Medicare EPMs Using Data to Paint a Clearer Picture
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Medicare Episode Payment Models
Takeaway from today s discussion The EPM programs are constantly evolving CMS is not providing enough information for you to take meaningful action. What you can do to stay ahead of the curve.
Medicare Episode Payment Models Recap and update
About the Programs CJR MS-DRGs 469 & 470 Participating hospitals are responsible for the total costs of the inpatient admission and all treatment 90 days post-discharge CJR (Comprehensive Care for Joint Replacement) is a mandatory bundled payment model in 67 MSAs (4/1/16-12/31/20) *Proposed: 34 MSA s starting in 2018 5-year program (4/1/16-12/31/20)
Program Details and Changes Target determined by a blend of the participating hospitals costs and the regional cost (1/3 rd, 2/3 rd, 100%) Discount determined by the participating hospitals Total Composite Score on Quality (1.5-3.0) Risk/reward capped at 5% PY1&2, 10% in PY3 and 20% in PY4&5 *no payback in PY1 2 Reconciliations - NPRA
Working with available data Making use of data you already have
Data files available through CJR portal Patient demographics Diagnosis information Acute claims data (includes anchor admission, readmissions, LTAC) HHA claims data SNF claims data (includes swing bed) Physician and Anesthesia claims data Part B claims data Rehab claims data
Evaluating Cost Compared to Target
ALOS and Profitability by Physician
Post Acute Impact on Target Price
What s wrong with this picture? Using other data of take it a step further
ALOS and Variance by Physician
Variance by Post Acute Provider
Variance by Discharge Status Code
Standard Analytic Files 2012 2016 (5 years) Inpatient, Outpatient, SNF, HHA, Hospice and Physician Roughly 12M Inpatient claims, 160M Outpatient claims, 5M SNF, 6M HHA and millions of hospice and physician claims (per year) Deidentified at the patient level but contain enough demographic information to analyze meaningful trends Allows you to take the provider specific CJR/EPM data provided by CMS analyze it at the regional and national levels
ALOS and Variance by Physician
ALOS and Variance by Physician
Variance by Post Acute Provider
Variance by Post Acute Provider
Variance by Discharge Status Code
Variance by Discharge Status Code
Moving beyond the CMS data
Regional and National Benchmarking 9.0 8.2 8.3 8.0 7.7 LOS Comparison Average 7.0 6.0 5.0 6.4 6.1 6.0 5.3 5.1 4.9 4.0 3.0 3.0 3.1 3.0 2.0 1.0-469 (with fracture) 469 (no fracture) 470 (with fracture) 470 (no fracture) Facility W/S Central National
Variance of DRG Transfers 80% Qualified DRG Transfer Episodes 2012-2015 2015 70% 60% 50% 40% 30% 20% 10% 0% 469-0 469-1 470-0 470-1 Facility National W/S Central
MCC Frequencies
Maria talks about quality data
Impact of Quality
Quality Trends
Using technology to fill in the gaps
Shortcomings of CMS Data The data CMS provides is somewhat complex taken in the format they provide Negative trends, once published, may take years to correct Providers need a tool to harnesses people s natural ability to spot visual patterns, reveal opportunities, identify trends and most importantly, make data-driven decisions. There is no ability for advanced planning
Drilling down
Drilling down
BESLER EPM Scorecard Acute facility scorecard listing key metrics to help providers understand their risk when it comes to Medicare s CJR programs Predicts CMS total spend Provides a facility with their quality scores Provides data on their total at risk dollars under the mandatory program
Sample Hospital Scorecard
CMI and the effect on EPM CMS assigns a unique weight to each DRG The weights are intended to account for cost variations between different types of treatments A hospital case mix index, or CMI is the relative average of all cases assigned a DRG for a given year and is a good metric to determine how CMS adjusts DRG payments to the facility Besler uses the national benchmark data to calculate a CMI for each DRG combination allowing our analysts to determine if a specific diagnosis is being reimbursed at a reasonable rate compared to other facilities within the comparison region
Heatmap
Tying it all together
Estimate your Composite Quality Score before Reconciliation Start with the raw data from hospital compare Calculate the weighted value and national percentile per the Final Rule Create a final score card that shows the expected Quality Category
Ely will talk about data and such
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