United States

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&#39;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&#39;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&lt;0.01). Stratified by clinic, maternal-fetal medicine (-1.47%; p=0.01), minimally invasive gynecology (+4.62%; p&lt;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&lt;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&lt;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.

AMA Research Challenge 2024 

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

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AMA Research Challenge 2023

Join the AMA Research Challenge poster symposium and semifinals to explore top research posters, score posters in the semifinals (an AMA member exclusive opportunity) and attend education sessions with experienced physicians to learn tips to advance your research and career. 

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

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The 2024 American Medical Association Research Challenge includes a poster symposium hall and a semifinals hall with the top scored research. **Log in at any time throughout the event to:**

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* **Score posters in the semifinals** to help decide the top five finalists (AMA member exclusive opportunity)
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At the conclusion of this event, the AMA will announce the top five finalists who will advance to the AMA Research Challenge finals, present their research to an elite panel of judges, and compete to win a $10,000 grand prize presented by Laurel Road. 

### Grand Prize Sponsor

AMA would like to thank Laurel Road for sponsoring a $10,000 grand prize for the winner of the AMA Research Challenge. Laurel Road is not involved in winner selection process. The winner will be announced on February 20, 2025 during the AMA Research Challenge finals. Payment of the grand prize will be directly from Laurel Road. See the AMA Research Challenge Official Rules for terms and conditions.

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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

Background
Adolescent behavioral health services face complex challenges characterized by patient demographics, treatment outcomes, and service utilization patterns. Limited data and research exist regarding behavioral health especially in this population demographic. The aim of our study was to analyze demographics of adolescent (10-17 years) patients at one hospital’s (LVHN Muhlenberg hospital) adolescent psychiatry inpatient unit from 2021 to 2023, examine the primary diagnoses and their impact on length of stay (LOS), and to assess how this facility compares to other similar facilities.

Methods
This project was conducted utilizing the Vizient clinical database and all patients under 18 years of age were queried for one single institution’s inpatient facility. Variables collected for each patient included age, sex, race, primary diagnosis, and LOS. Vizient was also utilized to compare the observed LOS against the expected LOS (LOS index) of our facility with that of similar size large specialized complex care medical center (Vizient hospital category) inpatient units in the United States.

Results
The results showed increased admissions for adolescents from 2021 to 2023, with prevalence in mood disorders, adjustment disorders, and anxiety disorders. When compared to 36 other similar facilities, our facility’s LOS index (LOS observed/LOS expected) ranked at number 30 in 2021 and 2023 and number 20 in 2022. White males (211 encounters, 17%) and white females (637 encounters, 51%) had the most inpatient encounters. The results indicated a significant variation in LOS from 2021-2023 based on diagnosis, with mood disorders (8.14 days vs. 7.33 days, p < 0.05) and schizophrenia (16.92 vs. 9.37 days, p < 0.05) showing the highest deviations from expected LOS. Racial and sex disparities were noted, with differences in LOS across various demographic groups.

Conclusion
Within the studied county and neighboring counties, 20% of the population is under 18 and our facility’s inpatient adolescent unit has a maximum of 13 beds. These findings support the need for more infrastructure, preventative measures, and methods for decreasing LOS. Comparisons with other facilities suggest collaborative opportunities to explore different methods of care.

Trends in Adolescent Behavioral Health Admissions at Lehigh Valley Health Network (LVHN)- Muhlenberg Hospital


Disparities in Adult Asthma Outcomes Among Disaggregated Asian Americans in the National Health Interview Survey


Rationale
Asthma is a chronic inflammatory lung disease disproportionately affecting racially diverse vulnerable populations. Asthma prevalence and control are not well described amongst Asian American (AA) populations.

Methods
We examined asthma outcomes from the National Health Interview Survey from 2006-2018 for non-Hispanic white adults (NHW) and AA adults (Chinese, Filipino, Asian Indian [AI], and Other Asians [OA]), including asthma prevalence and control (current asthma symptoms, ≥1 asthma attack or ED asthma visits within a year). Using logistic regression, we calculated odds ratios (OR) for AA asthma prevalence/outcomes compared to NHW adults, adjusting for demographic, health, and socioeconomic factors.

Results
Asthma prevalence varied for adults identifying as NHW (n=33764), Chinese (310, OR:0.54), Filipino (603, OR:1.03), AI (236, OR:0.43), and OA (601, OR:0.61).
Over half of the adults had poor control with ongoing asthma symptoms: 62% NHW, 53.5% Chinese (OR:0.72), 50.2% Filipino (OR:0.64), 54.8% AI (OR:0.75), and 59.2% OA (OR:0.82). Filipino adults reported more asthma (OR:1.37) but better control (symptoms [OR:0.74]), while Chinese (OR:0.39) and AI (OR:0.48) adults had fewer ED visits. Sociodemographic and health factors influenced asthma symptoms (OR:1.04-1.77), attacks (OR:1.26-2.04), and ED visits (OR:1.43-3.12).

Conclusion
Asthma prevalence and control varied widely amongst disaggregated AA populations. Sociodemographic and health factors influenced poor asthma control more than racial groups.

Clinical Implications
Our research reveals unique challenges faced by specific Asian subgroups, warranting interventions to reduce asthma-related disparities. Tailored strategies can improve asthma management, access to care, and health outcomes, advancing health equity for Asian Americans.


Disparities in Adult Asthma Outcomes Among Disaggregated Asian Americans

Background
Proton therapy is an advanced radiation treatment that uses a beam of protons to destroy cancer cells while better sparing adjacent tissue compared to conventional X-ray treatments. As of July 2024, there are only 45 operating proton therapy centers in the United States, with an additional 6 member centers in development. 25 states and the District of Columbia have at least 1 proton therapy center, while 25 states do not have a proton therapy center. With patients using Google to learn more about their medical conditions and available treatments, the purpose of this study is to determine the relationship between access to a proton therapy center
and search interest for proton therapy.

Methods
2004-2023 Google trends data for the search term “proton therapy” was analyzed by state using the Health filter. Google Trends used a scale of 0-100 to analyze search interest, with 0 representing the lowest search interest and 100 representing the highest search interest. A two tailed unpaired t-test was utilized to assess differences in search interest between states with a proton therapy center in comparison to states without a proton therapy center.

Results
There is a statistically significant difference in search interest for proton therapy between states with a proton therapy center versus states without a proton therapy center (p<0.0001). On a scale of 0-100, states with a proton therapy center had a mean search interest of 48.46 with a standard deviation of 18.37, while states without a proton therapy center had a mean search interest of 30.44 with a standard deviation of 11.80. Four states had a statistically significant higher search interest in proton therapy compared to other states (p<0.05): Tennessee, Oklahoma, Kansas, and Delaware. Of these four, only Delaware does not have a proton therapy center. Among the 16 states and District of Columbia with the highest search interest in proton therapy, only Delaware did not have a proton therapy center. Among the 15 states with the lowest search interest in proton therapy, only Utah had a proton therapy center.

Conclusion
This study highlights the association between proton therapy search interest and availability. The expansion of this novel treatment will likely impact distribution of patient resources for proton therapy.

Proton Therapy: Probing the Relationship Between Interest and Availability of Proton Therapy Centers

Background
Underserved communities in the USA face significant healthcare disparities, leading to poorer clinical outcomes and higher rates of chronic diseases. Socioeconomic barriers such as inadequate insurance coverage, poor health literacy, poverty, and cultural or linguistic differences impede patients' access to care and exacerbate these health inequities. Patient navigator programs, like the Pharmaceuticals-sponsored initiative at SUNY Downstate Health Sciences University and Kings County Hospital (KCH), help bridge gaps in healthcare and provide continuity and essential support for high-risk patients in underserved communities.

Methods
A novel multi-lingual medical student-staffed two-site, dual-departmental patient navigator program was established by the rheumatology and dermatology specialties at SUNY Downstate Health Sciences University and KCH in June 2021 and August 2022, respectively. 153 patients were recruited from the UHB Arthritis clinic and 26 patients from dermatology clinics, with identified needs such as severe disease manifestations, poor adherence to appointments or medications, and/or language difficulties. Patients were randomly assigned to patient navigators unless they had a specific language need. Patient navigators were recruited from second-year medical students at SUNY Downstate Medical School. Students underwent standardized training, shadowed in the rheumatology and/or dermatology clinics, and received structured didactics. Data was collected on patient demographics, their use of the patient navigator program, and their method of communication, and stored on a HIPAA-compliant secure REDCap database. 

Results
179 patients were enrolled in rheumatology and dermatology programs, paired with 30 active patient navigators, resulting in a PN-to-patient ratio of 1:5.97. The mean age of the patients was 48.5 years (SD 18.8). The male-to-female patient ratio was 1:5.13. Enrolled patients' most common rheumatological and dermatological conditions were lupus (53.6%) and hidradenitis suppurativa (46.2%), respectively. Since the program's initiation, 715 encounters occurred between patients and their navigators. The median number of encounters per patient was 4.00, with a mean of 5.439 (SD 4.98). The primary method of communication between PNs and patients was by telephone (50.4%), or text (30.1%). Navigators attended 91 clinic visits in person with their patients. Most encounters focused on appointment scheduling or reminders (40.3%), wellness checks (19.6%), and addressing patients' active medical issues unrelated to either specialty (14.9%). The mean number of issues addressed by the navigator per encounter was 1.45 (SD 0.849).

Conclusion
The multispecialty Patient Navigator Program demonstrated significant engagement and support for patients with rheumatological and dermatological conditions. Further research will compare patients’ clinic attendance rates before and after patient navigator assignment.




A Novel Multispecialty Patient Navigator Program for Underserved Brooklyn Communities

Background
Clustering is a machine learning technique that identifies distinct groups based on similarities in a given set of variables. This project leverages binary comorbidity, complication, or confounding data to identify distinct patient groups with varying risk profiles. Patients are grouped by similar combinations in a set of variables, this aims to identify key factors associated with an outcome as well as unique, nuanced interactions that are not apparent we term the process of clustering based on comorbidities, complications, or covariates, “CoClustering”. This study aims to guide researchers in applying CoClustering, including selecting appropriate clustering algorithms and understanding their statistical foundations.

Methods
For the illustrative example provided in this framework, the 2015-2019 National Inpatient Sample (NIS) was queried for patients diagnosed with Cerebral Infarction. Using the K-Modes algorithm, patients were clustered based on 18 clinically relevant comorbidities and age groups <65, 65-79, 80+). Cluster quality was assessed using the Davies-Bouldin Index (DBI) and Calinski-Harabasz Index (CHI) to determine the optimal number of clusters. Post-clustering analysis included Odds Ratio analysis of mortality amongst clusters, multivariate logistic regression adjusting for sex, race, income quartile and primary payer.

Results
In the illustrative example, nine unique clusters were formed. Post-clustering analysis showed statistically significant differences in mortality, the highest mortality group (Group 9) had an OR of 6.05 (95% CI: 4.99-7.33) and an AOR of 6.27 (95% CI: 5.11-7.69) when compared to Group 1.

Conclusion
This framework provides clinical researchers with a practical approach to apply clustering for the identification of subgroups in diverse clinical datasets, and a demonstration of the utility of clustering in analyzing highly dimensional comorbidity data. By effectively managing high dimensionality and sparsity in large datasets, machine learning clustering algorithms like K-Modes can reveal clinically relevant patterns and key variables associated with an outcome. The CoClustering technique can be applied broadly, ranging from offering a preliminary analysis of a clinical dataset to identify salient variables to further investigate, to grouping patients with a common diagnosis to better understand individual prognosis, further enabling highly personalized care.

CoCluster: A Framework for Comorbidity, Complication, and Covariate Clustering via Machine Learning

The patient-physician relationship begins when a doctor provides care for their patient. Trust is a fundamental component of the patient-physician relationship for achieving compliance and higher satisfaction rates with medical care. Previous studies have investigated the physician's emotional intelligence as a factor in patient-physician trust. However, this study examined the role of emotional intelligence from the patient’s perspective in the medical student population, compared to existing findings in college students. The study hypotheses stated that emotional intelligence will positively correlate with the trust in physicians in medical students and that the level of emotional intelligence from medical students will be higher, given their evolving level of education.

This IRB-approved study recruited 100 medical students who completed a survey through the online survey platform, Qualtrics. Survey questions included the 11-item Trust in Physician (TPS) Likert scale, a universal scale used to assess patients' views of their physician. Additionally, the survey included the Wong and Law Scale of Emotional Intelligence (WLEIS), which is a self-report Likert measure.

The emotional intelligence of the medical student was positively associated with trust in physician (r = 0.263, n =100, p < .001). In comparison to the study where emotional intelligence of the college student was positively associated with trust in physicians (r = 0.340, n = 202, p < .001). Additionally, on a scale of 1-7, the mean Emotional Intelligence score for medical students was 5.63 (SD = 0.578) and for college students was 5.72 (SD = 0.856), which were not significantly different (t = 0.958, p = 0.339).

This study found that as emotional intelligence of the patients increased, their trust in their physician also increased, in both college and medical student populations. These data indicate a robust generalizable relationship between emotional intelligence of patients and trust in physicians. Further study is needed to unpack the subcomponents and potential causation mechanisms underlying the relationship. We hypothesized that medical students who are exposed to healthcare systems as a patient and in the future as a physician, may self-report a higher level of emotional intelligence which could impact their patient-physician relationship. However, the level of emotional intelligence in the two samples was not significantly different. The findings of this study add to the existing knowledge on the patient-physician relationship and further study is warranted to assess the extent of the impact it has on patient outcome, compliance, and satisfaction with care.

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