Case-Mix and Risk Adjustment in Primary Health Care

Transcription

1 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 USA jweiner@jhsph.edu Presented in Lisbon, 29 October 2010

2 Goals of Presentation 2 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.

3 Not all persons have the same need for health care 3 % of Population % of Resources 1% 30% 10% 72% 50% 97%

4 Working Definitions 4 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.

5 The risk measurement pyramid 5 Management Applications High Disease Burden Single High Impact Disease Users Case- Management Disease Management Practice Resource Management Needs Assessment Quality Improvement Payment/ Finance Users & Non-Users Population Segment

6 Types of risk adjustment applications within health care 6 Financing, Payment, Planning Morbidity-adjusted capitation Allocation of budgets Service targets Forecasting healthcare spending Provider Performance Assessment Profiling Care Management Identification of high risk Quality patient Disease management Case management Quality assessment Quality monitoring Research and Program Evaluation Pay-for-Performance

7 7 OVERVIEW OF THE JOHNS HOPKINS ACG SYSTEM

8 Overview of Johns Hopkins ACG System 8 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.

9 ACG System s International Presence 9 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

10 No similar method is more fully tested by real world applications 10 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.

11 The Johns Hopkins ACG System: An Expanding Suite of Measures and Tools Diagnosis (ICD) based: 11 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 for more information on methodology).

12 Key components of the Johns Hopkins ACG System 12 Patient Info ID Age Gender Resource Use Resource Use $ - Counts ACGs 100 Diagnosis ICD 9 - ICD 10 - ICPC ACG System EDCs 300 / 30 Predictive Models Pharmaceuticals ATC Markers Frailty Hosdom Chronic Pregnancy - Delivery Rx-MG 60

13 ACGs capture the essence of a person s health status 13 Time Period (e.g., 1 year) Treated Morbidities Visit 1 Visit 2 Visit 3 Clinician Judgment Diagnostic Codes Code A Code B Code C Code D Morbidity Groups ADG10 ADG21 ADG03 Clinical Grouping Data Analysis ACG Category

14 ADGs for Diabetes: ICD Codes 14 ICD-9-CM D iabetes Mellitus ADG Code Label Code Label 2500 DIABETES MELLITUS UNCOMPLICATED DIABETES MELLITUS WITHOUT COMPLICATIONS UNCONTROLLED 2501 DIABETES WITH KETOACIDOSIS 3620 DIABETIC RETINOPATHY 10 CHRONIC MEDICAL: STABLE 11 CHRONIC MEDICAL: UNSTABLE 09 LIKELY TO RECUR: PROGRESSIVE 18 CHRONIC SPECIALTY: UNSTABLE-EYE

15 Examples of ACG Categories 15 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 ADGs, Age > 44, 2+ major ADGs 5322 Infants: 0-5 ADGs, 1+ major ADGs, low birthweight

16 The EDC tool represents over 200 individual disease markers : The Cardiovascular EDCs 16 CV signs/symptoms Hypertension Ischemic heart disease Congenital heart disease Congestive heart failure Cardiac valve disorder Cardiomyopathy Heart murmur Cardiac arrhythmia Thrombophlebitis Generalized atherosclerosis Peripheral vascular disease Edema

17 Co-morbidity is central to understanding resource use: ACG risk levels and patterns of resource use at an English Primary Care Trust 17 Level of Comorbidity (Based on ACGs) % Pop. Hospital Use Relative Ratio Est. % of Admissions at PCT Avg. # Out- Patient Episodes / Yr. Avg. # Prescripti ons / Yr. High 2% % Moderate 17% % Low 40%.6 26% None 41% >.1 2%.5 6 Data from several large GP practices within PCT for N= 20,500 all ages.

18 Distribution (%) of ACG case-mix morbidity bands across UK & US sample populations: Ages ACG- Morbidity Bands (1- healthy, 6 sickest) UK US-HMO Source: Forrest et al BMJ Data: UK - GPRD, n = 758K. US- private HMO, n = 70K (w/ no Medicaid or uninsured) 18

19 ACG Risk Scores Help Stratify Resource Use: NHS Consultant Referral Rates by ACG Morbidity Score* % Patients referred per year to one or more consultants 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.

20 20 Risk Adjusted Performance Profiles of Primary Care Doctor Groups

21 Comparing primary group actual resource use to expected use (based on case-mix) in US HMO based on illness burden 21 Primary Care Doctor Group Avg. Pt. Cost per Month Unadjusted cost / HMO Avg. Expected use based on Illness Burden * ACG Adjusted Efficiency Ratio (actual /expected) #1 $ # # # 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.

22 Breakdown of cost, illness burden and efficiency ratio for doctor group #3 ( see previous table) by service 22 category Type of Service Relative Cost ACG Illness Burden Efficiency Inpatient Primary Care Surgery Medical Specialists / consultants Lab & x-ray Pharmacy Total

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

24 Ambulatory Clinic Pharmacy Cost Profiling in Spanish Region 24 Pharmacy cost x patient: observed ( ) and expected ( ) Efficiency Index: 0,79 21% undercost Efficiency Index: 1,27 27% overcost 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 0,97 0,87 1,14 0,98 1,27 1,02 0,81 Impact ( )

25 25 Capitation, & Budgeting & Other Financial Issues

26 Determining the Healthcare Budget for a Population Involves a Variety of Factors 26 - Available Budget - Political Forces - Actuarial Forecasts Size of the Healthcare Pie

27 Risk adjustment can be used to fairly slice the health care budget pie 27 Risk Adjustment

28 Some Reasons Why Risk Adjusted Payment & Budgeting 28 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.

29 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.

30 30 Care Management & Predictive Modeling

31 Potential uses of ACG-based predictive risk modeling in care management 31 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.

32 ACG Predictive Modeling Combines Multiple Risk Factors into a Single Risk Score 32 Age Overall Disease Burden Gender Frailty Complicated Pregnancy Marker Selected Medical Conditions Hospital Dominant Conditions Pharmacy (Rx) Information (optional)

33 Risk Score Distribution of Risk Scores Across a Population Cumulative % x th Percentile 2.5

34 Year-2 Costs of Persons with High (Year-1) ACG Risk Score (Dx-PM) vs. Other Persons 34 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.

35 Using PM risk stratification to target and stratify disease management program participation for chronic 35 conditions Condition of Interest % Enrollees in ACG Risk Category Resource Use of Cohort Relative to Total Population Low Med. High Low Med. High Diabetes Congestive Heart Failure Tier 1 Tier 2 Tier 3 35

36 ACG-PM Risk Score (Baseline Year) as Predictor of Hospital Use (Year 2) 36 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)

37 ACGs Applied to EMR data to understand morbidity patterns among regions 37 Fitri, Presentation at Patient Classification System International, Munich Germany.

38 ACG Morbidity Index (1995/96) Crude Premature Mortality Ratio ACGs as a measure of need using Mortality Rates in rural & urban Manitoba (Reid et al, 2000) Rural Areas Urban Areas ACG Morbidity Index Premature Mortality Rate

39 Some Practical / Data Issues 39 Requires computerized diagnoses from ambulatory care sector Ideally in-patient data too. Ideally 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)

40 Possible Applications in Portugal 40 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.

41 Opportunities for learning more about Johns Hopkins ACGs 41 Web Site: To learn more, contact: Dr. Karen Kinder Director, ACG International Dr. Patricio Muñiz - Senior Consultant for Portugal, Spain and Latin America, ACG International patricio.muniz@jhu.edu