Khurana, Aditya

INTRODUCTION: Access to care is a predictor of health outcomes for the aged female population. The purpose of this study was to evaluate the accessibility of obstetrician–gynecologists (ob-gyns) and gynecologic oncologists to the U.S. Medicare population, over time and by state. METHODS: The supply of ob-gyns and gynecologic oncologists was extracted from the Physician and Other Supplier Public Use File (POSPUF) database of Medicare Part B claims submitted to the U.S. Centers for Medicare and Medicaid Services. This supply was compared to the number of providers normalized by the number of Original Medicare beneficiaries; values reported are providers per 100,000 beneficiaries by state from 2012 to 2019, obtained from the Kaiser Family Foundation. RESULTS: In 2019, the average number of ob-gyns per 100,000 beneficiaries across all states was 67.2 (standard deviation [SD] ±19.3), and ranged from 157.01 (District of Columbia) to 45.39 (Wyoming). The average number of gynecologic oncologists was 2.48 (SD±1.18), and ranged from 6.60 (District of Columbia) to 0.83 (Mississippi). From 2012 to 2019, the average absolute difference in number of ob-gyns across all states was −7.71±8.59 and ranged from+15.74 (Connecticut) to −37.32 (Alaska). For gynecologic oncologists, this difference was+0.10±1.04 and ranged from+5.22 (District of Columbia) to −1.84 (South Dakota). CONCLUSION: There is wide geographic variation in the accessibility and growth rate of ob-gyns and GYNONCs for the Medicare population. This provides insight into areas of the country where the supply of ob-gyns and gynecologic oncologists may not meet current and future demand.

Patient portals have been widely used by patients to enable timely communications with their providers via secure messaging for various issues including transportation barriers. The large volume of portal messages offers an invaluable opportunity for studying transportation barriers reported by patients. In this work, we explored the feasibility of cutting-edge deep learning techniques for identifying transportation issues mentioned in patient portal messages with deep semantic embeddings. The successful creation of annotated corpus and identification of 7 transportation issues showed the feasibility of this strategy. The developed annotated corpus could aid in developing an artificial intelligence tool to automatically identify transportation issues from millions of patient portal messages. The identified specific transportation issues and the analysis of patient demographics could shed light on how to reduce transportation gaps for patients.

Background . Patients increasingly use asynchronous communication platforms to converse with care teams. Natural language processing (NLP) to classify content and automate triage of these messages has great potential to enhance clinical efficiency. We characterize the contents of a corpus of portal messages generated by patients using NLP methods. We aim to demonstrate descriptive analyses of patient text that can contribute to the development of future sophisticated NLP applications. Methods . We collected approximately 3,000 portal messages from the cardiology, dermatology, and gastroenterology departments at Mayo Clinic. After labeling these messages as either Active Symptom, Logistical, Prescription, or Update, we used NER (named entity recognition) to identify medical concepts based on the UMLS library. We hierarchically analyzed the distribution of these messages in terms of departments, message types, medical concepts, and keywords therewithin. Results . Active Symptom and Logistical content types comprised approximately 67% of the message cohort. The “Findings” medical concept had the largest number of keywords across all groupings of content types and departments. “Anatomical Sites” and “Disorders” keywords were more prevalent in Active Symptom messages, while “Drugs” keywords were most prevalent in Prescription messages. Logistical messages tended to have the lower proportions of “Anatomical Sites,”, “Disorders,”, “Drugs,”, and “Findings” keywords when compared to other message content types. Conclusions . This descriptive corpus analysis sheds light on the content and foci of portal messages. The insight into the content and differences among message themes can inform the development of more robust NLP models.

AbstractObjectiveThe content of websites for fellowship programs is an important source of information for residents applying to breast imaging fellowship programs (BIFPs). The purpose of this study is to evaluate the comprehensiveness of online content of BIFPs.MethodsA list of BIFPs was obtained from the Society of Breast Imaging website. Each program’s website was evaluated for the presence of 19 training-relevant content variables. Impact of program characteristics on comprehensiveness scores was determined. For statistical analysis, Kruskal–Wallis tests were used to assess differences in comprehensiveness scores based upon region, and two-tailed t-tests were used to compare based upon program size.ResultsA total of 79 BIFP websites were analyzed. The mean comprehensiveness score of BIFP websites based on meeting the 19 criteria was 44.1% (8.4 ± 2.7/19). Program coordinator contact information (72/79, 91.1%), application requirements (71/79, 89.9%), and faculty information (56/79, 70.9%) appeared on >70% of websites. The majority of fellowships had a dedicated webpage for their program (71/79, 89.9%). Information regarding 12 of the 19 criteria appeared on fewer than 50% of websites. Program region (P = 0.32) and size (P = 0.16) were not associated with any differences in mean comprehensiveness score. Additionally, there was no significant difference in scores associated with filling all available positions for the 2020 match cycle (P = 0.77).ConclusionThere is a paucity of information commonly sought out by applicants on the websites of most BIFPs. Both programs and applicants may mutually benefit from increasing comprehensive online content.

Abstract PURPOSE Brain tumor related epilepsy (BTE) is a major co-morbidity in patients with glioma. It is difficult to determine whether the use of anti-epileptic drugs is necessary. We attempted to build a machine-learning model to predict the probability of seizure presentation (SP) with glioma. METHODS We trained a random forest classifier using the following variables: volumetric data of pre-treatment MR images (T1Gd and T2-FLAIR sequences), patient demographics (age; sex), and measurements of tumor proliferation (log(ρ)), invasiveness (log(D)) and their relative ratio (log(ρ/D)). Our cohort consisted of 221 patients total. Using an 80-20 ratio, we used 176 patients (76 SP, 100 nSP) for training and the remaining 45 patients (19 SP, 26 nSP) were used for testing. We also trained on male-only and female-only cohorts to evaluate any sex differences in prediction. For training, 108 males (53 SP, 55 nSP) were used and 28 for testing (14 SP, 14 nSP). We used 72 females (21 SP, 49 nSP) for training and 15 (7 SP, 8 nSP) for testing. We corrected for class imbalance in the female cohort before training. Using 10-fold cross-validation and a separate testing set, we measured performance by ROC curve (AUC), accuracy, sensitivity, and specificity of predictions (average of folds in cross validation). RESULTS The female model achieved the highest AUC (0.853) followed by the mixed model (0.726) and the male model (0.651). In the validation set, the accuracy/sensitivity/specificity of the three cohorts were as follows: mixed (0.726/0.696/0.750), female (0.853/0.830/0.875), and male (0.651/0.577/0.722). The performance of the testing set, in terms of accuracy/sensitivity/specificity were: mixed (0.733/0.74/0.73), female (0.8/0.57/1), and male (0.714/0.64/0.79). CONCLUSION We found a negative correlation between seizure probability and size and invasiveness of tumors. Our model shows promising performance on testing set data. Further cohort studies and training is warranted.

Abstract Sex differences in brain tumor related epilepsy (BTE) as an initial symptom of glioma are rarely investigated. Sex differences are being explored with increased interest more recently. Epidemiological studies have generally indicated overall incidence of epilepsy to be slightly higher in males than females, but a higher incidence of idiopathic generalized epilepsy has been seen in females in some studies. Clinicians regularly face the challenge of anti-epileptic drug prescription determination in patients with newly identified brain mass and would greatly benefit from knowing clinical characteristics in BTE. We analyzed clinical characteristics in patients with pathologically-determined glioma with known seizure status at clinical presentation using non-parametric and Fisher’s Exact tests with the entire cohort and male and female separately. From our clinical data repository, we identified 223 patients (females, n=86 (39%); males, n=137 (61%)) divided by those that presented with seizure (SP, n=96, 43%; F, n= 28; M, n= 68) and those that presented without seizure (nSP, n=127, 57%, F n=58, M n=69). In sex-specific analysis males had a significantly (x2=6.28, p=0.01) higher proportion of SP (49.6%) than females (32.6%). There was a dependence between seizure presentation status and tumor grade in males (x2=5.94, p=0.015). Tumor kinetics in the female cohort showed significantly (p< 0.0001) more diffuse tumors in SP compared to nSP. Right-sided tumors were predominantly nSP, while left-sided tumors were split more evenly between SP and nSP. The predominance of nSP among right-sided tumors seems to be driven by females (74%), male right-sided tumors were split more evenly between SP and nSP. Seizure status is dependent on tumor location for the combined cohort, however not for males (x2=5.23, p=0.16) or females (x2=4.34, p=0.23). Clearly there are sex differences and similarities in clinical characteristics at glioma presentation. Further characterization could lead to improved adherence of national standards for AED prescription.