No Evidence on Association Between Prospective Exposure to Out-of-Pocket Cost Information and Appointment Cancelations or No-Shows: A Case-Control Pilot Study

Abstract

Health care price transparency aims to empower patients to make better-informed purchasing decisions. However, the prospective availability of patients’ out-of-pocket costs may lead to an increased rate of forgone care. The objective of this study was to examine whether obtaining a prospective out-of-pocket cost estimate is associated with the likelihood of canceling or not arriving at a scheduled outpatient health care appointment. We surveyed adult individuals with scheduled outpatient imaging appointments at a large health care system in Georgia. In this case-control pilot study, we estimated the adjusted association between obtaining an out-of-pocket cost estimate for a scheduled imaging appointment (did not obtain an estimate, did not seek an estimate but received it via an unsolicited phone call from the health care system, and actively sought and obtained an estimate) and not attending the appointment using multivariable logistic regression that controlled for the type of primary health insurance and patient demographics. Actively seeking an out-of-pocket cost estimate was not associated with appointment cancelation or no-show (adjusted odds ratio [aOR] = 0.81, P = .75). Passively receiving an out-of-pocket cost estimate via an unsolicited phone call from the health care system was marginally associated with lower odds of appointment cancelation or no-show (aOR = 0.24; P = .076). This study did not find evidence of an association between prospective exposure of patients to out-of-pocket cost information and the likelihood of health care appointment cancelation or no-show.

Keywords

health policy price transparency imaging health care utilization patient cost-sharing

Introduction

As insured health care consumers in the US are required to pay an increasing share of the cost of their care out of pocket, so has increased the desire to know their cost-sharing obligations before—instead of after—care is delivered.^1
-4 Federal regulations newly require all US hospitals and health insurance plans to disclose prices of “shoppable” health care services (ie, health care services that can be scheduled) and offer online tools to allow customers to obtain personalized out-of-pocket cost estimates.^5
-8 Simultaneously, some health systems across the US initiated efforts to provide prospective elective care patients with estimated out-of-pocket cost information beyond the federal requirements, likely to reduce the volume of unpaid bills or advertise their competitive prices.^9,10 Thus, patients may have several options to prospectively obtain information about the cost of their care, although the cost information they receive is not always accurate or reliable.^11,12

Providing patients with out-of-pocket cost information before care is delivered aims to empower them to make better-informed purchasing decisions. In an ideal world, this would mean that patients would compare the cost of health care services across various providers in their local health care market and ultimately choose to seek care from the provider that offers the best overall value. However, this also means that some patients may decide to forgo or delay care after learning their estimated out-of-pocket cost obligations due to the inability to pay the proposed amount, and the forgone care may eventually translate into adverse health outcomes that may be associated with even higher out-of-costs.¹³ On the one hand, transparent price information is a necessary condition for patients to exercise their autonomy.¹⁴ Because patients in the US have been responsible for increasing portions of the cost of their care—and cost is a form of harm—experts have argued that health care price transparency has considerable importance.^14,15 On the other hand, patients may not make choices in their best interest when confronted with a seemingly high price because they may not fully understand the health and financial consequences forgoing care.^16,17

The potential for increased rates of forgone and delayed care has been a major concern since the inception of health care price transparency efforts in the US, but so far, evidence on this important outcome has been limited. To better understand consumer behavior within this context, we conducted a pilot study to investigate whether obtaining an out-of-pocket cost estimate was associated with the likelihood of a scheduled outpatient appointment cancelation or no-show. We conducted this study in the setting of outpatient advanced diagnostic imaging—a common, shoppable health care service with non-negligible cost implications for many patients.

Methodology

Conduct of this observational survey-based case-control study was approved by the Emory University Institutional Review Board (IRB). Informed consent was obtained from all research participants before initiating research activities. Individuals who were surveyed online or using a paper-based questionnaire provided written consent. For individuals who were surveyed via phone, the IRB waived the requirement for documentation of signature in the informed consent but required us to obtain verbal consent using an approved script. We recruited study subjects between February 2022 and January 2023 from a pool of adult individuals with scheduled outpatient magnetic resonance imaging (MRI) appointments at one of the imaging centers of a large metropolitan health care system in Georgia, where MRI was the most common type of imaging performed.

Patients scheduled for imaging had multiple options to obtain out-of-pocket cost estimates before their appointments. Since 2018, the health care system where this study was conducted phoned outpatients at least 14 days before their scheduled appointment to inform them about the estimated out-of-pocket costs for the visit. In compliance with the Centers for Medicare and Medicaid Services hospital price transparency rule,⁵ the health care system also launched in 2021 an online price estimator with the ability to integrate with most health plans and provide patients with out-of-pocket cost estimates. Finally, patients had the option to obtain an out-of-pocket cost estimate by calling the health care system’s billing office and also by inquiring with their health insurer.

We offered participation in this research study via a recruitment letter mailed to the address in their electronic medical records to all adult individuals who either attended or missed their scheduled appointments during the study period. Individuals who rescheduled their appointments were not eligible to participate in this research until they ultimately attended or missed their appointments. To minimize recall bias, we sent recruitment letters to eligible individuals within 7 days of their appointments. To maximize the response rate, individuals who did not respond to the recruitment letter were telephoned up to 3 times during different times of the day.

Respondents to the recruitment letter who canceled or missed their scheduled appointments were designated as cases, and those who attended their scheduled appointments were designated as controls. For ease of interpretation, however, we refer to cases as “cancelers” and to controls as “attenders” throughout this manuscript. We surveyed cancelers via phone and attenders using Research Electronic Data Capture (REDCap)—an online survey platform—or paper-based questionnaires. Survey respondents were asked about their (1) source and use of health care price transparency tools in anticipation of their imaging appointment, (2) anticipated out-of-pocket cost amount for the scheduled health care encounter before having received medical bills (regardless of obtaining an out-of-pocket cost estimate in advance of the health care encounter), (3) health insurance status, and (4) demographic information such as age, gender, race, ethnicity, highest level of education, and household income. Cancelers were also asked about the reasons that they did not attend their appointment. Survey questions inquiring about the use of price transparency tools and anticipated out-of-pocket cost amount were developed by the research team and pilot-tested in a small group of lay persons (n = 5). Demographic questions were validated in prior research.^4,18 The survey instruments for cancelers and attenders are included in sections A and B of the Appendix, respectively. Cancelers received $5 for responding to the phone-based survey. Attenders received $20 for completing the online or paper-based survey, which was longer because it was used to collect additional data beyond the scope of the research reported in this manuscript.

We measured prospective exposure to out-of-pocket cost information using a categorical variable with the following categories: (1) the patient did not obtain an out-of-pocket cost estimate in advance, (2) the patient did not seek an out-of-pocket cost estimate but received it via a phone call from the health care system, and (3) the patient actively sought and obtained an out-of-pocket cost estimate (eg, by using online price transparency tools, calling the hospital’s billing department, or contacting their health insurer). The primary outcome was a binary indicator of whether or not individuals attended their scheduled imaging appointments. The secondary outcome was the out-of-pocket cost amount that patients anticipated to incur for their imaging appointments.

Statistical Analysis

We summarized the collected data using appropriate descriptive statistics. We analyzed only responses with no missing values in the exposure and outcome variables. Missing values in patient demographics were replaced using the hot-deck imputation technique before the analysis.¹⁹ We estimated the association between obtaining a prospective out-of-pocket cost estimate and appointment cancelation or no-show using a multivariable logistic regression analysis. We estimated the association between obtaining a prospective out-of-pocket cost estimate and patients’ anticipated out-of-pocket cost amount for their imaging appointment using a multivariable generalized linear model with the log link and the Poisson distribution functions. All multivariable models controlled for the type of primary health insurance and patient demographics. The analysis was performed using Stata, version 18.0. We followed the STROBE case-control reporting guidelines.²⁰

Results

Study Sample

We contacted 524 individuals who canceled or did not arrive at their scheduled outpatient imaging appointments (cases/cancelers) and 1257 patients who arrived at their appointments (controls/attenders). Of those contacted, 63 cancelers and 71 attenders completed surveys, resulting in response rates of 12.0% and 5.6%, respectively. A comparison of survey responders and non-responders on key observable characteristics is summarized in Supplemental Table C1 in the appendix. In brief, responders were similar to non-responders in terms of age and gender but differed in race and ethnicity. Among cancelers, survey responders also differed from non-responders in the type of health coverage. Three cancelers and 1 attender did not answer the question regarding whether they had obtained an out-of-pocket cost estimate before their imaging appointment—the main exposure variable—resulting in the final analytic sample comprised of 60 cancelers and 70 attenders. Sample characteristics stratified by study group are described in Table 1.

Table 1.

Sample Characteristics.

Characteristic	Cases: Canceled or missed a scheduled appointment (n = 60)	Controls: Arrived at scheduled appointments (n = 70)
Age; mean (SD)	57.0 (14.9)	56.3 (17.0)
Gender; n (%)
Female	32 (53.3%)	47 (67.1%)
Male	26 (43.3%)	22 (31.4%)
Unknown	2 (3.3%)	1 (1.4%)
Race and ethnicity; n (%)
Asian	1 (1.7%)	4 (5.7%)
Black, non-Hispanic	28 (46.7%)	18 (25.7%)
White, Hispanic	2 (3.3%)	1 (1.4%)
White, non-Hispanic	20 (33.3%)	46 (65.7%)
Other	8 (13.3%)	1 (1.4%)
Unknown	1 (1.7%)	–
Highest level of education; n (%)
High-school or less	8 (13.3%)	11 (15.7%)
Some college or 2 year degree	22 (36.7%)	12 (17.4%)
College graduate	14 (23.3%)	17 (24.3%)
More than a college degree	14 (23.3%)	30 (42.9%)
Unknown	2 (3.3%)	–
Annual household income; n (%)
<$15 000	6 (10.0%)	8 (11.4%)
$15 000 to $29 999	6 (10.0%)	7 (10.0%)
$30 000 to $59 999	9 (15.0%)	9 (12.9%)
$60 000 to $99 999	14 (23.3%)	16 (22.9%)
$100 000 or more	14 (23.3%)	22 (31.4%)
Unknown	11 (18.3%)	8 (11.4%)
Type of primary health insurance; n (%)
Private	32 (53.3%)	32 (45.7%)
Medicare (traditional)	15 (25.0%)	19 (27.1%)
Medicare advantage	4 (6.7%)	12 (17.1%)
Medicaid	6 (10.0%)	5 (7.1%)
Veterans/Military	3 (5.0%)	1 (1.4%)
Uninsured	–	1 (1.4%)

Seeking Out-of-Pocket Cost Information

Attenders were as likely as cancelers to have actively sought an out-of-pocket cost estimate before their scheduled imaging appointment (n = 11, 15.7%, vs n = 9, 15.0%). However, attenders were more likely than cancelers to have obtained the out-of-pocket cost estimate by an unsolicited phone call from their health care provider (n = 9, 12.9%, vs 3, 5.0%; Table 2).

Table 2.

Association Between Exposure to Prospective Out-of-Pocket Cost Information and Appointment Cancelation or No-Show.

Exposure	Cases: Canceled or missed a scheduled appointment; n (%)	Controls: Arrived at scheduled appointments; n (%)	Adjusted OR(95% CI)	P-value
Did not obtain an out-of-pocket cost estimate in advance	48 (80.0%)	50 (71.4%)	Ref.	–
Did not seek an out-of-pocket cost estimate, but received it via a phone call from the health care system	3 (5.0%)	9 (12.9%)	0.24 (0.05-1.16)	.076
Actively sought and obtained an out-of-pocket cost estimate	9 (15.0%)	11 (15.7%)	0.81 (0.25-2.70)	.75

Note. The reported odds ratios have been adjusted for patients’ age, gender, level of education, household income, and type of primary health insurance using a multivariable logistic regression model.

OR = odds ratio; CI = confidence interval.

Association Between Obtaining an Out-of-Pocket Cost Estimate and Visit Attendance

After adjusting for primary health insurance type and patient demographics, actively seeking an out-of-pocket cost estimate was not associated with appointment cancelation or no-show (adjusted odds ratio [aOR] = 0.81; 95% confidence interval [CI]: 0.25-2.70; P = .75; Table 2). Receiving an out-of-pocket cost estimate passively via an unsolicited phone call from a health care provider was marginally associated with lower odds of canceling or not arriving at a scheduled appointment compared to not having obtained an out-of-pocket cost estimate at all (aOR = 0.24; 95% CI: 0.05-1.16; P = .076). The full model estimates are presented in Supplemental Table C2 in the Appendix.

Reasons for Not Attending Scheduled Appointments

Of the 12 cancelers who obtained an out-of-pocket cost estimate, 7 (58.3%) individuals indicated that the primary reason for not attending their scheduled appointment was the amount of the anticipated out-of-pocket cost. Of these 7 individuals, 4 (57.1%) stated that they were unable to afford the anticipated out-of-pocket cost, which ranged from $60 to $1400 with a mean of $640 (standard deviation [SD] = 558). The remaining 3 (42.9%) responded that they or their health insurance plan had found a less expensive health care provider. Nevertheless, we were unable to verify whether patients completed the imaging exam at the alternative location.

None of the 48 cancelers who did not obtain an out-of-pocket cost estimate indicated cost as a reason for their cancelation or no-show. Their reasons for not arriving at the scheduled outpatient imaging appointment included: communication problems (eg, not receiving reminders, receiving incorrect information about the appointment; n = 14; 29.2%), health issues (eg, being too sick at the time of the scheduled appointment, suffering from claustrophobia or other physical limitations, competing health priorities such as cancer treatment; n = 10; 20.8%), scheduling and timing issues (eg, conflicts with other appointments, conflicts with work schedules, late scheduling, appointments being too far out; n = 8; 16.7%), insurance denials (n = 6; 12.5%), transportation and accessibility issues (n = 6; 12.5%), or administrative errors (eg, scheduling the wrong type of MRI, failing to send necessary work orders; n = 2; 4.2%). The remaining 2 cancelers did not provide a reason for their appointment cancelation or no-show.

Association Between Obtaining an Out-of-Pocket Cost Estimate and the Anticipated Out-of-Pocket Cost Amount

Individuals who obtained an out-of-pocket cost estimate before their scheduled appointment anticipated incurring a higher out-of-pocket cost (mean = $571, SD = 695, among those who actively sought the out-of-pocket cost estimate; mean = $510, SD = 534, among those who passively received the out-of-pocket cost estimate) than those who did not obtain the out-of-pocket cost estimate (mean = $234, SD = 422). After adjusting for primary health insurance type and patient demographics, those who actively sought the out-of-pocket cost estimate and those who passively received it anticipated incurring $214 (95% CI: $200-$227) and $221 (95% CI: $205-236) more than those who did not obtain the out-of-pocket cost estimate, respectively (Table 3). The full model estimates are presented in Supplemental Table C3 in the Appendix.

Table 3.

Patients’ Anticipated Out-of-Pocket Cost for an Outpatient Imaging Appointment, by Exposure to Prospective Out-of-Pocket Cost Information.

Exposure	Unadjusted mean(95% CI)	Adjusted mean(95% CI)	Adjusted difference(95% CI)	P-value
Did not obtain an out-of-pocket cost estimate in advance	234 (110-358)	259 (254-264)	Ref.	–
Did not seek an out-of-pocket cost estimate, but received it via a phone call from the health care system	510 (171-850)	480 (466-493)	221 (205-236)	<.001
Actively sought and obtained an out-of-pocket cost estimate	571 (225-917)	473 (462-484)	214 (200-227)	<.001

Note. The reported means have been adjusted for patients’ age, gender, level of education, household income, and type of primary health insurance using a multivariable generalized linear model with the log link and Poisson distribution functions.

CI = confidence interval.

Discussion

The negative clinical, financial, and disparity-worsening impacts of cost-related medical non-adherence are well-documented.^13,21
-23 When prices of health care services are opaque, some patients delay or forgo care because they know from prior experiences they would be unable to afford it, others delay or forgo care because of cost uncertainty and the inherent mere possibility that they might be unable to afford it. Health care price transparency initiatives have been designed to address the latter scenario by reducing the cost uncertainty for patients, which simultaneously raises concerns about the possibility of worsening the former scenario. Because strengthening and expanding health care price transparency requirements is expected to be a key priority in the health policy space for the upcoming US administration, understanding the effects of enabling patients to obtain prospective out-of-pocket cost information on medical appointment attendance is timely and policy-relevant.²⁴

The existing evidence on the influence of prospective availability of out-of-pocket cost information on medical appointment cancelation or no-show is inconclusive. A recent cross-sectional study of 532 patients found that patients who obtained an out-of-pocket cost estimate before an imaging examination were more likely to cancel or not arrive at their appointments.²⁵ In contrast, another study of 470 422 imaging encounters found that patients for whom an out-of-pocket cost estimate was generated in an online patient portal were more likely to complete their imaging exams.²⁶ This discrepancy may be due to socioeconomic differences in studied patient populations or due to uncontrolled patient-, provider-, or system-level factors.

In the current study, individuals who obtained an out-of-pocket cost estimate anticipated incurring almost double the amount compared to those who did not obtain an out-of-pocket cost estimate, although this result is most likely due to selection among both active seekers and passive recipients of out-of-pocket cost estimates. Nonetheless, having obtained an out-of-pocket cost estimate was not associated with an increased likelihood of appointment cancelation or no-show despite the higher anticipated out-of-pocket cost obligations among those who obtained the estimate. This finding tempers the concerns that providing patients with prospective information on out-of-pocket costs would lead to increased rates of forgone or delayed care—a major critique of health care price transparency efforts.

In fact, this study found that receiving an out-of-pocket cost estimate passively via an unsolicited phone call from a health care provider was marginally associated with lower odds of canceling or not arriving at scheduled appointments compared to having not obtained the cost estimate at all. One prior study collected data on the source of upfront out-of-pocket cost information and whether patients attended their scheduled appointments.²⁵ Although that study did not explicitly estimate the association between passive receipt of out-of-pocket cost information and appointment cancelation or no-show, their reported data align with our findings. A possible explanation of this observed association is that patients who do not actively seek to obtain an out-of-pocket cost estimate may not be too concerned about the cost of their care, and receiving cost information engages them in attending their scheduled appointments. This finding further challenges the hypothesis that providing patients with upfront cost information would discourage care consumption.

Although we did not find an association between prospective out-of-pocket cost disclosure and increased likelihood of appointment cancelation or no-show, it is important to acknowledge that some patients in our study did act on the prospectively obtained cost information, pointing to a potential effect heterogeneity across patient groups. Over half of the patients in this study who obtained an out-of-pocket cost estimate and did not attend their appointments indicated cost as the main reason for the appointment cancelation. Some of the proactive out-of-pocket cost seekers who eventually canceled their appointments indicated that they were able to get the same procedure at a better price from a different health care provider, documenting that some patients were willing and able to engage in price-shopping—an outcome often perceived as difficult to achieve.²⁷ If patients did indeed undergo the procedure elsewhere—which we were unable to confirm—a cancelation of a more costly appointment is a desirable outcome from both the patient and payer perspectives.

Nevertheless, other patients in our study indicated that they had to cancel their imaging appointment because they were simply unable to afford it. Such a financial barrier to care is concerning as not receiving provider-ordered tests may have negative downstream health and financial consequences.^16,17 However, had these patients unable to afford their out-of-pocket cost obligation not received the estimate in advance, they might have undergone the procedure and subsequently received medical bills they would be unable to pay, forcing them into medical debt, which is associated with adverse consequences.²⁸ This finding highlights the tensions and tradeoffs involved in prospective cost disclosure to patients that warrant thoughtful approaches to improving access to care while preserving patient autonomy.

Limitations

This exploratory pilot study was not without limitations. First, the observational design of this study did not allow causal interpretation of the estimated associations. For the same reason, the estimates may also be affected by residual confounding due to unobserved differences between study groups. Future research will need to establish the causal effect—or the lack thereof—of providing patients with prospective out-of-pocket cost information on their decisions to consume medical care. Second, certain limitations of the electronic medical system at our institution influenced our choice of the retrospective case-control study design instead of a more powerful prospective cohort design. For the same reason, this research also had to rely on patient self-reported exposures to cost information.

Third, we used different modes of survey for cancelers and attenders because contacted cancelers in the early phases of this research were generally not willing to complete an asynchronous online survey. The different data collection methods for the 2 study groups may have introduced social desirability bias among cancelers who were surveyed by a human data collector. Fourth, as in any survey-based research, the imperfect response rate raises concerns regarding potential selection bias. Because outpatients at our institution were required to complete a basic demographic questionnaire at the time of scheduling an appointment, we were able to compare survey responders to non-responders across select observable characteristics. Although responders were similar to non-responders in terms of age and gender, they differed in terms of race and ethnicity. It is thus possible that survey non-responders had different reasons for appointment cancelation or no-show than responders, which may have also impacted their ability to respond to the survey.

Fifth, the estimated associations mostly did not reach the conventional level of statistical significance, possibly due to the limited statistical power of this pilot study. The sample size in this pilot study was determined by budget constraints rather than an ex-ante power analysis, partially because key parameters that would be needed to conduct a power analysis had been unknown. Findings from this pilot study now fill that gap.

Sixth, our findings may not generalize to other types of health care services and settings. Patients likely consider a variety of factors besides the cost of care—such as the urgency of their health care needs—when deciding whether to forgo care. Subjects for this study were recruited from an outpatient non-emergency imaging clinic, and thus, none of their appointments were critically urgent. Nevertheless, the unmeasured differences in the perceived urgency or severity of the health condition for which imaging was requested could still have influenced patients’ decisions and thus, biased our estimates.

Seventh, we did not collect information on the generosity of patients’ health insurance plans beyond the type of their primary health insurance or whether patients possessed supplemental health insurance coverage, which could further protect them from exposure to the cost of their care. Eight, we did not assess the accuracy of obtained out-of-pocket cost estimates, which may have varied depending on the source. Nevertheless, the decision to attend or not a scheduled health care appointment is likely influenced mainly by the value of the provided estimate regardless of its accuracy, which can be assessed only after care has been delivered and medical bills issued.

Finally, findings from this single-site pilot study may not generalize to other geographic regions of the United States or beyond. In spite of these limitations, we believe our findings provide valuable insight into patients’ use of prospective cost information and guide future policymaking and research on patient decision-making based on available cost information.

Conclusion

This pilot study examined whether obtaining a prospective out-of-pocket cost estimate was associated with the likelihood of canceling or not arriving at a scheduled outpatient appointment. We found that actively seeking an out-of-pocket cost estimate was not associated with appointment cancelation or no-show, while receiving an estimate passively via an unsolicited phone call from a health care provider was marginally associated with lower odds of appointment cancelation or no-show compared to not having obtained an estimate at all. These findings temper the concerns that providing patients with prospective information on out-of-pocket costs would lead to increased rates of forgone or delayed care. Although more evidence is necessary to fully understand patients’ decision-making processes depending on possessing prospective out-of-pocket cost information, this study provides a timely and policy-relevant contribution to the evolving literature on the effects of health care price transparency in a market-driven health care system.

Supplemental Material

sj-docx-1-inq-10.1177_00469580251320174 – Supplemental material for No Evidence on Association Between Prospective Exposure to Out-of-Pocket Cost Information and Appointment Cancelations or No-Shows: A Case-Control Pilot Study

Supplemental material, sj-docx-1-inq-10.1177_00469580251320174 for No Evidence on Association Between Prospective Exposure to Out-of-Pocket Cost Information and Appointment Cancelations or No-Shows: A Case-Control Pilot Study by Brae Mitchell, Gelareh Sadigh, A. Mark Fendrick and Michal Horný in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Acknowledgements

The authors thank Debrua Coleman, PhD, for assistance with project coordination and data collection activities. Preliminary findings from this research were presented at the 9th Annual Health Services Research Day held at Emory University, Atlanta, GA on May 3, 2023.

Author contribution statement

Conceptualization: GS, MH

Data curation: BM, MH

Formal analysis: BM, MH

Funding acquisition: GS, MH

Investigation: BM, GS, AMF, MH

Methodology: GS, MH

Project administration: BM, MH

Resources: GS, MH

Software: MH

Supervision: AMF

Validation: MH

Visualization: BM, MH

Writing—original draft: BM, MH

Writing—review & editing: GS, AMF

Data availability statement

Deidentified data are available upon request from the corresponding author.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Ms. Mitchell reported no conflict of interest. Dr. Sadigh reported receiving research funding from the National Cancer Institute and an honorarium from the Journal of the American College of Radiology where she serves as an associate editor. Dr. Fendrick reported being a consultant for AbbVie, CareFirst BlueCross BlueShield, Centivo, Community Oncology Association, Covered California, EmblemHealth, EBRI, Exact Sciences, GRAIL, Harvard University, Health & Wellness Innovations, Health at Scale Technologies, Hopewell Fund, Johnson and Johnson, Medtronic, MedZed, Merck, Mother Goose Health, Phathom Pharmaceuticals, Proton Intelligence, RA Capital, Sempre Health, the state of Minnesota, US Department of Defense, Virginia Center for Health Innovation, Washington Health Benefit Exchange, Wellth, Yale–New Haven Health System, and Zansors; receiving research support from the Agency for Healthcare Research and Quality, Gary and Mary West Health Policy Center, Arnold Ventures, National Pharmaceutical Council, Patient-Centered Outcomes Research Institute, Pharmaceutical Research and Manufacturers of America, Robert Wood Johnson Foundation, state of Michigan, and Centers for Medicare & Medicaid Services; serving as co–editor in chief of the American Journal of Managed Care; and being a partner in VBIDHealth, LLC. Dr. Horný reported receiving research support from Health Care Cost Institute Inc., the Commonwealth Fund, the Centers for Disease Control and Prevention, and the National Center for Advancing Translational Sciences for unrelated work; receiving speaker honoraria and travel support from Masaryk University, Brno, Czech Republic, and the Czech Academy of Sciences, Prague, Czech Republic; receiving payments from the American Medical Association for statistical review of manuscripts; and receiving consulting fees from VBIDHealth, LLC.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a Seed Grant Program of the Emory University School of Medicine, Department of Radiology & Imaging Sciences (PI Horný & Sadigh).

Ethical approval

Conduct of this research was approved by the Emory University Institutional Review Board (#STUDY00003540) on December 20, 2021.

Consent to participate

Informed consent was obtained from all research participants before initiating research activities. Individuals who were surveyed online or using a paper-based questionnaire provided written consent. For individuals who were surveyed via phone, the IRB waived the requirement for documentation of signature in the informed consent but required us to obtain verbal consent using an approved script, answers to which were recorded in an Excel spreadsheet by the research staff.

ORCID iDs

Gelareh Sadigh

Michal Horný

Supplemental Material

Supplemental material for this article is available online.

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