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Jonathan Weiner: Risk adjustment opportunities and challenges: US and UK experiences

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  • 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© Copyright 2011, Johns Hopkins University,.
  • 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%© Copyright 2011, Johns Hopkins University,.
  • 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© Copyright 2011, Johns Hopkins University,.
  • 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© Copyright 2011, Johns Hopkins University,.
  • 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© Copyright 2011, Johns Hopkins University,.
  • 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.© Copyright 2011, Johns Hopkins University,. 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.© Copyright 2011, Johns Hopkins University,.
  • 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.© Copyright 2011, Johns Hopkins University,.
  • 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© Copyright 2011, Johns Hopkins University,.
  • 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.© Copyright 2011, Johns Hopkins University,.
  • 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© Copyright 2011, Johns Hopkins University,.
  • 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© Copyright 2011, Johns Hopkins University,.
  • 15. 15 FINANCING, PAYMENT, RESOURCE PLANNING© Copyright 2011, Johns Hopkins University,.
  • 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© Copyright 2011, Johns Hopkins University,. 16
  • 17. Morbidity Risk Scores (RUBs) AcrossGP practices in a PCT
  • 18. And these risk levels vary across communitiesserved 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.© Copyright 2011, Johns Hopkins University,.
  • 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.© Copyright 2011, Johns Hopkins University,.
  • 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.© Copyright 2011, Johns Hopkins University,.
  • 22. 22 PROVIDER PERFORMANCE ASSESSMENT© Copyright 2011, Johns Hopkins University,.
  • 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.© Copyright 2011, Johns Hopkins University,.
  • 24. Comparison of Risk Adusted (ACG) and Age/Gender-based O:E Ratios for GPs across 24 Province of British Columbia Canada© Copyright 2011, Johns Hopkins University,.
  • 25. 25 CARE MANAGEMENT AND HIGH RISK PATIENT IDENTIFICATION© Copyright 2011, Johns Hopkins University,.
  • 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© Copyright 2011, Johns Hopkins University,. 26
  • 27. Long-term conditions for GP practice by RUB comorbidity strata
  • 28. Care Management ACG report for a GP’s “highrisk” 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.© Copyright 2011, Johns Hopkins University,.
  • 30. 30 SOME ISSUES AND OBSERVATIONS© Copyright 2011, Johns Hopkins University,.
  • 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.© Copyright 2011, Johns Hopkins University,.
  • 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,.