Nursing home (NH) private-pay residents account for 25.2% of the NH population. These private-pay residents bear substantial financial burden, which easily exceeds $80,000 annually and the price growth has consistently outpaced both the general and medical inflation. Theoretically, for a given price consumers will select the highest quality NH, or for the same quality, consumers will choose the lower-priced NH. Such decisions would serve to maximize the value of care. Yet, NH consumers may have limited cognitive abilities, NHs often have more quality information than consumers, and the number of NH choices and availability of beds in a local area can be limited. As a result, consumers may pay higher prices for NH care or receive lower quality than they would if these conditions did not exist. This leads to unaffordable, inefficient, and suboptimal quality of NH care. Due to data limitation, little is understood about consumer demand for the private-pay segment. The goal of this project is to enhance the affordability, efficiency, and cost transparency of NH care, by advancing our understanding of the roles of clinical quality, private-pay price, and market structure on consumer demand in the private-pay segment. The proposed study will be based on a unique and hand-collected price dataset (nine states) and use discrete choice models to estimate private-pay consumers' demand and preference for nursing home care. The price dataset will include 4,700 NHs in California, Florida, Georgia, New York, Ohio, Oregon, Pennsylvania, Texas, and Vermont from 2005 to 2010. Our study aims include: (1) Use the discrete choice framework and random coefficient logit model (mixed logit) to estimate the demand for NH care in the private-pay segment. (2) Estimate whether and to what extent the implementation of the 5-star quality rating improved consumers' welfare. (3) Determine how demand varies across NH markets with different levels of NH concentration and availability of NH beds. The adoption of the discrete choice framework enables us to quantify the willingness to pay (WTP) for clinical quality, amenities, and other NH attributes by consumers. With WTPs and prices, we can calculate consumer welfare stratified by cognitive function, reported quality rating, and market structure. This allows us to quantify consumer welfare gains/losses and inform policies enhancing the value of NH care. The study directly focuses on the Agency for Healthcare Research Quality (AHRQ) priority populations: the elderly and those who have special needs and need chronic care. The project also directly responses to AHRQ's priority areas. By evaluating the impacts of the quality rating, market competition, and capacity constraints on consumer welfare, we will provide results that inform policies that enhance the affordability, efficiency, and cost transparency. A more efficient private-pay segment is also likely to improve NH quality and resident safety. The study will provide knowledge to design policies that empower consumers, encourage them to make informed choices, and lead to more patient-centered care.
Nursing home (NH) private-pay prices for long-stay residents rose more than 50% during the past decade and easily exceeds $80,000 annually. However, due to limited cognitive function, asymmetric quality information, and market concentration and capacity constraints, consumers may not always select the highest value NHs. The project uses a unique NH price dataset (nine states) and the discrete choice framework including consumer characteristics to understand consumer demand for NH care in the private-pay segment and estimates consumer welfare gains and losses under various market and policy environments.
