspects of provider performance and outcomes.

Once you have tamed your statistical skew with the appropriate data science techniques and your operational skew with a new business model, you will be well on your way to developing actionable insights from your predictive modeling. With assistance from the appropriate technology and operational routines, the most uncooperative skewness generally can be tamed. Are you ready to "tame the skew"?

Read Dr. Gardner's first two articles in this series:

Five Best Practices to Ensure the Injured Workers Comes First

Cycle Time is King

As first published in Claims Journal.

###

Laura B. Gardner, M.D., M.P.H., Ph.D., is an expert in analyzing U.S. health and workers' compensation data with a focus on predictive modeling, outcomes assessment, design of triage and provider evaluation software applications, program evaluation and health policy research. She is a successful entrepreneur with more than 20 years of experience in starting and building Axiomedics Research, Inc.

Dr. Gardner earned her bachelor's degree in biology (magna cum laude) from Brandeis University, her M.D. from Albert Einstein College of Medicine and both an M.P.H. in health policy and a Ph.D. in health economics from the University of California at Berkeley. As a physician, she is board certified in General Preventive Medicine and Public Health and is a fellow of the American College of Preventive Medicine.

For more information, visit http://www.claraanalytics.com/ and follow CLARA Analytics on LinkedInFacebook and Twitter.

More Stories By CLARA Analytics

CLARA analytics empowers workers’ compensation claims teams to rapidly get injured workers back on track with easy-to-use artificial intelligence (AI)-based products. Its CLARA providers search engine is an award-winning provider scoring engine that helps rapidly connect injured workers to the right providers, while CLARA claims is an early warning system that helps frontline claims teams efficiently manage claims, reduce escalations and understand the drivers of complexity. CLARA’s customers include a broad spectrum — from the top 25 insurance carriers to small, self-insured organizations.

IoT & Smart Cities Stories
Dion Hinchcliffe is an internationally recognized digital expert, bestselling book author, frequent keynote speaker, analyst, futurist, and transformation expert based in Washington, DC. He is currently Chief Strategy Officer at the industry-leading digital strategy and online community solutions firm, 7Summits.
Digital Transformation is much more than a buzzword. The radical shift to digital mechanisms for almost every process is evident across all industries and verticals. This is often especially true in financial services, where the legacy environment is many times unable to keep up with the rapidly shifting demands of the consumer. The constant pressure to provide complete, omnichannel delivery of customer-facing solutions to meet both regulatory and customer demands is putting enormous pressure on...
IoT is rapidly becoming mainstream as more and more investments are made into the platforms and technology. As this movement continues to expand and gain momentum it creates a massive wall of noise that can be difficult to sift through. Unfortunately, this inevitably makes IoT less approachable for people to get started with and can hamper efforts to integrate this key technology into your own portfolio. There are so many connected products already in place today with many hundreds more on the h...
The standardization of container runtimes and images has sparked the creation of an almost overwhelming number of new open source projects that build on and otherwise work with these specifications. Of course, there's Kubernetes, which orchestrates and manages collections of containers. It was one of the first and best-known examples of projects that make containers truly useful for production use. However, more recently, the container ecosystem has truly exploded. A service mesh like Istio addr...
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Charles Araujo is an industry analyst, internationally recognized authority on the Digital Enterprise and author of The Quantum Age of IT: Why Everything You Know About IT is About to Change. As Principal Analyst with Intellyx, he writes, speaks and advises organizations on how to navigate through this time of disruption. He is also the founder of The Institute for Digital Transformation and a sought after keynote speaker. He has been a regular contributor to both InformationWeek and CIO Insight...
Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settlement products to hedge funds and investment banks. After, he co-founded a revenue cycle management company where he learned about Bitcoin and eventually Ethereal. Andrew's role at ConsenSys Enterprise is a mul...
To Really Work for Enterprises, MultiCloud Adoption Requires Far Better and Inclusive Cloud Monitoring and Cost Management … But How? Overwhelmingly, even as enterprises have adopted cloud computing and are expanding to multi-cloud computing, IT leaders remain concerned about how to monitor, manage and control costs across hybrid and multi-cloud deployments. It’s clear that traditional IT monitoring and management approaches, designed after all for on-premises data centers, are falling short in ...
In his general session at 19th Cloud Expo, Manish Dixit, VP of Product and Engineering at Dice, discussed how Dice leverages data insights and tools to help both tech professionals and recruiters better understand how skills relate to each other and which skills are in high demand using interactive visualizations and salary indicator tools to maximize earning potential. Manish Dixit is VP of Product and Engineering at Dice. As the leader of the Product, Engineering and Data Sciences team at D...
Dynatrace is an application performance management software company with products for the information technology departments and digital business owners of medium and large businesses. Building the Future of Monitoring with Artificial Intelligence. Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more busine...