Cloudy with a Chance of Readmissions Penalties Lynn Smith, Clinical Excellence Executive, Midas+ Solutions

Alycia R James, VP Care Performance Transformation Group, Midas+ Solutions

Objectives • Be able to describe the CMS readmission

reduction program penalty calculation • Be able to explain the analytics and insights

available to predict performance and target areas of opportunity for improvement

Readmissions! Readmissions!

Shifting From Volume to Value • Historically

readmissions were a revenue stream

• DRG-based payment caused intense focus on decreasing LOS with no provisions for readmissions

• “Frequent Flyers” common

• Hospitals struggle to make the shift – Not lack of intent/will – Like trying to turn a big ship quickly

• No longer responsible for JUST care within their four walls

• “Out of our control” no longer adequate

*Affordable Care Act (3/20/10) instigates MAJOR paradigm shift

*Section 3052 of Affordable Care Act established the Hospital Readmissions Reduction Program

Multiple TOP Priorities With Bottom-line Impact

• Value-Based Purchasing (VBP) • Hospital Readmissions Reduction Program

(HRRP) • Hospital Acquired Complications Reduction

Program (HACRP)

Payment Adjustment Hierarchy Payment reductions are made in a hierarchical order: 1. Value Based Purchasing 2. Hospital Readmissions

Reduction Program 3. Hospital Acquired

Complications Reduction Program

HOWEVER: • VBP and HRRP payment

adjustments are made independently of each other.

• Each applied to the base operating DRG payment amount.

• HAC adjustments are made after VBP and HRRP

Plethora of Published Data – 906,000 results on Google search

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Excessive Readmission Ratios Values > 1.0 = Financial Penalties

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Your Hospital ERR Other Hospitals

Hospital Readmission Reduction Program Penalties Forecasted to Impact More

Hospitals in Future

• Acute Myocardia Infarction

• Pneumonia

• Heart Failure

• Total Hip and Knee

• COPD

• CABG

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Excess Readmission Ratio’s > 1

in ANY one of the clinical cohorts

results in financial penalties

Excess Readmission Ratio = Predicted /Expected

A Primer on Predicted and Expected

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Predicted FY 2016

Excess Readmission Ratio = Predicted /Expected

=

Your patient’s risk factors for FY 2016 Part A & B Claims

(July 1, 2010 – June 30, 2014) x

Risk Coefficients

(July 1, 2011 – June 30, 2014)

+

Your hospital

provider Intercept

(July 1, 2011 - June 30, 2014)

Expected FY 2016 = +

Average hospital provider intercept for all Section(d) Hospitals in US

(July 1, 2011 – June 30, 2014)

Your patient’s risk factors for FY 2016 Part A & B Claims

(July 1, 2010 – June 30, 2014)

x

Risk Coefficients

(July 1, 2011-June 30, 2014)

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Midas+ Xerox

Hospital Readmission Penalty Forecaster

Hospital Readmission Penalty Forecaster Service explained

• Delivered via presentation with quarterly updates • Generated by Midas+ proprietary methodology • Executive-level analysis insights

– System-level rollups forecasts – Individual hospital view by cohort – Cohort detail – Cohort trend – Non-same hospital readmissions trend

• Cohort detail at patient-level files delivered

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How It Works • Utilizes advanced analytics, machine

learning technology, and clinical executive insight

• Informs you of your anticipated readmission penalties up to two years before your hospital-specific reports

• Help you focus improvement resources most effectively

Midas+ Predictive Analytics Creates a Proactive Response Strategy……

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Risk Factors Risk Coefficients

Midas+ Clients see their results

CMS Hospital Specific Reports

Adjusted Payments

Begin

July 2012 to June 2015

July 2011 to June 2015

October 2016 TODAY! August

2016

Midas+ Hospitals can

prioritize and intervene high risk populations to impact

financial penalties two years before they apply!

FY 17 Risk Factors and Risk Coefficients

calculated from Medicare Part A & B Claims July 1, 2011 – Dec 31, 2013

+ Midas+ Data Jan 1, 2014, to date

+ Midas+ Predictive Analytics

Understanding Your Tipping Point

• Which populations are at greatest risk?

• How many readmissions are too many?

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Readmission Penalty Forecaster Uses Medicare Part A & B Claims Data • Includes risk Factors and risk coefficients

Also includes Midas+ data from January 2014 through latest harvested quarter

RPF measures available to Readmission Penalty Forecaster clients only; delivered as a service including quarterly presentations and executive level insights

Unique Features

• Combines CMS Claims data with YOUR data (DV quarterly harvest) to identify patient level

• Provides visibility and predictive adjustment for “Non-Same” readmissions

• Defines how many readmissions are EXCESS causing penalties

• Calculates Expected Probability of Readmission to each INDEX case

16% 7%

33% 29%

Process • Contract Signed

• Financial Line Item data received

• Your Hospital Model developed/Data Generated by Advanced Analytics

• Data to Care Performance Transformation – Insights

• Presentation of YOUR results via web ex • Follow up with delivery of patient level (by cohort)

files

FY 18 Predictions available – Newest CMS data anticipated in August, 2-4 weeks to forecast

Financial Info Needed for Forecaster • Medicare Fee-for-Service In-Patient Volume

• FFS Part A inpatient Medicare claims as principal payer • May include Medicare Part A FFS as secondary is secondary paid • May include Medicare Part A FFS enrolled in state-buy-in program • Must NOT include Medicare Advantage or HMO

• CASE MIX for Medicare Fee for Service inpatient volume • Relative weights for DRGS that have been adjusted for transfer • Only Medicare populations previously defined

• TOTAL REVENUE for Medicare FFS Inpatient volume • By fiscal year 2012, 2013, 2014

• BASE Operating DRG VALUE for 3 years • DRG Weights x [(labor share x wage index)+(non-labor share x cost of living

adjustment)]+New technology if applicable • DO NOT include adjustments for graduate medical education, disproportionate

share, high-cost outliers or Value-Based Purchasing • Does NOT include adjustments or add-on payments for Medicaid payments to

Disproportionate Share Hospitals (DSH)

System-level Forecast

Individual Hospital View

Cohort Detail

Cohort Trend

Patient-level Files

SAME but Different DataVision Readmission Penalty Forecaster

DATA Hospital’s ADT/DAB All Patients with Medicare as payer

Primary: CMS Claims Secondary: Hospital’s Midas+ data CMS Claims – Medicare as principal/secondary

FACILITY Readmissions ONLY to same facility

Readmissions to same facility with adjustment for “non-same” Identification of “non-same” via Claims data

PRIMARY FOCUS

Patient care management at your facility

Predicting excess readmissions & associated penalties at the cohort & system level

Good Enough Today May Not Be Good Enough Tomorrow

“Even if you’re on the right track, you’ll get run over if you just sit there.”

Will Rogers

I’ve Got the Data – Now What?

• Financial planning (budget) – Reality (What is fluid/what is static?)

• Performance Improvement – Focus on “teaching” cases – Root Causation

• Expected Probability • Readmission to “Non-Same” hospitals • Discharge Disposition

Midas+ Findings • Much variation in Expected Probability of

Readmission by Cohort – Highest doesn’t always translate to most readmissions

• CABG and TH/TK typically highest cost/case

• Being discharged to a SNF or home with Home Health no guarantee

Importance of Documentation • Accurate & complete

documentation of patient’s clinical condition is of utmost importance

• “Not uncommonly, a measured risk-adjusted readmission rate is artificially high either because the conditions defining the cohort and/or the comorbidities defining the severity of the expected readmission rate were not documented by the provider or were not coded by the facility as to be reported, or both…” – https://www.nationalreadmissionp

revention.com/documents/snf-penalties-announced.pdf

LOST in TRANSLATION?

Client Findings - Forecaster

• “Non-same” readmit trends

• The amount of excess readmissions only has to be 1 (in 1 cohort) to initiate penalty

• Post acute care providers importance

• The “EXPECTED” is consistently lower for FY 17 than FY 16 (window tightening) – Above average in FY 16 is not a guarantee for

FY 17 … moving target

Quick Wins • Systems

– Communicate, communicate, communicate – “Non-same” readmissions

• What cohort is “almost” there? Minor issues

• Trends of decreasing Relative Weight/increasing provider intercepts – What Changed? Who Changed?

Long-range Improvement Strategies • Systems

– Transparency – ID Best Practices • Community Collaboration with Post Acute

Care providers • Cohorts with major opportunity

– Process evaluation / root causation – Care Transitions

• Talk to your patients

• There is no “one size fits all” to reduce readmissions

• Hospitals must deploy and support multiple strategies

• IMPORTANT – consistently monitor results • Reliable data is CRITICAL • The time for action is NOW – don’t delay

Summary

Guidance in the Storm • Readmission Penalty Forecaster • Midas+ Care Performance

– Additional Hands on Deck – Impartial Root Causation – Best Practices – Collaboration – DV Data

• Process Control

Thanks for attending. Are there any questions?

Lynn Smith, Clinical Excellence Executive, Midas+ Solutions

Alycia R James, VP Care Performance Transformation Group, Midas+ Solutions