Time is money: Outpatient waiting times and health insurance choices of elderly veterans in the United States

Pizer, Steven D.; Prentice, Julia C.

doi:10.1016/j.jhealeco.2011.05.004

Cited by 26 publications

(20 citation statements)

References 23 publications

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“…The existing literature on waiting time and demand for hospital services has focused on elective rather than emergency care (Gravelle, Dusheiko, & Sutton, , Martin et al , Martin and Smith , Sivey , Windmeijer, Gravelle, & Hoonhout, ) and has generally estimated relatively small waiting time elasticities of demand in the range of −0.1 to −0.4. A smaller literature has attempted to estimate the effect of waiting time on demand for nonemergency physician appointments (Lourenco & Ferreira, ; Pizer & Prentice, ). These studies find “appointment delay” reduces quantity of care demanded (Lourenco & Ferreira, ) or increases demand for substitute care (Pizer & Prentice, ).…”

Section: Introductionmentioning

confidence: 99%

Should I stay or should I go? Hospital emergency department waiting times and demand

Sivey

2017

Health Economics

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In the absence of the price mechanism, hospital emergency departments rely on waiting times, alongside prioritisation mechanisms, to restrain demand and clear the market. This paper estimates by how much the number of treatments demanded is reduced by a higher waiting time. I use variation in waiting times for low-urgency patients caused by rare and resource-intensive high-urgency patients to estimate the relationship. I find that when waiting times are higher, more low-urgency patients are deterred from treatment and leave the hospital during the waiting period without being treated. The waiting time elasticity of demand for low-urgency patients is approximately -0.25 and is highest for the lowest-urgency patients.

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Section: Introductionmentioning

confidence: 99%

Should I stay or should I go? Hospital emergency department waiting times and demand

Sivey

2017

Health Economics

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“…26 The results from these two studies are consistent in that veterans who are mainly accessing one health care system for their care prefer to continue accessing the same system even if required to wait longer. Established clinical relationships probably prevent most veterans from making major changes in response to changes in waiting times in the short run.…”

Section: Studies Examining Va Wait Times and Utilizationmentioning

confidence: 66%

“…14,26 More research is needed to determine the health consequences of a patient's preference to wait longer to obtain care from their usual source of care (and potentially forego care altogether) versus receiving care faster from unfamiliar providers. The effect of delayed access to care on intermediate health outcomes and chronic disease management also needs further research.…”

Section: S680mentioning

confidence: 99%

What Are the Consequences of Waiting for Health Care in the Veteran Population?

Pizer

Prentice

2011

J GEN INTERN MED

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National health reform is expected to increase how long individuals have to wait between requests for appointments and when their appointment is scheduled. The increase in demand for care due to more widespread insurance will result in longer waits if there is not also a concomitant increase in supply of healthcare services. Long waits for healthcare are hypothesized to compromise health because less frequent outpatient visits result in delays in diagnosis and treatment. Research testing this hypothesis is scarce due to a paucity of data on how long individuals wait for healthcare in the United States. The main exception is the Veterans Health Administration (VA) that has been routinely collecting data on how long veterans wait for outpatient care for over a decade. This narrative review summarizes the results of studies using VA wait time data to answer two main questions: 1) How much do longer wait times decrease healthcare utilization and 2) Do longer wait times cause poorer health outcomes? Longer VA wait times lead to small, yet statistically significant decreases in utilization and are related to poorer health in elderly and vulnerable veteran populations. Both long-term outcomes (e.g. mortality, preventable hospitalizations) and intermediate outcomes such as hemoglobin A1C levels are worse for veterans who seek care at facilities with longer waits compared to veterans who visit facilities with shorter waits. Further research is needed on the mechanisms connecting longer wait times and poorer outcomes including identifying patient subpopulations whose risks are most sensitive to delayed access to care. If wait times increase for the general patient population with the implementation of national reform as expected, U.S. healthcare policymakers and clinicians will need to consider policies and interventions that minimize potential harms for all patients.

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“…These models allowing for two separate types of zeros are known as zero‐inflated count models (Mullahy, , Lambert, ), the most prominent ones being the zero‐inflated Poisson (ZIP) and zero‐inflated negative binomial (ZINB) models. In health economics, they have been used, among others, for the number of physician visits (Yen et al ., ; Sarma and Simpson, ; Sari, ; Pizer and Prentice, ), the number of pharmacy visits (Chang and Trivedi, ), the number of prescriptions (Street et al ., ), the number of occupational injuries (Campolieti, ), and the number of cigarettes smoked (Sheu et al ., ; Bauer et al ., ).…”

Section: Introductionmentioning

confidence: 99%