Cost-effectiveness of an Emergency Department-Based Intensive Care Unit

IMPORTANCE

Value in health care is quality per unit cost (V = Q/C), and an emergency department-based intensive care unit (ED-ICU) model has been associated with improved quality. To assess the value of this care delivery model, it is essential to determine the incremental direct cost of care.

OBJECTIVE

To determine the association of an ED-ICU with inflation-adjusted change in mean direct cost of care, net revenue, and direct margin per ED patient encounter.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective economic analysis evaluated the cost of care delivery to patients in the ED before and after deployment of the Joyce and Don Massey Family Foundation Emergency Critical Care Center, an ED-ICU, on February 16, 2015, at a large academic medical center in the US with approximately 75 000 adult ED visits per year. The pre-ED-ICU cohort was defined as all documented ED visits by patients 18 years or older with a complete financial record from September 8, 2012, through June 30, 2014 (660 days); the post-ED-ICU cohort, all visits from July 1, 2015, through April 21, 2017 (660 days). Fiscal year 2015 was excluded from analysis to phase in the new care model. Statistical analysis was performed March 1 through December 30, 2021.

EXPOSURES

Implementation of an ED-ICU.

MAIN OUTCOMES AND MEASURES

Inflation-adjusted direct cost of care, net revenue, and direct margin per patient encounter in the ED.

RESULTS

A total of 234 884 ED visits during the study period were analyzed, with 115 052 patients (54.7% women) in the pre-ED-ICU cohort and 119 832 patients (54.5% women) in the post-ED-ICU cohort. The post-ED-ICU cohort was older (mean [SD] age, 49.1 [19.9] vs 47.8 [19.6] years; P < .001), required more intensive respiratory support (2.2% vs 1.1%; P < .001) and more vasopressor use (0.5% vs 0.2%; P < .001), and had a higher overall case mix index (mean [SD], 1.7 [2.0] vs 1.5 [1.7]; P < .001). Implementation of the ED-ICU was associated with similar inflation-adjusted total direct cost per ED encounter (pre-ED-ICU, mean [SD], $4875 [$15 175]; post-ED-ICU, $4877 [$17 400]; P = .98). Inflation-adjusted net revenue per encounter increased by 7.0% (95% CI, 3.4%-10.6%; P < .001), and inflation-adjusted direct margin per encounter increased by 46.6% (95% CI, 32.1%-61.2%; P < .001).

CONCLUSIONS AND RELEVANCE

Implementation of an ED-ICU was associated with no significant change in inflation-adjusted total direct cost per ED encounter. Holding delivery costs constant while improving quality demonstrates improved value via the ED-ICU model of care.

An Operational Framework to Study Diagnostic Errors in Emergency Departments: Findings From A Consensus Panel

OBJECTIVE

To create an operational definition and framework to study diagnostic error in the emergency department setting.

METHODS

We convened a 17-member multidisciplinary panel with expertise in general and pediatric emergency medicine, nursing, patient safety, informatics, cognitive psychology, social sciences, human factors, and risk management and a patient/caregiver advocate. We used a modified nominal group technique to develop a shared understanding to operationally define diagnostic errors in emergency care and modify the National Academies of Sciences, Engineering, and Medicine's conceptual process framework to this setting.

RESULTS

The expert panel defined diagnostic errors as "a divergence from evidence-based processes that increases the risk of poor outcomes despite the availability of sufficient information to provide a timely and accurate explanation of the patient's health problem(s)." Diagnostic processes include tasks related to (a) acuity recognition, information and synthesis, evaluation coordination, and (b) communication with patients/caregivers and other diagnostic team members. The expert panel also modified the National Academies of Sciences, Engineering, and Medicine's diagnostic process framework to incorporate influence of mode of arrival, triage level, and interventions during emergency care and underscored the importance of outcome feedback to emergency department providers to promote learning and improvement related to diagnosis.

CONCLUSIONS

The proposed operational definition and modified diagnostic process framework can potentially inform the development of measurement tools and strategies to study the epidemiology and interventions to improve emergency care diagnosis.

Predicting Direct-Specimen SARS-CoV-2 Assay Performance Using Residual Patient Samples

Background

Diagnostic sensitivities of point-of-care SARS-CoV-2 assays depend on specimen type and population-specific viral loads. Evaluation of these assays require ‘direct’ specimens from paired-swab studies rather than more accessible residual specimens in viral transport media (VTM).

Methods

Residual VTM and limit-of-detection studies were conducted on Abbott ID NOW™ COVID-19, Quidel Sofia 2™ SARS Antigen FIA, and DiaSorin Simplexa™ COVID-19 Direct assays, with cycle threshold (CT) adjustments to approximate direct-specimen testing based on gene-target doubling each PCR cycle. Logistic regression was used to model assay performance by specimen CT. These models were applied to CT distributions of symptomatic and asymptomatic populations presenting to emergency services to predict the percent of specimens that would be detected by each assay. A 96-sample paired-swab study was conducted to confirm model results.

Results

When using direct nasopharyngeal samples and fit with either VTM or limit-of-detection data, percent positivities for ID NOW (symptomatic 94.9%/97.4%; asymptomatic 88.4.0%/89.6%) and Simplexa (symptomatic 97.8%/97.2%; asymptomatic 91.1%/90.8%) were predicted to be similar. Likewise, fit with VTM data, percent positivities for ID NOW with direct nasal specimens (symptomatic 77.8%; asymptomatic 64.5%) and Sofia 2 with direct nasopharyngeal specimens (symptomatic 76.6%, asymptomatic 60.3%) were similar. The paired-swab study comparing direct nasopharyngeal specimens on ID NOW and nasopharyngeal VTM specimens on Simplexa showed 99% concordance.

Conclusions

Assay performance can be modeled as dependent on viral load, fit using laboratory bench study results, and adjusted to account for direct-specimen testing. When using nasopharyngeal specimens, direct testing on Abbott ID NOW and VTM testing on DiaSorin Simplexa have similar performance.