In-network Care Research Articles

Patient cost consciousness in the emergency department

Background‘Surprise billing’, or the phenomenon of unexpected coverage gaps in which patients receiving out-of-network medical bills after what they thought was in-network care, has been a major focus of policymakers and advocacy groups recently, particularly in the Emergency Department (ED) setting, where patients' ability to choose a provider is exceedingly limited. The No Surprises Act is the legislative culmination to address “surprise bills,” with the aim of promoting price transparency as a solution for billing irregularities. However, the knowledge and perceptions of patients regarding emergency care price transparency, particularly the degree to which ED patients are cost conscious is unknown. Accordingly, we sought to quantify that perception by measuring patients' direct predictions for the cost of their care. MethodsWe conducted an in-person survey of patients in Emergency Departments (EDs) over an 10-month period at two campuses within a large academic hospital system in southern Connecticut. We surveyed a convenience sample of patients at the bedside regarding demographics, care seeking perceptions and their estimates of the total and out-of-pocket costs for their ED care. Survey data was linked to institutional hospital finance datasets including actual charges and payments. We then later obtained the actual costs and billed amounts and compared these to the patients' estimates using a paired t-test. We also analyzed results according to certain patient demographics. ResultsA total of 600 patients were approached for survey, and data from 455 were available for the final analysis. On average, patients overestimated the cost of their care by $2484 and overestimated out-of-pocket cost by $144; both of these results met statistical significance (p < .005). Patients were better able to predict both total and out-of-pocket costs if they were: college educated or above; unemployed or retired; aged 65 or older; or had private insurance. Uninsured patients could better predict total cost but not out-of-pocket costs. One in 4 patients reported considering the cost of care prior to visiting the ED. Only 12 patients reported trying to look up that price before coming. ConclusionsThis study is the first to our knowledge that sought to quantify how patients perceive the cost of acute, unscheduled care in the ED. We found that ED patients generally do not consider the price before going to the ED, and subsequently overestimate the negotiated total costs of acute, unscheduled emergency care as well as their out-of-pocket responsibility for care. Certain demographics are less predictive of this association. Notably, patients with Medicare/Medicaid and those with high school education or below were of the furthest off in predicting the actual cost of care. This lends credence to the established trend of patients' limited knowledge of the total cost of healthcare; moreover, that they overestimate the cost of their care could serve as a barrier to accessing that care particularly in more vulnerable groups. We hope that this finding adds useful information to policymakers in sculpting future legislation around surprise billing.

Out-of-Network Care in Commercially Insured Pediatric Patients According to Medical Complexity.

Commercial health plans establish networks and require much higher cost sharing for out-of-network (OON) care. Yet, the adequacy of health plan networks for access to pediatric specialists, especially for children with medical complexity, is largely unknown. To examine differences in OON care and associated cost-sharing payments for commercially insured children with different levels of medical complexity. Cross-sectional study using a nationwide commercial claims database. Enrollees 0-18 years old in employer-sponsored insurance plans. The Pediatric Medical Complexity Algorithm was used to classify individuals into 3 levels of medical complexity: children with no chronic disease, children with non-complex chronic diseases, and children with complex chronic diseases. OON care rates, cost-sharing payments for OON care and in-network care, OON cost sharing as a proportion of total health care spending, and OON cost sharing as a proportion of total cost sharing. The study sample included 6,399,006 individuals with no chronic disease, 1,674,450 with noncomplex chronic diseases, and 603,237 with complex chronic diseases. Children with noncomplex chronic diseases were more likely to encounter OON care by 6.77 percentage points with higher cost-sharing by $288 for OON care, relative to those with no chronic disease. For those with complex chronic diseases, these differences rose to 16.08 percentage points and $599, respectively. Among children who saw behavioral health providers, rates of OON care were especially high. Commercially insured children with medical complexity experience higher rates of OON care with higher OON cost-sharing payments compared with those with no chronic disease.

Emergency Physicians Recover A Higher Share Of Charges From Out-Of-Network Care Than From In-Network Care.

Surprise medical bills occur when a patient unexpectedly or involuntarily receives care from an out-of-network provider and is billed for the amount not covered by insurance. Past studies were unable to observe whether bills for such care were sent to patients and, if so, how much patients paid directly to out-of-network providers. We used data from the Medical Expenditure Panel Survey to measure how much privately insured emergency patients paid when they likely received a surprise bill and how much physicians received in these situations. Physicians collected 65percent of the charged amount for likely surprise bills compared with 52percent for other cases. Patients who likely received a surprise out-of-network bill for emergency care paid physicians more than ten times as much as other emergency patients paid, on average.

A Predictive Model for Identification of Children at Risk of Subsequent High-Frequency Utilization of the Emergency Department for Asthma.

Asthma is the most common chronic condition among children with high-frequency emergency department (ED) utilization. Previous research has shown in outpatients seen for asthma that acute care visits predict subsequent health care utilization. Among ED patients, however, the optimal method of predicting subsequent ED utilization remains to be described. The goal of this study was to create a predictive model to identify children in the ED who are at risk of subsequent high-frequency utilization of the ED for asthma. We used 3 years of data, 2013-2015, drawn from the electronic health records at a tertiary care, urban, children's hospital that is a high-volume center for asthma care. Data were split into a derivation (50%) and validation/test (50%) set, and 3 models were created for testing: (1) all index patients; (2) removing patients with complex chronic conditions; and (3) subset of patients with in-network care on whom more clinical data were available. Each multivariable model was then tested in the validation set, and its performance evaluated by predicting error rate, calculation of a receiver operating characteristic (ROC) curve, and identification of the optimal cutpoint to maximize sensitivity and specificity. There were 5535 patients with index ED visits, of whom 2767 were in the derivation set and 2768 in the validation set. Of the 5535 patients, 125 patients (2.3%) had 4 or more visits for asthma in the outcome year. Significant predictors in models 1 and 2 were age and number of prior ED visits for asthma. For model 3 (additional clinical information available), the predictors were number of prior ED visits for asthma, number of primary care visits, and not having a controller medication. Areas under the ROC curve were 0.77 for model 1, 0.80 for model 2, and 0.77 for model 3. Administrative data available at the time of ED triage can predict subsequent high utilization of the ED, with areas under the ROC curve of 0.77 to 0.80. The addition of clinical variables did not improve the model performance. These models provide useful tools for researchers interested in examining intervention efficacy by predicted risk group.