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. Author manuscript; available in PMC: 2021 Jan 1.
Published in final edited form as: J Dev Behav Pediatr. 2020 Jan;41(1):16–22. doi: 10.1097/DBP.0000000000000727

Increasing Access to Autism Spectrum Disorder Diagnostic Consultation in Rural and Underserved Communities: Streamlined Evaluation within Primary Care

Jeffrey F Hine 1, Jessica Allin 2, Angela Allman 3, Michelle Black 4, Brooke Browning 5, Bobbie Ramsey 6, Amy Swanson 7, Zachary E Warren 8, Andrea Zawoyski 9, William Allen 10
PMCID: PMC6933088  NIHMSID: NIHMS1535035  PMID: 31490843

Abstract

Objective:

Preliminary feasibility and clinical utility research has demonstrated that implementation of a streamlined diagnostic model embedded within primary care clinics promotes early identification of young children with autism spectrum disorder (ASD). Use of this model results in dramatically reduced waits for diagnostic consultation, high levels of family/provider satisfaction, and reductions in referrals to overtaxed tertiary diagnostic centers. The current study extends this work by providing data pre/post implementation of a streamlined model across a diverse range of primary care clinics that provide healthcare to rural and underserved communities.

Methods:

The streamlined assessment involved record/history review, diagnostic interview, standard rating scales, and an interactive screening tool. Eighty children between the ages of 19 and 47 months old were seen across five different clinics. Data were collected via chart review.

Results:

Implementation of streamlined model resulted in a significant decrease in latency to diagnostic conclusion from a mean of 144.7 days to 49.9 days. Children were likely to experience a greater reduction in wait times if they were a primary care patient versus a non-primary care patient.

Conclusion:

Results show significant reduction in wait times for ASD diagnostic decisions across both primary care and non-primary care patients. By reducing waits and identifying concerns more efficiently, we may increase the ability of families to access early intervention and support services.

Keywords: autism spectrum disorder, diagnosis, medical home, primary care, rural

INTRODUCTION

With the increasing prevalence of autism spectrum disorder (ASD)1,2 and wide-spread delays for diagnostic evaluations and initiation of services,3,4 there is a growing need for novel systems of triage and care for children with ASD and their families. Recent research shows that even though a majority of children eventually diagnosed with ASD (85%) have mention of developmental concerns in their medical record before 36 months, far less than half receive an evaluation by that time.1 This contributes to an average age of diagnosis above four years of age and lack of timely access to critical specialized intervention services shown to increase short-term and lifespan functioning.5,6 Substantial delays in age of diagnosis are further documented among children impacted by a range of geographic, socioeconomic, and racial/ethnic factors.1,7-9

Given this need, a number of programs have developed novel models for identification and care for young children with ASD including primary care provider training in ASD identification/consultation,10 telemedicine-based diagnosis,11,12 and staffing developmental-behavioral clinics with general pediatricians.13 Across a series of works within our own program,14,15 we trained community providers to use the Screening Tool for Autism in Toddlers & Young Children (STAT)16 and a decision-making framework for within-practice diagnostic identification of ASD for young children (STAT-MD model). This preliminary work demonstrated that with formal tools and training, within-practice diagnostic consultation was an accurate and realistic practice model for many community pediatricians; however, this type of training was shown to be not easily scalable or sustainable due to cost, time, and trainer resources. We also implemented a similar model through telehealth by having an early interventionist perform the STAT in-person with a family while a psychologist observed remotely and provided diagnostic consultation.11,12 Findings from this assessment model supported feasibility, accuracy, and clinical utility of telediagnostic consultation; however, initiation of this service required ongoing partnerships with the state’s Part C system, two or more specialists at a time, as well as supported partner sites that could accommodate technology and staff. Thus, despite the well-documented impact of such models, further investigation of sustainable and accessible systems is needed to make an impact over time, especially regarding underserved communities.

Many families living in rural and underserved areas have unique challenges regarding access to health care, behavioral-health services, and specialized service providers. To combat these disparities within rural and underserved communities, integration of behavioral-health services within primary care settings is often the preferred service model.17-19 In fact, the National Committee for Quality Assurance for improving the quality of healthcare established a national initiative to employ a model of patient-centered medical homes that has been previously shown to reduce fragmentation of services, lower health care costs, improve patient-centered access, and better manage chronic conditions.20-24 In recent work, we embedded psychologist-lead ASD consultation and support clinics within our metropolitan medical center’s primary care clinics.25 This change resulted in dramatically reduced waits for diagnostic consultation, high levels of family/provider satisfaction, increased show rates, and reductions in referrals to an overtaxed tertiary diagnostic center and provided further support for use of a streamlined diagnostic model involving the STAT. Overall, embedding practitioners within primary care may represent a more viable model; however, no study has been able to demonstrate improvements relative to a control population and our preliminary study was completed in clinics closely linked to a large urban medical center.

The present study investigates the impact of generalizing this previously-documented model to a larger network of integrated primary care clinics. Our objective is to extend previous work by providing data pre/post implementation of the streamlined model across a diverse range of clinics that provide healthcare to rural and underserved communities. We hypothesized that wait times would be significantly reduced once the streamlined assessment model was introduced system-wide.

METHODS

Setting

Cherokee Health Systems (CHS) consists of a network of integrated health care clinics serving a wide range of patients across Tennessee. Primary care patients receive behavioral health services as needed during their medical visits and can schedule behavioral health appointments even if they do not need a medical service. All clinics within CHS are staffed with at least one behavioral health consultant (BHC), typically a licensed psychologist or clinical social worker. All BHCs see patients for intakes, behavioral consultation, and short-term focused therapy appointments; however, prior to the service system intervention, very few BHCs provided diagnostic consultation/assessment for neurodevelopmental disorders, as most did not have extensive training in ASD diagnostic consultation. Instead, most diagnostic testing was completed by the developmental psychology team, which consisted of eight psychologists or postdoctoral trainees with expertise in ASD diagnostic evaluation. The team worked within the same primary care clinics but primarily saw patients referred for ASD and other developmental concerns by both in-house and outside providers. Due to significant waits for children to be seen by the developmental psychology team, in October of 2017 CHS sought to implement a service system intervention that included embedding streamlined assessment for ASD within five primary care clinics. Vanderbilt University Medical Center (VUMC) was contacted to provide training in the streamlined model for all embedded behavioral health providers within each clinic (both BHCs and developmental psychologists)

Participants

In 2017, 6003 children under the age of four received services at CHS. Fifty-four percent were male, 46% were female. Ninety percent received a medical service, 15% received a behavioral or developmental service, and 6% received a dental service. The majority of these children (84%) utilized insurance through Medicaid. Just over 6% were uninsured, and just under 10% were covered by commercial insurance. This group of young children is ethnically diverse, with nearly equal numbers of children identified as White (29%), Black (27%), and Hispanic (31%). Participants for this study included 80 children between the ages of 19 and 47 months old seen in five different clinics. Data were collected on participants based on their status as a primary care patient (or outside referral) and whether they received an evaluation before or after implementation of the streamlined model (“STAT-BHC”) in October of 2017.

Primary Care (PC) Patients.

This group consisted of primary care patients whose in-house primary care providers referred them for evaluation due to concerns for ASD in one of the five clinics. Forty primary care patients were systematically selected: the 20 most recent patients seen before implementation of STAT-BHC and the next 20 patients seen after implementation. The pre-implementation patients were systematically selected, starting with the most recent patient and working backwards until 20 had been obtained.

Non-Primary Care (Non-PC) Patients.

This group included patients who were referred for an ASD evaluation by an outside source (e.g., other local pediatrician’s office, state-wide early intervention system, self-referred, or school district). Historically, CHS partnered with local pediatric clinics and the early-intervention system to complete ASD evaluations. A number of factors lead to this partnership including (a) these entities not having necessary training to complete ASD evaluations, (b) CHS being the only provider within the region that accepted all private/public insurance, and (c) the only other option for most families was a referral to a tertiary specialty diagnostic center over 200 miles away whose wait list was 8-10 months at the time of this study (VUMC). When a child was referred to CHS from an outside agency, all referrals were handled by a scheduling assistant who scheduled the child in one of the clinics based on proximity to the family’s home, wait time, and availability of ASD-trained provider. Similar to the primary care patients, the most recent 20 non-primary care patients were selected pre/post implementation of STAT-BHC (40 total).

Measures

The STAT is an individually-administered, play-based standardized assessment of early social communication skills. In 15-20 minutes, examiners administer 12 simple, standardized activities to elicit child behaviors in four core social communication and interaction categories: play, imitation, shared attention, and requesting. Each item is scored as pass/fail based on observation of a discrete operationalized behavior. Total failures across activities are then tallied to obtain an overall ASD-risk score based on child age. The STAT was originally developed for children ages 24-36 months but validated scoring cutoffs are available for children ages 14-47 months. Cutoffs differ based on a child's age such that older children are expected to show stronger social communication skills than younger children. Given the simplicity of tasks and the scoring system, high levels of inter-observer agreement and test-retest reliability are present. Validation data for the STAT suggests strong correlations with standard comprehensive assessment tools including high concurrent validity between the STAT and the Autism Diagnostic Observation Schedule 2nd Edition (ADOS-2)26 (Cohen’s kappa of .95).27 The ADOS-2 is considered the gold-standard assessment tool for evaluating ASD. It consists of semi-structured tasks to assess the child’s social, play, and communication skills, and provides data for determining the range of concern and/or classification for ASD. The ADOS-2 requires extensive training to administer, score, and interpret, and often takes 40-60 min to administer as part of a comprehensive evaluation.

Procedures

Pre-Implementation.

Before implementation of STAT-BHC, children were referred by their primary care providers to the developmental psychology team after failing developmental screening (e.g., Modified Checklist for Autism in Toddlers, Revised with Follow-Up; MCHAT-R/F)28, due to specific provider concerns for ASD, or referred by the state’s early intervention system. The evaluation process typically spanned three appointments: intake, testing, and feedback. The intake session included a comprehensive diagnostic interview and clinical observations. Testing sessions included administration of the ADOS-2, evaluation of cognitive functioning, and behavioral/adaptive rating scales. During the feedback session, providers summarized information from the evaluation, provided and discussed diagnoses if appropriate, provided psychoeducation about ASD, and discussed individualized recommendations.

STAT-BHC Training.

The STAT-BHC training was adapted from the STAT-MD model previously used to train pediatric providers to confidently discuss and accurately diagnose autism in young children within their own community settings.14,15 This training is an intensive 1.5 or 2-day workshop focusing on (a) administration and scoring of the MCHAT-R/F, (b) administration and scoring of the STAT, (c) clinical interviewing and use of caregiver observations/concerns to elicit diagnostically relevant information, (d) integration of data using a Diagnostic and Statistical Manual of Mental Disorders (DSM)-based29 framework to generate a diagnostic impression, (e) how to explain results and diagnosis to families, (f) service recommendations, and (g) coding processes for third-party reimbursement. Trained providers are also asked to complete standard fidelity procedures by sending two videotaped practice administrations for review or completing online supplemental training and passing a post-test. It is important to note that through this model, providers are trained to use the STAT as an objective decision-making tool as part of the diagnostic process and not to base diagnostic decisions solely on the STAT score. Whether providers used the STAT or the ADOS-2, diagnostic decisions were based on multiple sources of data (e.g., comprehensive review of medical and developmental history, caregiver interview, structured observations, use of standardized ASD-focused tools, and completion of DSM-5-based check sheet). Seven BHCs and eight members of the developmental psychology team completed this training.

STAT-BHC for PC Patients.

During medical well child checks in the primary care clinics, children completed screening for ASD and other developmental concerns (e.g., MCHAT-R/F). If a child screened positive for ASD, the family was scheduled for an evaluation with a BHC. During this evaluation, the BHC reviewed medical/developmental history, conducted an ASD diagnostic caregiver interview, set up structured observations, and administered the STAT. The medical/developmental history and the interview were part of the same form and completed either by the caregiver (pre-visit) or the BHC. The questionnaire reviewed social history and family information, birth history, trauma history, medical issues/medications, eating/feeding behaviors, sleep problems, motor development/milestones, self-care and adaptive skills, previous intervention/therapies, and language development/milestones. The questionnaire also consisted of ASD-specific questions to elicit information about social-communication, social functioning, peer relationships, play behaviors, stereotyped/repetitive movements or play, sensory concerns, and behavioral issues. The ASD-specific portion of the form was adapted from other DSM-5-based clinical interviews including the Early Screening for Autism and Communication Disorders (ESAC)30 and the Autism Diagnostic Interview-Revised (ADI-R).31 Whether completed by caregivers or the clinician, this form was also used to guide the parent interview and elicit specific information regarding ASD diagnostic criteria. Evaluation and feedback always occurred during the same appointment. Feedback included results of the evaluation, diagnostic decision, psychoeducation about ASD, and individualized recommendations. Patients were also seen by BHCs during sick or planned visits if ASD concerns arose. The decision whether to complete STAT on the same day of the appointment depended on the complexity of medical procedures required for the child’s visit and family availability. If the results of the streamlined processes above were inconclusive and all diagnostic symptoms were not clearly discernible, patients were referred to the developmental psychology team for further testing which most often included the ADOS-2, cognitive/adaptive testing, and other standardized rating scales if needed. Prior to initiation of the streamlined evaluation, families were made aware of the process and were scheduled to follow up with the BHC within 6 months regardless of diagnosis.

STAT-BHC for Non-PC Patients.

For children with ASD concerns referred by outside providers or the state early intervention system, the developmental history, caregiver interview, STAT testing, and feedback were completed within one 90-min appointment within a single clinic and diagnostic processes were nearly identical to the PC patients. To aid in the efficiency of the STAT-BHC process, the developmental history form was mailed to caregivers prior to their appointment. Feedback sessions included content similar to that described above for PC patients seen by BHCs.

Diagnostic Follow-up and Support.

The primary care team at CHS also includes bachelor’s level Community Health Consultants (CHCs) who provide case management services. Before the STAT-BHC was implemented in primary care, seven CHCs received a three-hour training on ASD and the STAT-BHC process and their role in connecting families with resources. When needed, CHCs were able to support families throughout the STAT-BHC assessment process. For example, they were asked to review the developmental history form with families to ensure comprehension of items and assist in completion if necessary. Additionally, if an ASD diagnosis was made, CHCs were available to support the family by reviewing recommendations, helping families write a list of questions for their provider, aiding families in initiating interventions services, and arranging for continued follow up with case management services if needed. For those children for whom a diagnosis of ASD was not appropriate, either the BHC or a CHC reviewed the current intervention plan and provided consultation for early intervention or speech evaluation, referrals for school evaluations, and general information on developmental play activities. If the child was a PC patient, then short-term skills-based therapy and caregiver training were offered through the integrated clinic setting.

Data Collection

Electronic medical health records of each participant were analyzed to assess for impact of the streamlined model pre/post implementation. To determine the date of the first concern recorded in the electronic health record, each participant’s chart was examined for any indication of concern about ASD. Specifically, charts were scanned for one of the following indicators: date of failed screening, physician concerns about ASD within a medical visit, or any contact with the developmental psychology scheduling secretary. To ensure conservative measurement, if a chart included more than one of the three indicators, then the earliest of the dates was selected as the date of initial concern. We obtained ethical approval from the CHS institutional review board.

RESULTS

We calculated descriptive statistics for participant demographics including gender, ethnicity, and diagnosis (Table 1). We then completed a 2-way between-groups analysis of variance (ANOVA) to examine significant differences between the PC and Non-PC groups for the number of days from first concern to diagnostic decision (both pre- and post-implementation of STAT-BHC). Due to positive skewness of the data for this variable (most of data clustering in the 0-100 day range), we performed a square root transformation to improve distribution.

Table 1.

Descriptive Statistics on Referral Sample Characteristics

Pre-Implementation
 STAT-BHC
PC
n = 20		 Non-PC
n = 20		 Combined
n = 40		 PC
n = 20		 Non-PC
n = 20		 Combined
n = 40
Gender, n (%)
 Male 16 (80) 15 (75) 31 (78) 20 (100) 18 (90) 38 (95)
 Female 4 (20) 5 (25) 9 (23) -- 2 (10) 2 (5)
Race/Ethnicity, n (%)
 African American 3 (15) -- 3 (8) -- 2 (10) 2 (5)
 Caucasian 12 (60) 19 (95) 31 (78) 9 (45) 18 (90) 27 (68)
 Hispanic/Latino 4 (20) 1 (5) 5 (13) 7 (35) -- 7 (18)
 Multiracial -- -- -- 1 (5) -- 1 (3)
 Asian, PI -- -- -- 1 (5) -- 1 (3)
 Unknown 1 (5) -- 1 (3) 2 (10) -- 2 (5)
ASD Diagnosis, n (%) 13 (65) 13 (65) 26 (65) 9 (45) 11 (55) 20 (50)
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Table 2 depicts the number of days from first concern to diagnostic decision (and age of diagnostic decision) for children referred for ASD concerns, both pre- and post-implementation of STAT-BHC for the PC group, Non-PC group, and combined groups. Analysis of variance yielded significant variation among conditions; F(1,76) = 46.43, p<.001. Figure 1 is a graphical representation of pre/post data across groups. When PC and Non-PC groups were combined, implementation of STAT-BHC resulted in a significant decrease in latency to diagnostic conclusion from a mean of 144.7 days to 49.9 days. A post hoc Tukey test showed similar significant pre/post reductions in wait times when children were split into PC and Non-PC patients (p<.001). Although not a statistically significant finding, we also discovered an influence of PC or Non-PC status; that is, children were likely to experience a greater reduction in wait times if they were a PC patient (mean reduction of 120.5 days) versus a Non-PC patient (mean reduction of 69.1 days); F(1,76) = 3.63, p=.060. Lastly, mean age at diagnostic decisions was 32.9 months pre-implementation and 31.9 months post implementation of STAT-BHC.

Table 2.

Number of days from first concern to diagnostic decision for children referred for ASD concerns.

Pre-Implementation
 STAT-BHC
PC Non-PC Combined PC Non-PC Combined
Mean number of days 154.7 134.7 144.7 34.2* 65.6* 49.9*
 SD 128.0 62.4 99.9 27.1 29.5 32.2
 Range 15-458 58-275 15-458 3-99 13-118 3-118
Mean age at diagnostic conclusion (months) 32.7 33.1 32.9 31.1 32.6 31.9
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*

Indicates significant difference between pre-implementation and post-implementation of STAT-BHC for designated group: Primary Care (PC), Non-Primary Care (Non-PC), and Combined (p<.001).

Figure 1.

Figure 1.

Open in a new tab

Mean number of days from first reported developmental concern to diagnostic decision for primary care patients, non-primary care patients, and all patients combined pre- and post-implementation of STAT-BHC. Data depicted are results after square root transformation.

Nine of the 40 children who received the STAT (PC and Non-PC combined), were referred for more comprehensive testing through the developmental psychology team. In only two of those cases, the STAT and ADOS-2 were not in agreement regarding ASD risk level. For one, the STAT was negative, but the child scored above-threshold on ADOS-2, leading to a formal diagnosis once all evidence was compiled. In the other case, the STAT was positive, but the child did not meet the ASD threshold on the ADOS-2, and a diagnosis was not applied. Although the wait time analyses above included the nine children who required follow-up evaluation, a separate analysis for just those nine children shows mean days from first concern to diagnostic decision was 72.8 for PC patients, 76.3 for Non-PC patients, and 74.5 combined. Thus, even though these children required more evaluation, wait times were reduced by 82, 58, and 70 days, respectively. Overall, 65% of the pre-implementation patients and 50% of the STAT-BHC patients received diagnoses of ASD (Table 1).

DISCUSSION

Our primary objective in this study was to further evaluate a streamlined model for early classification of children suspected of having ASD by extending to a variety of primary care clinics with a largely rural and underserved catchment. We examined the degree to which training and implementation of this model decreased the latency from first concerns to diagnosis. Results show significant reduction in wait times for ASD diagnostic decisions across both primary care and non-primary care patients. With implementation of STAT-BHC, families had to wait an average of 95 days less than families not having access to the streamlined model. By reducing waits and identifying concerns more efficiently, we may increase the ability of families to access early intervention and support services as well as prepare them for the school-age transition at 3 years.

An important finding includes the effect of being a PC patient versus a Non-PC patient. Reductions in the latency from first concern to diagnosis were greater for those patients who received all phases of the model within their primary care clinic, as opposed to children who were referred from outside sources. Also, mean age of diagnosis was 31.1 months for PC patients, compared to 32.6 for Non-PC patients post-implementation. These data may further support the importance of integrating behavioral-health services within primary care practices. In addition to implementation of the streamlined model, larger reductions in wait times for PC patients could be due to the substantiated benefits of integrated behavioral health services including reduced fragmentation of services, well-organized scheduling protocols, shared/integrated medical charting, increased provider consultation, family convenience/satisfaction, higher show rates, treatment/follow-up plan adherence, or reduction of transportation and other barriers.20-25

The primary limitation of this study is our somewhat limited sample. Generalization of these findings would be improved with increased heterogeneity of participant characteristics as well as more participants overall. However, this study extends past research by embedding this model across multiple primary care clinics and by including behavioral-health providers with varied experience and backgrounds. We also were not able to present specific clinical data for each participant. Although past research has demonstrated that this model results in accurate and efficient diagnostic decision making, this paper would be strengthened by having more comprehensive data for each participant and analysis on what factors increased a patient’s likelihood to be referred for further assessment. Similarly, since the STAT is considered a level 2 screening tool, there may always be a risk for identifying children inaccurately while balancing efficiency with accuracy. Without careful consideration and a decision framework for children in a diagnostic “gray area,” formal evaluation/services may erroneously not be pursued. Fortunately, the STAT-BHC model includes the option for follow-up comprehensive assessment when needed, thus, diagnostic decisions were never made if evidence was lacking. Providers also discussed with caregivers the advantages and disadvantages of the streamlined process and that formal assessment is always an option/recommended.

Another limitation includes lack of data on family characteristics and not collecting data regarding increasing families’ access to intervention services. A primary objective of removing barriers to diagnostic consultation is to engage families in intervention and support services more efficiently. Future work will examine stakeholder experiences and clinical outcomes for this model. Formal data regarding financial reimbursement and billing/coding structures also were not reported. However, the same billing codes were used pre/post implementation and we expected similar reimbursement rates regardless of the model used. Future work will examine the proportion of staff time devoted to intervention versus evaluation and the financial and system impacts of this shift. Lastly, while the wait time for children under 4 decreased dramatically with the implementation of STAT-BHC, we were not able to address the wait time for children older than 4. Although our focus was on early identification of young children with ASD, there are few well-research efficient options for addressing ASD diagnostic concerns in older children.

Despite limitations, the current work suggests that implementation of a streamlined model for early identification of ASD dramatically improves wait times for diagnostic consultation and the capacity of behavioral health providers within underserved and rural primary care settings. This study documents an extension of recent and past research suggesting developmental and behavioral concerns are better managed and identified earlier when behavioral-health providers are integrated within primary care. This is especially true for entities that focus on efficient triage, streamlined evaluation, and accessible follow-up support for children with chronic medical needs. Furthermore, this work highlights an opportunity for providers to more efficiently and effectively serve children from rural and other underserved groups who continue to face barriers to quality care.

Acknowledgments

Sources of Support:

This project was completed with support from the Katherine Dodd Faculty Scholars Program in the Department of Pediatrics at Vanderbilt University Medical Center, support from the Vanderbilt Kennedy Center Eunice Kennedy Shriver National Institute of Child Health and Human Development Intellectual and Developmental Disabilities Research Center U54 HD08321, support from the Vanderbilt University Medical Center CTSA award No. 1UL1TR002243-01 from the National Center for Advancing Translational Sciences, as well as support from Cherokee Health Systems for training and data collection. Contents are solely the responsibility of the authors and do not necessarily represent official views of any funding agency.

Footnotes

Author Disclosure Statement:

The authors declared no potential conflicts of interest with respect to research, authorship, and/or publication of this article.

Contributor Information

Jeffrey F. Hine, The Vanderbilt Kennedy Center Treatment and Research Institute for Autism Spectrum Disorders; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN.

Jessica Allin, Cherokee Health Systems, Knoxville, TN.

Angela Allman, Cherokee Health Systems, Knoxville, TN.

Michelle Black, Cherokee Health Systems, Knoxville, TN.

Brooke Browning, Cherokee Health Systems, Knoxville, TN.

Bobbie Ramsey, Cherokee Health Systems, Knoxville, TN.

Amy Swanson, The Vanderbilt Kennedy Center Treatment and Research Institute for Autism Spectrum Disorders; Vanderbilt University Medical Center, Nashville, TN.

Zachary E. Warren, The Vanderbilt Kennedy Center Treatment and Research Institute for Autism Spectrum Disorders; Department of Pediatrics, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN.

Andrea Zawoyski, Cherokee Health Systems, Knoxville, TN.

William Allen, Cherokee Health Systems, Knoxville, TN.

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