Hospital financing: Private health insurance and casemix funding

1. Hospital financing: Private health insurance and casemix funding James RG Butler Alexandra A Sidorenko ACERH ACERH Policy Forum 22 February 2008, Brisbane

2. Motivation • Growth in private hospital services utilisation and outlays • Declining memberships/ demographics • Private Health Insurers funding formula in need for improvement • Per diem formula provides weak incentives to control costs • Moral hazard problem in the length of stay (joint decision of hospital, doctor, patient) • Transition from a “passive payer” to an “active purchaser” role

3. Structure of the paper • Theoretical background on hospital services purchasing • Analysis of the hospital payment formulae in use • Review of hospital payment schemes used in Australia against criteria • Control of unit costs • Utilisation • Quality • Risk-sharing • Proposes a blended funding formula and discusses its implementation

4. Theoretical background • Hospital services purchasing models • Adverse selection (asym information about technology and patient’s severity) and moral hazard (endogenous effort) • Incentive contracts literature, Laffont and Tirole (1999) - typical procurement scheme where a is the fixed fee, is the power of the incentive scheme, and C is actual cost. The case b=0 represents a cost-plus contract, b=1 is a high-power fixed-price contract, for 0<b<1 – an incentive scheme

5. General framework • Hospital cost function • - technology parameter reflecting inefficiency, • e - effort, • s - quality. • An optimisation problem for a regulator/ purchaser maximising total surplus; • Risk neutrality assumption; if relaxed (risk-averse hospital), lower power is needed. Same to maintain quality • A multiproduct production technology - efficiencies of scope are possible • Multiple treatment paths – a range of costs to achieve the same health outcome

6. Hospital payment systems • A general move away from cost-plus reimbursement to higher-powered schemes (price and revenue caps) • Fixed funding for an episode of care based on the Diagnosis Related Group (DRG) classification • US Medicare patients since 1984 • Sweden and Norway since the 1990s • Australian public hospitals • Crucial question in deciding the applicability of a high-power incentive contract is whether the hospital can reject a patient or not (dumping)

7. Private hospitals • Private hospitals can exercise discretion with respect to the selection of patients: • cream-skimming (selecting favourable risks/severity of patients), • dumping (refusal to treat complicated and potentially expensive cases), and • under-supply of quality • A mixed hospital purchasing formula becomes optimal • Ellis and McGuire (1986), Pope (1989), Ellis and McGuire (1990), Pope (1990), Selden (1990), Ellis and McGuire (1993), Newhouse (1996), Ellis (1998), Sappington and Lewis (1999), Pauly (2000), Chalkley and Malcolmson (2002) • Cream-skimming • Use of risk adjustment instruments in the hospital funding formula helps alleviate the cream-skimming incentive (Barros (2003), Eggleston (2000), Keenan and et al. (2001), Gilman (1999), Folland and Hofler (2001) and Lu, Ma et al. (2000))

8. Hospital purchasing models: public sector • Casemix/episode payments based on Australian Refined Diagnosis Related Groups (AR-DRG) classification • NSW for acute inpatient services since 2000, emergency departments (ED) and intensive care units (ICU) incorporated in 2001-02. • NSW Costs of Care Standards (annually). 2006/07 Standards based on AR-DRG v5.0 were released in November 2007, and cover such service areas as acute care, emergency department care, outpatient, sub- and non-acute care and mental health.

9. Public hospitals

10. Acute care funding – public hospitals

11. Hospital purchasing models: Private health insurers

12. Assessment of purchasing models against specified criteria • Cost • Determination of a benchmark unit cost w=1 • Actual historic hospital costs – weak incentives • Average costs – the choice of a reference group; “allowable” cost differentials vs inefficiencies • Utilisation • Hard to control directly • Quality • Crowding-out under fixed price schemes; outliers/cost-sharing • Risk-sharing • Mixed formulas preferred under a range of scenarios

13. Alternative purchasing formula • A risk-sharing payment formula the payment to the hospital for an episode of hospitalisation in the ith casemix category; the fixed payment per episode (or case payment) in the ith casemix category; the average length of stay for an episode in the ith casemix category; the average cost per day in the ith casemix category; and the risk-sharing factor ()

14. Alternative purchasing formula - continued • If , and are all calculated for the same reference group of hospitals using the same data, • Risk-sharing formula

15. Example: AR-DRG O60D • NHCDC Round 6, 2001-02 • AR-DRG O60G Vaginal Delivery w/out Complicating Conditions • ALOS=4.51 • B=$2,264 • Examine various payouts scenarios, including ALOS for public hospitals and NHCDC Round 5, 2000-01 data

16. Example: Average episode payment for AR-DRG O60D, A$

17. Incentive effects of risk-sharing • LOS not independent from r • a reduction in LOS following introduction of an incentive contract • An uniform r across DRGs, to prevent gaming the system • Per diem in cost-sharing formula is DRG-specific • Direct effect of reduced payouts • Larger for lower r • Indirect effects through the likelihood of LOS reduction • Larger for higher r • Dynamic effect through subsequent reduction of national/ peer group benchmarks

18. Formula limitations • Higher financial risks for smaller/ less diversified hospitals; • Mechanism for risk-pooling across hospitals • Can be mitigatied through the use of an appropriate benchmark unit cost • Subject to negotiation with hospitals • Bargaining power of larger hospital groups • Two negotiation parameters – r and B

19. Coefficient of variation of LOS by number of separations

20. Negotiating r • A coefficient of risk-sharing • Higher r – smaller financial risk to insurer, larger to hospital • Quality! • Larger hospitals – better internal risk diversification • Risk aversion by hospitals • Bargaining position

21. Negotiating B • A baseline (unit weight) episode payment • Correct choice of a benchmark • National average vs hospital historic average • Allowable cost differentials vs inefficiency • A choice of a peer group • Data collection • In NHCDC Round 5, 2000-01 only 30.7% private hospitals/ 43% separations covered. In Round 7 (2002-03), data was received from 113 private hospitals/ 65% of all private hospital separations. Collections lapsed in Rounds 8 and 9 • Incentives to supply data to NHCDC and to improve reporting (eg Ungroupable 960Z cases) • Improved national private hospital DRG weights

22. Conclusions • A mixed reimbursement formula is useful to balance cost and quality incentives • Risk-sharing formula allows it to reduce LOS through the immediate incentive effect • Dynamic effect through the shift of a benchmark unit cost, s.t. improved data collection and wider applicability of the formula