Revealing the Value of a Bed Day: A Supply-Side Observational Analysis Using Australian Activity-Based Funding and Costing Data

Background & Objective: Health system planners frequently seek estimates of a hospital bed day’s value, to guide service expansion and redesign. Traditional accounting values do not capture the marginal value or opportunity cost of capacity. While a gold standard might involve measuring health outcomes gained by patients “next in line” for a bed, such data are seldom available. Contingent valuation surveys of hospital executives are prone to bias. This study adopts a pragmatic, supply-side approach by examining real-world resource allocation decisions at a hospital-clinic level to derive marginal cost— or “shadow price”—estimates of a hospital bed day.

Methods: We analysed five years of Australian Activity-Based Funding (ABF) and costing data from the Independent Health and Aged Care Pricing Authority (IHACPA) National Benchmarking Portal (NBP) for four large teaching hospitals in South Australia. Key variables (e.g., total episodes, mean cost per National Weighted Activity Unit [NWAU], mean NWAUs per episode) were combined with publicly available estimates of average lengths of stay. We calculated casemix-adjusted average and total costs and ABF prices paid by state and Commonwealth funders, before then examining the incremental changes in bed-day utilisation over time to estimate the marginal value of a bed day.

Results: Preliminary findings indicate that marginal bed-day costs and funding diverge substantially from simple “sticker prices” (total cost ÷ total bed days) and elicited values. Funding paid for an additional bed day varied over time and by hospital, with an average ICER of $AUD2,871 per inpatient bed day paid for through ABF. On average, the state government paid an additional $AUD323 per bed day in block grants to cover hospital losses. Funders paid more for fewer Emergency Department bed days, except for one hospital where additional bed days were bought at $AUD2,461 each, through ABF, and an additional $AUD20,563 through soft state money.

Conclusion: A supply-side analysis of funding behaviours and payer preferences can provide a transparent and context-specific estimate for economic evaluations, business case modelling, and strategic planning. Given that Australia’s ABF structure parallels similar cost collection and tariff payment systems internationally, these findings may be readily translatable and comparable to other settings.