Health policy/original research
Admit or Transfer? The Role of Insurance in High-Transfer-Rate Medical Conditions in the Emergency Department

https://doi.org/10.1016/j.annemergmed.2013.11.019Get rights and content

Study objective

We study the association of payer status with odds of transfer compared with admission from the emergency department (ED) for multiple diagnoses with a high percentage of transfers.

Methods

This was a retrospective study of adult ED encounters using the Healthcare Cost and Utilization Project 2010 Nationwide Emergency Department Sample. We used the Clinical Classification Software to identify disease categories with 5% or more encounters resulting in transfer (27 categories; 3.7 million encounters based on survey weights). We sorted encounters by condition into 12 groups according to expected medical or surgical specialist needs. We used logistic regression to assess the role of payer status on odds of transfer compared with admission and report adjusted odds ratios (ORs).

Results

Among high-transfer conditions in 2010, uninsured patients had double the odds of transfer compared with privately insured patients (OR 2.12; 95% confidence interval [CI] 1.72 to 2.62). Medicaid patients were also more likely to be transferred (OR 1.2; 95% CI 1.04 to 1.38). Uninsured patients had higher odds of transfer in all specialist categories (significant in 9 of 12). The categories with the highest odds of transfer for the uninsured included nephrology (OR 2.44; 95% CI 1.07 to 5.55), psychiatry (OR 2.26; 95% CI 1.65 to 3.25), and hematology-oncology (OR 2.21; 95% CI 1.50 to 3.25); the highest for Medicaid were general surgery (OR 1.61; 95% CI 1.09 to 1.83), hematology-oncology (OR 1.55; 95% CI 1.05 to 2.30), and vascular surgery (OR 1.55; 95% CI 1.02 to 2.28).

Conclusion

Insurance status appears to play a role in ED disposition (transfer versus admission) for many high-transfer conditions.

Introduction

Hospitals vary widely in the services available, and many lack access to certain specialists and diagnostic and treatment modalities. When a hospital does not have the resources to adequately care for a patient with an emergency condition, the patient may be transferred to another acute care hospital. Reasons for transfer may include patient needs (disease complexity1, 2 or specialist availability3, 4, 5), patient preference,6 protocols based on regionalized systems of care,7, 8 or hospital operations (bed availability8 or night versus day arrival9). Studies on the effect of transfer to a higher level of care have been mixed; some show mortality benefit as a result of transfer,10, 11, 12 whereas others show no effect on outcomes, with increases in cost.13, 14

Editor's Capsule Summary

What is already known on this topic

Patients are transferred for many reasons, including subspecialist availability, patient preference, and disease severity.

What question this study addressed

The relation between insurance status and the likelihood of transfer from an emergency department in a nationally representative weighted sample.

What this study adds to our knowledge

Uninsured and Medicaid patients are transferred more frequently than privately insured patients with the same conditions.

How this is relevant to clinical practice

Should these disparities in transfer rates be shown to cause differential outcomes, this would be yet another example of inequities in our health care system.

Both clinical and nonclinical factors affect the decision to transfer a patient. Economic reasons, in particular insurance status, have been found to influence transfer decisions. Payer status, especially lack of insurance, has been found to play a role in transfers of orthopedic emergencies,15, 16 traumatic injuries,17, 18 psychiatric emergencies,19 and neurosurgical emergencies,1 but had no effect in other studies.20, 21 The effect of payer status on transfer for a broad variety of disease categories has not been studied, and no studies to our knowledge have used data at a national level.

Understanding the influence of insurance status on likelihood of transfer is important in the context of the Emergency Medicine Treatment and Labor Act (EMTALA), which was passed by Congress in 1986 and mandates that hospitals conduct a medical screening examination and stabilize all patients presenting to the emergency department (ED) regardless of their citizenship, legal status, or ability to pay.22, 23, 24 EMTALA was enacted in response to evidence about denial of care for medical emergencies to the poor and uninsured, a problem known as "patient dumping." Its purpose is to ensure that all patients with an emergency medical condition, regardless of any factor other than the need for care, are appropriately examined and stabilized in the ED. EMTALA bars transferring medically unstable patients with emergencies except under appropriate and medically justifiable conditions; therefore, a key question is whether transfer patterns suggest that factors other than medical appropriateness are involved in transfer decisionmaking.

In this study, we use the Nationwide Emergency Department Sample (NEDS) to study the association of insurance status with odds of transfer compared with admission. Specifically, we identify diagnoses with high frequencies of transfer in adults (5% or more encounters resulting in transfer), group these high-transfer diagnoses according to predicted specialist need, and assess the association of payer status with the odds of transfer versus admission among these specialist categories.

Section snippets

Study Design

This was a retrospective study of ED encounters for adult patients (aged 18 years and older), using the Healthcare Cost and Utilization Project (HCUP) NEDS for 2010.25 HCUP is maintained by the Agency for Healthcare Research and Quality (AHRQ). The NEDS is the largest all-payer ED database in the United States. The 2010 NEDS is a nationally representative, weighted sample that estimates approximately 129 million ED encounters. Data come from 961 hospital-based EDs in 28 states and can be used

Characteristics of Study Subjects

In 2010, according to sample weights, there were 128,970,364 ED encounters (95% CI 123,579,723 to 134,361,005). Of these, 19,733,530 encounters, or 15.3%, resulted in admission at the same hospital (95% CI 19,241,557 to 20,225,503) and 1,942,692, or 1.5%, led to transfer to another acute care hospital (95% CI 1,823,736 to 2,061,648). Although overall about 1.5% of encounters led to transfer, certain diagnostic categories had a higher percentage of encounters resulting in transfer.

Table 1

Limitations

The main limitation is that large administrative databases such as the NEDS are unable to fully adjust for several factors that can influence transfer decisions. We controlled for patient comorbidities, age, sex, zip code income quartiles, and county-level racial demographics, and compared admitted and transferred patients with the same diagnostic category; however, our results might have been different had we had access to physiologic information, laboratory test results, or radiology reports

Discussion

Among high-transfer diagnoses in US EDs in 2010, we found significant patient- and hospital-level predictors of transfer compared with admission. Our results suggest that many factors may affect the decision to transfer a patient, including clinical need and hospital resource availability. The majority of diseases with the highest transfer frequencies are complex and high-acuity conditions, and many require specialized centers for definitive care. Many transfers likely result in better care by

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    Please see page 562 for the Editor's Capsule Summary of this article.

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    Supervising editor: Brendan G. Carr, MD, MS

    Author contributions: DRK and RLM had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the results. DRK, RLM, and JMP were involved in the study concept and design and statistical analysis. DRK and RLM were involved in data acquisition. RLM provided administrative and technical support. All authors were involved in data analysis and interpretation. DRK was primarily involved in drafting the article and all authors took part in its critical revision. DRK takes responsibility for the paper as a whole.

    Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). The authors have stated that no such relationships exist.

    This article does not represent the policy of either the Agency for Healthcare Research and Quality (AHRQ) or the US Department of Health and Human Services (DHHS). The views expressed herein are those of the authors and no official endorsement by AHRQ or DHHS is intended or should be inferred.

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