Abstract
The Emergency Medical Treatment and Active Labor Act (EMTALA) requires that Medicare-participating hospitals screen and stabilize all individuals appearing in their emergency departments, regardless of expected compensation. To counter the incentive to prioritize revenue-generating patients, the law also prohibits facilities from delaying care to under-insured individuals. I estimate whether timeliness of emergency care is, in fact, unaffected by payer source as mandated. Using the National Hospital Ambulatory Medical Care Survey, I first examine the direct effect of under-insurance and find that under-insurance is associated with an approximately 6–10 % increase in emergency department wait time. Because of concerns that the effects of under-insurance may be mediated by triage assignment, I subsequently estimate the relationship between under-insurance and triage assignment, using the office hours of general practitioners as an exogenous source of variation in payer source. Instrumental variable results suggest that under-insured patients are inexplicably assigned higher triage scores which are known to lengthen waits. Contrary to the stipulations of EMTALA, discrepancies in timeliness of care do exist. Yet, this noncompliance is not readily apparent; roughly 80 % of the increase in under-insured individuals’ wait times are masked by adjustments to triage scores.
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Notes
Data from 2001 and 2002 is not used because wait times were not measured in those years.
To verify that the omission of unknown triage observations does not bias wait time results, independent sample t tests were run to compare the ‘unknown triage’ sample with the ‘known triage’ sample. After controlling for the wait time predictors used in this paper, I find that the difference between the average adjusted wait time of individuals in my sample (with known triage) and the average wait time of the omitted sample (with unknown triage) is only 36 s.
According to the NHAMCS instructions, the self-pay category, “Includes visits for which the patient is expected to be ultimately responsible for most of the bill, not whether the patient actually pays the bill.”
The distribution of wait times across triage assignments are shown in Fig. 1 of the Appendix. This distribution uses the smaller (\(n=92{,}587\)) sample where observations with unknown waits and/or who left the ED before being seen by a physician are omitted.
These include 44 dummy variables which represent different reasons for visiting the ED. Each variable takes the value of one if the individual cited that item as either the first, second, or third reason for visiting the ED, and zero if the individual did not cite that item as a reason for visiting the ED.
Flores and Flores-Lagunes’ 2009 unpublished manuscript represents recent work done in the area of causal mechanisms Flores and Flores-Lagunes (2009). However, the use of instrumental variables has yet to be fully developed. Existing IV methods are not yet at a stage where they can be straightforwardly applied in this context.
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Appendix
Appendix
See Appendix Fig. 1.
Wait time distributions, by triage assignment. Whisker ends denote the fifth and 95th percentiles, outer box edges denote the 25th and 75th percentiles, and center lines mark the median
See Appendix Tables 13, 14, 15 and 16.
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Woodworth, L. The doctor will be with you ... shortly?. J Regul Econ 45, 138–174 (2014). https://doi.org/10.1007/s11149-013-9235-6
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DOI: https://doi.org/10.1007/s11149-013-9235-6