cracking the code to better quality and financial outcomes · cracking the code to better quality...

1

Cracking the Code to Better Quality and Financial Outcomes

Session 37, February 12, 2019 (1:30-2:30)

James Grana, Ph.D., Chief Analytics Officer, Rush Health

Bala Hota, MD, Vice President & Chief Analytics Officer, Rush University Medical Center

2

James R. Grana, Ph.D.

Bala Hota, MD

Has no real or apparent conflicts of interest to report.

Conflict of Interest

3

• Review Market Justification for Code Capture Optimization

• Review Relationship Between Code Capture and Financial Performance

• Review Practical Challenges to Implementing a Code Capture Strategy

• Review Relationship Between Code Capture and Select Quality Indicators

Agenda

4

• Learning Objective 1: Identify the correlation between accurate documentation and improved quality scores and financial outcomes

• Learning Objective 2: Analyze the steps the Rush Health and RUMC team used to drive process improvements at the provider, practice, and departmental levels

• Learning Objective 3: Demonstrate the improvements in quality scores and risk adjustment Rush Health and RUMC achieved by increasing documentation accuracy

Learning Objectives

5

Evolving Health System

• Fee For Service

• Fee For Value

– Shared Savings

– Shared Risk

– Bundles

– MSSP Changes

– Medicare Advantage

– Oncology Care Model

6

Risk Adjusted Performance Measurement and Compensation

• Medicare

• Medicaid

• Commercials

7

Benefits of Code Capture Optimization and Consistency • Reimbursement

• Quality

– Follow-Up Trigger

– Follow-Up Indicator

• Identify Care Variation Rather than Coding Variation

8

Simple Case is Observed to Expected Ratio

• Simple to Understand

– O/E = 1 suggests that you are performing as expected.

– O/E = 1.1 suggests that you are performing 10% higher than expected

– O/E = .90 suggests that you are performing 10% lower than expected

• Considerations

– Comparison Cohort

– Factors Included In Adjustment

– Data Staging and Timing

– Sample Size

9

Risk Adjustment Factors and Cost Targets

$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

Moderate RAF: Target $1,000

PM

PM

Performance Relative to TargetAssuming a PMPM of $1,000

RAF- Driven Target

PMPM: $1,000

RAF- Driven Target

PMPM: $1,000

Achieved PMPM:

$1,000

Achieved PMPM:

$1,000

When the RAF-driven

Target PMPM and

Achieved PMPM are

equal, dollars are

neither available for

shared savings nor at

risk.

10$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

Moderate RAF: Target $1,000

PM

PM

Performance Relative to TargetAssuming a PMPM of $1,000

RAF-Driven Target

PMPM: $750

RAF-Driven Target

PMPM: $750

Achieved PMPM:

$1,000

Achieved PMPM:

$1,000

Dollars at RiskWhen the RAF-driven

Target PMPM is below the

Achieved PMPM, dollars

are at risk.

Risk Adjustment Factors and Cost Targets

11

$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

Moderate RAF: Target $1,000

PM

PM

Performance Relative to TargetAssuming a PMPM of $1,000

11

Achieved PMPM:

$1,000

Achieved PMPM:

$1,000

Shared SavingsWhen the RAF-driven

Target PMPM is above

the Achieved PMPM,

dollars are available for

shared savings.

RAF-Driven Target

PMPM: $1,250

RAF-Driven Target

PMPM: $1,250

Risk Adjustment Factors and Cost Targets

12

$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

Low RAF: Target $750 Moderate RAF: Target $1,000 High RAF: Target $1,250

PM

PM

Performance Relative to TargetAssuming a PMPM of $1,000

Dollars at

Risk

Shared

Savings

Increasing your RAF, increases your

likelihood of benefiting from shared savings

Risk Adjustment Factors and Cost Targets

13

Reduce health care spend

• Curb avoidable utilization

• Utilize lower-cost care

alternatives

Boost premium revenues

• Increase RAF through accurate

HCC capture

• Grow (high-risk) covered lives

Medical Spend

Premium Revenue and Targets

Two Ways to Improved Your Shared Risk Performance

14

• Hierarchical Condition Categories (HCC) are groupings of ICD-10 diagnosis

codes. Each HCC is assigned a weight.

• RAF scores are the sum HCC weights and demographic weights to reflect the

burden of illness associated with group of patients. CMS uses these scores in its

compensation systems.

Sicker Patients

-> Higher RAF

-> More dollars

Healthier Patients

-> Lower RAF

-> Fewer dollars

What They Are

15

• Providers assign diagnoses for a patient’s conditions

• Conditions are sorted into HCCs and assigned a corresponding

numeric weight

• HCCs are re-determined each calendar year, requiring revalidation of all relevant

diagnoses through claims to CMS

Hierarchal Condition Categories

16

• Serve as a payment multiplication factor for relevant populations

• Patient HCC weights are summed to create a RAF score

• Scores are recalculated annually

Base Payment Final RAF Final Payment

$1,000 1.0 $1,000

$1,000 1.2 $1,200

$1,000 0.8 $800

Risk Adjustment Factors

17

All Codes Missing Diabetes

Age/Gender: 72/M 0.379 0.379

Vascular disease with complications (HCC 107) 0.4 0.4

Morbid obesity (HCC 22) 0.273 0.273

Congestive heart failure (HCC 85) 0.323 0.323

- CHF/diabetes interaction (HCC 85d) 0.154 Not coded

Diabetes with chronic complications (HCC 18) 0.318 Not coded

RAF Sum 1.847 1.375

PMPM $2,124.05 $1,581.25

PMPY $25,488.60 $18,975.00

The Importance of ICD 10 Code Accuracy

18

• Missed diagnoses

• Failing to document/bill for an unresolved condition present in a previous calendar year

• Using a “history of” diagnosis when condition is actively occurring and not yet resolved

• Using a generalized or non-specific diagnosis code

• Failing to document completion of medication list review

Common Coding Errors

19

• Patient JW

• Provider: Dr. X

• Conditions not re-validated

• Congestive Heart Failure 0.368

• Chronic Obstructive Pulmonary Disease 0.346

• Coagulation Defects and Other Hem. Disorders 0.252

*Denotes the revenue from CMS; actual payments to provider may vary by payer arrangement

HCC Score Revenue

2016 1.261 $1,450

2017 (October YTD) 0.295 $339

-$1,111*

MA HCC Example

20

194 (29.8%) of 651 patients experienced a decrease in HCC score from 2016 to 2017 (October YTD)

– Average decrease of 0.56

– Lost plan revenue* approximately $1,499,232

– If provider were to reach 2,000 members lost revenue would amount to approximately $4,605,888

Common conditions not revalidated

*Denotes the revenue received by insurance carrier from CMS for the population.

Missing Condition Patient Count

Vascular Disease 37

Congestive Heart Failure 24

Chronic Obstructive Pulmonary Disease 20

Coagulation Defects and Other Specified Hematological Disorders 20

Breast Prostate and Other Cancers and Tumors 18

Acute Renal Failure 18

Specified Heart Arrhythmias 16

Ischemic or Unspecified Stroke 16

Morbid Obesity 14

Diabetes with Chronic Complications 14

Sample Reporting: MA HCC YTD Performance

21

Impediments to Code Capture Improvement Strategies

• Historical Inpatient Focus is Not Enough

• Outpatient Opportunities

• Multiple Risk Adjustment Models (some requiring recoding prior to each new “episode” or “trigger admission.”)

• Coding Value Chain and Possible Leakage

– Provider

– Input Mechanism

– Internal Coding Team

– Provider IT and ETL

– Payer IT and ETL

22

• Legal Considerations

• Disproportionate PCP Burden

• Specialist Reluctance

• Workflow Inefficiencies

• Need for Ongoing Provider Education

Additional Code Capture Considerations and Impediments

23

Comorbidity Capture and Inpatient Quality

16

24

• Complex space of multiple rating agencies

• Common factors but areas of difference

• Common themes with outpatient ACO risk adjustment

17

Quality Measurement & Inpatient Care

25

Vizient CMS US News LeapfrogConsumer

ReportsTruven

Mortality 26% 22% 38% 20% 20%

Efficiency / Cost 6% 8%* 20% 30%

Safety 26% 22% 5% 50% 20% 20%

Effectiveness /

Readmission and

Throughput21% 26%** 20% 20%

Patient Centeredness 16% 22% 15% 20% 10%

Equity 5%

Reputation 28%

Structural Measures 30% 35%

*4% - effectiveness of care/ 4% Efficient use of medical

imaging**22% - readmission/4% - Timeliness 18

Do These Systems Complement Each Other or Conflict?

26

19

27

• Overall composite score for hospitals based on Hospital Compare Data

• 64 potential quality measures in 7 domains

• Not all measures reported by all hospitals

• In general:

– Reporting fewer measures was better: fewer than 10% of hospitals

reporting fewer than 38 measures included received either 1 or 2 stars

– AAMCs disproportionately received 1 or 2 stars (62%) (worst)

– Rush: 4 stars; best AAMC in Chicago area, top 15.8% of teaching

hospitals nationally

20

CMS Stars Rating

28

• Mortality (MI, CABG, COPD, CHF, PNA, CVA)*

• Safety of Care (CLABSI, CAUTI, SSI, MRSA BSIs, C diff, surgical

complications and PSIs)

• Readmission (unplanned readmissions)*

• Patient Experience (Patient Sat Surveys)

• Effectiveness of Care (Vaccination, Screening, Protocol Driven Care)

• Timeliness of Care (ED throughput, time to care for MI)

• Efficient Use of Imaging (Outpatient MRI, CT, and Stress Test Use)

• Dates of Data: July 1, 2013 – June 30, 2016

*Risk Adjusted Measure, with comorbidities affecting expected rate

Star Rating CMS Measure Components

29

Based on measured values and case mix adjustment rates

Denominator

Claims Based

(ICD/CPT

Code)

Risk

Adjustment

Expected Rte

Claims Based

Numerator

Claims, Chart

Abstraction

Based

O:E Ratio

Quality Rank Contingent

on Documentation and

Hospital Billing Codes

(HCCs)

CMS Mortality, Safety, & Readmission Rates

30

• By-products of risk adjustment methodology

– Comorbidity adjustment

– Hospital size adjustment (small hospitals get handicap)

– No adjustment for SES

Factors in risk adjustment

31

Clinical documentation impacts reimbursement and quality measurement for

hospitals

• Medicare Reimbursements: Maximizing revenue requires determining the

correct Medicare DRG

• Quality: Accurately reflecting outcomes requires fully recording patient and

treatment data, including any complications or comorbidities (CC) and/or major

complications or comorbidities (MCC). Quality risk adjustment models align with

HCCs.

Documentation is often incomplete due to:

- Omission of chronic conditions

- Diagnostic laboratory tests are not fully recorded

Incomplete documentation affects Medicare reimbursement and accurate quality

measurement

Problem: Incomplete Documentation

32

20% of a hospital’s revenues are fixed rate Medicare payments

Mechanism DescriptionImpact of Incomplete

Documentation

Centers for Medicare and Medicaid

(CMS) Reimbursements- Reimbursements based on illness severity Lost Revenue

Hospital Value Based

Purchasing (HVBP)

&

Hospital Readmission

Reduction Program (HRRP)

- Patients are assigned a benchmark

probability of readmission/mortality

- Hospitals are incentivized based on

readmission/mortality vs. benchmark

Financial Penalties

Incomplete documentation affects Medicare reimbursement

through three primary mechanisms:

Financial Impact

33

Inaccurate quality measurement affects national

reputation

• Quality metrics such as Patient Safety Indicators

(PSI) influence national rankings and

benchmarking

• Correctly reporting case mix and mortality

measures is critical for accurate quality metrics

• Reputation affects the ability to attract patients

and top staff

Reputational Impact

34

Health and Human Services’

“Triple Aim” Goal

1. Improving health care quality

2. Improving population health

3. Reducing unnecessary health care costs

34

Quality Metrics and Outcomes will Drive Medicare

Reimbursements in the Future

Also have reputational impact –public information

Increasing Focus on Quality

35

Chronic Conditions

Cancer Ischemic heart disease

Major depression Osteoporosis

Epilepsy COPD

Hypercholesterolemia Osteoarthritis

Obesity Dementia

Malnutrition Cerebrovascular disease

Hypertension Asthma

Chronic kidney disease Bipolar disorder

Congestive heart failure Diabetes melitus

• Chronic conditions are critical common factors between DRG and

HCC coding

• Additional codes determine DRG code reclassification and optimize

Hierarchical Condition Classification (HCC) scoring

• Results in a greater reimbursement and improved quality scores

Conditions Detected

36

Perc

entile

1 - 9 points0 points

10 pointsPercentile of evaluated hospitals

Rush’s Risk Adjusted Survival Rate

Included in Medicare Data

Missing from Medicare Data

*Only the conditions with missing

codes are shown (8 of 25)

Hypothetical

1%

improvement:

94th percentile

Original:

77th

percentile

Impact of Conditions on AMI 30-Day Mortality

Rush’s Actual Survival Rate

Rush’s Expected Survival Rate, with the missing condition

37

29

38

Results of Implementation

39

31

Total Hip/Total Knee Readmission Penalty

40

Implementation of software resulted in

pay-for-performance improvements at Rush

Federal Fiscal YearValue Based

Purchasing

Hospital Readmission

Reduction Program

Hospital Acquired

Conditions Reduction

Program

Net Pay for

Performance

FY2015 $550 K ($1.2 M) ($1.7 M) ($2.4 M)

FY2016 $676 K ($1.1 M) No Penalty ($676 K)

FY2017 $958 K ($483 K) No Penalty $475 K

Pay for Performance Initiatives

41

CMS Pay for Performance Estimated Returns for Federal FY2017 CMS Star Rankings

VBPReadmission

Reduction HAC Total

Patient Experience

StarsOverall Ranking

RUMC $958K ($483K) – $475K

U of C $19K ($341K) – ($322K)

NM $562K ($386K) ($1,989K) ($1,813K)

UIC ($129K) ($136K) – ($265K)

Loyola ($35K) ($320K) ($1,611K) ($1,967K)

Post-Implementation: Rush’s outperforms its peers

Rush’s Comparison to Peers

42

James Grana, Ph.D., Chief Analytics Officer, Rush Health

Bala Hota, MD, Vice President & Chief Analytics Officer, Rush University Medical Center

43

James Grana, Ph.D.

Chief Analytics Officer

Rush Health

James_Grana@rush.edu

Bala Hota, MD

Vice President & Chief Analytics Officer

Rush University Medical Center

Bala_Hota@rush.edu

Contact Information