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


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

 


Five Questions EMPAQ Can Help You Answer


By John Azzolini/Tuesday, May 10, 2016

The National Business Group on Health®, in partnership with Truven Health AnalyticsTM, an IBM Company, will be collecting EMPAQ® data until May 20, 2016. EMPAQ® (Employer Measures of Productivity, Absence and Quality™) is an online survey-based measurement tool ― developed by employers for employers ― that helps quantify the costs of poor health, low productivity, and absence. It provides employers with a framework by which to measure and monitor the return on investment they’re receiving from their human capital investments. 

Here are five questions you’ll be able to answer with the customized, benchmarking data you’ll receive when you participate:

If you would like to participate in the EMPAQ® program, you’ll need to submit your data to http://submission.empaq.org/ by May 20, 2016. Once the survey is initiated, you can authorize a consultant or vendor to complete the form on your behalf. We’ve set up a dedicated phone line and email to assist you with your submission. Call 855-878-8367 (855-TRUVENQ) or email empaq@truvenhealth.com.


 


EMPAQ® Is Open for Data Submission Through May 20


By John Azzolini/Friday, May 6, 2016

The National Business Group on Health®, in partnership with Truven Health AnalyticsTM, will be collecting EMPAQ® data between March 1 and May 20, 2016.  EMPAQ (Employer Measures of Productivity, Absence and Quality™) is an online survey-based measurement tool ― developed by employers for employers ― that helps quantify the costs of poor health, low productivity, and absence.

Why Should I Submit Data?

Your participation in EMPAQ will give you:

     A framework to monitor and measure your ROI from human capital investments

     Valuable insights to help you manage your health and productivity programs

     An individualized report that details how your program performs compared with similar employers and all respondents

     Specific recommendations based on your results

Read More About EMPAQ Data

Read the results from the 2014 data submissions to learn how the EMPAQ program can help you.

If you would like to participate in the EMPAQ® program, you’ll need to submit your data to http://submission.empaq.org/ by May 20, 2016. Once the survey is initiated, you can authorize a consultant or vendor to complete the form on your behalf.

We’ve set up a dedicated phone line and email to assist you with your submission. Call 855-878-8367 (855-TRUVENQ) or email empaq@truvenhealth.com.

John Azzolini
Senior Consulting Scientist


Congratulations to the 2016 Advantage Award Winners


By Mike Boswood/Wednesday, May 4, 2016

At our annual customer conference last week, I had the honor of presenting the 2016 Advantage Awards to clients who documented significant results from projects that required innovative thinking and analytics, and produced greater value in healthcare.

Georgia Health Information Network (GaHIN) was the overall winner this year. GaHIN created a “network of networks” wherein patient information remains with the treating provider and flows only when there is authorization. Caregivers can now instantly access information from a multitude of sources through their EMR, enabling more informed decisions about treatments and avoiding unnecessary, expensive tests.

In addition to the overall winner, the following organizations were honored with Advantage Awards for their impressive results:

  • DeKalb Regional Health System: achieved reduced lengths of stay and cost savings from a systemic commitment to greater quality.
  • IASIS Healthcare: reduced readmissions through near-real time analytics and by improving the transition of care from hospital to home.
  • Liberty Mutual Insurance: exceeded enrollment goal by enabling members to understand their likely costs if they switched to the CDHP or stayed in a PPO.
  • Lockheed Martin: used analytics to understand costs of various treatments for lower back pain and to identify targeted intervention opportunities.
  • Prime Healthcare Services – Centinela Hospital Medical Center: turned a negative margin positive and improved patient satisfaction, with pervasive quality-oriented management.
  • State of Michigan: reduced invoice-to-claim discrepancies by over $30 million by automating the comparison process.
  • Trinity Mother Frances Hospitals: realized over $14M in cost reductions by guiding all departments to operate off standardized definitions, metrics, benchmarks and goals.

We support our clients’ commitment to building greater value in healthcare with our expanding range of solutions and services; we anticipate that as part of IBM Watson Health we will be able to provide ever more compelling solutions to the evolving challenges facing healthcare across the U.S. and globally.

Our congratulations to the success of all this year’s Advantage Award winners.

Mike Boswood
President and CEO
Truven Health Analytics


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