United States

Background: To assess school-aged children’s wellbeing and to characterize changes in school-related needs from 2021 to 2022.
Methods: We surveyed a nationally representative sample of parents of children enrolled in grades K-12 in the 2021-2022 academic year in June 2021 and again one year later (June 2022). Parents completed the Strengths and Difficulties Questionnaire (SDQ) and reported their child’s general health and needs for school-related services. Weighted regressions examined associations among wellbeing, needs, and socio-demographic characteristics.
Results: 1,504 participants completed baseline and 1,199 completed follow up surveys. The percentage of children with deficits in total difficulties, hyperactivity, and prosocial behavior decreased from baseline to follow up, but a high percentage of children continued to have peer problems. Parents reported receiving support from their school for 45.3% of needs during the 2021-2022 school-year but 72.5% of parents continued to have at least one school-related need. Highest priority needs were academic enrichment, socialization, physical activity, tutoring, and coping with stress.
Implications for School Health Policy, Practice, and Equity: While many children with school-related needs received school support, a large percentage continue to have unmet needs and experience peer-related problems.
Conclusions: Continued investment in school-based services is necessary to support child health.

2023 AMA Research Challenge

Changes in School-Age Children’s Wellbeing and School-Related Needs during the COVID-19 Pandemic

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Background: The Adverse Childhood Experiences (ACEs) questionnaire is a widely used screener for pediatric trauma exposure and general household dysfunction. On a population level, ACEs are cumulative risk factors for a range of mental health and physical conditions. ACEs research has been largely based on self-reported ACEs exposures and health conditions, anthropometric measures, and biomarkers. In contrast, data mined directly from the electronic health record (EHR) allows for more objective analysis and reduces potential harms and limitations of self-report. The objectives of this pilot were to generate and test an algorithm for deriving Proxy ACEs values from existing EHR data, and to study the population-level association between derived Proxy ACEs, medical morbidity, and healthcare utilization. 

Methods: Proxy ACE scores were derived from structured EHR data including diagnoses, chief complaints, and social and family history sections. Medical morbidity was operationalized using the total number of medical diagnoses and prescribed medications, while healthcare utilization was represented by the number of emergency room visits, number of hospitalizations, and total hospital length of stay. We generated a dataset of 142,719 patients <18 years old who received care at Yale, spot-checked the derivation algorithm, and iterated modifications to obtain their final Proxy ACE scores. 

Results: Proxy ACE scores were positively correlated with all medical morbidity and healthcare utilization outcome variables at a significance level of P<0.01, with the strongest associations for number of medical diagnoses (r=0.30), number of prescribed medications (r=0.19), and number of emergency room visits (r=0.17). After accounting for outliers using Cook’s d and applying hierarchical multiple regression, Proxy ACE scores predicted an additional 9.2% of the variability in number of medical diagnoses after controlling for age, sex, race, and ethnicity (β=.31). 

Conclusion: Our findings are proof of concept that Proxy ACEs scores can be meaningfully derived from the EHR. Future directions include incorporating natural language processing methodology to account for unstructured data stored in the EHR and creating and validating an individual-level multivariable predictive model using our Proxy ACEs dataset.

Harnessing electronic health record data to examine the association between adverse childhood experiences, medical morbidity, and healthcare utilization

Background: Depression is a public health concern affecting the well-being and development of adolescents. Despite evidence-based guidelines promoting routine depression screening, many youths remain unidentified and untreated. Quality improvement (QI) initiatives may enhance depression screening practices. This retrospective chart review assesses a QI initiative's impact on depression screening rates among 12 to 18-year-old patients at four pediatric clinics in urban Nebraska from 2018 to 2022. The study evaluates trends in positive depression screens and the effectiveness of targeted interventions to increase adolescent depression screening. 
Methods: Electronic health record data was used to calculate adolescent depression screening rates and the percentage of positive depression screens. The screening rate equaled the total number of completed adolescent depression screens divided by the total number of well child exams for 12 through 18-year-old patients. The percentage of positive depression screens was the percentage of all adolescents who were screened and were positive on the Patient Health Questionnaire-4 (PHQ-4). The QI initiative comprised a “Lunch and Learn" session to raise provider awareness about the importance of depression screening. Additional interventions involved creating supportive materials for managing positive screenings, integrating depression screening into well-child exam rooming, and posting educational materials in the clinic's computer system for easy access by patients and providers. The impact of the initiative on depression screening rates was analyzed annually over five years. 
Results: The data revealed variations in depression screening rates and stability in the percentage of positive depression screens over the five-year study period. In 2018, the screening rate at well- child exams was 25.9%, with 8.1% of patients positive for depression. In 2019, the screening rate decreased to 21.6%, while the percentage of positive screenings increased to 13.1%. Following the initiation and continuation of the QI initiative in 2019, screening rates notably increased, reaching 37.2% with 11.7% positive patients in 2020. Screening rates further improved to 48.4% and 88.6% in 2021 and 2022, respectively, while the percentage of positive screenings remained relatively stable at 11.2% and 10.1%. 
Conclusion: The QI initiative positively impacted depression screening rates among adolescent patients at the four pediatric clinics, empowering healthcare providers to recognize and support adolescents facing depression. Continuous screening practice improvement efforts are crucial for early identification and intervention. The initiative offers a promising approach to enhance adolescent mental health care. 


Impact of Targeted Interventions on Depression Screening Rates among Adolescent Patients: A Retrospective Chart Review

Background
Pediatric hypertension is underrecognized, affecting 4% of adolescents in the USA with an additional 10% having elevated BP. Disparities exist among racial/ethnic groups. The global aim of this initiative is to improve recognition of elevated BP and hypertension and eliminate disparities in adolescents at the three NYU Langone Hospital Long Island primary care ambulatory sites.

Methods
Data between 2019-2021 was analyzed to determine prevalence and distribution of elevated BP and hypertension in patients >13 years by sex, race/ethnicity, and preferred language. Continuous variables were compared using Wilcoxon rank-sum test, and Chi-square/Fisher’s exact tests utilized for categorical variables. SAS v9.4 was used with statistical significance if p-value <0.05.

An interprofessional QI team was chartered to standardize BP measurement and improve recognition/management of adolescent hypertension. QI measures included percentage of appropriate documentation of elevated BP and hypertension in the care plan, timely primary care follow-up, and appropriate nephrology referral. Random sampling of 10-15 adolescents/month per site with elevated BP were reviewed. Data for 6 months was utilized for baseline QI measures. Our SMART aim is to increase recognition and appropriate follow-up by 25% from baseline across all sites in twelve months. Key driver diagram was constructed, and change ideas implemented using PDSA cycles. Interventions included multi-modal education and site visits to standardize BP measurement. BP reference and management algorithm handouts were distributed. Data were analyzed quarterly and plotted using run charts.

Results
Retrospective data from 2019-2021 (n=6158) showed 32% prevalence of elevated BP and hypertension in our cohort, 66.3% of which were male. Race/ethnicity distribution of adolescents with elevated BP and hypertension were 37.1% Black, 31.9% Hispanic, 31.7% White, and 27.7% Asian. Distribution by preferred language revealed 33.1% Spanish and 32.1% English (remainder other).

Appropriate documentation of elevated BP and hypertension in the care plan improved by 4.5 % (from 2% to 6.5%). Inter-site comparison revealed site-site variations, with the largest improvement of 11% at our Patient-Centered Medical Home. Twelve-month baseline for timely primary care follow-up and appropriate nephrology referral were 7% and 13%, respectively. 

Conclusion
The prevalence of elevated BP was higher in our cohort, potentially due to variations in BP measurement, and was targeted by our interventions. We achieved our SMART aim of increased recognition and documentation of adolescents with elevated BP and hypertension through standardization of BP measurement and provider education. Our next step is to target timely primary care follow-up and appropriate nephrology referral. An additional goal is to identify and reduce disparities across race, ethnicity, and preferred language for recognition and management of adolescent hypertension.

Measurement of Ambulatory Blood Pressure (BP) Standardization (MAPS) in Adolescents: A Quality Improvement (QI) Initiative

Background: Predicting the discharge destination, otherwise known as discharge disposition, can offer several advantages for the inpatient rehabilitation patient, including treatment and discharge planning. Machine learning (ML) models have demonstrated a strong predictive capacity for discharge disposition, particularly for patients with specific diagnoses such as traumatic brain injuries, or in specific settings such as acute neurologic care, or among specific populations. 



Objective: Explore various ML models’ ability to predict inpatient rehabilitation discharge disposition to home versus non-home using patient encounter data. 



Methods: Patient data included demographics, diagnoses, lab values, length of stay, and insurance status from all inpatient rehabilitation encounters at a large midwestern health system occurring between 2015 and 2022 (N=4919). We compared two commonly used ML approaches: a random forest (RF) classifier and a decision tree (DT) classifier. Two bagging classifiers, which aggregate the results of multiple RF or DT classifiers to improve predictions, were also tested. Because the imbalance in number between home (N=3687) and non-home discharges (N=1232) can impair predictions, balancing techniques were used prior to training the ML classifiers. The ML classifiers were then optimized with grid search and cross validation. Performance of the ML classifiers was determined by the receiver operating characteristic area under the curve (AUC) – a measure of the ability to discriminate the true discharge disposition, accuracy (computed using the Jaccard Index), and macro f1-score (indicative of classification ability across classes). 



Results: 

A bagging classifier using RF showed highest predictive performance in terms of AUC (0.68) and accuracy (0.75), however it struggled to predict non-home discharges (macro f1-score = 0.54). A bagging DT classifier improved prediction of non-home discharges (macro f1-score = 0.59), however AUC (0.65) and accuracy (0.69) were lower. Generally, bagging classifiers combining the predictions from multiple RF or DT classifiers improved predictive performance compared to single RF or DT classifiers. 



Conclusions: As artificial intelligence and ML continue to grow in healthcare applications, this study showcased the effectiveness of ML approaches to predict discharge disposition from inpatient rehabilitation. Our findings complement previous studies in other diagnoses and settings, due to our diversity of available variables and differences in our patient population and treatment paradigms. Of note, utilization of bagging classifiers proved valuable in enhancing the performance of the tested base classifiers. Our results may help guide optimization of ML approaches to predict discharge disposition, in hopes of improving patients' hospital courses and post-hospital planning and treatment.

Evaluation of Machine Learning Approaches for Predicting Inpatient Rehabilitation Patient Discharge Disposition

Background
Post Traumatic Stress Disorder (PTSD) is a disorder characterized by exposure to some traumatic event leaving the individual with a variety of symptoms that can result in debilitating disability. Treatment has historically been centered around Cognitive Processing Therapy (CPT) and literature has shown a significant risk of suicide in those left untreated. Thus, the importance of appropriate healthcare utilization is important for addressing morbidity and mortality of PTSD. Determining appropriate utilization is not well recorded in the literature though some studies show increased utilization with increased symptom severity. The aim of
this study is to replicate these findings and determine if there exists any range of symptom severity that over or under-utilizes the healthcare system.

Methods
Data was collected by Rosen et al. 2012 through Project VALOR, a longitudinal registry of PTSD in combat exposed veterans of Afghanistan and Iraqi military theaters. PTSD checklist (PCL) scores and data regarding number of healthcare utilizations for PTSD treatment year to year were recorded over a five-year period. Utilization of healthcare services for PTSD were stratified by this study into categories and compared against PCL scores using an ordinal logistical regression model to predict probability of being placed in a higher utilization category. Quadratic analysis was conducted on the PCL scores to determine linear relationship of findings.

Results
Ordinal logistical regression of mean centered PCL value was 1.042 (p=0.000). Quadratic effect of ordinal logistical regression of mean centered PCL2 was determined to be 0.99 (p=0.061).

Conclusion
Self-reported PCL scores are positively predictive of healthcare utilization. For every 1 increase in PCL score, there is a predicted 4.29% increased chance of being placed into the next highest utilization category (p=0.000). In addition, the relationship between PCL score and healthcare utilization was determined to be log-linear due to quadratic effect being found not significant 0.99 (p=0.061). This indicates no PCL score is correlated with increased probability of increased or decreased healthcare utilization. This likely represents healthcare services are appropriately being administered based on symptom severity and no group is over or under-utilizing resources. Ultimately, this study replicates previous literature that increased severity is associated with increased healthcare utilization while also quantifying the predictive value of increasing PCL scores. Currently the healthcare system is correctly identifying and capturing individuals who would in theory benefit from increased healthcare utilization. Additional research into capture strength would be beneficial in determining impact of hospital system capture rate of at risk patients.

Increasing PTSD Symptom Severity Predicts Increasing Healthcare Utilization Among OEF/OIF Veterans.

Obstructive sleep apnea (OSA) affects millions of U.S. Veterans and is linked to severe illnesses such as stroke and heart failure. However, understanding how OSA interacts with common comorbidities and the additive risk of OSA in developing severe illnesses is lacking. Large language models (LLMs) are promising tools for clinical natural language processing (NLP). These models are pre-trained on vast amounts of text data and learn to generate coherent and contextually appropriate text by predicting tokens in a sequence. This study explores the quality of LLM embeddings in clustering disease phenotypes related to OSA and its comorbidities such as atrial fibrillation, coronary artery disease, heart failure, hypertension, stroke, and type 2 diabetes mellitus. By identifying sub-cohorts of patients with similar diagnoses, procedures, medications, and adherence to continuous positive airway pressure (CPAP) treatment, we aim to score LLM embeddings by how well pre-trained LLMs discriminate OSA-related phenotypes. Document-level embeddings for discharge reports in the MIMIC-IV database were computed using NERSC's Perlmutter supercomputer. Twelve clinical LLMs of different sizes and pre-training schemes were evaluated including BioBART, BioBERT, BioGPT, BioMegatron, Gatortron, and RadBERT. To visualize the LLMs embedding, UMAP was used to enable plotting of embedded documents in 2D. Each set was clustered using K-Means with increasing values for K, and the purity of the clusters was measured using the Shannon entropy of cluster assignments. Finally, we test the performance of quantized document-level data for supervised phenotype classification.
Qualitatively, we examined the LLM embeddings along UMAP plots and reviewed K-Means cluster assignment by reading sampled text. Gatrotron_medium, BioGPT_large, and BioBERT_base were the best models at separating OSA from its comorbidities. Quantitatively, the clusters were measured on their Shannon entropy factor in which the lower value correlated to greater purity of clusters and vice versa. Gatortron_medium, Gatortron_base, and BioBART_base consistently scored with low entropy across comorbidities. The developed method selected the Gatortron_medium LLM as the best pre-trained model for OSA-related phenotypes both qualitatively and quantitatively. The method also has potential for assessing bias in LLMs by measuring how embeddings group patients according to social determinants of health such as gender and race. In the context of the Department of Veterans Affairs (VA) healthcare, there is a need for early OSA detection and prevention. Leveraging LLMs for feature discovery and risk factor identification, this research can provide valuable insights for allocating CPAP treatment effectively by grouping patients with similar health histories and OSA outcomes.

Utilizing Large Language Models for DiseasePhenotyping in Obstructive Sleep Apnea

Background: Breast cancer is the second most common cancer amongst women in the United States. From 2013 to 2018, the breast cancer death rate fell by 1%; this decline was attributed to earlier screening and awareness. Early diagnosis of breast cancer can reduce morbidity and the intensity of treatment plans (ex: chemotherapies). There are many different screening tests to predict 5 year and lifetime risk of breast cancer. These screening tests and software programs have large variabilities in their risk prediction. Recent publications show a 5% to 19% overdiagnosis of breast cancer in women. In this study we predicted the 5-year risk, 10-year risk, and lifetime risk of breast cancer for mammogram patients using four different breast cancer screening tools: Mammorisk, Tyer Cuzick 2013 (TC 2013), Tyer Cuzick 2017 (TC 2017), and Gail. We compared their variability using t test statistical analysis. The purpose of our study is to determine the precision of these screening tests and begin streamlining the screening process for breast cancer. 

Methods: We collected Mammorisk data from 242 participants at Southwood Imaging (Youngstown, OH) who came in for their annual mammogram. Most women were between 40 to 70 years old. While participants waited to be screened, we asked them to fill out a questionnaire. The questionnaire contained 19 questions about the patient’s personal health, prior breast biopsies, and family history of breast cancer. We put this additional patient information into the additional screening tests Tyer Cuzick 2013, Tyer Cuzick 2017, and Gail. We calculated the 5-year risk and lifetime risk for each patient. We then measured the 5-year risk, 10-year risk, and lifetime risk assessment using t-test statistical analysis (two tailed). 

Results: The 5-year risk did not significantly differ between breast cancer risk models. The 10-year risk did not significantly differ between breast cancer risk models. The results showed statistically significant differences between lifetime risk for breast cancer risk models. Mammorisk and TC2017 lifetime risks differed by 0.544. The difference in percent lifetime risk between the Mammorisk and TC2013 models was -2.666. Mammorisk and Gail lifetime risks varied by 0.104. Even though our lifetime risk data was significant, the effect size was low . So some of our data may be inconclusive.

Conclusion: Our study indicated a lack of significant difference in 5-year and 10-year risk between models, but lifetime risk did significantly differ between breast cancer risk models. This creates variability that is difficult for physicians to interpret. This can affect physicians’ decision making and future patients’ health outcomes. Additional studies with a larger and more diverse population are needed for definitive evidence in variability between breast cancer risk models. Our study is a first step in finding a streamlined way to approach breast cancer risk in patients. In creating a streamlined approach radiologists will be able to care for cancer patients in a more organized manner.

Variability Between Different Breast Cancer risk Models and Effects on Various Parameters

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**Abstract:**



Evaluating Demographic Reporting in Publicly Available Prostate MRI Data Sets

Background:

The medical field is known to be male-predominated and in 2019, 64% of the total number of physicians were males. However, in 2017, females (50.5%) outnumbered males (49.4%) entering medical school for the first time. The gender disparity gap in medicine has been closing due to the increased number of women entering the field. The goal of this analysis is to predict gender differences in the medical profession. 


Methods: 

Data was collected from AAMC Physician Specialty Data Reports’, from 2007-2021. Statistical analysis, including a linear forecast, was used to determine predicted values implementing the equation. 

Results: 

Based on data from the AAMC, in 2007, 2011, 2013, 2015, 2017, 2019, and 2021, when looking at all subspecialties combined, there were 71.7%, 69.6% 67.4%, 66%, 64.8%, 63.7%, and 62.9% active male physicians, compared to 28.3%, 30.4%, 32.6%, 34%, 35.2%, 36.3%, and 37.1% active female physicians, respectively. Using the slopes of each data set (y = -0.6586x + 1393.5 for males and y = 0.6586x - 1293.5 for females), it is predicted that by the year 2040, there will be 50% active female physicians with a 95% CI [49.64-51.01] and 50% active male physicians with a 95% CI [48.99-50.36]. Furthermore, orthopedic surgery is the specialty with the greatest proportion of male physicians (94.1% male in 2021) while pediatrics is the specialty with the greatest proportion of female physicians (65% female in 2021). 


Conclusion:

These results conclude that females are underrepresented in medicine when looking at all specialties combined, however there has been a progressive rise in active female physicians. Moreover, 2017 was the first year females comprised the majority of medical students. It is predicted that the number of active female and male physicians will become equal by the year 2040.

A Predictive Analysis of Gender Disparities in the Medical Profession

Background: There is no standardized sexual assault (SA) or trauma-informed care (TIC) curriculum in medical education, resulting in little to no exposure to these topics for many medical students. The goal of this project is to evaluate medical student perceptions of TIC before and after a weeklong SA/TIC curriculum. We hypothesize that students will be more willing to use TIC strategies and think that it is more important after they have been introduced to instruction on the topic.
Methods: All second-year medical students at MCG participated in a weeklong SA/TIC curriculum in August 2021. Students were tasked with learning about TIC during the Case-Based Learning (CBL) sessions and a standardized patient encounter in Patient-Centered Learning (PCL). Students were given a voluntary survey on their perceptions regarding TIC before (pre-survey) and after (post-survey) this week as part of the curriculum. The survey was adapted from Cannon, et al. 2020 and used 5-point Likert scale (1 = strongly disagree, 5 = strongly agree) responses to 17 questions. Data were coded by the curriculum office and de-identified. After IRB approval, quantitative data were analyzed using descriptive statistics and paired t-tests.
Results: In total, 193 medical students participated in the SA/TIC curriculum. There were 101 pre-surveys and 99 post-week surveys completed for a response rate of 52.3% and 51.3% respectively. Results from the survey demonstrated significant gains in knowledge, skills, and attitudes and significant improvement towards trauma informed care overall on average (+0.360, p<0.001).
Conclusion: Our results demonstrate that having a TIC curriculum significantly improved medical students’ knowledge of and attitude towards providing trauma-informed care. This curriculum can be reproduced at other institutions to introduce future physicians across all specialties to principles of SA/TIC care to improve health outcomes.

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