Options for Chronic Care Under Activity Based Funding

1. Options for Chronic Care Under Activity Based Funding IHPA Activity Based Funding Conference 13-16 May 2013 Steve Gillett John Pilla KPMG National Health Care Group

2. Purpose of Paper • ABF has largely focussed around products as defined by specific service events (separations, occasions of service, attendances etc). This approach works reasonably well for acute care, but less well for other types of care. • This is largely an artefact of history. It reflects how information was collected 50 years ago when Fetter started developing DRGs • Preferred treatment models have changed with shorter episodes and greater use of outpatient and out of hospital care. Increased emphasis on the patient journey and care coordination, especially for chronic care. Emphasis on keeping people healthlyand out of hospital. • Steven Duckett talked about funding the episode and being more focussed on patient outcomes. How can we fund care in ways that encourage this? • In this paper we start to explore funding models other than the traditional “casemix” models and consider how they might coexist with the the existing IHPA model. We will focus on the longest “episodes of care”, those associated with chronic conditions

3. The Rise and Rise of Chronic Conditions 1. Factors driving increases in chronic care requirements:- • Population aging (more elderly with chronic conditions)  reduced mortality • 2.Expect increased age specific prevalence 3. Prevalence = Existing Cases + Incidence – Deaths + Cure • 4. Reductions in the age specific mortality rates:- • Little recovery because of the definition of “chronic” • Incidence (new cases)  uncertain? 5. Greater demand for services

4. Current incentives are wrong • Under ABF the incentive is to admit multiple times. • Hospital admitted care can be the most appropriate care setting for “acute episodes” of many chronic conditions. However it is better to keep people in good health and minimise “acute episodes”. • Under ABF there is no budget or incentive to spend to keep people living with chronic care health problems and out of hospital? • GP fund holder arrangements • Budgets for hospitals  Explored in this talk

5. Objectives • Overall aim is to appropriate care in an appropriate stetting:- • Allow for substitution for less intensive care • Allow preventative measure to prevent severity increasing

6. Desirable Requirements of a Funding Model • Process must be compatible with the National pricing framework. • Be revenue neutral compared to episodic funding (unless there is a specific incentive to move to one funding model). Why? • no cost data • Based on feasibly collected data. • Creates the right incentives and difficult to game • Be able to be consistently reproduced

7. Approaches to Funding Models Model 1 Bundling services for the treatment of a specific condition over a predefined period. Model 2 Paying for the full health needs for an individual over a predefined period. Model 3 Offering incentive payments for reduction in hospitalisation for patients in the target population.

8. Australian Examples • GP antenatal care for a period after the delivery has been roled into the price for the delivery. • Renal Dialysis in some jurisdictions has a mixed model with an annual payment and a episode based payment.

9. Comparison of Approaches

10. Condition Hierarchy ( Model 2:example using DxCG) In capitation funding: • the concept of a Principle Diagnosis has no meaning as this can change with different episodes. • Don’t pay twice for similar conditions  which is more important Could AR-DRG repetitive exclusion used in defining complications be used for this?

11. One approach to setting Weights under Model 1 • Calculate total NWAU for each patient episode normally and sum across all the health encounters over the period. • Calculate a revise NWAU score and sum for all patients. Revise by:- • Remove all secondary diagnoses relevant to the condition . Regroup and recalculate NWAU • Zero weight cases with a relevant principle diagnosis that :- • Do not use ICU • Do not have surgery • Other criteria ? • Sum across all episodes during the period • Subtract the Revised NWAU from the actual NWAU for each patient and calculate the difference • Find the average difference across all patients in the group

12. Constructing Regression Data for setting weights under Model 2 NOTE: WIES can be replaced with NWAU

13. Regression Model using HCCs for Weights under Model 2 WIES = ∑βi × AgeSexi + ∑γi × HCCi Estimate βi s and γi s - these become the weights where AgeSexi are dummy variables (0,1) for 18 age sex cohorts HCCi are dummy variables (0,1) for 184 HCCs Note: requires large amounts of data can give odd results like negative amounts

14. Regression Model using HCCs WIES = ∑βi × AgeSexi + ∑γi × HCCi Estimate βi s and γi s - these become the weights where AgeSexi are dummy variables (0,1) for 18 age sex cohorts HCCi are dummy variables (0,1) for 184 HCCs Note: requires large amounts of data can give odd results like negative amounts

15. UseRatio Health Service Actual (12 Month*) Concurrent Prospective Region 1 4.7 5.2 0.92 3.0 Region 2 4.0 4.8 0.83 3.2 Region 3 5.3 4.7 1.13 2.6 Region 4 5.0 5.6 0.89 3.4 Example of running a version of Model 2 using regression derived weights(Actual and predicted for chronic care patients who did not die during the period) Close alignment with concurrent and Capitation Lower prospective. Possibilities:- 1) Acute illness not picked up in age/sex adequately 2) Deaths not fully excluded (Only in hospital deaths) 3) Timing of 12 month period 4) Using trimmed data for weights (unlikely….similar when all results used) 5) Less resources actually required due to stablisation after and acute period.

16. Predictive Power of regression derived weights in the Model 2 example(on a cohort of chronic care patients) • Concurrent Models (predict WIES in the year that the diagnoses were reported) • HCCs only Rsquare 60.3% • HCCsand Age/Sex groups Rsquare 60.4% • Prospective Models (predict WIES for the year after the diagnoses were reported) • HCCs only Rsquare 16.2% • HCCsand Age/Sex groups Rsquare19.1% • Trimmed data HCCs only Rsquare 59.4% • Trimmed data HCCs + Age/Sex groups Rsquare 65.5% Note: Higher RSquares expected on trimmed data (2.5% high and low WIESuseage).

17. Diagnostic Codes (ICD-9-CM) (n=14,000) ADGs (n=32) 1 ICD1 ADG Age, Sex Adjusted Clinical Group (ACG) (n=92) Mutually Exclusive 1 Person1 ACG A different approach: John Hopkins Adjusted Care Groups (ACGs) Not Mutually Exclusive

18. Predictive Modelling using ACGs Age Multi-Morbidity Disease Burden (ACGs) Gender Pharmacy Morbidity (Rx-MGs) Risk Score Selected Medical Conditions (EDCs) Selected Prior Use Measures (optional) Special Population Markers

19. Defining the Payment Model Considerable debate about the parameters within the funding model eg • Should it be based upon actual performance or based upon improved performance or a combination of both. • Should it be based upon reaching a threshold or on a continuous scale or a combination of both. If based upon a threshold how should the thresholds be determined • Improved performance requires change. How can change be funded with certainty. The CEO does not know if he will achieve target performance until after the event. • Should indicative budgets be allocated at the start of the year assuming each hospital receives a share for reaching targets. What happens to “unallocated” money where targets are not reached. Experience suggests that where hospitals do poorly under the model they will lobby to change the rules

20. The HARP Experience in Victoria Hospitals give a fixed amount of dollars for each enrolled chronic care patient to “trial” new treatment modes designed to keep people out of hospital. This was additional to any ABF funding under existing arrangements. Analysis of enrolled patients showed:- • Patients were allocated to multiple programs at the same time (often in the same hospital) • Patients were allocated to programs, subsequently discharged from the program and re-enrolled some time later • Admissions through the emergency department went down but admissions from other sources went up

21. Capitation NWAU Low Percentage High Percentage CasemixWIES Normal ABF NWAU Developing a Safety Net For Chronic care funding Fixed episode based payment End of year Adjustment if minimum percentage is not reached Additional episode based NWAU if high point is exceeded * * Simpler than an outlier model

22. Setting the percentage boundaries Set the lower boundary by Policy (eg 25%) Calculate the high boundary to be as close to revenue neutral as possible Capitation NWAU 25% High Percentage? Normal ABF NWAU

23. Thank You Contact: email sgillett@kpmg.com.au phone (+61) 03 9288 6289 mobile (61+) 0407 72240