Helping Hospitals Understand and Embrace Bundled Payments Gloria Kupferman, Vice President, DataGen Kelly Price, Director, DataGen Group A 2 HA March 20, 2013

Key Lessons Data is king, be ready to deal with large amounts of it Know the game and why you’re playing Fill all positions on the team Understand variation, risk and opportunity There’s a difference between reducing costs at the provider level and reducing costs for the payer (program costs)

Remember the Payer’s POV Initial Inpatient Stay Readmission Dr. Hospital Visit Dr. Office Visit Dr. Hospital Visit Inpatient Post-Acute Stay (Rehab, Psych, LTC, SNF, HH) Other Part B Services (Hospital Outpatient, Labs, Durable Medical Equipment, Part B Drugs)

Metrics Must support decision-making – Focus on decision-making and application – Avoid interesting but useless information Must be understandable Can’t be static Here’s how we analyzed the Medicare BPCI data:

Analytic Decision Drivers Volume – i.e. critical mass Payment variation Discernible, manageable care patterns Benchmark comparisons

Identify Episodes of Interest

Episodes of Interest

Evaluate Payment Variation Within Bundles Where is the variation? Remember the payer’s POV. Start thinking in terms of potentially preventable costs (to the payer).

Cost Composition and Variation Initial Inpatient Stay What’s happening within the hospital? Identify outliers and short stays. Identify variation in physician care. Are all these patients the same?

Cost Composition and Variation 90 Days Post Initial Stay What happens after the patient leaves the hospital? Identify readmissions. See hand-offs to post-acute settings. Find sources of variation. What is potentially preventable? Where is the potential for standardization?

Physician Claims by Specialty What physician specialties are predominant? Who do we need to align with? How do services vary? Why? Do the patterns make sense? Is the variation due to lack of coordination?

Pricing Comparisons What do my neighbors look like? Are the volumes in other hospitals high enough to be reliable?

Pricing Comparisons What components do I need to include in my price? How does my price compare to benchmark? Where am I different and why?

First Post-Anchor Site of Care

Regional Variation AMI Major Joint – Lower Extremity

Readmissions Drive Costs

Where is the volume...

Are we done yet?

Lessons Learned From the Data Analytics Payment variation – Medical vs surgical DRGs – Payment variation is due to post-acute care and readmissions Episode length selection – Depends on the diagnosis Hospital portion of the discount Quantifying the impact/effect of exclusions

Effective Hospital Discount Total Payment $7,643,138 3% Discount $229,294 Hospital payment $3,794,889 Effective hospital discount6.04%

Episode Length Risk Full Episode31-90 Day Episode Period

Discounts and Episode Length Joint ReplacementTotal Discount 3% of 30 days $209,103 2% of 90 days $152,863 Difference $56,241 Chest PainTotal Discount 3% of 30 days $13,087 2% of 90 days $12,606 Difference $481

Additional Lessons Learned Data may not support intuition Understand what data matters Don’t over-analyze (until you have to) Have a multi-disciplinary team together first – Physician leadership is a key advantage – Clinical interpretation of the data facilitates decision-making Real program savings come from changing utilization and care patterns both within and outside the facility

Leveraging the Experience Analytics/metrics can be applied to: – Commercial contracts – Other innovative payment arrangements New payment arrangements are an opportunity to cover costs for lost volume (e.g. readmissions) If you are not part of the process, you will be caught downstream of the savings Beware of the halo effect

Unanswered Questions Who will succeed? What will drive success? How will success stories be shared? How reproducible is success?

Contact Information Gloria Kupferman Vice President, National Information Products DataGen, a HANYS Solutions Company Kelly Price Director, DataGen Group DataGen, a HANYS Solutions Company

Where are the average dollars...