Accounting for $46-billion - or 36% of public spending on health care [1
] - hospitals are a major target for cost reductions through efficiency initiatives in Canada. Some provinces are considering payment reform as a vehicle to achieve this goal. The use of financial incentives to increase hospital efficiency is now widespread in Europe and has occurred in the United States (US) since 1983 [2
]; however, the approach remains largely untested in Canada [5
In April 2010, the British Columbia (B.C.) provincial government implemented activity based funding (ABF) for hospitals, marking a fundamental change to the method of funding acute hospital care. ABF will direct up to 20% of available funds to acute hospitals on the basis of types and volume of services. This change is significant in an industry accustomed to historically-based global budgets and unaccustomed to detailed scrutiny of what services are provided to B.C.'s 4.5 million residents and at what cost.
While this change in hospital funding is being implemented in B.C., a debate over the relative merits of hospital ABF is unfolding across Canada [6
]. For example, the Canadian Medical Association and the B.C. Medical Association have recently staked positions supportive of ABF [8
], although support for ABF among the medical community is mixed [11
]. The Canadian Nurses Association supports an increasing emphasis on output measurement [13
] but falls short of endorsing ABF, while other groups are calling for more comprehensive baseline data [14
]. Although there has been no formal examination of the effects of ABF policies in Canada - in part because B.C. is the first province to adopt ABF for acute hospitals on a large scale - several other provinces are considering adopting similar funding models.
This project will provide an evidence base on the impacts of ABF, including changes in the type, volume, cost, and quality of services provided. Policy- and decision-makers in B.C. and elsewhere in Canada will be able to use this evidence as a basis for policy adaptations and modifications.
What is Activity Based Funding?
In B.C., regional Health Authorities (HA) are responsible for providing health services to their region's population. The HAs derive their operating revenue from multiple sources, though the vast majority is provided by the provincial government [15
]. Provincial funds are allocated to HAs through a mix of: 1) block operating grants (which are historically- and population-based, independent of the volume and type of activity provided to the region's residents), 2) targeted line items linked to specific policy objectives, and 3) to a very minor degree, pay for performance initiatives [16
]. Health authorities then allocate from their block grants to the various community-based providers (excluding physicians) in their regions, with a significant portion of those resources going to acute care hospitals.
ABF is a variant of fee-for-service in which funds are allocated based on the volume and type of services provided, including considerations of patient case-mix. ABF is an alternative to the traditional Canadian block operating grant funding for hospitals but has been widely used outside of Canada for some time now [2
]. A small-scale implementation in Canada was abandoned by the Ontario Ministry of Health and Long-Term Care [17
] in 2007 due to poor data quality [18
] and lack of support across affected stakeholders. In the B.C. program, as an addition to a base block grant, a portion of hospital funding will flow based on the number of cases, with remuneration adjusted for the mix of patient diagnoses and the services and procedures provided to those patients.
Information Base for Supporting ABF
The success of ABF is critically linked to the ability to measure 'weighted' hospital output accurately. Methods for characterizing hospital output according to the patients' mix of clinical conditions and interventions have become common and are known as case mix adjustments. The most popular method is diagnosis related groups, or DRGs [20
], though variants of DRG have been developed, each customized to address local policy and health delivery characteristics and objectives [21
]. In Canada, all acute inpatient discharge data submitted to the Canadian Institute for Health Information's (CIHI) Discharge Abstract Database (DAD) are case-mix-adjusted using their Case Mix Group (CMG+) methodology [23
]. Case mix adjustment is important since it weights hospital discharges according to their expected relative use of resources (and therefore cost). Each CMG+'s expected relative cost is represented by a resource intensity weight (RIW). As the length of stay has no bearing on the RIW value assigned (with minor exceptions for patients with exceptionally long lengths of stay), the incentive is to shorten lengths of stay in order to keep actual costs below the RIW-based case-specific relative amounts.
The veracity of weighting hospital output by RIW is contingent on accurately reported clinical data. CIHI's clinical chart re-abstractions have shown that Canadian hospitals have modified their clinical coding behaviour to maximize RIWs even in the absence of direct financial incentives to do so [18
]. This behavior introduces an additional challenge to our study and requires us to be able to differentiate actual changes in activity mix as a result of the changing financial incentives from apparent changes in activity mix that are merely shifts in coding practices.
Gaps in knowledge
The introduction of payment tied to particular types of activity represents a major change in hospital payment policy in Canada. We lack a knowledge base in Canada regarding the actual effects of these policies; and the (also relatively sparse) lessons from other health care systems cannot be necessarily generalized to the Canadian context. The natural experiment that is unfolding in B.C. thus provides an important opportunity to examine whether the theoretical incentive effects of ABF for hospitals, tied to efficiencies sought by B.C.'s Ministry of Health Services (MoHS), will actually materialize.
This project also provides a first-ever opportunity to examine the subsequent impact of ABF on the non-hospital sector. From a policy perspective, our results will provide as close to real-time impact information to B.C. decision makers as is possible in the world of health services research regarding the effects of ABF on costs, quality and access to care for residents.
The primary objective of this study is to examine the impact of ABF on acute care hospitals and related services in B.C. This objective will be fulfilled by meeting two specific aims.
Aim 1: To measure internal (to the hospital) changes resulting from the shift toward ABF. In a longitudinal analysis of observational data, we will measure whether and how hospitals respond to the changed financial incentives. Related hypotheses include:
Hypothesis 1.1: Lengths of stay will decrease and case mix adjusted volume of patients will increase from baseline levels. We will test for changes in trend over time.
Hypothesis 1.2: There will be changes in in-hospital quality measures (mortality and adverse events) for specific conditions that are characterized by their variability in clinical utilization patterns. We will test for changes in trend over time.
Hypothesis 1.3: Hospitals' expenditures will increase from baseline levels. We will construct a model to detect changes in trend over time of hospital expenditures.
Aim 2: To measure external (to the hospital) changes. In a longitudinal analysis of observational data, we will measure whether other components of the health care system are affected by the shift toward ABF. Related hypotheses include:
Hypothesis 2.1: There will be an increase in 30 day readmission rate, rate of admissions from emergency department (ED) and rate of admissions for ambulatory sensitive conditions, all indicators of sub-optimal acute care. We will test for an increase in the rates over time.
Hypothesis 2.2: There will be an increase in the number of new home care patients subsequent to being discharged from acute care. We will construct a model to detect changes in the trend of: 1) new home care patients over time and 2) readmissions to acute care from home care over time.
Hypothesis 2.3: There will be an increase in expenditures on physician services. We will test for an increase in physician expenditures over time.