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Key Performance Indicators Examples - Business Analytics

Alberto Roldan's Profile
Payers are facing serious challenges that require finding ways to quickly adapt to change or perish. Whether the increasing cost of chronic diseases, prescription drugs, fraud and abuse, new government regulations, or the decrease in membership due to the recession the answer requires predictive analytics and fresh thinking in how to align different techniques so that healthcare payers quickly adapt to change in the reset economy by decreasing costs and increasing profits.  
Business Analytics is the effective use of data and information to drive positive business actions. The body of knowledge for this area includes both business and technical topics, including concepts of performance management, definition and delivery of business metrics, data visualization, and deployment and use of technology solutions such as OLAP, dashboards, scorecards, analytic applications, and data mining techniques.
There are five different concepts that form the basis of a comprehensive business analytics program in healthcare:
·         Comparative Analytics uses basic mathematical concepts like sums, subtraction, multiplication, correlation and division to compare key performance indicators (KPI) in an organization. For example, these concepts can compare and calculate revenue, expenditures and profits by product and LOB during a specified time period. Also, correlation is used to assess the degree of similarity between two independent variables.
·         Outlier Detection uses concepts like average, standard deviation and Z-scores to determine whether a determined data point is abnormal in the same classification or category. Outlier detection analytics are applied to a fraud and abuse, and utilization review programs for healthcare payers.
·         Pattern Detection covers the reverse side of outlier detection but applies the same methodologies. It is used in many industries to determine best practices. In the healthcare industry, for instance, it is also used for outcomes research. Also, it covers the discovery of patterns in unstructured data or text data mining algorithms in call centers to analyze trends in membership and managed care products.
·         Predictive Modeling refers to the ability to create a score that determines the probability that an event will occur based on prior experience. Concepts like regression and neural networks are used to determine the relationship between different variables and the probability that a specific event will occur. Organizations use predictive modeling to do forecasting in many situations. In the healthcare industry, for example, it is used by Medicare to determine capitation payments to payers using hierarchical condition category (HCC). 
·         Segmentation, Classification, Clustering and Spatial Analysis is used to determine the different classes or categories among different variables. In retail, these concepts are used for categorization of customers, using variables like level of spending, gender, geography and products. In the healthcare industry, it is used in the analysis of different diagnoses like diabetes, cardiovascular and cancer.
·         Linkage Analytics aligns new strategic KPIs built from financial metrics to customer satisfaction surveys and employee satisfaction surveys. Using these analytics, companies can detect patterns in a three-dimensional view of the resultant data to visualize best practices that should be implemented across the enterprise or, alternatively, avoid practices that negatively impact revenues, costs or profitability. 
·         Optimization refers to the speed in which a decision support system (DSS) can correctly process key performance indicators (KPI) in a timely manner. The ability to process and analyze large data sets is the keystone for a flexible analytics solution.
Three additional concepts must also be considered by companies looking to apply advanced analytics more holistically. They include:
  1. The Visualization of Analytics: It has been said that a picture is worth a thousand words. Companies, governments and organizations are challenged every day to store large datasets that contain structured and unstructured data. Science has progressed to the point that it can deliver the capacity to store and analyze large volumes of data. Advances in business, science and technology can now be combined with the capacity of the human brain to visualize and comprehend analytical data.
  2. Software-as-a-Service (SaaS): In any rapidly changing economic environment, cost flexibility must be accounted for and measured against revenues, expenditures or profitability. SaaS, with its pay-as-you-go, low-cost-of-ownership and hosted model, could become the most cost-efficient analytics business model for some companies. The main issue is whether prospective SaaS vendors have the domain expertise to successfully implement analytics projects that deliver measureable results. 
  3. Real-Time Analytics: A thoughtful evaluation regarding the need of real-time analytics becomes crucial to any company, regardless of the economic cycle. Because companies need flexibility to make quick decisions, real-time analytics are often overlooked because they are time-intensive and expensive to build and maintain within budgetary constraints. This area also encompasses the ability to implement automated decisions in real-time, not just analyze data. For example, in the chemical engineering manufacturing industry outlier analysis (statistical control process) is already used in real-time to automate decisions.
An analytics Center of Excellence (CoE) is a mechanism that allows the CIO organization to serve multiple LOBs in the area of predictive analytics efficiently. The advantage of an Analytics CoE is that it creates an infrastructure that leverages data governance, change management, KPIs and advanced analytics models in one centralized area, while simultaneously allowing business stakeholders to customize analytics to their specific needs. For example, a project to forecast additional revenue stream in a Medicare Advantage program from HCC reconciliation of members with diabetes should only take 2-4 weeks in an analytics COE. Such a project in healthcare organizations without an analytics COE can take 2-4 months.
A challenge in establishing an analytics CoE is how to adequately staff it. Some companies have their analytics capabilities spread across multiple business units, and if they are organized in silos, there may be little cooperation among those units. Another challenge is that analytics professionals are typically weak in soft skills, command high compensation levels, are not easy to find and have a low tolerance to ambiguity. Therefore, companies should consider taking a partnership approach to analytics PoCs and CoEs to share risk and rewards, especially where offshoring is lynchpin of the organization’s business model and has proven to reduce costs or accelerate time to market. Once a company is able to successfully measure the value of two to three analytics PoCs, they are better able to transfer important business processes, statistical techniques, technology tools and knowledge gained during the PoC into an analytics CoE. The main benefits accrued include reduced costs by streamlining analytics headcount and increased revenues from using statistical modeling to gain better insights into data. Well-implemented analytics PoCs and CoEs have saved costs and generated revenues to early adopters in the realm of hundreds of millions of dollars per year. 
Leveraging analytics to gain a competitive advantage and improve business decision-making is now the top priority for chief information officers, according to a global study of more than 2,500 CIOs by a major business IT services player. More than four out of five (83%) survey respondents identified business intelligence and analytics -- the ability to see patterns in vast amounts of data and extract actionable insights -- as the way to enhance their organizations’ competitiveness.  
As a starting point, CoEs can help ensure that companies do miss an opportunity to leap ahead (or, conversely, not be left behind) when the global economy turns around in this reset economy. It can also ensure that companies are working toward a knowledge-driven platform for seizing business opportunities and shielding or correcting operational weaknesses that can undermine long-term success.
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