Implementing a Bundled Payment ProgramPayment Program Bundled Payment Summit June 16th, 2014 Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc. Agenda •

Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.

Implementing a Bundled Payment Program

Bundled Payment Summit

June 16th, 2014


Agenda •  Implementation Overview: Step-by-Step •  Data Preparation •  Data Driven Metrics and Decision Making •  “Big Picture” Analytical Tools •  Budget Creation •  Risk Adjustment •  Assessing Quality •  Scaling BP •  The HCI3 Learning Center

2


Implementation Overview: Step 1 Data Analysis •  The beginning and end of all successful

payment reform program is robust data analysis

•  Otherwise, all parties are steering in the dark

•  Seeing is believing; and believing is committing

•  Payment reform, in the final analysis, is really about transforming information

3


The Contracting Dyads

Governments (Medicare /Medicaid / State Employee Programs)

Health Plans, HMOs, PPOs, Co-ops, TPAs

Employers Direct Contracting*

ACOs and Large Health Systems

Free Standing Surgical Centers

IPAs and Hospital / Physician Groups

Specialist Line of Service Groups (Ortho, OB/GYN, etc)

Primary Care / Medical Homes

Buyers Sellers

*this one alone is sending shock waves through the “planosphere”

4


Step 2: Leadership Commitment

C-Suite

Data Analysts

IT

Business Management

Plan / Payer(s)

Physician Leadership

Quality Management

IT

Network Management

Providers

Convening Organization

Implementation Methods

Steering Committee

Leadership Plan PM Prov. PM

Clinical Staff

5


Step 3: Payer / Provider Engagement

Kick-off Meeting •  A well crafted agenda that both parties have agreed to •  Sets the tone and tenor of the entire project going

forward •  Assign roles and responsibilities: Good PMs •  Star Agenda Item: data analytics (which is really the

beginning of contract negotiations) •  Retrospective vs. Prospective payment •  Action items and deliverables •  Dashboards with (realistic) milestones •  Follow-through procedures to Launch

6


Step 4: Scoping the Project

Scoping comes in 4 “Boxes” The Bundle Set

Responsible Individuals

Quality Scorecard

The Engine

Which and how many episodes are in the dyad?

Who is responsible for what within the dyad?

How will we measure (and operationalize) effectiveness?

When will we move to scale?

7


Managing Dyads – Layering Scope

Orthopedic Bundles

Coronary Bundles

Condition Bundles (PCMH)

Master Scope

8


Step 5 and Beyond: Launch

Observation Year: ECR Analytics over FFS

PAC Reduction: Upside only Contract

PAC Reduction: Contract with Downside

Full Prospective Payment

•  “Brass Tacks” of operationalizing BP program

•  And negotiating contracts •  Benefits redesign (?) •  Launch date(s) •  Pathway and program expansion

9


For contact information: www.HCI3.org www.bridgestoexcellence.org www.prometheuspayment.org

Questions?


Data Preparation: Clearing the Pathway for Analytics

Bundled Payment Summit June 16 2014

Jenna Slusarz, Program and Operational Support, HCI3


Basic Files Required

•  Member File –  one record per member

•  Enrollment File –  Multiple records per member

•  Provider File –  One record per provider_id present in the stay and professional

files •  Inpatient Stay File

–  All claims for a single admission rolled up into one record with all codes and final allowed amount

•  Professional File –  Line level file –  Contains professional, outpatient facility and ancillary claims

•  Pharmacy File

12


Know Your Data

•  What do you expect to see? –  What range of costs would you consider

normal for each claim type within your data? •  What are your data limitations?

–  Missing fields? –  Quirks?

§  Missing costs for a certain payer §  Putting all unknowns under 1 provider id

or member id –  Unreliably reported fields

13


Are Data Supplements Available?

•  If you have missing or unreliably filled-in fields, is there a secondary data source you can use?

– Ex. NPI database to fill in missing specialties

14


Know Your Data Structure

•  If you don’t understand how your data is structured and stored you wont be able to accurately structure it for analysis and can’t rely on the results to be accurate

15


Data Needed for Episode Analytics •  Claim Information

–  Allowed amounts (claim and line level) –  Diagnosis and procedure codes

§  ICD- 9 DX §  ICD-9 PX §  CPT/HCPCS §  Revenue codes

–  Dates of service –  Facility type

•  Consistent member ids and provider ids •  Member information

–  Enrollment –  Age/YOB

•  Provider information (specialty, group, etc.) 16


Data Structure: Why Use PX And DX Versus DRG? •  DRG matches DX, doesn’t take PX into account

MS_DRG_CODE

DRG description DXCCS DXCCS_DESC

Principal_proc_code PXCCS PXCCS_DESC

313 CHEST PAIN '102' 'Chest pain' 4523 '76' 'Colonoscopy ' 313 CHEST PAIN '102' 'Chest pain' 4525 '76' 'Colonoscopy ' 313 CHEST PAIN '102' 'Chest pain' 4292 '69' 'Esoph dilat ' 313 CHEST PAIN '102' 'Chest pain' 3995 '58' 'Hemodialysis' 313 CHEST PAIN '102' 'Chest pain' 0392 '5' 'Inject spine' 313 CHEST PAIN '102' 'Chest pain' 9390 '216' 'Mech ventil ' 313 CHEST PAIN '102' 'Chest pain' 8605 '174' 'nOR Rx skin ' 313 CHEST PAIN '102' 'Chest pain' 8843 '191' 'Ot arterio ' 313 CHEST PAIN '102' 'Chest pain' 8838 '180' 'Ot CT scan ' 313 CHEST PAIN '102' 'Chest pain' 9205 '209' 'Ot fct scan ' 313 CHEST PAIN '102' 'Chest pain' 0017 '231' 'Ot Rx procs ' 313 CHEST PAIN '102' 'Chest pain' 3893 '54' 'Ot vasc cath' 313 CHEST PAIN '102' 'Chest pain' 3782 '48' 'Pacemaker ' 313 CHEST PAIN '102' 'Chest pain' 8941 '201' 'Stress tests' 313 CHEST PAIN '102' 'Chest pain' 8942 '201' 'Stress tests' 313 CHEST PAIN '102' 'Chest pain' 3142 '35' 'Tracheoscopy' 313 CHEST PAIN '102' 'Chest pain' 4513 '70' 'UGI endosc ' 313 CHEST PAIN '102' 'Chest pain' 4516 '70' 'UGI endosc ' 313 CHEST PAIN '102' 'Chest pain' 5794 '108' 'Urine cath ' 313 CHEST PAIN '102' 'Chest pain' 9955 '228' 'Vaccinations'

17


Data Structure: Why Use PX And DX Versus DRG?

ms_drg_code DRG Description

Principal_proc_code

PXCCS

PXCCS_DESC

principal_diag_code DXCCS DXCCS_DESC

235 CORONARY BYPASS W/O CARDIAC CATH W MCC 3611 '44' 'CABG ' 41041 '100' 'Acute MI'

235 CORONARY BYPASS W/O CARDIAC CATH W MCC 3611 '44' 'CABG ' 41071 '100' 'Acute MI'

236 CORONARY BYPASS W/O CARDIAC CATH W/O MCC 3611 '44' 'CABG ' 41011 '100' 'Acute MI'




•  DRG matches PX, doesn’t take DX into account

18


Data Structure: Why Not Roll Up OP/PB? •  Lines/costs can be allocated separately •  Claim lines below were attributed to 2 episodes and costs

were split by line into typical and complications (PACs), if this were rolled up all costs would have gone to PAC

condi&on

CLAIM_ID

LINE_ID

allocated_amt

CLAIM_TYPE

assignment_type

PLACE_OF_SVC_CODE

HCPCS_PROC_CODE HCPC_descrip&on

PRINCIPAL_DIAG_CODE dx_descrip&on

ASTHMA claim1 1 10 PB T 22 71020 RADIOLOGIC EXAMINATION, CHEST, 2 VIEWS, FRONTAL AND LATERAL 78605 shortness of breath

CAD claim1 1 10 PB T 22 71020 RADIOLOGIC EXAMINATION, CHEST, 2 VIEWS, FRONTAL AND LATERAL 78605 shortness of breath

ASTHMA claim1 2 10.67 PB C 22 93971

DUPLEX SCAN OF EXTREMITY VEINS INCLUDING RESPONSES TO COMPRESSION AND OTHER MANEUVERS; UNILATERAL OR LIMITED STUDY 45111

PhlebiJs and thrombophlebiJs of femoral vein (deep) (superficial)

CAD claim1 2 10.67 PB C 22 93971

DUPLEX SCAN OF EXTREMITY VEINS INCLUDING RESPONSES TO COMPRESSION AND OTHER MANEUVERS; UNILATERAL OR LIMITED STUDY 45111

PhlebiJs and thrombophlebiJs of femoral vein (deep) (superficial)

19


Data Structure: Why Roll up Stays?

•  Complete stay, not pieces of stay across multiple claims which would look like the patient had multiple stays during the same time window

•  All codes on the claim taken into account at once to bucket the stay

•  Costs come on one line (e.g., room and board)

20


Data Checks for Running Analytics

•  Compare your data to the specifications –  Are all the fields named correctly? –  Are the fields the correct type and size? –  Is the content in those fields correct?

•  If there are mapped fields do only the appropriate values show up?

•  Are the required fields populated? •  Is there a large percent missing from any required fields? •  If you are submitting data to someone else to run are

they aware of any quirks or missing information in your data?

21



Questions?


Data Driven Metrics and Decision Making: Insights from Episode of Care Analytics

Bundled Payment Summit June 16 2014

Stacey Eccleston Program Implementation and

Research Leader, HCI3


What can we learn from analyzing episodes of care? •  Analysis of episodes can help inform:

–  Policy decisions •  What kinds of reforms will be most effective? •  Where should those reforms be focused? •  What is the potential for savings/improvement?

–  Provider price and quality transparency •  Who are the most efficient and highest quality providers?

–  Provider process improvement •  How do I compare to my peers on cost and quality? •  Which patients experienced defects in care and when?

24


What metrics can be used to inform policy decisions? •  Compare episode costs and potentially

avoidable complication (PAC) rates –  Where are opportunities

•  Analyze additional drivers of cost variation—Is it price, volume or service mix? –  Target your efforts

•  Evaluate the potential savings from reducing variation –  Know potential yield

25


Focus on episodes with high costs, PACs and variation - commercial

Less variation but considerable proportion of costs and PAC rate

Significant proportion of costs and high cost variation

26


Price, volume, and service mix contributions to cost variation •  Episode costs within ECRs vary widely

•  Epi Costs = f(Price, Volume, Service-Mix)

•  What proportion of the cost variation across distribution of episodes within ECR are explained by these three factors? –  Differences in volume and service mix often tied to

presence of complications –  Provide a basis for further investigation and developing

focused interventions.

27


Understanding the causes of variation; example: diabetes

28

Diabetes Episode Costs:

13.1%

78.5%

8.5%

0%

20%

40%

60%

80%

100%

Diabetes

Most Costly 20% vs Median

Volume Service Mix Price

•  Holding changes in volume and price constant, service mix explains the vast majority (~80%) of higher costs in the uppermost quintile of episodes relative to median episode costs.

•  Drilldown to service level to identify specific drivers of service-mix: ü  Higher inpatient days and facility-level

E&M codes ü  Decrease in office-based E&M codes

•  Take-away: •  Exacerbations of illness major driver

of cost variation •  Redesign payments to reduce

incidence of hospitalizations


Translate the results into actionable feedback

•  Potential solutions may involve:

•  Where price is the driver, innovation solutions may include:

–  reference pricing –  pricing transparency –  formulary management –  tiered networks to guide patients to efficient providers

•  Where service mix and/or volume is driver, innovation solutions may include:

–  bundled payments –  gain sharing –  P4P –  Reducing co-pays for high valued services

29

V-BID

Provider Payment Reform


For most episodes the tail wags the dog – opportunity for savings

!$#!!!!

!$20,000!!

!$40,000!!

!$60,000!!

!$80,000!!

!$100,000!!

!$120,000!!

!$140,000!!

!$160,000!!

!$180,000!!

!$200,000!!

1! 33!

65!

97!

129!

161!

193!

225!

257!

289!

321!

353!

385!

417!

449!

481!

513!

545!

577!

609!

641!

673!

705!

737!

769!

801!

833!

865!

897!

929!

961!

Episodes((cumula/ve(count)(

Average(Costs(8(HIP(Replacement(

Stop Loss to protect providers and patients from catastrophic losses

98th Percentile

80th Percentile

$2 million in potential savings

Example: hip replacement

30


Potential Savings Can Be Substantial Across All Episodes

CondiJon Total Savings % Episode Savings % Total Savings

HTN $9,484,851 20% 1.5% PREGN $8,895,520 9% 1.4% CAD $8,753,610 34% 1.4% COLOS $7,087,935 11% 1.1% DIAB $6,312,965 20% 1.0% STR $5,175,083 30% 0.8% KNARTH $5,007,218 14% 0.8% HYST $4,087,835 12% 0.6% GERD $4,022,260 20% 0.6% GBSURG $3,905,899 11% 0.6% ASTHMA $3,726,895 18% 0.6% PNE $3,582,603 27% 0.6% KNRPL $3,529,483 7% 0.6% EGD $3,145,564 13% 0.5% CHF $3,100,427 32% 0.5% COLON $2,758,976 15% 0.4% AMI $2,388,896 14% 0.4% HIPRPL $1,958,383 6% 0.3% CXCABG $1,853,715 12% 0.3% COPD $1,718,238 21% 0.3% PCI $796,928 9% 0.1% Total $91,293,284 14.3%

107k patients; $639 million in episode costs

$547 million

86% of episode costs

Savings = $91 million 14% of total episode costs

Simulated savings at 80th percentile with 98th percentile stop loss

31


Analyzing episodes of care: Price and Quality Transparency ü Analysis of episodes can help inform:

ü Policy decisions •  What kinds of reforms will be most effective? •  Where should those reforms be focused? •  What is the potential for savings/improvement?

Ø Provider price and quality transparency •  Who are the most efficient and highest quality providers?

–  Provider process improvement •  How do I compare to my peers on cost and quality? •  Which patients experienced defects in care and when?

32


Substantial variation in costs/PACs across PCPs treating chronic care patients

+238%

0%#

5%#

10%#

15%#

20%#

25%#

30%#

35%#

40%#

45%#

#$*####

#$1,000.00##

#$2,000.00##

#$3,000.00##

#$4,000.00##

#$5,000.00##

#$6,000.00##

PCP#1# PCP#2# PCP#3# PCP#4# PCP#5# PCP#6# PCP#7# PCP#8# PCP#9# PCP#10# PCP#11#

Chronic(Care(Cluster:(Average(Costs(by(PCP(Costs(to(Treat(Chronic(Care(Pa7ents/Annual(

Average#Typical# Average#PAC# PAC#rate#

+238%

*Providers are compared that have at least 200 chronic care patients/episodes

9%

41%

33


Providers may be outliers on typical costs, PACs or both

!$200%

$0%

$200%

$400%

$600%

$800%

$1,000%

$1,200%

$1,400%

$1,600%

$1,800%

$0% $500% $1,000% $1,500% $2,000% $2,500% $3,000% $3,500% $4,000% $4,500%

Average'PA

C'Co

sts'

Average'Typical'Costs'

Average'Typical'&'Average'PAC'Costs'by'PCP'Chronic'Care'Pa7ents'

Highest efficiency and quality

Outlier provider on cost and quality

34


Analyzing episodes of care: Provider Process Improvement ü Analysis of episodes can help inform:

ü Policy decisions •  What kinds of reforms will be most effective? •  Where should those reforms be focused? •  What is the potential for savings/improvement?

ü Provider price and quality transparency •  Who are the most efficient and highest quality providers?

Ø Provider process improvement •  How do I compare to my peers on cost and quality? •  Which patients experienced defects in care and when?

35


Comparison of MDs treating GERD patients

Select a gastroenterologist to view his/her patients with GERD episodes; Is it a few outlier patients or are higher costs pervasive across all this physician’s patients?

36


Distribution of GERD patients within a single MD

Select a patient to view detail on his/her episodes; What were the typical and complication events and when did they occur?

37


Examine “encounters” over treatment period for this patient

•  This patient had three episodes: GERD, Colonoscopy, Endoscopy •  The endoscopy is associated to the GERD episode as “typical” •  Intense activity (medical claims) throughout the year of treatment

with complications occurring throughout •  Complications include ED visits for exacerbations

38


•  Unwarranted variation in episode costs is an indicator that incentives aren’t working –  Patient compliance; treatment practices; price transparency

•  Rates of avoidable complications and associated costs are a powerful mechanism to assist in benefit design and patient engagement

•  Interactive data tools allow providers to benchmark against peers and develop best practices

Who benefits from episode of care analysis?

39



Questions?


“Big Picture” Analytic Tools and Techniques: Super Utilizers and Total Cost of Care

Andrew Wilson, MPH, MA Research Leader, HCI3

Bundled Payment Summit June 16, 2014


Health care use over time

Getting the “Big Picture”

Inpatient Outpatient Professional Pharmacy

Chronic (e.g., Diabetes)

Procedural (e.g., Knee Replacement)

Acute (e.g., AMI)



•  Accountable Care Organizations •  Care coordination of complex patients •  Population health/use assessments •  Performance reporting

42


Completing the picture

•  Big picture analytic tools can be used to: 1.  Understand overall scope of costs and utilization 2.  Identify “problem” areas and possible targets for

interventions/reforms •  But only a starting point •  Combine with episode of care analyses to zero

in on specific cost drivers and opportunities.

43


Super-Utilizers (HCI3)


Background

•  Dr. Jeff Brenner and the “Frequent Flyers” •  High utilizers of ER and admissions

45


Who are the Super-Utilizers?

•  Definition – 6+ ED visits over a two-year period – 3+ inpatient stays over a two-year period – Both

•  Purpose is to be able to identify greatest need/opportunity for targeted interventions – Access, disease management, care

coordination •  Reduce costs, potentially improve quality

46


Identifying Opportunities to Reduce Resource Use/Costs

# Admissions 0 – 1 2 3+

# ED

Vis

its

0 – 3 Low opportunity Medium Opportunity High Opportunity

4 – 5 Medium Opportunity

Medium Opportunity High Opportunity

6+ High Opportunity High Opportunity Significant Opportunity

Combined with information demographics, location, diagnoses, costs, etc. to gain a more detailed picture

47


Distribution of Super-Utilizers (% of all members)

# Admissions 0 – 1 2 3+

# ED

Vis

its

0 – 3 86.6% 5.3% 5.5%

4 – 5 0.6% 0.3% 0.8%

6+ 0.3% 0.1% 0.5%

22% of total health spending 6% of total health spending

Opportunity to address ~1/3 of total spend simply by reducing unnecessary IP stays and ED visits among ~7% of all members!

48 Based on a Sample of Medicare patients


How big an opportunity?

!"!!!! !200!! !400!! !600!! !800!! !1,000!!!1,200!!!1,400!!!1,600!!!1,800!!

Abdomnl!pain!!Chest!pain!!

Oth!low!resp!!Headache/mig!!Back!problem!!

UTI!!Other!injury!!Superfic!inj!!

COPD!!Fluid/elc!dx!!

Nausea/vomit!!Dysrhythmia!!

Most%Frequent%Diagnoses%of%ED%SUs%4%Medicare%

ED!Visit!N! PaRent!N!

Many of these diagnoses appear to be related to ambulatory sensitive conditions, or diagnoses that may not have required an ED visit

49


Who are the major users?

Top 0.05% of ED Users

Top 0.1% of IP Users

50


Where are these Individuals?

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

0.70%

0.80%

1 2 3 4 5 6 7 8 9 10 11 12

% o

f all

Ben

es in

HSA

s th

at a

re S

Us

Health Service Area

ED SUs

IP SUs Avg. IP%

Avg. ED%

51


Total Cost of Care (TCOC) (HealthPartners)


Background

•  Problems with measuring resource use based on cost alone – Different payment methods for determining costs

•  Inpatient=DRGs, Professional=CPT codes

•  Provide a pure measure of resource use, independent of price –  “Apples-to-Apples” comparison between care

settings and types of services. •  Provide feedback to plans, providers, etc. for

further investigation

53


Total Cost Relative Resource Value Units (TCRRVs) •  Based on existing payment weighting schemes (MS-DRGs,

RVUs, APCs) and an average paid per weight for each •  Applies algorithm to express a service or procedure in

terms of its TCRRVs, which are additive across all services –  Ex: outpatient procedure vs. office-based procedure

TCRRV

Facility

Inpatient

DRGs

Outpatient

APCs

Professional

Office-based

RVU

Facility

RVU

Pharmacy

NDC

Claim Type

Weight

54


Practice-Level Analyses

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

0.4 0.6 0.8 1 1.2 1.4 1.6

Tota

l Cos

t Ind

ex (T

CI)*

Resource Use (TCRRV) Index (RUI)*

High cost Low resource

High cost High resource

Low cost High resource

Low cost Low resource

*TCI and RUI=Provider Avg / Total Avg (>1: higher than avg, <1: lower than avg)

High Prices?

Overutilization?

55


Provider Cost and Resource Use for Chronic Conditions – Complimenting Episodes of Care Analyses

Provider Metrics Rela0ve Performance

ECR Episodes Avg $/Episode PAC* Rate Cost

Resource Use PACs

Asthma 98 $2,171 14% 1.01 0.92 0.99

CAD 51 $15,155 44% 1.05 1.16 1.01

COPD 28 $5,141 25% 0.78 0.76 0.90

Diabetes 120 $3,894 16% 0.97 1.07 1.12

HTN 165 $3,177 15% 0.82 0.84 0.88

Minimize typical services to reduce resource use in CAD episodes Reduce PACs to reduce resource

use in Diab episodes

56 *PAC=Potentially Avoidable Complications


Summary

•  Big picture data tools help reveal the full magnitude of high cost users – SUs: 7% of population, but 33% of costs

•  Identify potentially high cost patients and providers – Overuse vs overpriced providers

•  When combined with episodes of care, provides a complete yet detailed view of use and the underlying drivers of costs – Typical vs PAC use

57


For more information

•  Program documentation and code •  Super-utilizer

– http://www.hci3.org/content/super-utilizer-freeware

•  Total Cost of Care – https://www.healthpartners.com/public/tcoc/

58



Questions?


Recap and Break



Recap

•  Committed leadership from the top with a sense of urgency is indispensible

•  Payment reform requires renewed level of data integrity – reverse FFS claims management trend

•  Payment reform is really about information reform – use data analytics for precise decision making and make it actionable by creating new feedback loops that make sources of variation available to payers and providers

•  Go after the tails! For one payer in only 21 episodes, paying at the 80th percentile and capping at the 98th percentile would save $91 million over 107k population

61


Recap

•  A Big Data, Big Picture approach that searches an entire population to hot spot SU has big implications: 1/3 of total spend can be eliminated by eliminating unnecessary ER / ED visits for 7% of population!

•  It is now possible to unite TCC trend with PAC analysis to zero in on micro opportunities within an entire population for plan comparative analysis and provider reporting – especially important for ACO arrangements

•  The Big Takeaway: these new reporting and payment techniques create a different psychology between payers and providers by forging a new, common, mutually beneficial objective – lower the defect rate!

62


Budget Creation: Putting Numbers to Bundles for

Contracting


Elizabeth Bailey, MPH

Program Implementation Leader, HCI3


Budget Use Cases

•  Performance Measurement – Provider comparisons of budgeted costs vs.

actual costs •  Making a Bundled Payment

– Setting a prospective budget against which the claims stream can be debited in an episode of care construct

64


Reconciliations are designed to compare budget to actual

•  Each episode has a budget, severity-adjusted to the patient.

•  The budget is compared to

actual expenses in order to perform the reconciliations across budgets for all episodes.

65


Key considerations for building a budget •  What behavior are you trying to influence?

1.  Motivate high performers or squeeze low performers?

2.  Save over prior year by reducing total spend or reduce on-going increases in costs?

•  The answer to these questions will determine how the components of a budget are established upfront in a contract

66


Prospective Budget Components

•  For each episode, patient-specific predicted budgets are calculated based on underlying comorbidities and risk factors

•  Budget components are negotiated upfront in the contracting phase and are dictated by the type of behavior you are trying to influence

67

Final Budget

Expected Cost of

Typical Care

Expected Cost of

Complications

Underuse Allowance

Complication Allowance

Margin


Risk Adjustment

•  Implementation partners must agree upon a risk adjustment methodology prior to budget creation

•  They may opt to not apply any risk adjustment and use the average episode cost – this decision likely hinges on the type of episodes selected for payment (e.g. common elective procedures vs. complex chronic conditions)

68


Accounting for recommended services •  Underuse = observed difference between

the recommended number of core services and actual

•  Underuse allowance = the observed number of underused core services * the average observed apportioned cost of those services

69


Tunable Budget Parameters

•  Allowance for Complications •  Margin •  Stop loss provisions

70


Accounting for Complications Under the Bundle •  Allowance for Complications

– A percentage negotiated by the payer and provider, which is applied to the expected or budgeted cost of complications

– An allowance of less than 100% indicates an overall reduction in the budgeted cost of complications

– The extent to which physicians and hospitals manage complications below the allowance becomes an upside “savings” opportunity

71


Margin

•  A percentage negotiated by the payer and provider, which is added to the expected or budgeted Typical costs

•  Margins are usually set to 0% at the outset of a bundled payment arrangement

•  When the provider has squeezed out as many inefficiencies under the bundle as possible, a margin can be added as a continued incentive

72


Stop Loss

•  The provider is at risk for the excess costs over the prospective budget, up to the stop loss per episode

•  There can be an aggregate stop loss in addition to a per episode stop-loss

•  In an “upside only” model, the episode of care stop loss = budget – But the budget can have a built-in “haircut” by

simply keeping future costs = current costs and not allowing for price inflation

73


The Donut Hole P

erce

nt o

f Bun

dle

Pric

e 100%

Risk

Payer Risk

Payer Risk

Provider Risk – “Donut Hole”

Stop Loss

74


High Level Process for Calculating Budgets

75

Run Risk Adjustment

Models

Run Provider Attribution

Methodology

Calculate Underuse Allowance

(if applicable)

Subtract underuse from Complication

Budgets & add to Typical Budgets

(if applicable)

Apply the Allowance for Complications

Apply the Margin to Typical Budgets

Add the Final Adjusted Typical

Budget to the Final Adjusted Complication

Budget


Budget Calculation for Provider X

Variable Value Typical Budget $20,000 Negotiated Margin 10% Final Adjusted Typical Budget $20,000 + $2,000 = $22,000 Complication Budget $3,000 Negotiated Complication Allowance 90% Final Adjusted Complication Budget $3,000 - $300 = $2,700 Total Budget $22,000 + $2,700 = $24,700

76

Provider X’s budget for a Total Knee Replacement Episode:



Questions?


Risk-Adjustment: Making Bundles Fit Real Patients

Bundled Payment Summit June 16th 2014

Amita Rastogi, MD, MHA, MS

Chief Medical Officer, HCI3


Agenda

•  What is Risk Adjustment ? –  A means to “level” the playing field –  What is risk and what are we modeling - Outcomes –  What are we adjusting for - Risk Factors

•  Risk Models in three Use cases: –  Patient level – “explain” drivers of cost variation –  Provider level - “Apples to Apples” comparison for

provider Performance Measurement –  Payers - Help create Fair Budgets based on

expected resource use due to severity of patient

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Level the playing field….

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•  Demographics: age, gender, educational status •  Comorbidities: other illnesses •  Severity of illness: current illness

Avoids selecting only healthy patients aka “cherry-picking”

- but my patients are “sicker”


•  What is risk? –  For physicians: Clinical Risk, e.g., risk of mortality,

hospitalizations, ER visits, complications –  For payers: Financial Risk, e.g., risk of increased costs,

resource use

•  Separate risk adjustment models should be created for different outcomes –  High mortality may be associated with low resource use –  Predictors of clinical risk may be different than predictors

of high resource use

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What are we modeling? - outcomes


Sources of Variations

•  Patient outcomes could vary based on a variety of factors: –  Patient factors – age, gender, socioeconomic factors,

education level, health conscious, self-care, patient compliance, social support, other comorbid illnesses, patient debility, severity of present illness

–  Geographic factors – proximity to health care, hours of operation, other access issues

–  Insurance – affordability, level of coverage, fee schedules –  Hospital factors – pre-set processes of care, protocols,

pathways, teaching facility –  Provider practice patterns, types and quantity of services,

ownership of diagnostic facilities, referral network –  Provider competence and expertise

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Modeling for Risk Adjustment

•  Science vs. Art –  Science: Modeling for risk adjustment is the easy part – many

statistical programs can do this –  Art: What goes into the models is where the secret sauce lies

•  What to adjust for: –  Adjust for factors that “cannot” be controlled by providers

(certain patient factors) –  This reveals variability due to provider factors (discretionary

services, practice patterns, quality issues, complications) –  Best is to create a patient profile based on historic

demographic factors and comorbidities to minimize gaming and perverse incentives or unintended consequences

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Avoid unintended consequences

•  What “not” to adjust for: •  We should not adjust for factors that will “adjust away”

sources of variation that should really be revealed •  If we use complications as risk factors, we will adjust

away the differences in costs due to complications

•  We should be careful we do not create “perverse incentives” for providers: –  Site of care (e.g. cause shift from outpatient to inpatient) –  Procedures: may shift care to more financially lucrative

treatments or procedures, instead of managing medically

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Setting the Regression Model (1)

•  Unit of inference: “Episode-of-care” •  We determine the study period for the costs to be

analyzed – e.g., episode time window •  Additionally, we define the boundaries of the services

that are included in our analysis e.g., if we are looking at costs related to CAD, we have to define what services are relevant to CAD and create the entire episode of CAD first

•  Dependent Variable: “Allowed Amounts” / Costs •  We take the relevant costs ”during” the study period

to create the dependent variable

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Setting the Regression Model (2)

•  Independent Variables: Risk Factors •  We take the risk factors from say,12 months period

“prior” to the study period to identify historic patient level factors

•  Unit of Analysis: Components of an Episode •  We may decide we want to calculate expected costs

for typical and reliable care separately from costs of complications; and facility costs separately from others

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Analyzing different components separately

87 87 87

Hospitalizations

Key:

Claims for typical care and services

Claims with potentially avoidable complications (PACs)

Begin End

Professional services, including Labs, DME and Rx

Inpatient Stays

One Year from the trigger claim

ER visits

Inpatient Professional


Use Case 1: Patient level analysis “Explain” drivers of variation


Regression Coefficients/ Estimates

•  The regression model generates coefficients for each risk factor and shows their contribution towards the dependent variable, e.g., costs

•  In linear regression cost models, these coefficients represent dollar values and are simply additive

•  The intercept is a hypothetical parameter and represents the base population and all the costs unexplained by the risk factors

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Regression Model: Interpretation •  A standard linear regression equation is as below:

Mean (Y|X) = β0+β1X1 + β2X2 + βnXn + … + ε

CAD Example: Let us assume our model coefficients are as below: Expected Cost of CAD = 315 + -37*(Female) + 51*(Age)+ 250*(Diabetes)

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Parameters Value Interpretation / Explanation

Intercept (β0) 315 Base Cost of CAD Care is $315 in a Male with hypothetical age = 0 & no Risk Factors

Gender (Female = 1) -37 Cost of CAD care is $37 lower in Females than in

Males, holding all other risk factors constant

Age 51 For every rise in age by 1 yr, the cost of care of CAD increases by $51

Diabetes 250 Cost of care of CAD is $250 more in diabetics than others, holding all other risk factors the same


Use Case 2: Physician Performance Measurement


Calculating Predicted Costs •  Predicted costs can be calculated for each patient by

simply inserting the values of each risk factor in the equation below (this technique is called scoring): Expected Y = β0+ β1X1 + β2X2 + β3X3

Continuing the CAD Example: Expected Cost of CAD = 315 + -37*(Female) + 51*(Age)+ 250*(Diabetes)

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Intercept β0

Female β1

Age β2

Diabetes β3

Cost of CAD care (Y)

315 -37 51 250 Patient 1 0=No 50 0=No $2,865 Patient 2 0=No 75 1=Yes $4,390 Patient 3 1=Yes 90 1=Yes $5,118


Developing a Case Mix Index / Risk Score for each provider •  Expected values for all patients on a physician’s panel can be

aggregated and compared to the average expected values across all providers to give us the case mix index for each physician

•  The Case-mix index or Risk Score is used to adjust physician’s actual costs to create risk-adjusted costs for each physician

CAD Example:

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Expected Costs per 100

patients

Case-Mix Index / Risk

Score

Actual Costs per 100 patients

Risk-Adjusted Costs per 100

patients

Provider A $1,000,000 2.00 $920,000 $460,000 Provider B $250,000 0.50 $295,000 $590,000 Provider C $400,000 0.80 $352,000 $440,000 Population Average $500,000 1.0 $500,000


Calculating Performance Scores

•  Performance scores expressed as a ratio of the average risk-adjusted costs across the entire sample gives the physician’s risk-adjusted performance

CAD Example:

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Risk-Adj Costs per 100

patients

Performance Score

Costs Relative to Total (%)

Provider A $460,000 0.90 -9.8% (better) Provider B $590,000 1.15 +15.6% (worse) Provider C $440,000 0.86 -13.7% (better) Population $510,000 1.00 Average


Use Case 3: Payer - Budget Creation


Creating a Budget

•  When budgets are created for providers, we want to make sure: – They are created prospectively – Budgets take into account patient’s severity –

use expected costs from a risk-adjustment model for the physician’s patient panel to create their budgets

–  Incentives are built in for the right provider behavior – e.g., reduce waste (ER visits, hospitalizations, complications), reduce underuse etc.

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Summary & Best Practices


A Complete Model

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Typical ComplComplication-Indicator 0 1 Typical Compl Typical Compl Typical ComplIntercept 315.00 156.35 1 1 1 1 1 1Female 937.00 72.54 0 0 0 0 1 1Recent-Enrollee 150.65 20.21 1 1 0 0 1 1Age- 51.00 68.24 50 50 75 75 90 90Diabetes 250.00 65.23 0 0 1 1 1 1Diabetes,-poor-control 779.95 990.81 0 0 0 0 1 1Hyertension 663.93 16.56 1 1 1 1 1 1Hyperlipidemia 9216.39 12.11 1 1 0 0 1 1Obesity 504.88 261.23 1 1 0 0 1 1

3,968$- 3,878$- 5,054$- 5,356$- 7,001$- 7,737$-1.00 0.20 1.00 0.30 1.00 0.50

3,968$- 776$---- 5,054$- 1,607$- 7,001$- 3,868$-

Patient-3

Expected-CostsProbability-of-UseExpected-Costs-Conditional-on-Use

Patient-1 Patient-2

Model Coefficients explain the influence of each risk factor on costs

Expected costs are used for performance measurements, and for creating budgets


Summary: Use Cases •  Patient level Analyses: explain “determinants” of costs

–  Determine contribution of each risk factor towards expected costs

•  For Performance Measurement: –  Using risk models, determine each patient’s severity score –  Aggregate severity scores of patients on a provider’s panel –

determine case mix index of provider –  Adjust actual costs by case mix index to get risk-adjusted costs

•  Budget Creation: –  Calculate expected costs for typical care –  Calculate expected costs of complications –  Aggregate expected costs for entire episode –  Use expected costs for entire patient panel to create budgets:

•  Expected adjusted typical costs + Underuse Allowance + Expected adjusted PAC allowance + Allowance for SRFs + Margin

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Best practices in Risk Adjustment

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•  Risk adjust separately typical and complication costs –  Expected costs can be handled separately for performance

measurement and for budget purposes

•  Risk-Adjustment specific for user’s data: –  Best is to build severity scores from the user’s own data –  Reflects their own specific fee schedules and practice patterns –  Do not base them on a reference population where coefficients

may not reflect different patterns of use and cost

•  Risk adjustment on a continuum: –  Best is to build severity scores on a patient-by-patient basis

along a continuum –  Do not create arbitrary strata and force patients into risk strata


Assessing Quality: Pursuing Excellence in Care

Jessica DiLorenzo, MA Program Implementation Leader, HCI3



Why Assess Clinician Performance?

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Why Assess Clinician Performance?

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Whats’ in It for the Clinician?

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Creating an Assessment Program: to-do list ü  Nationally Endorsed Measures ü  Meaningful and Comprehensive

Measures ü  Clinical Data & Full Patient Panel ü  Neutral & Objective Performance

Assessor ü  Electronic Data Sources ü  Timely Feedback Report ü  Publicly Reported

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Choosing Meaningful Clinical Indicators •  Start with an inventory of established

measures that have been tested, validated and approved

•  Sources to reference: –  National Quality Forum (NQF) –  The National Committee for Quality Assurance

(NCQA) –  AQA Alliance –  American Medical Association (AMA) –  Professional Associations, Societies and Colleges

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Partnering with Professional Organizations •  Mission alignment •  Professional Organization’s Role:

– Convenes Clinical Quality Committee – Data aggregator or registry – Participant recruitment – Advises on scoring calibration – Supports Program participant recruitment

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Use Measures that Matter

•  Process measures have very low relationships to decreasing costs of care (they actually increase costs because they’re tied to production of CPTs)

•  Intermediate outcome measures have a much stronger effect

•  We calibrated our scoring on these findings

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Towers Perrin analysis of actuarial savings

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Economic value of blood pressure management

1.  Burt, V., et al.(1995). Prevalence of Hypertension in the US Adult Population Results From the Third National Health and Nutrition Examination Survey, 1988-1991. Hypertension 25(3):305-313.

Haroun, M., et al. (2003). Risk Factors for Chronic Kidney Disease: A Prospective Study of 25,534 Men and Women in Washington County, Maryland. Journal of American Society of Nephrology 14(11): 2934–2941.

2.  Haroun, M., et al. (2003). Risk Factors for Chronic Kidney Disease: A Prospective Study of 25,534 Men and Women in Washington County, Maryland. Journal of American Society of Nephrology 14(11): 2934–2941.

Joyce, A., et al. End-Stage Renal Disease-Associated Managed care Costs Among Patients With and Without Diabetes. Diabetes Care 2004; 27:2829–2835. US Census Bureau (2003). Statistical Abstract of the United States. http://www.census.gov/prod/www/statistical-abstract-2001_2005.html. Accessed February 29, 2008. 3.  Burt, V., et al.(1995). Prevalence of Hypertension in the US Adult Population Results From the Third National Health and Nutrition Examination Survey, 1988-1991.

Hypertension 25(3):305-313. Wilson, P., et al. (1998). Prediction of Coronary Heart Disease Using Risk Factor Categories. Circulation. 97(18):1837-1847. 4.  MedStat 2005 data inflated to 2006. 5.  Burt, V., et al.(1995). Prevalence of Hypertension in the US Adult Population Results From the Third National Health and Nutrition Examination Survey, 1988-1991.

Hypertension 25(3):305-313. 6.  MedStat 2005 data inflated to 2006. 7.  Treatment was a straight average of supply costs for Enalapril 10mg daily, Hydrochlorothiazide 25mg daily, and Metoprolol 25mg three times a day.

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What isn’t Measured Doesn’t Improve

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The savings come from fewer PACs

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Colorado Health Matters 2011/2012 Newsletter

Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc. 113

BTE Recognition Programs

•  Collection of nationally endorsed and/or guidelines informed measures which together delineate high quality care delivery for patients

•  Developed in collaboration with physician experts and healthcare leading organizations

•  Focus quality accountability on the improvement of intermediate outcomes and better adherence to good processes, measuring the effects of proper management of patients and the delivery of good results


BTE Recognition Programs •  Currently available

–  Asthma –  Cardiovascular program in collaboration with American College

of Cardiology –  Coronary Artery Disease –  Congestive Heart Failure –  COPD –  Diabetes –  Hypertension –  IBD Program in collaboration with the American

Gastroenterological Association –  IVD/Stroke –  Depression – In development –  Medical Home


BTE Program Features Measures •  Mix of intermediate outcome measures and process

measures, with greater weight placed on outcomes.

•  Intermediate outcome measures include metrics focused on optimal control (superior control) and reduction of poorly controlled patients (poor control), with greater weight placed on the poor control measures

•  Poor control measures recognize greater required efforts to treat and manage sickest patients even if optimal goals can not be met

•  Levels of recognition to encourage and distinguish achievement.


Diabetes Care Program Clinical Measures Thresholds Min/Max Criteria Level 1 Level 2 Level 3

Poor Control Measures

HbA1c Control > 9.0 ≤ 27.5% of pt sample 15 40 40

BP Control ≥ 140/90 ≤ 40% of pt sample 15

LDL Control ≥ 130 mg/dl ≤ 40% of pt sample 10

Superior Control Measures

HbA1c Control < 7.0 ≥ 40% of pt sample 5 5 5

HbA1c Control < 8.0 ≥ 40% of pt sample 5 5 25

BP Control < 130/80 ≥ 30% of pt sample 10 10

LDL Control < 100 mg/dl ≥ 35% of pt sample 10 10

Process Measures

Ophthalmologic Exam N/A N/A 10 10 10

Nephropathy Assessment N/A N/A 5 5 5

Podiatry Exam N/A N/A 5 5 5

Smoking status and cessation advice and tx

N/A N/A 10 10 10

Total Possible Points 100 100 100

Points to Pass 60 60 60

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•  Level I: –  Focuses on a physician-centric view of measurement –  Individual metrics summed to produce a composite score –  Inclusion of “minimum” performance requirements for all intermediate

outcome control measures, both poor and superior (i.e., BP control and LDL control).

–  Thresholds have been set to focus on above average performance. •  Level II:

–  Focuses on a combination of physician and patient-centric measurements. –  Level II includes the measurement of individual metrics summed to produce

a composite score, with the inclusion of “minimum” performance requirements for all intermediate outcome superior control measures.

–  Defect rate of care delivery across poor control measures on a per patient basis.

–  Thresholds have been set to focus on very good performance. •  Level III:

–  Focuses on patient-centric view of measurement –  Defect rate of care delivery across superior control measures on a per

patient basis. –  Physicians must demonstrate that they are using advanced processes and

delivering all the right care on a per patient basis. –  Thresholds have been set to focus on exceptional

Diabetes Care – Levels

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•  Continuous scoring methodology •  Measure score is a function of the applicant’s actual performance on

individual measures

e.g. 30% (Num/Den) compliance on BP <130/80 measures

earns 30% of max allotted points for the measure

•  Continuous points distribution with an opportunity for partial credit

•  Score tied to actual performance on measures

•  Scoring strategy reduces the incentive for patient dumping

•  60 points of a possible 100 needed to achieve recognition at each level

Diabetes Care - Scoring

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Diabetes Recognition Scoring Example

The majority of the points are focused on reducing poor control and, secondarily, optimizing control. For every next patient that is well managed, the physician gets

additional points. 119


Data Sources

•  Administrative Data –  Not intended for

performance measurement

–  Retrospective look back

–  Not actionable –  Limited to process

measures –  Not representative of

patients health outcomes

•  Clinical Data –  Intermediate outcome

data available –  Timelier –  Reporting flexibility and

comparative reporting –  Leverage electronic

data sources –  Population

management –  Treatment gaps are

actionable

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Manual Chart Review and Extraction

•  30 charts per clinician •  1890 charts needed to be pulled

and reviewed •  Labor time to pull charts

•  30 charts per hour = 63 hours •  $10 labor time/hour = $630

•  Labor time for chart review •  15 minutes/chart = 473 hours •  $25 labor time/hour = $11,825 •  $12,455 total cost

Electronic Data Submission •  15 minutes to run electronic data •  2 hours of internal validation •  4 hours for external BTE

certification of data after BTE assessment

•  Labor costs •  $22 labor time for 6.25 hrs •  $156.25 total cost

What is the advantage of EMR submission to a practice?

Cost comparison: example from the field


Manual Chart Review and Extraction

•  Point in time audit – once per every 2 years

•  Only reflects care of 30 charts per clinician

•  No method of proactive management

•  Impact limited due to resource demand

Electronic Data Submission •  Quarterly submission and review •  Includes ALL patients seen with

diabetes during a period •  Reports available for real time

population management and patient level management

•  Can extend patient impact through all BTE chronic care programs

What is the advantage of EMR submission to a practice?

Clinical comparison: example from the field


EMR’s as the data aggregator

Diabetes Asthma HTN Cardiac CAD CHF

Athena health

✔

In process ✔

eCW ✔

✔

In Process ✔

In Process

NextGen ✔

In Process ✔

✔

Meridios ✔

✔

In Process ✔

In Process

Meditab ✔

Forward Health

✔

✔

EPIC Clients

✔

✔

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It is a “win-win” for EMR and Clinician •  National and Regional health plan

rewards and recognitions •  Quarterly data submission – frequent

feedback reports, track progress over time

•  EMR’s get standard set of specifications, file format, validation, data integrity checks

•  Measure alignment with PQRS 124


Performance Assessment •  Neutral – third party objective organization •  No conflict of interest •  Standard set of measures and criteria

regardless of location, specialty or patient mix

•  Minimum of 25 patients •  Individual or group assessment •  Conducts Audits •  Confidential results and feedback report

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Current results

Reporting Period

Levels of Assessment

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Measure Scoring detail: •  N/D results •  Points •  Drill down for

each level

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Gaps in

Care

Patient Outliers

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Scaling Bundled Payment: The Great Legacy Barrier (with wrap up)

Doug Emery, MS Program Implementation Leader, HCI3



Recap •  Prospective budgeting widens the network contacting

opportunity by allowing both retrospective and prospective BP

•  Good RA levels the playing field and also widens the opportunity space by capturing a greater percentage of eligible patients

•  Balancing efficiency (i.e., PAC rate reduction) with effectiveness allows us to create a powerful two-channel feedback system leading to global system transparency – this has never existed in US healthcare

•  Big Takeaway: Coupled with the range of data analytics, payers can now play a new and powerful role: infomediary (if they so chose)

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Scaling Inputs, Processes & Outputs

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The HCI3 Learning Center •  An RWJ sponsored TTT program for AF4Q

sites, but available to all •  An undergraduate-style curriculum that breaks

all this implementation material into 100, 200, 300 series modules (downloadable from HCI3 website) with graduate level hands-on workshops

•  Will build in a testing and certification program •  First tranche of LC course release by the end

of June

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HCI3 Learning Center: Training the Trainer




Documents

Implementing a Bundled Payment ProgramPayment Program Bundled Payment Summit June 16th, 2014 Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc. Agenda •