Are you a Health Insurer that wants to improve your STAR Rating? Bring On Control-M Big data and reduce your Medicare member complaints and grievances, and generate more revenue.
Is your Star score improving? Is your score staying the same or declining? Do you want to generate additional revenue? Drive higher quality? Improve your ratings by before they are released this fall? Control-M for Hadoop is the answer!
Each year, Medicare checks each Insurers plans to see if there are problems with the plan related to members having problems getting service and the plans are following all of Medicare’s rules. Medicare also provides insight into the Insurer’s drug plan’s performance. Whether the drug plan has improved or declined from one year to the next year as drug benefits are an area of major concern to Health Insurers. Medicare compares the plan’s previous scores to its current scores and averages the results to give the Insurers plan their improvement rating.
- If a plan receives 1 or 2 stars, it means, on average, the plan’s scores have declined (gotten worse).
- If a plan receives 3 stars, it means, on average, the plan’s scores have stayed about the same.
- If a plan receives 4 or 5 stars, it means, on average, the plan’s scores have improved.
Star ratings are critical to the Insurer’s business performance through these Medicare Advantage plans. The star rating system is important because it determines if the Insurers plans are eligible for bonus payments. Plans earning at least three stars are entitled to higher payment rates. Each half star is worth between $15 and $50 each month per enrollee, insurance. That potentially adds up to a substantial amount of money hinging on star ratings.
With Control-M for Hadoop, getting out in front of the plans data and understanding where the issues are as they relate to improving customer satisfaction with the quality of care the patients are receiving. What is the overall perception of customer service and how can you get in front of the data before the member or patient files a complaint or has issues with the drugs that being prescribed to treat their conditions? Providing accessibility and understanding of patients with multiple chronic conditions and complex care needs is often critical challenges for Insurers that offer Medicare Advantage plans. This is where the “Getting out ahead, Outrunning, Outpacing” the competition with Control-M for Hadoop will lead you towards outperforming and improving your Star Rating from year to year. Big Data for Insurer’s will ultimately assist their members and their patients with improved management of long term chronic conditions, assist consumers with improving their quality of life and avoiding preventable deaths. Insurers are ultimately held accountable for their members, and the responsiveness of their care and any issues they have with accessing quality services.
With Control-M for Hadoop and accessibility to these data models, Insurers can make decisions on the effectiveness of their plans and the overall consumer satisfaction of their plans. It is all about improving the models of how customers are driving the demand for models of treatment and how successful they are able to access these rapidly changing treatments. The ability to dive deeper into the analytics and each patient to understand where we can improve this patients experience, earlier in the process, before they are engaged in far more serious stage of illness that ultimately drives move expensive treatment and ultimately increases the complaints and grievances to Medicare.
A little Background to begin with on the Star Rating System
Before we begin on how Control-M for Hadoop can improve your Star Rating, let’s understand how the Centers for Medicare & Medicaid uses the Star Rating System to measure how well Medicare Advantage and prescription drug (Part D) plans perform. Medicare scores how well plans did in several categories, including the quality of care the patients receive and the overall perceived view of customer service from members. Ratings range from 1 to 5 stars, with five being the highest and one being the lowest score. Medicare uses this data to assign plans one overall star rating to summarize the plan’s performance as a whole. Plans also get separate star ratings in each individual category reviewed. The overall star rating score provides a way to compare performance among several plans. Medicare reviews these plans performances each year and releases new star ratings each fall. This means that your plan ratings change from one year to the next.
What does Medicare Rate for the Star System? The Centers for Medicare & Medicaid insurers are held accountable “for the care provided by physicians, hospitals and providers to their enrollees.”
Medicare health plans are rated on how well they perform in the following five different categories:
1. Consumers staying healthy: screenings, tests, and vaccines
2. Managing chronic (long-term) conditions for their members
3. Plan responsiveness and care
4. Member complaints, problems getting services, and choosing to leave the plan
5. Health plan customer service
Medicare drug plans are rated on how well they perform in four different categories:
1. Drug plan customer service
2. Member complaints, problems getting services, and choosing to leave the plan
3. Member experience with drug plan
4. Drug pricing and patient safety
Private Medicare plans are still failing on many levels, particularly when it comes to prescription drug benefits.
So how can Insurer’s generate more revenue?
The Centers for Medicare & Medicaid have tied financial incentives to the STAR rating system for Insurer’s. Plans that earn at least four stars earn extra revenue via a 5% boost to monthly per-member payments from Medicare. The Insurer’s Plans that receive lower scores do not receive any incentives from Medicare.
How can Control-M for Hadoop improve Insurer’s Star Rating?
In assisting Insurer’s with this very dilemma, it was critical to get in front of these complaints to Medicare and understand how to remediate the issues and make sense of the data. Providing a solution that gives the data scientists the ability to measure and provide predictive analytics so that they can get in front of the data and assist with diving deeper into the analytics. Where can we assist Insurer’s with the technology they need to generate improvements in the patient’s experience? How can we get in front of the data so that the patients who have chronic conditions and complex care needs are receiving the preferred high touch, differentiated level of service, one that is committed to helping their members navigate the complex world of health care.
Control-M for Hadoop will assist Insurer’s with understanding where the issues are within their Plans. Understanding what can be done to drive higher quality for their patients, continuous experience improvements and stem the issues related to prescription drug benefits. Escalations related to the Drug Benefit plans are generated from companies and Insurer’s that have outdated technology and this ultimately causes systemic issues with rejection of prescriptions and this ultimately escalates the complaints to Medicare and lowers the overall satisfaction of the Insured’s plan and the overall non-responsiveness.
Control-M for Hadoop is assisting the very largest Insurer’s today to build out advanced analytical procedures to generate statistic based models to understand where their plans are exposed to multiple variables that would cause complaints to Medicare. Control-M for Hadoop delivers a highly usable interface that allowed the Data Engineers and Scientists to streamline the Hadoop processing and remove the complexity that they were experiencing with Oozie. The difficulty for the Data Scientists with Oozie was preventing the Insurer’s from getting in-front of the data as close to real-time as possible. The goal was to implement a big data solution that would increase the speed and meet the critical timelines via a simple intuitive interface that the Data Engineers and Data Scientists could drag and drop with no scripting to consolidate simply into a workflow from different sources across the enterprise: data warehouses, legacy applications, relational databases, ETL’s. The complexity of Oozie and the inability to not be able to integrate all aspects of the data in a “Single Pane of Glass” caused much frustration with the Data Scientists and ultimately these Insurers were not able to implement the predictive, statistical based modeling that was needed to meet the Project timelines and ultimately stem the complaints to Medicare. The Centers for Medicare has levied millions of dollars of fine on Insurers this past year due to pharmacy benefit (PBM) violations as well as Insurer’s that have a difficult appeals process for their members that have complaints. Every complaint to Medicare is a direct cost to the Insurers.
Control-M for Hadoop streamlined the entire implementation and the Data Engineers and Scientists were able to integrate the workflow at all levels to meet their critical project timeline and improve their quality scores. Not only have they been able to implement predictive, statistical based modeling but also they have been able to design models for the future as the Insurers have been able to utilize the data that they have collected in the past as part of the complaints process. Future models have been built to analyze the data from the past, and this ultimately provides the big data scientists with exactly what they need to define more predictable results moving forward. This strategic approach will ultimately benefit the patients via improving customer satisfaction within all levels of their plan, but also generate revenue based on predictive results.
Improve your Star Rating with Control-M for Hadoop. “Get out ahead, outrun, outpace” the competition with Control-M for Hadoop! This will lead you towards outperforming and improving your Star Rating from year to year as well as improving customer satisfaction within the plan from year to year. Remember the past holds a vast amount of data to model your plans future outcomes!
These postings are my own and do not necessarily represent BMC's position, strategies, or opinion.
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