All Publications

  • Changes in Medicaid enrollment during the COVID-19 pandemic across 6 states. Medicine Sun, R., Staiger, B., Chan, A., Baker, L. C., Hernandez-Boussard, T. 2022; 101 (52): e32487

    Abstract

    The coronavirus disease 2019 public health emergency (PHE) caused extensive job loss and loss of employer-sponsored insurance. State Medicaid programs experienced a related increase in enrollment during the PHE. However, the composition of enrollment and enrollee changes during the pandemic is unknown. This study examined changes in Medicaid enrollment and population characteristics during the PHE. A retrospective study documenting changes in Medicaid new enrollment and disenrollment, and enrollee characteristics between March and October 2020 compared to the same time in 2019 using full-state Medicaid populations from 6 states of a wide geographical region. The primary outcomes were Medicaid enrollment and disenrollment during the PHE. New enrollment included persons enrolled in Medicaid between March and October 2020 who were not enrolled in January or February, 2020. Disenrollment included persons who were enrolled in March of 2020 but not enrolled in October 2020. The study included 8.50 million Medicaid enrollees in 2020 and 8.46 million in 2019. Overall, enrollment increased by 13.0% (1.19 million) in the selected states during the PHE compared to 2019. New enrollment accounted for 24.9% of the relative increase, while the remaining 75.1% was due to disenrollment. A larger proportion of new enrollment in 2020 was among adults aged 27 to 44 (28.3% vs 23.6%), Hispanics (34.3% vs 32.5%) and in the financial needy (44.0% vs 39.0%) category compared to 2019. Disenrollment included a larger proportion of older adults (26.1% vs 8.1%) and non-Hispanics (70.3% vs 66.4%) than in 2019. Medicaid enrollment grew considerably during the PHE, and most enrollment growth was attributed to decreases in disenrollment rather than increases in new enrollment. Our results highlight the impact of coronavirus disease 2019 on state health programs and can guide federal and state budgetary planning once the PHE ends.

    View details for DOI 10.1097/MD.0000000000032487

    View details for PubMedID 36596028

  • Exploring Reasons for Non-Use of Hydroxychloroquine in SLE Pregnancy Chan, A., Hirz, A., Chaichian, Y., Rector, A., Druzin, M., Simard, J. WILEY. 2022: 1901-1902

    View details for Web of Science ID 000877386501477

  • Using deep learning-based natural language processing to identify reasons for statin nonuse in patients with atherosclerotic cardiovascular disease. Communications medicine Sarraju, A., Coquet, J., Zammit, A., Chan, A., Ngo, S., Hernandez-Boussard, T., Rodriguez, F. 2022; 2: 88

    Abstract

    Background: Statins conclusively decrease mortality in atherosclerotic cardiovascular disease (ASCVD), the leading cause of death worldwide, and are strongly recommended by guidelines. However, real-world statin utilization and persistence are low, resulting in excess mortality. Identifying reasons for statin nonuse at scale across health systems is crucial to developing targeted interventions to improve statin use.Methods: We developed and validated deep learning-based natural language processing (NLP) approaches (Clinical Bidirectional Encoder Representations from Transformers [BERT]) to classify statin nonuse and reasons for statin nonuse using unstructured electronic health records (EHRs) from a diverse healthcare system.Results: We present data from a cohort of 56,530 ASCVD patients, among whom 21,508 (38%) lack guideline-directed statin prescriptions and statins listed as allergies in structured EHR portions. Of these 21,508 patients without prescriptions, only 3,929 (18%) have any discussion of statin use or nonuse in EHR documentation. The NLP classifiers identify statin nonuse with an area under the curve (AUC) of 0.94 (95% CI 0.93-0.96) and reasons for nonuse with a weighted-average AUC of 0.88 (95% CI 0.86-0.91) when evaluated against manual expert chart review in a held-out test set. Clinical BERT identifies key patient-level reasons (side-effects, patient preference) and clinician-level reasons (guideline-discordant practices) for statin nonuse, including differences by type of ASCVD and patient race/ethnicity.Conclusions: Our deep learning NLP classifiers can identify crucial gaps in statin nonuse and reasons for nonuse in high-risk populations to support education, clinical decision support, and potential pathways for health systems to address ASCVD treatment gaps.

    View details for DOI 10.1038/s43856-022-00157-w

    View details for PubMedID 35856080

  • IDENTIFYING REASONS FOR STATIN NONADHERENCE IN A DIVERSE, REAL-WORLD POPULATION USING ELECTRONIC HEALTH RECORDS AND NATURAL LANGUAGE PROCESSING Sarraju, A., Coquet, J., Chan, A., Ngo, S., Lossio-Ventura, J., Hernandez-Boussard, T., Rodriguez, F. ELSEVIER SCIENCE INC. 2021: 1665

    View details for Web of Science ID 000647487501671

  • Medical Student Values Inform Career Plans in Service & Surgery-A Qualitative Focus Group Analysis. The Journal of surgical research Carroll, A. L., Chan, A. n., Steinberg, J. R., Bryant, T. S., Marin-Nevarez, P. n., Anderson, T. N., Bereknyei Merrell, S. n., Lau, J. N. 2020; 256: 636–44

    Abstract

    Diversifying the surgical workforce is a critical component of improving care for underserved patients. To recruit surgeons from diverse backgrounds, we must understand how medical students choose their specialty. We investigate how preclinical students contemplate entering a surgical field.We conducted semistructured focus groups during two iterations of a seminar class called Service Through Surgery. Discussion goals included identifying student values and assessing how they inform early career decisions. We used a systematic, collaborative, and iterative process for transcript analysis, including developing a codebook, assessing inter-rater reliability, and analyzing themes.Twenty-four preclinical medical students from diverse backgrounds participated in seven focus groups; most were women (16; 67%), in their first year of medical school (19; 79%), and interested in surgery (17; 71%). Participants ranked professional fulfillment, spending time with family, and serving their communities and/or underserved populations among their most important values and agreed that conducting groundbreaking research, working long hours, and finding time for leisure activities were the least important. We constructed a framework to describe student responses surrounding their diverse visions for service in future surgical careers through individual doctoring interactions, roles in academia, and broader public service.Our framework provides a basis for greater understanding and study of the ways in which preclinical medical students think about their personal values and visions for service in potential future surgical careers. This research can guide early interventions in medical education to promote diversity and care for the underserved in surgery.

    View details for DOI 10.1016/j.jss.2020.07.030

    View details for PubMedID 32810664

https://orcid.org/0000-0002-7496-9581