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Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
Implementing a Bundled Payment Program
Bundled Payment Summit
June 16th, 2014
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
Managing Dyads – Layering Scope
Orthopedic Bundles
Coronary Bundles
Condition Bundles (PCMH)
Master Scope
8
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
For contact information: www.HCI3.org www.bridgestoexcellence.org www.prometheuspayment.org
Questions?
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
Data Preparation: Clearing the Pathway for Analytics
Bundled Payment Summit June 16 2014
Jenna Slusarz, Program and Operational Support, HCI3
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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'
236 CORONARY BYPASS W/O CARDIAC CATH W/O MCC 3611 '44' 'CABG ' 41041 '100' 'Acute MI'
236 CORONARY BYPASS W/O CARDIAC CATH W/O MCC 3611 '44' 'CABG ' 41071 '100' 'Acute MI'
236 CORONARY BYPASS W/O CARDIAC CATH W/O MCC 3611 '44' 'CABG ' 41091 '100' 'Acute MI'
• DRG matches PX, doesn’t take DX into account
18
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
For contact information: www.HCI3.org www.bridgestoexcellence.org www.prometheuspayment.org
Questions?
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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 = $91 million 14% of total episode costs
Simulated savings at 80th percentile with 98th percentile stop loss
31
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
Analyzing episodes of care: Price and Quality Transparency ü Analysis of episodes can help inform:
ü Policy decisions • What kinds of reforms will be most effective? • Where should those reforms be focused? • What is the potential for savings/improvement?
Ø Provider price and quality transparency • Who are the most efficient and highest quality providers?
– Provider process improvement • How do I compare to my peers on cost and quality? • Which patients experienced defects in care and when?
32
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
Substantial variation in costs/PACs across PCPs treating chronic care patients
+238%
0%#
5%#
10%#
15%#
20%#
25%#
30%#
35%#
40%#
45%#
#$*####
#$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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
• 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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
For contact information: www.HCI3.org www.bridgestoexcellence.org www.prometheuspayment.org
Questions?
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
“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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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)
Inpatient Outpatient Professional Pharmacy
Inpatient Outpatient Professional Pharmacy
• Accountable Care Organizations • Care coordination of complex patients • Population health/use assessments • Performance reporting
42
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
Super-Utilizers (HCI3)
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
Background
• Dr. Jeff Brenner and the “Frequent Flyers” • High utilizers of ER and admissions
45
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
Who are the major users?
Top 0.05% of ED Users
Top 0.1% of IP Users
50
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
Total Cost of Care (TCOC) (HealthPartners)
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
For contact information: www.HCI3.org www.bridgestoexcellence.org www.prometheuspayment.org
Questions?
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
Recap and Break
Bundled Payment Summit June 16, 2014
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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
Proprietary & Confidential. Health Care Incentives Improvement Institute, Inc.
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!
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Budget Creation: Putting Numbers to Bundles for
Contracting
Bundled Payment Summit June 16, 2014
Elizabeth Bailey, MPH
Program Implementation Leader, HCI3
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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
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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.
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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
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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
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Final Budget
Expected Cost of
Typical Care
Expected Cost of
Complications
Underuse Allowance
Complication Allowance
Margin
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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)
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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
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Tunable Budget Parameters
• Allowance for Complications • Margin • Stop loss provisions
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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
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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
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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
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The Donut Hole P
erce
nt o
f Bun
dle
Pric
e 100%
Risk
Payer Risk
Payer Risk
Provider Risk – “Donut Hole”
Stop Loss
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High Level Process for Calculating Budgets
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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
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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
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Provider X’s budget for a Total Knee Replacement Episode:
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For contact information: www.HCI3.org www.bridgestoexcellence.org www.prometheuspayment.org
Questions?
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Risk-Adjustment: Making Bundles Fit Real Patients
Bundled Payment Summit June 16th 2014
Amita Rastogi, MD, MHA, MS
Chief Medical Officer, HCI3
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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”
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• 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
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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
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Use Case 1: Patient level analysis “Explain” drivers of variation
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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
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Use Case 2: Physician Performance Measurement
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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
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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
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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 $510,000 1.00 Average
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Use Case 3: Payer - Budget Creation
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Creating a Budget
• When budgets are created for providers, we want to make sure: – They are created prospectively – Budgets take into account patient’s severity –
use expected costs from a risk-adjustment model for the physician’s patient panel to create their budgets
– Incentives are built in for the right provider behavior – e.g., reduce waste (ER visits, hospitalizations, complications), reduce underuse etc.
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Summary & Best Practices
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A Complete Model
98
Typical ComplComplication-Indicator 0 1 Typical Compl Typical Compl Typical ComplIntercept 315.00 156.35 1 1 1 1 1 1Female 937.00 72.54 0 0 0 0 1 1Recent-Enrollee 150.65 20.21 1 1 0 0 1 1Age- 51.00 68.24 50 50 75 75 90 90Diabetes 250.00 65.23 0 0 1 1 1 1Diabetes,-poor-control 779.95 990.81 0 0 0 0 1 1Hyertension 663.93 16.56 1 1 1 1 1 1Hyperlipidemia 9216.39 12.11 1 1 0 0 1 1Obesity 504.88 261.23 1 1 0 0 1 1
3,968$- 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
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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
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Assessing Quality: Pursuing Excellence in Care
Jessica DiLorenzo, MA Program Implementation Leader, HCI3
Bundled Payment Summit June 16, 2014
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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
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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
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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
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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.
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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
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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
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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
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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
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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
127
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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
Bundled Payment Summit June 16, 2014
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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
Bundled Payment Summit June 16, 2014
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For contact information: www.HCI3.org www.bridgestoexcellence.org www.prometheuspayment.org
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