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The Truven Health Blog


The latest healthcare topics from a trusted, proven, and unbiased source.


EDGE Server Data Submissions: Do You Need Help?


By Bryan Briegel/Thursday, July 7, 2016



Now that health plans have a couple of years of EDGE server data submissions under their belts, it’s a great time to step back and evaluate how your organization did.

We think a few key questions to ask are:

  • How well did our approach to EDGE work?
  • Did we have clean data to optimize our risk adjustment?
  • Were we able to respond to the changes in CMS requirements in a timely and effective manner?
  • What operational improvements do we need to make?

  • The fact is, many health plans — busy serving their members by providing quality care at a reasonable cost — simply don’t have the proper resources or experience in place to complete the arduous tasks needed to comply with the Premium Stabilization Programs. The EDGE server requirements are challenging — and missteps in meeting them have led to disappointing results, including leaving reinsurance dollars and understated risk scores on the table . Even large health plans with corporate supports in place have been challenged to meet the requirements.

If you think there’s a potential for improvement, now is the time to consider a new plan for your EDGE server submissions. Should you continue to go it alone or stick with your current TPA? Before you decide, consider all the things that a proper EDGE server process should entail.

Your solution should give you:

  • On-time, accurate submissions
  • Dynamic data management services, with constant updating to meet CMS changes
  • At a minimum, quarterly analytic reporting
  • Support staff to keep current with CMS changes and respond to their EDGE server inquiries and mandated server updates
  • Peace of mind and the ability to focus internal resources  attending to your day-to-day responsibilities

If complying with CMS’s EDGE server requirements is taxing your organization’s resources, it’s time to consider partnering with a qualified EDGE server administrator, so you can get back to the business of offering quality health care. Contact us to learn more.


Bryan Briegel, Healthcare Reform Solutions Specialist 
Anita Nair-Hartman, Senior Vice President, Payer Strategy and Business Operations



Here’s What you Missed at AHIP Institute 2016


By Truven Staff/Wednesday, June 29, 2016

Last week, representatives from Truven Health Analytics, an IBM Company, and nearly 3,000 other healthcare professionals attended AHIP Institute 2016 in Las Vegas, NV to learn from health plan industry leaders, see the newest products and services, and get an idea of what’s ahead in healthcare.

Here’s what you missed from Truven Health at this year’s AHIP Institute:

  1. Some things just work well together: A Toast to Truven Health Analytics and IBM Watson Health - During the opening night reception, Truven Health Analytics and IBM Watson Health celebrated our new relationship: Truven Health is now a wholly owned subsidiary of IBM Watson Health.

  2. Custom LEGO® Bridge Build - Our booth included a custom LEGO bridge build to show how our portable analytics solution can bridge the gap between your enterprise data warehouse and the answers you need. Watch our video for more information on our portable analytics solution.

  3. Truven Health Concurrent Session: Bundled Payments: Evaluating the Opportunities for Your Business -  During our concurrent session on Thursday, June 16, Truven Health experts Kevin Ruane and David Jackson discussed the experiences of payers and providers in various stages of implementing bundled payments. They also reviewed methods used to determine preparedness, how to evaluate opportunities and risks, and key metrics for success. 

If you missed our session at AHIP Institute, the presentation will also be included in our upcoming AHIP webinar. Contact us to register.

Contact us for more information on AHIP Institute, or to find out more about our solutions. 


2015 Was a Big Year for Bundled Payments. Here’s Why.


By Tom Halvorson/Tuesday, June 28, 2016

On November 16, 2015, CMS announced the Final Rule for the Comprehensive Care for Joint Replacement (CJR) model, instituting mandatory bundled payments for hip and knee total joint replacement episodes for hospitals in select geographies across the country. The bundles would cover all related services from inpatient admission to 90 days after discharge.

Unlike the earlier bundled payment initiatives, in which hospitals success was defined by their ability to reduce episode spending from historic levels, success under the CJR program is based on a health system’s ability to become and remain an efficient provider of joint replacement episodes in their region.

Case Study: Bundled Payments in Action

Although the new CJR payment model will continue for five performance periods, through December 31, 2020, it’s important for health systems to evaluate and monitor their CJR deployment as soon as possible. To position one regional health plan’s health system for success, Truven Health Analytics provided a high-level assessment, implementation services, and ongoing monitoring support. With the assessment, Truven Health:

●      Analyzed historical spending and utilization of post-acute service trends

●      Evaluated historical utilization of post-acute services against regional benchmarks using Truven Health MarketScan® data and the Medicare Standard Analytical File

●      Analyzed skilled nursing providers used by CJR patients

●      Performed a variation analysis of high-volume surgeons’ spend, utilization of post-acute services, and readmissions

●      Forecasted annual financial wins and losses

●      Projected performance using current internal data

As a result of the Truven Health review and modeling, the health system was able to develop and implement a standard care pathway to address variation in post-acute care, achieve consensus from the physician practice on the newly formed pathway, institute discharge planning protocols, and define a clear post-acute network. These efforts allowed the system to reduce single joint replacement costs by $400 and bilateral joint replacements by $3700.

How Ready Are you to Implement Bundled Payment Pricing?

For answers to your questions about bundled payments, or for help devising a bundling strategy, contact us at payersolutions@truvenhealth.com.

Tom Halvorson
Director, Analytic Consulting

*These results are a statistical narrative represented by a number of Truven Health client projects

 


Deciding Whether to Implement a TJR Readmissions Intervention Program


By Bob Kelley /Thursday, June 23, 2016

In my last post related to our bundled payments research series, I discussed how it’s possible to identify the clinical factors that are most predictive for a commercially insured total joint replacement (TJR) patient hospital readmission. That type of identification could be beneficial since one goal of bundled payment models is to reduce costs, including those associated with readmissions.

But even with the ability to identify readmission risk factors, should we? Is using a predictive model better than a random assignment of intervention? And if it is better, is the value of predictive modeling worth the cost of intervention?

To answer these questions, statistical methods are required.

Consider the predictive accuracy of one model: We analyzed the trade-off between sensitivity (the ability to correctly identify high-risk patients) and specificity (the ability to minimize the number of patients incorrectly identified as high risk). The AUC (area under the ROC curve) score was 0.62, which is distinctly better than the 0.50 that represents a random assignment of risk — but still a fair distance from a perfect 1.00 assignment of risk. Nevertheless, this scoring shows that a statistical approach could be used to set a useful threshold for assigning high risk for an intervention program.

To further assess the value of a readmissions model, we then worked from the equation of: Expected Value = Expected Benefit − Expected Cost.

  • Expected cost is the average cost per patient intervention multiplied by the number of interventions. The average cost depends on the mix of interventions and their duration.
  • Expected benefit is the cost of an avoided readmission multiplied by the number avoided. The number avoided is the product of the number of interventions and the probability of success of each intervention.

To test our calculations, we performed a simplified version of how a hospital might assess the value of a model. Somewhat arbitrarily but informed by experience, we chose a cost per intervention of $500, the cost of an avoided readmission of $10,000, and an expected success rate of 50 percent.

We then set a risk threshold at 0.07 (above which the patient will be classified as high risk). We found that the intervention would target 26 patients per thousand at a total cost of $13,000. We would expect three of these patients to be readmitted without intervention, and with a 50-percent success rate, the intervention would prevent 1.5 readmissions or $15,000. That’s an expected net savings of $2,000.

Based on this simple example, the results suggest the accuracy of the model in identifying high-risk patients is marginally sufficient to support a targeted intervention. But actual readmission cost, success rate, and risk threshold will change the results in every instance, so understanding the true experience of each organization is critical to deciding whether to apply an intervention model.

There is much more, of course, to this approach than we’ve shown here. For details and more insights from our bundled payments research, you may want to download our latest brief, Total Joint Replacements in the Commercially Insured Population: Predicting Risk of Readmission.

Bob Kelley

Senior Research Fellow, Advanced Analytics


New Bundled Payments Research: Identifying the Most Predictive Risk Factors for TJR Readmissions


By Bob Kelley /Thursday, June 2, 2016

As part of our continuing research into bundled payments for commercially insured total joint replacements (TJRs), we recently turned our focus to patient readmissions — which we already know vary in cost and occurrence by geographic area and type of post-acute care a patient receives.

Logical thinking tells us that if we could accurately identify TJR patients who are at high risk for readmission due to certain clinical factors, we could reduce or prevent readmissions. And any change in TJR readmissions could then impact TJR value-based bundled payments. The goal, of course, would be to identify at-risk patients early in the care process, so that they could be targeted for special care or intervention programs.

But can we identify the top clinical risk factors?

To find out, we used TJR claims data from the Truven Health MarketScan® Commercial Database to analyze 84,648 simulated bundles. Each bundle was characterized by more than 35 data attributes, including patient demographics, presence of readmission, post-acute care usage, payments by component service type, comorbidities, utilization of healthcare services in the six months prior to the surgery, and more.

 Across several models, we found that the most predictive risk factors for TJR patient readmissions were:

  • Hospitalizations in the six months prior to the TJR
  • Emergency room visits in the six months prior
  • Unique condition/disease diagnoses in the six months prior

More specifically, when we looked at odds ratios estimated in a logistic regression model, additional significant TJR readmission risk factors were:

  • Number of comorbid, high-stage chronic conditions
  • Number of high-stage acute episodes in prior six months
  • Comorbid cardiovascular disease
  • Comorbid cerebrovascular disease

In a future blog post, we’ll take a look at whether or not using these clinical insights to identify and target at-risk patients provides better results than a random selection of patients.

Full details on this study, and additional findings, are available by downloading our latest research brief, Total Joint Replacements in the Commercially Insured Population: Predicting Risk of Readmission.

Bob Kelley
Senior Research Fellow, Advanced Analytics

 


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