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Abstract

Objective:

This study characterized telemedicine utilization among Medicaid enrollees by patients’ demographic characteristics, geographic location, enrollment type, eligibility category, and clinical conditions.

Methods:

This study used 2008–2009 Medicaid claims data from 28 states and the District of Columbia to characterize telemedicine claims (indicated by GT for professional fee claims or Q3014 for facility fees) on the basis of patients’ demographic characteristics, geographic location, enrollment type, eligibility category, and clinical condition as indicated by ICD-9 codes. States lacking Medicaid telemedicine reimbursement policies were excluded. Chi-square tests were used to compare telemedicine utilization rates and one-way analysis of variance was used to estimate mean differences in number of telemedicine encounters among subgroups.

Results:

A total of 45,233,602 Medicaid enrollees from the 22 states with telemedicine reimbursement policies were included in the study, and .1% were telemedicine users. Individuals ages 45 to 64 (16.4%), whites (11.3%), males (8.5%), rural residents (26.0%), those with managed care plans (7.9%), and those categorized as aged, blind, and disabled (28.1%) were more likely to receive telemedicine (p<.001). Nearly 95% of telemedicine claims were associated with a behavioral health diagnosis, of which over 50% were for bipolar disorder and attention-deficit disorder or attention-deficit hyperactivity disorder (29.3% and 23.4%, respectively). State-level variation was high, ranging from .0 to 59.91 claims per 10,000 enrollees (Arkansas and Arizona, respectively).

Conclusions:

Despite the touted potential for telemedicine to improve health care access, actual utilization of telemedicine in Medicaid programs was low. It was predominantly used to treat behavioral health diagnoses. Reimbursement alone is insufficient to support broad utilization for Medicaid enrollees.
Telemedicine has been in use for decades, and its potential to improve health care access and to reduce costs has propelled it into the ongoing health care reform discussion (1,2). Telemedicine has the potential to improve health outcomes for vulnerable populations, especially those that lack access to primary care providers and mental health specialists because of geographic isolation and mobility impairments (3). Telepsychiatry is one of the most promising and best-studied applications of telemedicine and might help address behavioral health workforce shortages facing many regions (47). Even with rapidly growing evidence supporting telemedicine, its use in Medicare has been historically low, with only about 14,000 beneficiaries having a claim for telemedicine in 2009 (3).
Medicaid is the leading payer for mental health services in the United States, but even when there is a payment source, lack of access to mental health services due to provider shortages is a persistent problem (8). Some states leverage telepsychiatry services to ease emergency department congestion and coordinate mental health services, often preventing expensive inpatient services (9). Telepsychiatry has also been effective in reducing Medicaid costs and the need for psychotropic medication among children with high needs (10). Collaborative care that includes video access to behavioral health specialty providers has the potential to increase access, improve quality, and lower costs, but adoption of the model is limited by the lack of enabling policies, reimbursement options, clinical training and education, and technology at the point of care.
Despite telemedicine’s potential to improve access to treatment and the fact that most state Medicaid programs pay for at least some forms or components of telepsychiatry, there has been limited investigation about the extent to which these health care innovations reach underserved populations. In fact, we could find no studies specifically measuring telemedicine utilization in state Medicaid programs across the United States. The purpose of this study was to describe the characteristics of Medicaid patients who had used telemedicine by analyzing Medicaid claims. Patients’ demographic characteristics, geographic location, enrollment type, basis of eligibility, and clinical conditions were examined.

Methods

Data and Study Population

This study used claims data extracted from the 2008 and 2009 Medicaid Analytic Extract files obtained from the Centers for Medicare and Medicaid Services. From the Medicaid personal summary file, we identified individuals ages 0 to 64 residing in Alabama, Arkansas, Arizona, California, Colorado, Connecticut, Florida, Georgia, Illinois, Indiana, Louisiana, Massachusetts, Maryland, Michigan, Mississippi, Missouri, New Jersey, New Mexico, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, Washington, D.C., and Washington. Patients with both Medicare and Medicaid (dual eligible) were excluded. Patients enrolled in fee-for-service or managed care Medicaid for at least one month were included. The rural-urban continuum codes from the Area Health Resources File were used to distinguish individuals residing in big metropolitan, small metropolitan, and nonmetropolitan (rural) areas.
Telemedicine utilization was established by using GT, the Current Procedural Terminology (CPT) modifier indicating a claim for professional services, and Q3014, the Healthcare Common Procedure Coding System (HCPCS) code indicating a claim for the facility fee (11). Both types of claims could be submitted for the same encounter, and state Medicaid plans varied in their reimbursement rates for both types of claims. Telemedicine utilization rates and encounter rates were calculated on the basis of total number of Medicaid enrollees. An initial frequency analysis showed that a number of states in the 2008 data set had zero claims for telemedicine, an indication that the state did not reimburse for this service. To make sure that we excluded states lacking reimbursement policies for telemedicine, we supplemented the 2008 claims data with the findings of a 2006 study of Medicaid reimbursement policies by Brown (12). States that had no Medicaid policies for telemedicine reimbursement according to the Brown study and no Medicaid claims for telemedicine in the 2008 data were assumed not to have a Medicaid reimbursement policy for telemedicine in 2008 and were excluded from the analysis. States that did not have Medicaid reimbursement policies according to the 2006 study but that had Medicaid telemedicine claims in 2008 were assumed to have adopted a policy between 2006 and 2008 and therefore were included in the study. On the basis of these criteria, 22 states were included in the descriptive analysis: Alabama, Arkansas, Arizona, California, Colorado, Florida, Georgia, Illinois, Indiana, Louisiana, Michigan, Missouri, North Carolina, New Mexico, New York, Oklahoma, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, and Washington.

Statistical Analysis

We collected descriptive statistics to characterize the sample by telemedicine records, demographic covariates, ICD-9 codes, and state-level variation in telemedicine encounters. Chi-square tests were used to compare telemedicine utilization rates and one-way analysis of variance was utilized to estimate differences in mean number of telemedicine encounters among subgroups of Medicaid enrollees. All analyses were performed by using SAS, version 9.3.

Results

Demographic Characteristics and Telemedicine Utilization

Table 1 describes the demographic characteristics of the study population. Table 2 shows differences in both telemedicine utilization rates and average number of telemedicine encounters across subgroups. Five times as many enrollees lived in metropolitan areas compared with rural areas, but individuals living in the rural areas were 17 times more likely to receive telemedicine services compared with individuals in the big metropolitan areas. Similarly, although a small proportion of enrollees qualified for Medicaid on the basis of the aged, blind, and disabled eligibility category, they were four and six times more likely than enrollees in the adult and child categories, respectively, to receive health care services via telemedicine. The results showed significant racial and ethnic disparities, with white enrollees two times as likely as African-American and Hispanic enrollees and 16 times as likely as Asian patients to receive services via telemedicine.
TABLE 1. Characteristics of 45,233,602 Medicaid enrollees from 22 states with Medicaid reimbursement policies for telemedicine, 2008–2009
CharacteristicN%
Age group  
 0–1726,468,45758.5
 18–299,481,83821.0
 30–445,744,28912.7
 45–643,539,0187.8
Race-ethnicity  
 White15,865,49635.1
 African American10,326,60022.8
 Hispanic12,126,25226.8
 Asian1,262,5202.8
 Other5,652,73412.5
Sex  
 Female26,546,42858.7
 Male18,687,17441.3
Metropolitan area  
 Big25,327,08756.0
 Small13,139,04229.0
 Rural6,767,47315.0
Plan type  
 Fee for service24,598,50654.4
 Managed care20,635,09645.6
Basis of eligibility  
 Aged, blind, and disabled4,399,5869.7
 Child26,474,80758.5
 Adult14,154,53631.3
 Unknown74,201.2
 Not eligible130,472.3
Telemedicine utilization  
 Yes33,329.1
 No45,200,27399.9
TABLE 2. Utilization of telemedicine among 45,233,602 Medicaid enrollees from 22 states with Medicaid reimbursement policies for telemedicine, 2008–2009, by enrollee characteristic
CharacteristicNTelemedicine utilization rateapTelemedicine encounter ratebp
Total45,233,6027.4 28.7 
Age group  <.001 <.001
 0–1726,468,4575.9 22.7 
 18–299,481,8385.8 22.7 
 30–445,744,28911.1 44.8 
 45–643,539,01816.4 63.6 
Race-ethnicity  <.001 <.001
 White15,865,49611.3 45.8 
 African American10,326,6004.8 17.7 
 Hispanic12,126,2525.8 19.9 
 Asian1,262,520.7 2.5 
 Other5,652,7345.9 25.4 
Sex  <.001 <.001
 Female26,546,4286.6 24.9 
 Male18,687,1748.5 34.1 
Metropolitan area  <.001 <.001
 Big25,327,0871.5 7.9 
 Small13,139,0429.0 29.1 
 Rural6,767,47326.0 105.9 
Plan type  <.001 <.001
 Fee for service24,598,5066.9 27.7 
 Managed care20,635,0967.9 29.9 
Enrollment type  <.001 <.001
 Aged, blind, and disabled4,399,58628.1 108.0 
 Child26,474,8074.6 16.8 
 Adult14,106,1596.2 26.5 
 Unknown74,2012.4 7.3 
 Not eligible130,4723.8 29.5 
a
Rate of utilization per 10,000 enrollees
b
Mean encounters per 10,000 enrollees per year

Common Diagnoses

Table 3 lists the top 20 ICD-9 codes for which telemedicine was used. The top 20 diagnosis codes accounted for 95% of all telemedicine claims. Nearly 93% of telemedicine encounters were for treatment of behavioral health diagnoses. The two diagnosis codes used most frequently in telemedicine claims, bipolar disorder and attention-deficit disorder (ADD) or attention-deficit hyperactivity disorder (ADHD), accounted for just over 50% of all telemedicine claims (29.3% and 23.4%, respectively).
TABLE 3. Diagnosis codes for 101,769 claims for telemedicine among Medicaid enrollees from 22 states with Medicaid reimbursement policies for telemedicine, 2008–2009
RankICD–9 codeDiagnosisN%
1296Bipolar disorder29,82129.3
2314ADD or ADHDa23,80123.4
3295Schizophrenia7,3197.2
4309Adjustment disorder6,2586.1
5304Opioid dependence5,2625.2
6300Anxiety4,7744.7
7313Overanxious disorder4,7194.6
8311Depressive disorder4,5154.4
9312Undersocialized conduct disorder3,4623.4
10299Autistic disorder2,1452.1
11298Depressive-type psychosis1,2691.2
12–20305, 303, 293, 250, 995, V61, 692, 706, and 307Nondependent alcohol abuse, acute alcohol intoxication, delirium, diabetes mellitus, other anaphylactic reaction, family disruption, contact dermatitis, acne varioliformis, and adult onset fluency disorder4,204<1%
a
Attention-deficit disorder or attention-deficit hyperactivity disorder

State-Level Variation

Table 4 shows the number of Medicaid enrollees who received telemedicine services by state. These claims showed high levels of variation of telemedicine utilization. In seven states (Arizona, Missouri, New Mexico, North Carolina, Oklahoma, Tennessee, and Texas), more than ten per 10,000 enrollees had a telemedicine encounter. In the remaining 15 states, fewer than ten per 10,000 enrollees received telemedicine services.
TABLE 4. Telemedicine encounters among 45,233,602 Medicaid enrollees in 22 states with Medicaid reimbursement policies for telemedicine, 2008–2009
StateEnrollees with telemedicineTotal enrolleesRate per 10,000 enrollees
AL16807,169.20
AR0702,405.00
AZ10,1861,700,12759.91
CA1,54610,281,4501.50
COa640,333.02
FL1313,006,115.44
GA9301,772,4305.25
IL1452,505,678.58
IN1961,115,0291.76
LA171,147,169.15
MI4071,988,5662.05
MO2,0211,014,95619.91
NC5,4841,711,76032.04
NM1,933578,97233.39
NY984,465,999.22
OK1,292814,33215.87
PA172,094,261.08
SC263880,9212.99
TN3,1931,415,19922.56
TX4,5074,502,02610.01
VA830900,5319.22
WA1161,188,174.98
Total33,32945,233,6027.37
a
Actual number was not reported to preserve patient privacy.

Discussion

The results of this descriptive analysis raise a number of important issues warranting further discussion and research. Most significant, the current findings show that in 2008–2009, telemedicine was predominantly used in the Medicaid population to treat mental health conditions, specifically bipolar disorder and ADD or ADHD. According to the media, professional organizations, and the scientific literature, telemedicine is used for a number of primary care, emergency, and specialty conditions, yet these findings show otherwise (1315). Neufeld and Doarn’s (16) analysis of 2012 Medicare claims found a similar—yet less extreme—proportion, with mental health professional fees accounting for 70% of total expenditures for telemedicine.
The disproportionate use of telemedicine for behavioral health services in the Medicaid program may have a number of explanations. First, the overall prevalence of mental health conditions is considerably higher in the Medicaid population than in the general population, in large part because of the social determinants of mental health and also because prior to enactment of the Affordable Care Act, most adult Medicaid enrollees were required to meet categorical eligibility criteria based on a disability (17). Second, the level of sophistication of the technology needed to provide mental health treatment via telemedicine is minimal compared with other telemedicine applications. In many cases, a mental health encounter can be conducted with a Webcam and a secure Internet connection, whereas treatment of general medical ailments, such as otitis media or heart disease, requires specialized equipment that is more costly and less widely available. Third, reimbursement policies in some states may incentivize the use of telemedicine for mental health services. For example, telemedicine mental health services are almost universally covered by Medicaid programs, whereas many other services are explicitly excluded for delivery via telemedicine (18). Conversely, some state-level policies disincentivize the use of telemedicine for mental health. In some states, only psychiatrists and psychologists are eligible for reimbursement, whereas licensed therapists, social workers, and counselors, who are often reimbursed for providing these services in person, are not paid for services delivered via telemedicine (18).
A striking profile emerged of the patient who was most likely to receive telemedicine services: a white male, age 45 to 64, who lives in a rural area with a managed care plan and who is eligible for Medicaid in the category of aged, blind, and disabled. This profile warrants further research because of some of the limitations of this study, including the overrepresentation of claims from a small number of states and the likely impact of confounding variables. The rural-urban disparity was likely due to a characterization of telemedicine as a solution to rural provider shortages, even though Medicaid enrollees living in urban areas face similar access issues, especially those in need of mental health providers.
Underutilization in urban areas may also align with Medicare reimbursement policies—whereas Medicare restricts reimbursement for telemedicine to nonmetropolitan statistical areas and health professional shortage areas, most state Medicaid policies do not make this distinction and reimburse for services provided in both urban and rural areas (18). A recent study by Neufeld and colleagues (19) found a substantial increase in Medicare utilization of telemedicine in Illinois following the expansion of the state’s Medicaid telemedicine reimbursement policy. Thus this relationship may go both ways—Medicare policies may also drive Medicaid utilization patterns. Further research is needed to test this hypothesis.
As was observed in this study, racial and ethnic disparities have been found in the use of new lifesaving innovations (20). However, the racial and ethnic variation found here was likely confounded by the urban-rural disparity. In contrast, telemedicine utilization was high for people with disabilities compared with the overall study population, which may indicate benefits of this technology for people with mobility impairments. Further research is needed to better understand the diffusion of telemedicine across diverse racial and ethnic populations and people with disabilities.
The results of this study highlight the chasm between the robust public discourse surrounding telemedicine policies and actual utilization of the services. When Medicare reimbursement of telemedicine became effective in 1999, expectations were high. Congress expected expenditures for telemedicine to reach at least $60 million in the first year, but only 301 encounters occurred and actual expenditures reached only $20,000 (16). Since then, Medicare reimbursement policies for telemedicine have been in flux, with some loosening of the geographic, provider, and CPT code limitations. Despite nearly 15 years of reimbursement, the low utilization rates for telemedicine persist even in the Medicare population. Neufeld and Doarn’s (16) analysis found only $5 million in total Medicare telemedicine expenditures in 2012, and the authors characterized the current state of nationwide utilization as merely “a pilot study.” Furthermore, as demonstrated by the lack of telemedicine claims in Arkansas despite a Medicaid reimbursement policy, state reimbursement practices may lag behind policy adoption for a number of reasons, including the possibility that no claims for telemedicine were submitted, claims were submitted but issues with third-party payers prevented payment, or the billing codes were not open or active despite the state policy. It is also possible that the state developed unique telemedicine modifier codes that were not picked up in this analysis, which only flagged the GT modifier and the Q3014 HCPCS code. On a more promising note, Adler-Milstein and colleagues (21) found that 42% of U.S. hospitals surveyed in 2012 by the American Hospital Association reported telehealth adoption. However, self-reported adoption may not directly correlate with utilization.
The low utilization of telemedicine by Medicaid providers and high level of variability among states in use of telemedicine demonstrate that reimbursement alone is insufficient to advance clinical practice. There are many obstacles (for example, legal, financial, cultural, and professional obstacles) to implementation of telemedicine services. Payment for services is a major step, but governments cannot simply reimburse their way to clinical practice changes. Best practices, clinical guidelines, education and training, and established practice networks may help bridge the gap between policy and practice. Ultimately, expansion of telemedicine requires further research into the impact of state-specific reimbursement and licensure policies on telemedicine utilization (22,23). Examination of how financial incentives affect the development of telemedicine services is needed, particularly the use of different fee structures for aspects of telemedicine. For example, the facility fees paid to presenting sites where the patient is located are typically much lower than the professional or service fees paid to distant sites where the provider is located. Nominal facility fees fail to cover the costs incurred by presenting sites, such as appointment scheduling and providing staff to serve as “presenters” of the patient to the distant provider during the telemedicine encounter. Without appropriate payment, potential presenting sites may decline to enter the telemedicine market, resulting in a limited patient supply. In addition, further research is needed to evaluate return on investment and to distinguish between short-term policy impacts versus long-term cost savings and improved health outcomes.
This study had all of the known limitations of claims-based health services research. There was also the potential that use of telemedicine was underestimated because of the use of the GT and Q3014 modifier codes to identify telemedicine services. Some states may have established state-specific procedure codes or modifiers that were not picked up in our analysis. In addition, issues with third-party intermediaries may require providers to submit claims without the telemedicine-specific modifiers in order to receive reimbursement, which may also have resulted in underestimation. The high level of state variation potentially skewed the results by overrepresentation of states with more telemedicine claims. For example, nearly one-third of the cases analyzed were accounted for by Arizona alone, and nearly two-thirds were accounted for by the addition of North Carolina, Texas, and Tennessee. Therefore, the patient profile that emerged from the data may have been confounded by geographic and state-level characteristics associated with this imbalance of claims. Finally, there is some indication that providers may be utilizing telemedicine without reimbursement, especially for facility fees and for patients in HMOs or other capitated-payment plans (24).

Conclusions

Telemedicine has tremendous potential to decrease access barriers for underserved communities and is the subject of much policy and practice debate, yet actual utilization of telemedicine in Medicaid programs in this study was low. The overwhelming majority of Medicaid telemedicine claims were for behavioral health treatments, a pattern similar to one seen in the Medicare program. Public policies that support the expansion of telemedicine services are needed, and further research is needed to determine how specific policies affect utilization. However, public policies alone are insufficient to maximize the potential of telemedicine. Best practices, clinical guidelines, education and training, and established practice networks will all be needed to support the clinical implementation of telemedicine services.

Footnote

The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of NIMHD or NIH.

References

1.
LeRouge C, Garfield MJ: Crossing the telemedicine chasm: have the US barriers to widespread adoption of telemedicine been significantly reduced? International Journal of Environmental Research and Public Health 10:6472–6484, 2013
2.
Bashshur RL, Shannon GW, Krupinski EA, et al: National telemedicine initiatives: essential to healthcare reform. Telemedicine Journal and e-Health 15:600–610, 2009
3.
The Role of Telehealth in an Evolving Health Care Environment: Workshop Summary. Washington, DC, Institute of Medicine, 2012
4.
Richardson LK, Frueh BC, Grubaugh AL, et al: Current directions in videoconferencing tele-mental health research. Clinical Psychology 16:323–338, 2009
5.
Zeller S: Models of psychiatric emergency care. Current Emergency and Hospital Medicine Reports 3:169–175, 2015
6.
Conn D, Gajaria A, Madan R: Telepsychiatry: effectiveness and feasibility. Smart Homecare Technology and TeleHealth 3:59, 2015
7.
Thiele JS, Doarn CR, Shore JH: Locum tenens and telepsychiatry: trends in psychiatric care. Telemedicine Journal and e-Health 21:510–513, 2015
8.
Thomas KC, Ellis AR, Konrad TR, et al: County-level estimates of mental health professional shortage in the United States. Psychiatric Services 60:1323–1328, 2009
9.
States leverage telepsychiatry solutions to ease ED crowding, accelerate care. ED Management 27:13–17, 2015
10.
Hilt RJ, Barclay RP, Bush J, et al: A statewide child telepsychiatry consult system yields desired health system changes and savings. Telemedicine Journal and e-Health 21:533–537, 2015
11.
Medicare Payment of Telemedicine and Telehealth Services. Washington, DC, American Telemedicine Association, 2013. https://www.americantelemed.org/docs/default-source/policy/medicare-payment-of-telemedicine-and-telehealth-services.pdf
12.
Brown NA: State Medicaid and private payer reimbursement for telemedicine: an overview. Journal of Telemedicine and Telecare 12(suppl 2):S32–S39, 2006
13.
Wootton R: Twenty years of telemedicine in chronic disease management—an evidence synthesis. Journal of Telemedicine and Telecare 18:211–220, 2012
14.
Frist B: Telemedicine is a game-changer for patients, the system. Forbes, March 12, 2015. http://www.forbes.com/sites/billfrist/2015/03/12/telemedicine-is-a-game-changer-for-patients-the-system/
15.
Telemedicine Practice Guidelines. Washington, DC, American Telemedicine Association. Accessed Oct 26, 2015. http://www.americantelemed.org/resources/telemedicine-practice-guidelines/telemedicine-practice-guidelines#.V9CKDE0rJD8
16.
Neufeld JD, Doarn CR: Telemedicine spending by Medicare: a snapshot from 2012. Telemedicine Journal and e-Health 21:686–693, 2015
17.
Compton MT, Shim R: The Social Determinants of Mental Health. Washington, DC, American Psychiatric Publishing, 2015
18.
Thomas L, Capistrant G: State Telemedicine Gaps Analysis: Coverage and Reimbursement. Washington, DC, American Telemedicine Association, 2015
19.
Neufeld JD, Doarn CR, Aly R: State policies influence Medicare telemedicine utilization. Telemedicine Journal and e-Health 22:70–74, 2016
20.
Levine RS, Rust GS, Pisu M, et al: Increased black-white disparities in mortality after the introduction of lifesaving innovations: a possible consequence of US federal laws. American Journal of Public Health 100:2176–2184, 2010
21.
Adler-Milstein J, Kvedar J, Bates DW: Telehealth among US hospitals: several factors, including state reimbursement and licensure policies, influence adoption. Health Affairs 33:207–215, 2014
22.
Saliba V, Legido-Quigley H, Hallik R, et al: Telemedicine across borders: a systematic review of factors that hinder or support implementation. International Journal of Medical Informatics 81:793–809, 2012
23.
Uscher-Pines L, Kahn JM: Barriers and facilitators to pediatric emergency telemedicine in the United States. Telemedicine Journal and e-Health 20:990–996, 2014
24.
Whitten P, Buis L: Private payer reimbursement for telemedicine services in the United States. Telemedicine Journal and e-Health 13:15–23, 2007

Information & Authors

Information

Published In

Go to Psychiatric Services
Go to Psychiatric Services

Cover: Love of Winter, by George Wesley Bellows, 1914. Oil on canvas. Friends of American Art Collection (1914.1018), the Art Institute of Chicago. Photography ©The Art Institute of Chicago, www.artic.edu.

Psychiatric Services
Pages: 173 - 178
PubMed: 27691381

History

Received: 30 November 2015
Accepted: 6 July 2016
Published online: 3 October 2016
Published in print: February 01, 2017

Keywords

  1. Utilization patterns &amp
  2. review, Telecommunications, Public policy issues
  3. telemedicine
  4. telepsychiatry
  5. Medicaid
  6. health policy

Authors

Details

Megan Daugherty Douglas, J.D.
Ms. Douglas, Mr. Xu, Dr. Mack, and Dr. Rust are with the National Center for Primary Care and Dr. Wrenn is with the Satcher Health Leadership Institute, Morehouse School of Medicine, Atlanta (e-mail: [email protected]). Ms. Heggs is with the Center for Leadership in Disability, School of Public Health, Georgia State University, Atlanta.
Junjun Xu, M.P.H.
Ms. Douglas, Mr. Xu, Dr. Mack, and Dr. Rust are with the National Center for Primary Care and Dr. Wrenn is with the Satcher Health Leadership Institute, Morehouse School of Medicine, Atlanta (e-mail: [email protected]). Ms. Heggs is with the Center for Leadership in Disability, School of Public Health, Georgia State University, Atlanta.
Akilah Heggs, M.A.
Ms. Douglas, Mr. Xu, Dr. Mack, and Dr. Rust are with the National Center for Primary Care and Dr. Wrenn is with the Satcher Health Leadership Institute, Morehouse School of Medicine, Atlanta (e-mail: [email protected]). Ms. Heggs is with the Center for Leadership in Disability, School of Public Health, Georgia State University, Atlanta.
Glenda Wrenn, M.D., M.S.H.P.
Ms. Douglas, Mr. Xu, Dr. Mack, and Dr. Rust are with the National Center for Primary Care and Dr. Wrenn is with the Satcher Health Leadership Institute, Morehouse School of Medicine, Atlanta (e-mail: [email protected]). Ms. Heggs is with the Center for Leadership in Disability, School of Public Health, Georgia State University, Atlanta.
Dominic H. Mack, M.D., M.B.A.
Ms. Douglas, Mr. Xu, Dr. Mack, and Dr. Rust are with the National Center for Primary Care and Dr. Wrenn is with the Satcher Health Leadership Institute, Morehouse School of Medicine, Atlanta (e-mail: [email protected]). Ms. Heggs is with the Center for Leadership in Disability, School of Public Health, Georgia State University, Atlanta.
George Rust, M.D., M.P.H.
Ms. Douglas, Mr. Xu, Dr. Mack, and Dr. Rust are with the National Center for Primary Care and Dr. Wrenn is with the Satcher Health Leadership Institute, Morehouse School of Medicine, Atlanta (e-mail: [email protected]). Ms. Heggs is with the Center for Leadership in Disability, School of Public Health, Georgia State University, Atlanta.

Competing Interests

The authors report no financial relationships with commercial interests.

Funding Information

National Institute on Minority Health and Health Disparities10.13039/100006545: U54MD008173
The project described was supported by a grant from the National Institute on Minority Health and Health Disparities (NIMHD) (U54MD008173), a component of the National Institutes of Health (NIH).

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