Out Of Network Research Articles

The Controversy over Price Transparency: Patients and Plastic Surgeons Disagree.

The No Surprises Act (NSA) went into effect on January 1, 2022. This federal law is designed to minimize surprise bills for emergency care, out of network (OON) care, and care for uninsured or self-pay individuals. To achieve these goals, the NSA relies heavily on physicians providing cost estimates to patients prior to administering care. Because healthcare is often price-opaque, this study was conducted to assess physician and patient attitudes toward price transparency in plastic surgery and aesthetic/self-pay medicine. Two cohorts of providers received a survey asking whether and how their practice offers pricing information prior to a consultation and their views regarding the NSA. Patients who had previously used an online price estimator tool also received a survey regarding their experience and accuracy of estimates received online. Most surveyed providers do not share the costs of their services online, whereas patients had a positive experience when receiving estimates of medical services before consultation. Although many providers cited the inaccuracy of price estimator tools as a reason not to incorporate them into their website, most patients reported that their final cost of service was close to (or even less than) the estimate received online prior to the consultation. This study suggests that price transparency is a patient satisfier. Therefore, plastic surgeons should exercise price transparency to be compliant with federal law and to gain a competitive advantage in the market, as the decision to implement price transparency can be mutually beneficial to patients and providers.

Radiologists’ Out-of-Network Billing Trends, 2007 to 2021

PurposeGiven the financial hardships of surprise billing for patients, the aim of this study was to assess the degree to which radiologists effectively participate in commercial insurance networks by examining the trend in the share of radiologists’ imaging claims that are out of network (OON). MethodsA retrospective study over a 15-year period (2007-2021) was conducted using claims from Optum’s deidentified Clinformatics Data Mart Database to assess the share of radiologists’ imaging claims that are OON. Radiologists’ annual OON rate was assessed overall as well as for claims associated with inpatient stays and emergency department (ED) visits. Rates were assessed for all imaging studies as well as by modality. Linear regression was conducted to assess OON rate time trends. ResultsFrom 2007 to 2021, 5,039,142 of radiologists’ imaging claims (6.3%) were OON. This rate declined from 12.6% in 2007 to 1.1% in 2021. Over the study period, the OON rate was 5.0% during an inpatient stay and 2.1% on the same day as an ED visit that did not lead to an inpatient admission. The linear trend in the overall OON rate declined 0.74 percentage points annually (95% confidence interval [CI], −0.90 to −0.58 percentage points) over the study period. Likewise, the annual declines were 0.54 percentage points (95% CI, −0.71 to −0.36) and 0.26 percentage points (95% CI, −0.33 to −0.20 percentage points) for imaging claims associated with inpatient stays and ED visits, respectively. ConclusionsRadiologists’ imaging claims that are OON has significantly declined from 2007 to a minimal level in 2021. This may indicate effective negotiations between radiologists and commercial payers and new state-level surprise billing laws.

The US Supreme Court and Future of Financing Dialysis Care.

The US Supreme Court and Future of Financing Dialysis Care.

Probing the Gendered History of Aegean Survey Archaeology

Probing the Gendered History of Aegean Survey Archaeology

Class Action Win Gives Doctors Appeals Rights for Repayments

Class Action Win Gives Doctors Appeals Rights for Repayments

Frequency and Costs of Out-of-Network Bills for Outpatient Laboratory Services Among Privately Insured Patients

Patients may be unaware of which laboratory is processing their clinical tests, limiting their ability to choose an in-network laboratory. Out-of-network laboratory services could increase patients' out-of-pocket costs and their reluctance to obtain necessary tests. To evaluate the frequency and cost of out-of-network bills for outpatient laboratory services compared with other services. This retrospective cohort study of claims data from the Truven MarketScan Commercial Claims database evaluated claims from 3 946 210 individuals (30.5% of the total) in the MarketScan database who were continually enrolled in health maintenance organization plans, preferred provider organization plans, exclusive provider organization plans, or consumer-driven health plans/high-deductible health plans with at least 1 outpatient clinical laboratory service in 2018. Outpatient laboratory services occurred in independent laboratories, physician offices, and outpatient centers. Laboratory bills from January 1, 2010, to December 31, 2018, were studied. Receipt and cost of outpatient laboratory service. The primary outcome was the proportion of outpatient laboratory services billed as out of network. The secondary outcome was the total potential out-of-pocket cost associated with the out-of-network bill, the sum of observed cost sharing, and the potential balance bill. Of the 12 958 130 in the total sample, 30.5% (3 946 210) had a laboratory test, of whom 5.9% received an out-of-network laboratory test. In comparison, 7.1% of the total sample had an emergency department visit, of whom 4.9% had a service billed as out of network, and 1.6% had an inpatient anesthesiology service, of whom 3.4% had an out-of-network service. Observed out-of-pocket spending was $24.59 higher for an out-of-network laboratory service than an in-network laboratory service. In addition, patients with an out-of-network laboratory service may receive an additional balance bill from the laboratory service; the estimated mean balance bill was $80.63. For the most common laboratory services, the total potential out-of-pocket cost associated with an out-of-network bill ranged from $15.68 for venipuncture to $88.09 for lipid panel but was as high as $303.18 for a drug screening test. In this cohort study, out-of-network laboratory services were 5 times more common than out-of-network emergency department visits and 34 times more common than out-of-network anesthesiology services. It is important for patients that consumer protections against out-of-network bills apply to laboratory services.

Prices And Cost Sharing For Psychotherapy In Network Versus Out Of Network In The United States

Patients in the US are more likely to receive out-of-network behavioral health care, including treatment for mental health or substance use disorders, than they are to receive other medical and surgical services out of network. To date, out-of-network and in-network trends in the prices and use of ambulatory behavioral health care have been seldom described. Here we compare levels and growth of insurer-negotiated prices (allowed amounts), patient cost sharing, and use of psychotherapy services in network and out of network in a large, commercially insured US population during 2007-17. For both adult and child psychotherapy, prices and cost sharing were substantially higher out of network than they were in network. These gaps widened during the eleven-year period. Prices and cost sharing for in-network psychotherapy decreased during this period, whereas prices and cost sharing for out-of-network psychotherapy increased. Use of adult and child psychotherapy increased during this period, driven by growth of in-network rather than out-of-network use. The increasing gap in prices and cost sharing between out-of-network and in-network psychotherapy, viewed in the context of a shortage of behavioral health providers who accept insurance, may limit access to ambulatory behavioral health care.

Differential Reimbursement of Psychiatric Services by Psychiatrists and Other Medical Providers.

Given low psychiatrist participation in insurance networks, this study examines how psychiatrists are reimbursed in network and out of network under commercial insurance relative to other providers for the same diagnoses and services. Paid private insurance claims from the 2014 Truven Health Analytics MarketScan Commercial Claims and Encounters Database were analyzed. The sample included all services billed for 3.8 million individuals with a mental disorder as the primary diagnosis by psychiatrists, nonpsychiatrist medical doctors, psychologists, social workers, or psychiatric nurses. The authors determined the most common services provided by each provider type, the median reimbursement and median out-of-pocket payment for the services by provider type and by network status (in or out of network), and the proportion of bills for services delivered out of network. "Evaluation and management" services for presenting problems of low to moderate and moderate to high severity were the two procedures most frequently billed by psychiatrists and nonpsychiatrist medical doctors. The median reimbursement for services for presenting problems of low to moderate and moderate to high severity was 13% less ($66 versus $76) and 20% less ($91 versus $114), respectively, for psychiatrists versus nonpsychiatrist medical doctors if the services were provided in network but 28% higher ($100 versus $78) and 6% higher ($122 versus $115), respectively, for psychiatrists versus nonpsychiatrist medical doctors for services provided out of network. Psychiatrists receive lower in-network reimbursement than nonpsychiatrist medical doctors for many of the same services. This may contribute to psychiatrists' lower participation in insurance networks relative to other providers and has implications for patient cost sharing and access to psychiatrists.

Factors associated with neurokinin-1 receptor antagonist use among commercially insured cancer patients receiving highly emetogenic chemotherapy.

24 Background: Neurokinin-1 receptor antagonists (NK1RA) reduce chemotherapy-induced nausea/vomiting (CINV) among patients undergoing highly emetogenic chemotherapy (HEC). We evaluated factors associated with the use of NK1RA in patients treated with HEC. Methods: We performed a retrospective cohort study using Truven Health Marketscan to identify subjects who initiated HEC (doxorubicin or cisplatin) between 2009 and 2013, and concurrently received a NK1RA. Multivariable logistic regression was used, to determine the association of clinical and demographic factors and NK1RA use. Results: Of 32,004 subjects with cancer who were treated with either doxorubicin or cisplatin-containing regimens, 11,325 (33.3%) did not receive an NK1RA. From 2009 to 2013, NK1RA use increased from 53.2% to 73.5% (p < 0.0001). Compared to non-users, NK1RA users were more frequently treated with cisplatin-based regimens (67.9% vs. 32.1%, p < 0.0001), had in-network claims (66.7% vs. 33.3%, p < 0.0001), were younger, and without other comorbidities. Multivariate analysis demonstrated that NK1RA use was more likely with cisplatin compared to doxorubicin (OR = 1.71, 95% CI 1.62-1.81) and with female compared to male patients (OR = 1.75, 95% CI 1.65-1.84). In-network claims (OR 1.26, 95% CI 1.16-1.36) and comprehensive benefit plans (OR = 1.14, 95% CI 1.04-1.25) were associated with increased NK1RA use, compared to out-of network claims and preferred provider organizations (PPO), respectively. Age ≥ 65 years (OR = 0.64, 95% CI 0.5-0.69) and increased comorbidities (comorbidity score of 1: OR 0.88, 95% CI 0.83-0.94; score of 2- OR 0.61, 95% CI 0.56-0.67) versus a score of 0 were associated with decreased NK1RA use. Conclusions: A substantial proportion of patients receiving HEC do not receive NK1RA, and type of insurance coverage was associated with receipt.

Social network modulation of reward-related signals.

Everyday goals and experiences are often shared with others who may hold different places within our social networks. We investigated whether the experience of sharing a reward differs with respect to social network. Twenty human participants played a card guessing game for shared monetary outcomes with three partners: a computer, a confederate (out of network), and a friend (in network). Participants subjectively rated the experience of sharing a reward more positively with their friends than the other partners. Neuroimaging results support participants' subjective reports, as ventral striatal BOLD responses were more robust when sharing monetary gains with a friend as compared to the confederate or computer, suggesting a higher value for sharing with an in-network partner. Interestingly, ratings of social closeness covaried with this activity, resulting in a significant partner × closeness interaction; exploratory analysis showed that only participants reporting higher levels of closeness demonstrated partner-related differences in striatal BOLD response. These results suggest that reward valuation in social contexts is sensitive to distinctions of social network, such that sharing positive experiences with in-network others may carry higher value.

Learning Curves of Agents with Diverse Skills in Information Technology Enabled Physician Referral Systems

To improve operational efficiencies while providing state of the art healthcare services, hospitals rely on IT enabled physician referral systems (IT-PRS). This study examines learning curves in an IT-PRS setting to determine whether agents achieve performance improvements from cumulative experience at different rates and how information technologies transform the learning dynamics in this setting. We present a hierarchical Bayes model that accounts for different agent skills (domain and system), and estimate learning rates for three types of referral requests: emergency (EM), non-emergency (NE), and non-emergency out of network (NO). Further, the model accounts for complementarities among the three referral request types and the impact of system upgrade on learning rates. We estimate this model using data from more than 80,000 referral requests to a large IT-PRS. We find that (1) The IT-PRS exhibits a learning rate of 4.5% for EM referrals, 7.2% for NE referrals, and 12.3% for NO referrals. This is slower than the learning rate of manufacturing (on average 20%) and more comparable to other service settings (on average 8%). (2) Domain and system experts are found to exhibit significantly different learning behaviors. (3) Significant and varying complementarities among the three referral request types are also observed. (4) The performance of domain experts is affected more adversely in comparison to system experts immediately after system upgrade. (5) Finally, the learning rate change subsequent to system upgrade is also higher for system experts in comparison to domain experts. Overall, system upgrades are found to have a long term positive impact on the performance of all agents. The learning curve estimation of this study contributes to the development of theoretically grounded understanding of learning behaviors of domain and system experts in an IT enabled critical healthcare service setting.