1. Risk adjustment opportunities
and challenges:
US and UK experiences
Professor Jonathan P. Weiner
Johns Hopkins Bloomberg School of Public Health
Baltimore Maryland, USA
Jweiner@jhsph.edu
Presented at Nuffield Trust Conference, London, 29/6/11
© Copyright 2011 Johns Hopkins University,.

2. A bit about me 2
Jonathan Weiner, DrPH
From The Johns Hopkins
University
 Professor of Health Policy &
Management
 Professor of Health
Informatics
 CEO of the Johns Hopkins
ACG R&D Team, based at
University
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3. During the next 25 minutes I will offer some
insights into: 3
• The conceptual domains of risk adjustment / case-mix
/ predictive modeling.
• The Johns Hopkins ACG risk adjustment / predictive
modeling method.
• Experiences with risk adjustment applications in the
US & UK related to budgeting / financing and beyond.
• Some issues, opportunities and challenges
associated with risk adjustment in the English primary
care context.
© Copyright 2011, Johns Hopkins University,.

4. Risk adjustment is necessary because not all
persons have the same need for health care 4
Percent of Health
Percent of
Care Resources
Population
Consumed
1% 30%
10% 70%
50% 97%
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5. Working Definitions 5
• Case mix / risk adjustment (RA) - taking
health status / risk into consideration for health
care finance, payment, provider performance
assessment and patient outcome monitoring.
• Predictive modeling (PM) - prospective (or
concurrent) application of risk measures and
statistical technique to identify “high risk”
individuals who would likely benefit from care
management interventions.
5
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6. Co-Morbidity is key – Multiple morbidities
encountered in UK GP practices 6
Average consultation in elderly involves someone with
1.9 QOF diseases and 6.7 chronic diseases using
ACG/EDC chronic disease designations
Source: Salisbury et al. From GPRD data, 488 practices 2005-2008
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7. Co-morbidities are the norm for those
with common “index” chronic conditions 7
(US 65+)
Diabetes 9% 22% 21% 21% 27%
Heart Disease 11% 21% 25% 24% 19%
Arthritis 12% 22% 23% 22% 21%
Hypertension 17% 24% 23% 20% 16%
0% 20% 40% 60% 80% 100%
Single Condition Condition + 1 Condition + 2 Condition + 3 Condition + 4+
Source: From US Medicare (65+) data . Partnership for Solutions, Johns Hopkins University
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8. Co-morbidities are central to understanding
resource use: ACG risk levels and patterns of 8
resource use at an English PCT
Level of Co- % of Hospital Est. % of Avg. # Avg. #
morbidity PCT’s Use Admissions Out- Prescripti
(Based on Pop. Relative at PCT Patient ons / Yr.
ACGs) Ratio Episodes
/ Yr.
High 2% 11.5 25% 11.0 93
Moderate 17% 3.0 47% 7.1 66
Low 40% .6 26% 3.0 28
None 41% >.1 2% .5 6
Data from several large GP practices within PCT for 2005. N= 20,500 all ages.
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8

9. Johns Hopkins ACGs - 1 9
• One of first case-mix / risk adjustment method for
categorizing diagnosis codes outside of hospitals.
• 30 years of ongoing R&D at Johns Hopkins.
• Original version based on primary care morbidity
patterns (ADGs) developed by Prof. Barbara Starfield.
• Billions of dollars per year are now routinely
exchanged using ACGs in US, Canada, Spain,
Sweden and other nations.
• Care of 80+ million patients is budgeted, managed and
monitored using ACGs in 16+ nations.
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10. Johns Hopkins ACGs - 2 10
• Comprehensive measure of a population’s risk and
morbidity burden. They do not just categorize
organ system-based diseases.
• Roots were primary care / population based.
• New collaboration with WONCA to integrate ACGs
with ICPC.
• Method owned and maintained by University.
• There is a comprehensive computerized suite of
ACG measures using most international diagnosis
and pharmacy codes.
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11. Key components of the Johns Hopkins ACG
System (www.acg.jhsph.edu) 11
ADGs
32
Patient Info
ID – Age – Gender –
Resource Use ACGs
102 / 6
Diagnosis ACG EDCs Predictive
Read- ICD 9 - ICD 10 - ICPC
System 220 / 30 Models
Markers
Frailty – Hosdom –
Chronic
Pharmaceuticals Pregnancy - Delivery
ATC, Read, BNF
Rx-MG
60
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12. ACG System Use in the NHS
(Currently 6+ million pts. across 12 PCTs) 12
• 2001 - Academic research at UCL and Imperial
using GPRD data.
• 2005 – Initial pilot project with 3 PCTs (w/ Imperial)
• 2007 – Collaborated with King’s Fund on the Person
Based Resource Allocation (PBRA) project
• 2008 – Ashton, Leigh, and Wigan PCT
• 2009 –North Yorkshire and York PCTs
• 2010 – South Central – 9 PCTs (150 practices with
uptake increasing)
• 2011- Sutton & Merton PCT
• Universities: Imperial College, UCL, Bristol,
Manchester, Glasgow.
• Several academics groups and many other PCTs /
NHS organizations have expressed interest.
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13. The Risk Adjustment “Application
Pyramid” 13
Management Applications
High Case-
Disease
Management
Burden
Disease Needs
Single High Management Practice
Assessment
Impact Resource
Disease Management
Quality
Improvement
Users Payment/
Finance
Users & Non-Users
Population Segment
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14. Types of risk adjustment
applications within health care 14
 Financing, Payment,  Care Management
Planning  Identification of high risk
 Morbidity-adjusted patient
capitation  Disease management
 Allocation of budgets  Case management
 Service targets  Quality
Forecasting healthcare  Quality assessment
spending  Quality monitoring
 Provider Performance  Research and Program
Assessment
Evaluation
 Profiling
 Pay-for-Performance
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15. 15
FINANCING, PAYMENT,
RESOURCE PLANNING
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16. Resource Use Varies by Risk of GP Patients: NHS
Consultant Referral Rates by Morbidity Score 16
50
40
% Patients
referred per
year to one
30
or more
consultants
20
10
0.5 1.0 1.5 2.0 2.5
ACG Morbidity Score
Source: Forrest, Majeed, Weiner, et al. BMJ 2002:
325;370
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16

17. Morbidity Risk Scores (RUBs) Across
GP practices in a PCT

18. And these risk levels vary across communities
served by practices in this PCT

19. State of Minnesota “Health Care Home” program pays
a monthly care coordination fee based on 5 patient
complexity tiers
19
Source: Minnesota Department of Health 2010.
PMPM = per member per month.
Patient complexity tiers based on ACG/EDC categories.
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20. Maryland Medicaid risk adjusted (ACG) payment to
capitated “HMO” health plans 20
Average
Risk
Using ACGs, risk ratios were determined for each contracting managed care organization /
health plan. Expected values were determined separately for the two enrollee groups with this
State Medicaid program.
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21. Some potential financial applications within context of
English PCTs, GPCCs, Clusters, GP practices (etc.) 21
• Setting aspects of budgets for GP Consortia / GP
practices.
• To help GPCCs determine the budgets for downstream
health care commissioning.
• Monitor resource use and adjusting various pay for
performance “P4P” performance measures.
• Special payment to GPs or others for special need
patients.
• Adjusting/stratifying cases or episode payments as part of
commissioning of secondary services.
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22. 22
PROVIDER PERFORMANCE
ASSESSMENT
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23. Risk-Adjusted O/E (Efficiency) Profiling Ratios
for GPs Across a Primary Care Trust (PCT) in UK 23
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
GP1 GP2 GP3 GP4 GP5 GP6 GP7 GP8 GP9 GP10 GP11 GP12
No of referrals No of unique prescriptions / month No of unique radiology tests
Observed = actual avg. use by patients.
Expected = based on ACG case-mix of pts. Above 1.0 = higher than expected.
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24. Comparison of Risk Adusted (ACG) and
Age/Gender-based O:E Ratios for GPs across 24
Province of British Columbia Canada
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25. 25
CARE MANAGEMENT AND
HIGH RISK PATIENT
IDENTIFICATION
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26. Using risk stratification to target disease management program
participation for chronic conditions
26
% Enrollees in ACG Risk Resource Use of Cohort
Category Relative to Total Population
Condition of Low Med. High Low Med. High
Interest
Diabetes 44.97 42.1 11.9 1.34 4.90 7.44
Congestive
Heart 19.75 53.5 26.75 1.14 6.02 7.93
Failure
Tier 1 Tier 2 Tier 3
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26

27. Long-term conditions for GP practice by RUB co
morbidity strata

28. Care Management ACG report for a GP’s “high
risk” Patient in GPCC / PCT

29. Possible applications of PM / RA for
improving care & accountability in England 29
• To comprehensively assess the needs of
communities and special populations
• Long-term (chronic) conditions
• Disparities / social need.
• To support “predictive” case finding to improve
coordination and avoid unnecessary hospital and
other types of care.
• By regulators /evaluators to fairly monitor outcomes
and financial performance.
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30. 30
SOME ISSUES AND
OBSERVATIONS
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31. Data Accuracy Issues: Condition identification using GP
diagnosis and pharmacy data from EMRs. Also
31
comparison to US results using 1º + 2º provider claims.
% UK
% UK and US % UK patients
patients patients uniquely
Condition identified identified by identified by
(ICD or GP GP
pharm.) diagnoses* prescriptions
Hypertension 13%, 19% 5.0% 8.1%
Disorders of Lipoid metabolism 5%, 15% 1.5% 3.8%
CHF 3%, 2% .2% 2.3%
Asthma 9%, 10% 4.4% 4.4%
Depression 6%, 10% 1.6% 4.6%
Diabetes 4%, 5% 3.9% .1%
UK results from two PCTs w/2005 data, n=500K, based on ACG’s EDCs and
RxMGs. US results based on 2M health plan sample using both primary and
secondary care claims. *Many pts. had both diagnosis and pharmacy codes.
© Copyright 2011, Johns Hopkins University,.

32. NHS Reform will lead to opportunities for expanding
the use of risk adjustment and predictive modeling
tools 32
• The English reform plans include many shifts in paradigms
that will enable RA and PM tools to have a positive impact
on system efficiency, effectiveness and equity.
• Many innovations are possible in English context that
could allow us to improve the state-of-the-art of risk
adjustment (e.g., social care integration, application of
electronic patient records).
• There is considerable overlap between GPCCs and
aspects of past and current US reform. There are lessons
and synergies in risk adjustment domain and beyond.
© Copyright 2011, Johns Hopkins University,.

33. There will also be challenges related to the
application of RA/PM 33
• Need to integrate risk adjustment / predictive modeling
into dynamically changing (and charged) administrative
and fiscal situation.
• There will be many challenges associated with
integrating and using data that previously had other
purposes.
• Some cautions: be wary of “code creep”, maintaining
zero sum game, negative impact of potential pharmacy
code gaming.
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34. Need for risk adjustment is universal, but of course all of
this will be impacted by future polices in both nations 34
© Copyright 2011, Johns Hopkins University,.
34

35. For more information on Johns Hopkins
experiences and tools 35
• Johns Hopkins web site:
– www.acg.jhsph.edu
• Contacts:
– Dr. Karen Kinder: Director, ACG International
(Based in Germany) kkinder@jhsph.edu
– Steve Sutch: Senior ACG Consultant (based in
England) ssutch@jhsph.edu
© Copyright 2011, Johns Hopkins University,.