Category Archives: Big Data

Predictive Analytics in Life Sciences: Co-Payment Mitigation

Specialty pharmacies have emerged as a strategic distribution channel for pharmaceutical and biotechnology companies, as the dollar share of distribution by this channel will soon reach 40% of the U.S. market. Specialty pharmacies provide a wide variety of services beyond filling an RX.  Patient support coordinators assist patients in administration of drugs, coordination of insurance benefits, adherence to therapy and scheduling of refills

Privacy concerns over patient data can be addressed by the process of de-identifying patient data which removes all personal information from prescription records while maintaining important data on demographic groups, insurance coverage and medical conditions which are necessary to model behavior at the patient level.  Patient level analytics allow drug companies to model persistency of therapy and adherence, which are often critical to patient outcomes.

Co-Payment Mitigation

With rising prescription costs and economic pressures, payers have shifted more specialty drugs into higher co-payment tiers.  Without co-pay mitigation, many patients face hundreds of dollars of copayments per month, which have an enormous impact on adherence to therapy.  With patient analytics, life science companies can develop and optimize programs to buy down co-payments for patients, therefore maintaining high quality access to needed medications.

Utilizing Big Data For Strategic Patient Insights:

Patient-Level Modeling Ensures That All Impacts on Patient Behavior Are Captured.

Not so long ago, pharmaceutical companies had to try to analyze the impact of marketing programs using monthly RX data by sales territory or physician decile.  Insights on patient behavior were gleaned from pharmaceutical call reporting systems and survey research with doctors.

RoadMap utilizes state of the art nonlinear statistical modeling techniques that are specifically designed to model the behavior of hundreds of thousands of individual patients on a daily basis.  Unlike linear models used for aggregate RX data, nonlinear models use statistical distributions designed for discrete patient choices such as Binomial and Multinomial Logits.

Event based models of individual patient persistency on therapy also require nonlinear methods like the Cox Proportional Hazards model.  Despite the sophistication of the models, RoadMap is committed to explaining and documenting all modeling results, so that management does not have to rely on a fragile “Black-Box” approach for critical strategic insights.Image


Developing Predictive Analytical Applications with Big Data

How to Quench Your Thirst for Knowledge from Big Data

  • Daily Point of Sale Data from 3,000 stores for Ten of Thousands of SKUs
  • Daily RX Data for Hundreds of Thousands of Individual Patients
  • Real Time Demand for Electricity At Each Point Along a 250,000 Square Mile Electric Grid

We all thirst for information and learning from these huge data sets. But, in the time it take to read this post, terabyte upon terabyte of transactions data has been piling up in corporate databases around the world.  How can we harness the power of Big Data, while not drowning in it? Identify the Strategic Drivers of Your Business By identifying what is really important to know in your business and focusing on analyzing just those issues, you can cut down the real time and daily data requirements for your business. Key Performance Indicators or KPIs have been around for a generation.  It’s very important to have a subclass of these indicators which are real time KPIs. This enables you to whittle down you Big Data set into hopefully a manageable one. These KPIs can be as unique as the industries that they are in.

  • Mobile Phones: Pre-orders for Soon to be Launched Phones
  • Fashion Apparel: Color Mix Across all Styles and Lines
  • Pharmaceuticals: List of Patients More than 7 Days Overdue for a Refill
  • Utilities: Hourly Sunshine and Temperature Forecasts by City
  • On-line Retailer: Recommendation Engine

Using Automated Tools to Analyze the Data As Yogi Berra once said, “you can’t think and hit at the same time” – meaning that a ball player does not have a lot of time to think about whether or to swing or not and what type of swing to take, it must be a trained instinct.   In a similar manner, predictive analytics for Big Data must be analyzed automatically.  In other words, if it takes you a day to some up with a forecast of tomorrow’s sales, you are never going to be able to come up with a forecast in time to make a decision based on the forecast.  RoadMap’s forecasting algorithms make extensive use of re-entrant coding as well as in-memory data caching which allows for fast and efficient automated processing. Look for a Total Solution, Not an ERP Suite or a Single Package Depending on the complexity of the predictive model, your existing suite of software may just not have enough horsepower.  That’s why open source  projects such as the R Project for Statistical Computing can be such as valuable part of the total solution.  R, an Open Source project which is supported by numerous corporations, universities and hospitals has developed over 5000 statistical models called packages.  So, if your Big Data analysis engine needs to be turbocharged, it’s a good bet that there will be some predictive model in R which will help fill the bill. R Statistical Packages Your analytic needs may also exceed your IT infrastructure. That’s why cloud based solutions work so well with Big Data.  IT departments usually work off a 18 month long backlog of applications requests.  Strategic projects can’t wait 18 months; they have to be done immediately, with whatever resources are available. In summary, corporations that can cut their predictive analytics down to size by focusing on strategic imperatives, develop automated systems, and use multi-vendor approaches, which may even include open source or could solutions, stand the best chance of gaining business insight.