Case-Mix and Risk Adjustment in Primary Health Care A tool for equitable budget setting, performance improvement and chronic care management Jonathan P. Weiner, DrPH Professor of Health Policy & Management Johns Hopkins Bloomberg School of Public Health Baltimore, Maryland 21205 USA [email protected] Presented in Lisbon, 29 October 2010 © Copyright 2010, Johns Hopkins University. Goals of Presentation • To introduce concepts surrounding population-based risk adjustment and “predictive risk” modeling in the primary health care context. • To describe the Johns Hopkins “ACG” case-mix adjustment / predictive risk modeling methodology as a method widely used within primary care sector. • To show practical, real-world examples of how these tools can be applied. • To begin to discuss potential application of these methods within the Portuguese context. © Copyright 2010, Johns Hopkins University. 2 Not all persons have the same need for health care 3 % of Population % of Resources 1% 30% 10% 72% 50% 97% © Copyright 2010, Johns Hopkins University. 4 Working Definitions • Case mix / risk adjustment is the process by which the health status of a population is taken into consideration when setting budgets or capitation rates, evaluating provider performance, or assessing outcomes of care. • Predictive risk modeling is the prospective (or concurrent) application of risk adjustment measures and statistical forecasting to identify individuals with high medical need who would likely benefit from care management interventions. © Copyright 2010, Johns Hopkins University. 5 The risk measurement pyramid Management Applications High CaseDisease Management Burden Single High Impact Disease Users Users & Non-Users Population Segment © Copyright 2010, Johns Hopkins University. Disease Management Practice Resource Management Needs Assessment Quality Improvement Payment/ Finance Types of risk adjustment applications within health care  Financing, Payment, Planning  Morbidity-adjusted capitation  Allocation of budgets  Service targets Forecasting healthcare spending Provider Performance Assessment  Profiling    Pay-for-Performance © Copyright 2010, Johns Hopkins University. Identification of high risk patient Disease management  Case management     Care Management  Quality  Quality assessment  Quality monitoring Research and Program Evaluation 6 7 OVERVIEW OF THE JOHNS HOPKINS ACG SYSTEM © Copyright 2010, Johns Hopkins University. Overview of Johns Hopkins ACG System • The ACG - “Adjusted Clinical Group” system provides conceptually simple, statistically valid, and clinically relevant measures of need/risk for health services. • The grouping process has been computerized. The “grouper” and “predictive modeling” software requires diagnosis information from encounter data, electronic medical records, or insurance records. • ACGs are generally applied using all diagnoses describing the person. They do not focus on individual visits. Ideally they are derived from primary and specialty ambulatory contacts as well as inpatient • There is a comprehensive ACG “suite” of risk/case-mix measures. © Copyright 2010, Johns Hopkins University. 8 ACG System’s International Presence • • • • • • • • • • • Several Provinces in Canada Numerous County Councils in Sweden Several Regions of Spain Multiple Primary Care Trusts in the UK Two Sickness Funds in Germany The largest Health Plan in Israel Two Medical Schemes in South Africa The Ministry of Health in Malaysia Active piloting in Brazil and Chile Research in Lithuania, Korea, Thailand, Taiwan Interest expressed in numerous other countries © Copyright 2010, Johns Hopkins University. 9 No similar method is more fully tested by “real world” applications • Billions of dollars per year are now routinely exchanged using ACGs in US and Canada and in several other nations. • Healthcare of 80+ million patients is actively managed and monitored using ACGs on several continents. • The practices of hundreds of thousands of physicians in over a dozen many nations are now more equitably assessed on an ACG case-mix adjusted basis. © Copyright 2010, Johns Hopkins University. 10 The Johns Hopkins ACG System: 11 An Expanding Suite of Measures and Tools Diagnosis (ICD) based: • ADGs classify diagnoses into a limited number of clinically meaningful, but not diseasespecific, morbidity groups. (For example “chronic unstable”) • EDCs classify diagnoses based on specific diseases. They represent disease markers and can be used to determine disease prevalence (For example, Type I Diabetes, w/o complications). • ACGs (Adjusted Clinical Groups) represent a single, mutually exclusive actuarial cell based on overall disease burden. (For example, 6-9 ADG Combinations, Age >34, 2 major ADGs) The system also includes national references for average cost by ACG • Dx-PM – a “predictive model” of future risk and need based on ACGs, EDCs and special high risk markers. (Formerly referred to as ACG-PM.) Pharmacy based: • Rx-PM - a predictive model of future risk and expected resources use based only on pharmacy. (See http://www.acg.jhsph.edu for more information on methodology). © Copyright 2010, Johns Hopkins University. Key components of the Johns Hopkins ACG System Resource Use $ - Counts Patient Info ID – Age – Gender – Resource Use ACGs 100 Diagnosis ICD 9 - ICD 10 - ICPC ACG System EDCs 300 / 30 Markers Pharmaceuticals Frailty – Hosdom – Chronic Pregnancy - Delivery ATC Rx-MG 60 © Copyright 2010, Johns Hopkins University. Predictive Models 12 ACGs capture the essence of a person’s health status Time Period (e.g., 1 year) Treated Morbidities Visit 1 Diagnostic Codes 13 Morbidity Groups Code A ADG10 Code B Visit 2 Code C ADG21 Visit 3 Code D ADG03 Clinician Judgment © Copyright 2010, Johns Hopkins University. Clinical Grouping ACG Category Data Analysis 14 ADGs for Diabetes: ICD Codes D iabetes Mellitus ICD-9-CM Code Label ADG Code Label 2500 DIABETES MELLITUS UNCOMPLICATED 10 CHRONIC MEDICAL: STABLE 25003 DIABETES MELLITUS WITHOUT COMPLICATIONS UNCONTROLLED 11 CHRONIC MEDICAL: UNSTABLE 2501 DIABETES WITH KETOACIDOSIS 09 LIKELY TO RECUR: PROGRESSIVE 3620 DIABETIC RETINOPATHY 18 CHRONIC SPECIALTY: UNSTABLE-EYE © Copyright 2010, Johns Hopkins University. Examples of ACG Categories ACG Code Description 0200 Acute Minor, Age 2-5 years 0600 Likely to Recur, without Allergies 1722 Pregnancy: 2-3 ADGs, no major ADGs, not delivered 2800 Acute Major and Likely to Recur 4430 4-5 ADGs, Age > 44, 2+ major ADGs 5322 Infants: 0-5 ADGs, 1+ major ADGs, low birthweight © Copyright 2010, Johns Hopkins University. 15 The EDC tool represents over 200 individual “disease markers”: The Cardiovascular EDCs • • • • • • • CV signs/symptoms Hypertension Ischemic heart disease Congenital heart disease Congestive heart failure Cardiac valve disorder Cardiomyopathy © Copyright 2010, Johns Hopkins University. • • • • Heart murmur Cardiac arrhythmia Thrombophlebitis Generalized atherosclerosis • Peripheral vascular disease • Edema 16 Co-morbidity is central to understanding resource use: ACG risk levels and patterns of resource use at an English Primary Care Trust Level of Comorbidity (Based on ACGs) High Hospital Use % Pop. Relative Ratio 17 Est. % of Admissions at PCT Avg. # OutPatient Episodes / Yr. Avg. # Prescripti ons / Yr. 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 2010, Johns Hopkins University. Distribution (%) of ACG case-mix “morbidity bands” across UK & US sample populations: Ages 0-65 40 35 30 25 UK US-HMO 20 15 10 5 0 1 2 3 4 5 6 ACG- Morbidity Bands (1- healthy, 6 – sickest) Source: Forrest et al BMJ 2002. Data: UK - GPRD, n = 758K. US- private HMO, n = 70K (w/ no Medicaid or uninsured) © Copyright 2010, Johns Hopkins University. 18 •ACG Risk Scores Help Stratify Resource Use: NHS Consultant Referral Rates by ACG Morbidity Score* 50 % Patients referred per year to one or more consultants 40 30 20 10 0.5 1.0 1.5 2.0 ACG Morbidity Score Source: Forrest, Majeed, Weiner, et al. BMJ 2002: 325;370 (From a sample of 758,000 Electronic Patient Records from GPs offices. GPRD database) * An “expected resource use” score, where 1.0 is average, is based on patient’s ADGs. © Copyright 2010, Johns Hopkins University. 2.5 19 20 Risk Adjusted Performance Profiles of Primary Care Doctor Groups © Copyright 2010, Johns Hopkins University. Comparing primary group actual resource use to expected use (based on case-mix) in US HMO based 21 on illness burden Avg. Pt. Cost per Month Unadjusted cost / HMO Avg. Expected use based on “Illness Burden”* ACG Adjusted Efficiency Ratio (actual /expected) #1 $157 1.22 1.02 1.20 #2 153 1.19 1.21 0.99 #3 144 1.12 0.92 1.22 #4 98 0.76 0.69 1.11 $129 1.00 1.00 1.00 Primary Care Doctor Group Entire HMO (includes other groups) * Case-Mix adjusted expected use based on ACG illness burden of patients in practice. 1.0 is average case-mix. This would be the commissioning budget. © Copyright 2010, Johns Hopkins University. •Breakdown of cost, illness burden and efficiency ratio for doctor group #3 ( see previous table) by service 22 category Relative Cost ACG Illness Burden Efficiency Inpatient 0.91 0.90 1.01 Primary Care 1.20 1.15 1.04 Surgery 2.23 0.91 2.45 Medical Specialists / consultants 1.61 0.92 1.75 Lab & x-ray 1.77 0.85 2.08 Pharmacy .86 0.85 1.01 1.12 0.92 1.22 Type of Service Total © Copyright 2010, Johns Hopkins University. Risk-Adjusted O/E (Efficiency) Profiling Ratios 23 for GPs Across a Primary Care Trust (PCT) in UK 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 No of referrals No of unique prescriptions / month Observed = actual avg. use by patients. Expected = based on ACG case-mix of pts. © Copyright 2010, Johns Hopkins University. GP9 GP10 GP11 GP12 No of unique radiology tests Above 1.0 = higher than expected. Ambulatory Clinic Pharmacy Cost Profiling in 24 Spanish Region Pharmacy cost x patient: observed ( ) and expected ( Efficiency Index: 0,79 ) Efficiency Index: 1,27 21% undercost 943.000 € 27% overcost 737.000 € 400 350 300 250 200 150 100 50 0 001 002 003 004 005 006 007 008 009 Average Mean Cost (€) 182,58 291,57 274,75 212,19 337,71 289,03 328,99 287,14 196,36 270,49 Mean cost (€) expected 231,02 271,59 293,94 243,63 296,59 295,57 258,10 280,21 241,01 270,49 Overcost or undercost, related to standard Efficiency Index 0,79 1,07 Impact (€) 943.068 510.658 © Copyright 2010, Johns Hopkins University. 0,97 0,87 1,14 0,98 1,27 280.254 481.278 715.386 121.540 736.869 1,02 144.487 0,81 281.209 25 Capitation, & Budgeting & Other Financial Issues © Copyright 2010, Johns Hopkins University. Determining the Healthcare Budget 26 for a Population Involves a Variety of Factors - Available Budget - Political Forces - Actuarial Forecasts © Copyright 2010, Johns Hopkins University. Size of the Healthcare Pie Risk adjustment can be used to fairly slice the health care budget “pie” Risk Adjustment © Copyright 2010, Johns Hopkins University. 27 Some Reasons Why Risk Adjusted Payment & Budgeting28 May Be Necessary • To protect doctors, clinics, or organizations that care for costlier than average patient populations. • To help ensure that government or others that finance care pay their fair share (neither too high or low). • To deter providers from selectively attracting healthier patients. • To facilitate organizations or providers wishing to specialize in treating people with higher than average illness burden. © Copyright 2010, Johns Hopkins University. State of Maryland Medicaid program risk adjusted (ACG) 29 payment to capitated “HMO” health plans 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 2010, Johns Hopkins University. 30 Care Management & Predictive Modeling © Copyright 2010, Johns Hopkins University. Potential uses of ACG-based predictive risk modeling in care management • To identify persons for inclusion in care management programs: – multi-disease (case-management) and – single disease programs. • To provide information to help manage their ongoing care. © Copyright 2010, Johns Hopkins University. 31 ACG Predictive Modeling Combines Multiple Risk Factors into a Single Risk Score Age Overall Disease Burden Gender Complicated Pregnancy Marker Frailty Selected Medical Conditions © Copyright 2010, Johns Hopkins University. Hospital Dominant Conditions Pharmacy (Rx) Information (optional) 32 Distribution of Risk Scores Across a Population 9500 40 Risk Score 30 20 10 2.5 0 0 © Copyright 2010, Johns Hopkins University. 2000 4000 6000 Cumulative % x 100 8000 10000 95th Percentile 33 Year-2 Costs of Persons with High (Year-1) ACG Risk 34 Score (Dx-PM) vs. Other Persons Risk Ratio (High/Low) for Total Costs is 14.8 For Rx Costs Ratio is 18.3 Data derived from a 115,000 member US Medicaid HMO. “High” is the top 1% of ACG-PM scorers. © Copyright 2010, Johns Hopkins University. Using PM risk stratification to target and stratify disease management program participation for chronic 35 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 © Copyright 2010, Johns Hopkins University. 35 ACG-PM Risk Score (Baseline Year) as Predictor of Hospital Use (Year 2) Type of Use ACG-PM Top 5% ACG-PM Lower 95% Risk Ratio >1 Hospitalization 27.0% 5.7% 4.7 >1 ICU admit 1.9% 0.2% 8.1 >1 CCU admit 2.0% 0.2% 9.9 Source: British Columbia linked database (n=3.8M) 1998-99 © Copyright 2010, Johns Hopkins University. 36 ACGs Applied to EMR data to understand morbidity patterns among regions Fitri , 2010. Presentation at Patient Classification System International, Munich Germany. © Copyright 2010, Johns Hopkins University. 37 1.8 1.8 1.6 1.6 ACG Morbidity Index (1995/96) Rural Areas Urban Areas 1.4 1.4 1.2 1.2 1 1.0 0.8 0.8 0.6 0.6 0.4 0.2 ACG Morbidity Index Premature Mortality Rate 0.4 0.2 Crude Premature Mortality Ratio 1991-95 ACGs as a measure of need using Mortality Rates in rural & urban Manitoba (Reid et al, 2000) Some Practical / Data Issues 39 • Requires computerized diagnoses from ambulatory care sector – Ideally in-patient data too. • Ideally 12 –24 months of data. • Diagnosis codes in ICD-9 or ICD-10, ICPC, or Read code format. • “Outcome measures” (e.g,. clinical or cost) on each patient desirable. If not available, can apply resource use weights from US or other settings. • Possible applications with pharmacy data as source of risk information. (requires WHO –ATC codes) © Copyright 2010, Johns Hopkins University. Possible Applications in Portugal • Population based need-assessment across patient populations (e.g. regions, vulnerable patient groups) • Assessing performance of providers (e.g. primary care clinics, USFs, regional health administrations). • Resource allocation / budgeting across clinics, regions or other care units. • “Predictive Risk” measurement to assist in chronic care management. • Quality improvement comparisons. © Copyright 2010, Johns Hopkins University. 40 Opportunities for learning more about Johns 41 Hopkins ACGs • Web Site: – www.acg.jhsph.edu • To learn more, contact: – Dr. Karen Kinder – Director, ACG International • [email protected] – Dr. Patricio Muñiz - Senior Consultant for Portugal, Spain and Latin America, ACG International • [email protected] © Copyright 2010, Johns Hopkins University.