United States

With limited healthcare resources, clinics and physicians face productivity challenges. Currently, minimal workflow standardization exists. As electronic health record systems (EHR) offer more capabilities to track times for which clinic stakeholders – physicians, patient flow coordinators, nurses, technicians, trainees, and other staff – interact with the system, such secondary data can reflect clinic activities. Despite the large data repository, little is known about its accuracy and applications. Therefore, this study aimed to understand the accuracy of time-motion metrics within EHR data and demonstrate preliminary trends in the clinic workflow.

Methods
This study was conducted at the Ambulatory Cardiac Clinics at Peter Munk Cardiac Center, University Health Network in Toronto, Canada. Two trained observers collected a convenient sample of over 60 hours of observational data using a time-motion approach during 7 clinic days. Automated data captured by this clinic’s EHR, EPIC, of all in-person outpatient visits during regular hours between January to December 2023 were used. Time-related and descriptive variables with &gt;10% missingness were excluded. The acceptable error for accurate timestamp and interval variables was ±5 minutes and ±5% of the total interval time, respectively, for ≥ 90% of EPIC data compared to observational data. Using acceptable variables as metrics, EPIC data was analyzed using summary statistics and multivariable regressions to capture preliminary clinic workflow trends.

Results
Across 9 cardiologists, 158 observational data records were compared with EPIC data. Of 49 time- and date-related variables, only 6 had less than 10% missingness (appointment date, appointment weekday, appointment time, appointment length, patient check-in time, and department-recorded check-in time), all of which were accurate. 15 descriptive variables also had less than 10% missingness. During the study period, 51,887 records from EPIC during weekdays 7 am - 5 pm were analyzed. The median patient appointment lasted 20 minutes [interquartile range 15, 60]. Furthermore, morning and afternoon clinic transitioned from 12 – 1pm and the 30-minute appointment load peaked during the middle of each morning and afternoon clinic. 

Conclusion
Secondary data automatically collected as part of the EPIC system is accurate and can be used to measure, manage, and improve clinic efficiency. However, further standardization of when stakeholders engage with the EHR is necessary to reduce missingness and increase the number of variables as clinic efficiency metrics. Additionally, more variables allow for regionalization, physician workload, and patient satisfaction to be determined for clinic function and management in addition to appointment-related metrics.

AMA Research Challenge 2024 

Standardizing Secondary Data from Electronic Health Record System to Assess Clinic Workflow – A Time-Motion Approach

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With limited healthcare resources, clinics and physicians face productivity challenges. Currently, minimal workflow standardization exists. As electronic health record systems (EHR) offer more capabilities to track times for which clinic stakeholders – physicians, patient flow coordinators, nurses, technicians, trainees, and other staff – interact with the system, such secondary data can reflect clinic activities. Despite the large data repository, little is known about its accuracy and applications. Therefore, this study aimed to understand the accuracy of time-motion metrics within EHR data and demonstrate preliminary trends in the clinic workflow.

Methods
This study was conducted at the Ambulatory Cardiac Clinics at Peter Munk Cardiac Center, University Health Network in Toronto, Canada. Two trained observers collected a convenient sample of over 60 hours of observational data using a time-motion approach during 7 clinic days. Automated data captured by this clinic’s EHR, EPIC, of all in-person outpatient visits during regular hours between January to December 2023 were used. Time-related and descriptive variables with >10% missingness were excluded. The acceptable error for accurate timestamp and interval variables was ±5 minutes and ±5% of the total interval time, respectively, for ≥ 90% of EPIC data compared to observational data. Using acceptable variables as metrics, EPIC data was analyzed using summary statistics and multivariable regressions to capture preliminary clinic workflow trends.

Results
Across 9 cardiologists, 158 observational data records were compared with EPIC data. Of 49 time- and date-related variables, only 6 had less than 10% missingness (appointment date, appointment weekday, appointment time, appointment length, patient check-in time, and department-recorded check-in time), all of which were accurate. 15 descriptive variables also had less than 10% missingness. During the study period, 51,887 records from EPIC during weekdays 7 am - 5 pm were analyzed. The median patient appointment lasted 20 minutes [interquartile range 15, 60]. Furthermore, morning and afternoon clinic transitioned from 12 – 1pm and the 30-minute appointment load peaked during the middle of each morning and afternoon clinic. 

Conclusion
Secondary data automatically collected as part of the EPIC system is accurate and can be used to measure, manage, and improve clinic efficiency. However, further standardization of when stakeholders engage with the EHR is necessary to reduce missingness and increase the number of variables as clinic efficiency metrics. Additionally, more variables allow for regionalization, physician workload, and patient satisfaction to be determined for clinic function and management in addition to appointment-related metrics.

poster

Background
Developments in artificial intelligence (AI) have enabled programs to mimic human behaviors, including thought and speech. Capabilities of AI (e.g. ChatGPT) include summarizing findings of previous research, generating and analyzing data, and assisting in writing and editing. However, these capabilities also raise concerns surrounding copyright, attribution, plagiarism, authorship, accuracy. This cross-sectional study aims to assess how dermatology journals address the use of AI for authors and reviewers and determine if there is a difference in impact factor (IF) between journals with and without AI statements.

Methods
Dermatology journals, ranks, and IFs were identified from The Observatory of International Research in July 2024. Journals’ statements on AI were collected from their guidelines pages. When the journal deferred to the publisher for AI guidance, statements were also collected from the publishers’ guidelines pages. The Shapiro-Wilk test assessed the distribution of the data. The Mann-Whitney-Wilcoxon test examined differences in IFs between journals with and without AI statements. Significance was set at p<0.05.

Results
A total of 59 journals were included in the study, 29 (49.2%) and 41 (69.5%) of which did not have AI statements for authors and reviewers, respectively. The most common author guidelines included: requiring disclosure of AI use (n=29, 49.2%), prohibiting listing AI as author (n=27, 45.8%), and allowing AI to enhance readability and language (n=12, 20.3%). For reviewers, the most common guidelines included: cautioning against AI use due to confidentiality concerns (n=16, 27.1%), requiring declaration of AI use in peer review reports (n=5, 8.5%), and prohibiting AI use in peer review (n=3, 5.1%). No significant difference in IF was found between journals with and without AI statements for authors (p=0.26); however, journals with higher IF were more likely to have AI statements for reviewers (p=0.03).

Conclusion
We found that nearly half of the dermatology journals included in our study lacked AI guidelines for authors, and even more did not have guidelines for reviewers. Journals of all impacts have begun to release AI statements for authors, but higher impact journals are more likely to provide guidance to reviewers. As AI technology continues to evolve, it is crucial for journals to establish comprehensive and clear guidelines to ensure ethical and accurate use of AI in research. In the meantime, dermatology researchers should adhere to these guidelines to ensure transparency in their work, and readers should be aware of these standards when interpreting research findings.

A cross-sectional study assessing AI guidelines across impact factors in dermatology journals: implications for authors and reviewers

Background: The average cost for tuition, fees, and living expenses at 50% of United States medical schools exceeds $275,000. In addition, a career in medicine requires 3-7 years of residency training and sometimes 1-3 years of fellowship training at a fraction of their projected income. Virtually no data exists on the credit profiles of internal medicine borrowers and the reasons for their borrowing. In the first analysis of its kind, we assessed the credit profiles of physician borrowers by FICO score, training or career stage, and reasons for borrowing.

Methods: This cross-sectional study was based on pooled FICO scores from 974 internal medicine trainee and attending borrowers from 2019-2023 sourced from a privately held physician and dentist financial services company in compliance with the Equal Credit Opportunity Act (ECOA). Borrowers applied for loans from October 1, 2019 to May 31, 2023. Descriptive analyses were performed characterizing the median borrower FICO score based on status (trainee vs. attending), years from training, and reasons for loan requests. FICO score excludes age, employment history, gender, location, income, marital status, national origin, and race to protect customers from potentially discriminatory credit decisioning. The FICO score pulls data from the three credit bureaus (Equifax, Experian, and Transunion) weighing the following categories when being calculated: payment history (35%), amounts owed (30%), length of credit history (15%), recent credit applications (10%), and credit mix (10%).

Results: The national median FICO scores were 679 (n=500; IQR=648-721) among trainees and 731 (n=474; IQR=683-778) among attending physicians (p<0.001). There was no statistically significant difference among physician FICO scores by geography. Credit card consolidation was the most common reason for borrowing across both cohorts followed by moving/relocation among trainees and home improvement among attendings. 

Conclusion: The median FICO scores of trainees is 11 points lower than the average American of their generation; for attendings, the median FICO score is 4 points higher. This is surprising given the personal character and comparative salary of physicians. Financial stresses reflected in FICO score can contribute to broader physician burnout and further the cycle of stress and shortage.

Financial Wellness in Medicine: A Fair Isaac Corporation (FICO) Score Cross-Sectional Analysis of United States Internal Medicine Trainees and Attendings

Studies also show that the effect of social risk factors on health is cumulative such that patients who experience more social risk factors have poorer health-related quality of life. Although low educational attainment, specifically, not completing high school, is known to lead to poor health and possible premature death; little is known about the association between low educational attainment and other social risk factors that can impact health. We identified and compared the prevalence of social risk factors in patients with less than high school education and patients with high school education or greater to determine what factors may be further contributing to poor health outcomes in patients with low educational attainment.

We reviewed results from social determinants of health screenings performed from October 2021 to October 2023 at a primary care clinic in Hartford, Connecticut. We compared the prevalence of social risk factors among patients who did and did not complete high school using Chi-square test of independence (p <0.05).

Volunteers screened 2,153 patients, 386 (17.9%) of whom did not complete high school. Compared to patients with high school education or greater, patients with less than high school education had a higher prevalence of unemployment (17.4% v. 13.4%, p=0.041), food insecurity (15.5% v. 9.8%, p=.0067), difficulty affording utilities (16.9% v. 9.8%, p=.0001), lack of transportation (30.8% v. 14.8%, p<0.001), and difficulty accessing healthcare/medication (5.1% v. 2.4%, p=0.007). The two groups had a similar prevalence of homelessness (9.0% v. 6.7%, p =0.17).

Our results show that failing to complete high school is associated with a higher prevalence of numerous social risk factors. Independently low educational attainment and the individual risk factors we investigated negatively impact health, but together their effects can compound and contribute to further worsening health outcomes. These findings argue that patients with low educational attainment should be routinely screened for social risk factors so, all factors that negatively impact their health can be identified and addressed.

Low Educational Attainment Linked to Social Risk Factors that Worsen Health Outcomes

Background: Health literacy (HL) is a cornerstone for favorable and equitable health outcomes, yet nearly 9 in 10 U.S. adults struggle with limited HL associated with higher rates of hospital readmissions, medication nonadherence, and all-cause mortality. This disproportionately affects marginalized populations, especially those from racial and ethnic minorities. While the 21st Century Cures Act aims to mitigate differential access to health information by providing patients with access to their clinical notes in electronic health records (EHRs), most are unable to utilize them for health-based decisions and adequate management of care. As health systems shift towards a culture of shared decision-making, limited readability of EHRs remains a key barrier in our goal of empowering patients as agents of their own care. Thus, there is a critical need to improve comprehensibility of clinical notes and facilitate equitable outcomes at a systems level. Here, we evaluate the utility of GPT4-based plain language translation of discharge summary notes (DSNs) on subjective and objective comprehension, confidence, and time spent reading as well as its differential effect on various patient demographics. 

Methods: 553 patients were enrolled from December 2023 to February 2024 and read four DSNs (two untranslated, two GPT4-translated) on common admission diagnoses on an inpatient general medicine service: congestive heart failure (CHF), community acquired pneumonia (CAP), diabetic ketoacidosis (DKA), acute ischemic stroke (AIS). After reading each DSN, patients answered questionnaire items assessing subjective perceptions and objective accuracy of comprehension. Linear mixed models were used to analyze effects of translation on comprehension outcomes. 

Results: Across all four DSNs, GPT4-based translation improved objective comprehension by 61% (β=1.18, p<0.001), reduced time spent reading by 51% (β=-0.57, p<0.001), improved self-reported confidence by 45% (β=1.94, p<0.001), and improved subjective comprehension by 18% (β=2.32, p<0.001). It also strengthened the association between patient confidence and objective comprehension by 67% (p<0.01). Improvements were greater in Black and Hispanic patients, older patients, male patients, and those who reported limited health knowledge (p<0.01). In particular, objective comprehension improved an additional 22% and 39% among Black and Hispanic patients respectively (p<0.01). 

Conclusion: GPT4-based translation of DSNs substantially improved patient comprehension of disease course and management while optimizing time spent reading them, and to a greater degree in marginalized populations with historically low rates of HL. Implementation of GPT4-translation in EHR interfaces for a patient-friendly supplement to provider notes offers great promise in closing the health literacy gap across a spectrum of patient demographics in medicine.

Using OpenAI to Improve Patient Comprehension of Clinical Notes and Bridge Health Literacy Gaps in Internal Medicine

Summary of Background Data 
Concordance with evidence-based medicine (EBM) guidelines is associated with improved clinical outcomes in spine surgery. The North American Spine Society (NASS) has published coverage guidelines on indications for lumbar fusion surgery, with a recent survey demonstrating a 60% concordance rate across its members. GPT-4 is the latest popular deep learning model that receives knowledge training across public databases including those containing EBM guidelines. There is a paucity of information regarding the utility of artificial intelligence in the clinical decision-making process for patients with lumbar pathology. 

Purpose 
To assess GPT-4’s responses to validated clinical vignettes and evaluate them for concordance with NASS clinical guidelines and recommendations for lumbar fusion surgery. 

Study Design 
Comparative Analysis and Narrative Review 

Patient Sample 
Adult patients in the United States evaluated for lumbar spine fusion surgery. 

Methods 
Seventeen well validated clinical vignettes with specific indications for or against lumbar fusion based on NASS criteria were obtained from a prior published research study. Each case included history of present illness, physical exam findings, previous treatments attempted, and definitive imaging interpretations. The cases were transcribed into a standardized prompt and entered into GPT-4 to obtain a decision whether fusion is indicated. The query for each case was repeated three times under identical conditions to evaluate the model’s inter-query reliability. If there was internal disagreement between the queries for a given case, GPT-4’s decision for that case was determined by majority rule. Queries were all entered in separate strings to ensure that no contextual memory in the software influenced subsequent responses. The investigator entering the prompts was blinded to the NASS-concordant decisions for the cases prior to complete data collection. GPT-4 inter-query reliability was assessed using the Fleiss’ Kappa statistic. Differences in decision-making between GPT-4 and NASS guidelines were analyzed with Chi-square analysis. 

Results 
GPT-4 responses for 14/17 (82.4%) of the clinical vignettes were in concordance with NASS EBM lumbar fusion guidelines. There was a significant association in clinical decision-making when determining indication for spine fusion surgery between GPT-4 and NASS guidelines (χ² = 7.14; p < 0.01). There was substantial agreement among the sets of responses generated by GPT-4 for each clinical case (K = 0.71; p < .001). 

Conclusions 
There is significant concordance between GPT-4 responses and NASS EBM indications for lumbar fusion surgery. Artificial intelligence and deep learning models may prove to be an effective adjunct tool for clinical decision-making and medical education within modern spine surgery practices.

Evaluation of GPT-4 Concordance with North American Spine Society Guidelines for Lumbar Fusion Surgery

Academic outpatient clinics report appointment non-adherence rates between 8-20%.As unfilled appointments represent a financial loss to the system, patient-directed financial penalties emerged as a strategy to offset departmental losses. 
We aimed to examine the impact of patient financial penalties on appointment non-adherence within a single academic institution's obstetrics and gynecology (OBGYN) outpatient clinics.
This retrospective policy effectiveness-implementation hybrid study included administrative data from all appointments at Eastern Virginia Medical School's OBGYN outpatient clinics from May 1, 2018 to April 30, 2022.
The institution-wide patient financial penalty policy for appointment non-adherence was implemented on May 1, 2020. Institutional Review Board approval was waived for this study. 
We conducted an interrupted time series analysis (ITS) stratified by clinic and insurance after adjusting the model for the effect of COVID-19.
There were 414,006 OBGYN outpatient clinic appointments; 58,473 (14.1%) were not attended. The mean (standard deviation) per month was 8391 (727) appointments and 14.3% (1.48%) non-adherence rate. Medicare/Medicaid patients had a higher non-adherence rate (36,872/188,343 [19.6%]).
COVID-19 was a significant confounder across all clinics and insurances. Adjusted ITS models observed a significant sustained effect on percent of non-adherent appointments per month (+0.18%; p<0.01). Stratified by clinic, maternal-fetal medicine (-1.47%; p=0.01), minimally invasive gynecology (+4.62%; p<0.01) and urogynecology (+4.42%; p=0.01) observed a significant immediate effect. Maternal-fetal medicine (+0.09%; p=0.02) and generalists (-0.33%; p<0.01) observed a significant sustained effect. 
Stratified by insurance, publicly insured patients (-3.99%; p=0.048) observed a significant immediate effect. Publicly (+0.21%; p<0.01) and non-publicly (+0.09%; p=0.03) insured patients observed a significant sustained effect.
A clinically significant reduction in appointment non-adherence was not observed 
in a single academic center’s OBGYN outpatient clinics after institutional implementation of patient-directed financial penalties for appointment non-adherence. The impact may differ based on clinic or patient insurance type.

Impact of Financial Penalties on Appointment Non-Adherence in Academic Obstetrics & Gynecology Clinics

Background: 
Uveitis is the fifth most common cause of preventable blindness in the United States and the mean per-patient total all-cause healthcare cost for noninfectious inflammatory eye disease was estimated to be $15,651 between 2006 and 2015. Without treatment, uveitis can lead to blindness through different causes, including macular edema and glaucoma. Topical corticosteroid eye drops are typically the first-line treatment, yet minimal research exists surrounding adherence to corticosteroid therapy among patients with uveitis. This study aimed to assess the effect of patient barriers, including medication cost, polypharmacy, unclear prescriber instructions, and side effects, on adherence with corticosteroid eye drops in patients with ocular inflammatory disorders throughout the United States. 

Methods:
This was a multicenter study of adult and pediatric patients with ocular inflammatory diseases undergoing treatment with corticosteroid eye drops. Sites included Johns Hopkins, University of Texas at Austin, University of Wisconsin, University of Minnesota, Minneapolis Veterans Administration Hospital, and Washington University of St. Louis. Eligible patients completed a survey consisting of 45 questions to assess barriers to adherence with corticosteroid drops. Reasons for medication non-adherence and the importance of patient demographics, including age, race, sex, region, insurance type, education level, and drop frequency were assessed for the impact on corticosteroid eye drop adherence. 

Results:
The survey was completed fully by 94 subjects and partially by 20 (114 in total), of whom were 66% white, 60% female, and 47% had a college or advanced degree. Fifty-six percent of patients used a corticosteroid drop more than once a day; 13% found frequency a limitation to adherence. Many patients also had polypharmacy–43% took 4 or more medications and 50% took 1 or more drops in addition to a corticosteroid drop. Patients also reported difficulty reading the writing on drop bottles (39%) and poor vision limiting application into the eye (17%). Almost a quarter (24%) of patients occasionally ran out of their drop prior to the next refill. Most patients did not report cost (98%) or insurance loss (97%) as a barrier to obtaining a refill. 

Conclusion:
Socioeconomic barriers to topical corticosteroid treatment for uveitis include difficulty with medication frequency, polypharmacy, medication administration, missing refills, and running out of medication early. No patients missed doses due to insurance loss and the majority did not report cost as a barrier to obtaining refills. Addressing these barriers to treatment with patients can promote adherence with corticosteroid drops to achieve treatment goals.

Barriers to Adherence with Corticosteroid Eye Drops in Patients with Uveitis

Background: Uveitis is a leading cause of preventable blindness in the United States and has been estimated to account for 10-15% of total blindness in the developed world. In order to prevent complications, non-corticosteroid immunomodulatory therapies (IMT) play an important role in achieving long-term remission in patients with chronic ocular inflammatory diseases. However, these medications require routine laboratory monitoring, have significant out-of-pocket costs for uninsured patients, and often require more than one agent to adequately control ocular inflammation. This study aims to evaluate the barriers to adherence with IMT for patients with uveitis and ocular inflammatory diseases across multiple regions of the United States.

Methods: A multicenter survey of adult and pediatric patients with ocular inflammatory diseases undergoing treatment with immunomodulatory therapy was conducted between September 2021 and August 2022. Participating sites included the Johns Hopkins Wilmer Eye Institute, Wong Eye Institute of the University of Texas at Austin, the University of Wisconsin, the University of Minnesota, Veterans Affairs Hospital of Minneapolis, and Washington University of St. Louis. Each patient completed a self-reporting survey of 45 questions to identify barriers to adherence.

Results: The survey was completed by 98 subjects, of whom were 71% white, 67% female, and 61% had a college or advanced degree. Nearly half (49%) were on two or more IMTs of which the most common were methotrexate (37%), mycophenolate (36%), or adalimumab (36%). The most frequent dosing regimen was twice a day (34%) and in addition to the IMT, 20% of patients took 8 or more medications. Limitations to completing laboratory studies to monitor for drug-related side-effects included finding time (10%), and cost (22%). Nearly half (52%) of patients required reminders to take their medications and 20% found it difficult to take IMT regularly with 12% struggling to take medications multiple times a day. A lack of refills resulted in 15% of patients missing doses. The cost of monthly refills ranged widely between less than $5 (27%), to over $1000 (4%). The majority of patients (98%) did not report the cost of their IMT refill as a barrier.

Conclusions: Socioeconomic barriers to IMT treatment include laboratory study cost, difficulty with medication administration, and adhering to medication schedules. Monthly cost of medication was high for some patients, but no patients were unable to take IMT due to insurance loss. Addressing these barriers can help increase IMT adherence for uveitis patients and promote better clinical outcomes.

Barriers to Adherence with Immunosuppressives: Evaluating Social Determinants of Health in Uveitis

Background
Many patients suffering from androgenetic alopecia seek out hair transplantation as a surgical treatment option, with over 700,000 hair transplantation procedures being performed annually worldwide. Oftentimes, patient selection of hair transplant clinics is influenced by social media advertising. Characteristics of hair transplant clinics including the credentials of surgeons, documentation of prior patient results, and the extensiveness to which a surgeon is involved in the procedure have not been described in the literature. Specifically, these characteristics have not been comparatively evaluated between clinics that are advertised on social media platforms against those that are advertised in hair transplant specific physician directories. This study aims to evaluate the quality of hair transplant clinics available on social media against physician directories.

Methods 
We compiled a list of the top 50 posts from various social media platforms (Instagram, TikTok, YouTube, Reddit) as a result of searching “hair transplant clinic selection” on each platform using an incognito window. We evaluated the characteristics of the hair transplant clinics being advertised in each social media post. Characteristics included the credentials of the primary provider, whether the clinic used surgery standardized photography to advertise patient results, documented patient Norwood level, documented graft number, and the country in which the clinic is located. We also randomly selected 50 physicians from well-known hair transplantation physician directories (International Society of Hair Restoration Surgery, International Alliance of Hair Restoration Surgeons, American Board of Hair Restoration Surgery) to comparatively evaluate the same characteristics. Statistical analysis was done by aggregating data from all social media platforms and comparing against aggregated data from the physician directories using a chi-squared analysis.

Results
Our analysis revealed a significant difference in the characteristics of hair transplant clinics advertised on social media versus those listed in physician directories. Clinics advertised on physician directories were more likely to contain dermatologists, plastic surgeons, or otolaryngologists (p < 0.001), use standardized photography (p < 0.001), document patient Norwood level (p < 0.001), document graft number used in procedures (p < 0.001), and contain a greater number of United States based clinics (p < 0.001) compared to social media platforms.

Conclusion
There is a significant difference in the quality of hair transplant clinics that are advertised via social media posts compared to those that are advertised in physician directories. Patients should thoroughly evaluate hair transplant clinics, particularly those on social media, by reviewing credentials, physician involvement, and documented results to make informed, safe decisions.

Choosing a Hair Transplant Clinic: Evaluating Social Media and Physician Directories

Background
Facial feminization surgery (FFS) is a crucial component of gender-affirming care (GAC) for transgender women, significantly improving quality of life and mitigating mental distress. Despite its importance, FFS coverage remains contentious due to the nuanced distinction between cosmetic procedures and medically necessary interventions. Massachusetts stands out as one of only three states offering extensive coverage for facial gender-affirming surgeries. This study aims to examine what "extensive coverage" truly means for those seeking FFS under MassHealth insurance. We investigated insurance coverage, average cost, and claim denials of three commonly desired FFS procedures in Massachusetts: rhinoplasty, frontal lobe reduction, and genioplasty.

Methods
This retrospective study utilized data from the Massachusetts Center for Health Information and Analysis (CHIA) claims database from 2016 to 2021. Transgender patients were identified by ICD-10-CM codes. The data was filtered for CPT procedure codes for the three FFS of interest. Analysis was performed to compare the cost of procedures for patients with MassHealth versus private insurance, including mean amount paid and percentage of denied claims.

Results
From 2016-2021, there were 454 rhinoplasties, 260 frontal bone reductions, and 226 genioplasties billed as GAC FFS in Massachusetts. After filtering to include only the highest version of paid claims, the dataset was reduced to 165 claims for rhinoplasty (18 MassHealth, 147 private), 93 for frontal bone reduction (7 MassHealth, 86 private), and 80 for genioplasty (2 MassHealth, 78 private).

Under MassHealth, the average costs for rhinoplasty, frontal bone reduction, and genioplasty were $1,596, $167, and $395, respectively. For private insurance, the mean costs were $5,443, $2,268, and $5,801 respectively. MassHealth denied no claims for rhinoplasty and genioplasty, while private insurance denied 12.3% and 8.1% respectively. For frontal bone reduction, MassHealth denied 0.4% of claims, compared to 7.1% denied by private insurance.

Conclusion
Our findings highlight significant differences in FFS procedure coverage in Massachusetts, with privately insured transgender individuals facing greater financial burdens and higher denial rates. This research contributes to evidence demonstrating disparities in access to and coverage for FFS. We hope these results will inform future policy decisions to improve equitable access to these medically necessary procedures. Insurance companies across the United States may consider modeling their coverage policies after MassHealth to offer a more comprehensive approach to GAC, potentially improving health outcomes and reducing disparities for transgender individuals.

Disparities in Facial Feminization Surgery Coverage: A Comparative Analysis of MassHealth and Private Insurance Coverage for Rhinoplasty, Frontal bone reduction, and Genioplasty in Massachusetts

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