jonathan weiner: risk adjustment opportunities and challenges: us and uk experiences

© Copyright 2011 Johns Hopkins University,.

Risk adjustment opportunities and challenges:

US and UK experiencesProfessor Jonathan P. Weiner

Johns Hopkins Bloomberg School of Public HealthBaltimore Maryland, USA

[email protected]

Presented at Nuffield Trust Conference, London, 29/6/11

mailto:[email protected]�

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© Copyright 2011, Johns Hopkins University,.

A bit about me

Jonathan Weiner, DrPHFrom 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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During the next 25 minutes I will offer some insights into:

• 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.

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Risk adjustment is necessary because not all persons have the same need for health care

Percent of Population

Percent of Health Care Resources

Consumed

1% 30%10% 70%50% 97%

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Working Definitions

• 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.

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Co-Morbidity is key – Multiple morbidities encountered in UK GP practices

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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17%

12%

11%

9%

24%

22%

21%

22%

23%

23%

25%

21%

20%

22%

24%

21%

16%

21%

19%

27%

0% 20% 40% 60% 80% 100%

Hypertension

Arthritis

Heart Disease

Diabetes

Single Condition Condition + 1 Condition + 2 Condition + 3 Condition + 4+

Source: From US Medicare (65+) data . Partnership for Solutions, Johns Hopkins University

Co-morbidities are the norm for those with common “index” chronic conditions (US 65+)

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Co-morbidities are central to understanding resource use: ACG risk levels and patterns of

resource use at an English PCT

Level of Co-morbidity (Based on ACGs)

% of PCT’sPop.

Hospital Use

Relative Ratio

Est. % of Admissions

at PCT

Avg. # Out-

Patient Episodes

/ Yr.

Avg. # Prescriptions / 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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Johns Hopkins ACGs - 1

• 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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Johns Hopkins ACGs - 2

• 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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Key components of the Johns Hopkins ACG System (www.acg.jhsph.edu)

DiagnosisRead- ICD 9 - ICD 10 - ICPC

Patient InfoID – Age – Gender –

Resource Use

PharmaceuticalsATC, Read, BNF

ACG System

ACGs102 / 6

EDCs220 / 30

MarkersFrailty – Hosdom –

Chronic Pregnancy - Delivery

PredictiveModels

ADGs32

Rx-MG60

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ACG System Use in the NHS (Currently 6+ million pts. across 12 PCTs)

• 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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Case-Management

The Risk Adjustment “Application Pyramid”

Disease Management Practice

ResourceManagement

HighDisease Burden

Single High Impact Disease

Users

Users & Non-Users

Population Segment

NeedsAssessment

Quality Improvement

Payment/Finance

Management Applications

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Financing, Payment, Planning Morbidity-adjusted

capitation Allocation of budgets Service targets Forecasting healthcare

spending Provider Performance

Assessment Profiling Pay-for-Performance

Care Management Identification of high risk

patientDisease managementCase management

Quality Quality assessment

Quality monitoring Research and Program

Evaluation

Types of risk adjustment applications within health care

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FINANCING, PAYMENT, RESOURCE PLANNING

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Resource Use Varies by Risk of GP Patients: NHS Consultant Referral Rates by Morbidity Score

50

40

30

2010 0.5 1.0 1.5 2.0 2.5

Source: Forrest, Majeed, Weiner, et al. BMJ 2002: 325;370

% Patients referred per year to one or more consultants

ACG Morbidity Score

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

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

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Source: Minnesota Department of Health 2010. PMPM = per member per month. Patient complexity tiers based on ACG/EDC categories.

State of Minnesota “Health Care Home” program pays a monthly care coordination fee based on 5 patient complexity tiers

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Maryland Medicaid risk adjusted (ACG) payment to capitated “HMO” health plans

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.

AverageRisk

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Some potential financial applications within context of English PCTs, GPCCs, Clusters, GP practices (etc.)

• 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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PROVIDER PERFORMANCE ASSESSMENT

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Risk-Adjusted O/E (Efficiency) Profiling Ratios for GPs Across a Primary Care Trust (PCT) in UK

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

GP1 GP2 GP3 GP4 GP5 GP6 GP7 GP8 GP9 GP10 GP11 GP12No 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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Comparison of Risk Adusted (ACG) and Age/Gender-based O:E Ratios for GPs across Province of British Columbia Canada

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CARE MANAGEMENT AND HIGH RISK PATIENT

IDENTIFICATION

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Using risk stratification to target disease management program participation for chronic conditions

% Enrollees in ACG Risk Category

Resource Use of Cohort Relative to Total Population

Condition of Interest

Low Med. High Low Med. High

Diabetes 44.97 42.1 11.9 1.34 4.90 7.44

Congestive Heart Failure

19.75 53.5 26.75 1.14 6.02 7.93

Tier 1 Tier 2 Tier 3

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

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

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Possible applications of PM / RA for improving care & accountability in England

• 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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SOME ISSUES AND OBSERVATIONS

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Data Accuracy Issues: Condition identification using GP diagnosis and pharmacy data from EMRs. Also comparison to US results using 1º + 2º provider claims.

Condition% UK and US

patients identified

(ICD or pharm.)

% UK patients

identified by GP

diagnoses*

% UK patients uniquely

identified by GP

prescriptionsHypertension 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.

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NHS Reform will lead to opportunities for expanding the use of risk adjustment and predictive modeling tools

• 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.

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There will also be challenges related to the application of RA/PM

• 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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Need for risk adjustment is universal, but of course all of this will be impacted by future polices in both nations

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For more information on Johns Hopkins experiences and tools

• Johns Hopkins web site:– www.acg.jhsph.edu

• Contacts:– Dr. Karen Kinder: Director, ACG International

(Based in Germany) [email protected]

– Steve Sutch: Senior ACG Consultant (based in England) [email protected]

http://www.acg.jhsph.edu/�

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jonathan weiner: risk adjustment opportunities and challenges: us and uk experiences

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