Jessica Herrmann

All Publications

• Leveraging a Large Language Model to Assess Quality-of-Care: Monitoring ADHD Medication Side Effects. medRxiv : the preprint server for health sciences Bannett, Y., Gunturkun, F., Pillai, M., Herrmann, J. E., Luo, I., Huffman, L. C., Feldman, H. M. 2024

  Abstract

  To assess the accuracy of a large language model (LLM) in measuring clinician adherence to practice guidelines for monitoring side effects after prescribing medications for children with attention-deficit/hyperactivity disorder (ADHD).Retrospective population-based cohort study of electronic health records. Cohort included children aged 6-11 years with ADHD diagnosis and ≥2 ADHD medication encounters (stimulants or non-stimulants prescribed) between 2015-2022 in a community-based primary healthcare network (n=1247). To identify documentation of side effects inquiry, we trained, tested, and deployed an open-source LLM (LLaMA) on all clinical notes from ADHD-related encounters (ADHD diagnosis or ADHD medication prescription), including in-clinic/telehealth and telephone encounters (n=15,593 notes). Model performance was assessed using holdout and deployment test sets, compared to manual chart review.The LLaMA model achieved excellent performance in classifying notes that contain side effects inquiry (sensitivity= 87.2%, specificity=86.3/90.3%, area under curve (AUC)=0.93/0.92 on holdout/deployment test sets). Analyses revealed no model bias in relation to patient age, sex, or insurance. Mean age (SD) at first prescription was 8.8 (1.6) years; patient characteristics were similar across patients with and without documented side effects inquiry. Rates of documented side effects inquiry were lower in telephone encounters than in-clinic/telehealth encounters (51.9% vs. 73.0%, p<0.01). Side effects inquiry was documented in 61% of encounters following stimulant prescriptions and 48% of encounters following non-stimulant prescriptions (p<0.01).Deploying an LLM on a variable set of clinical notes, including telephone notes, offered scalable measurement of quality-of-care and uncovered opportunities to improve psychopharmacological medication management in primary care.

  View details for DOI 10.1101/2024.04.23.24306225

  View details for PubMedID 38712037

  View details for PubMedCentralID PMC11071552

• Rapid model-guided design of organ-scale synthetic vasculature for biomanufacturing. ArXiv Sexton, Z. A., Hudson, A. R., Herrmann, J. E., Shiwarski, D. J., Pham, J., Szafron, J. M., Wu, S. M., Skylar-Scott, M., Feinberg, A. W., Marsden, A. 2023

  Abstract

  Our ability to produce human-scale bio-manufactured organs is critically limited by the need for vascularization and perfusion. For tissues of variable size and shape, including arbitrarily complex geometries, designing and printing vasculature capable of adequate perfusion has posed a major hurdle. Here, we introduce a model-driven design pipeline combining accelerated optimization methods for fast synthetic vascular tree generation and computational hemodynamics models. We demonstrate rapid generation, simulation, and 3D printing of synthetic vasculature in complex geometries, from small tissue constructs to organ scale networks. We introduce key algorithmic advances that all together accelerate synthetic vascular generation by more than 230 -fold compared to standard methods and enable their use in arbitrarily complex shapes through localized implicit functions. Furthermore, we provide techniques for joining vascular trees into watertight networks suitable for hemodynamic CFD and 3D fabrication. We demonstrate that organ-scale vascular network models can be generated in silico within minutes and can be used to perfuse engineered and anatomic models including a bioreactor, annulus, bi-ventricular heart, and gyrus. We further show that this flexible pipeline can be applied to two common modes of bioprinting with free-form reversible embedding of suspended hydrogels and writing into soft matter. Our synthetic vascular tree generation pipeline enables rapid, scalable vascular model generation and fluid analysis for bio-manufactured tissues necessary for future scale up and production.

  View details for PubMedID 37645046

  View details for PubMedCentralID PMC10462165

• Anxiety and Depression Treatment in Primary Care Pediatrics. Pediatrics Lester, T. R., Herrmann, J. E., Bannett, Y., Gardner, R. M., Feldman, H. M., Huffman, L. C. 2023

  Abstract

  Primary care pediatricians (PCP) are often called on to manage child and adolescent anxiety and depression. The objective of this study was to describe PCP care practices around prescription of selective serotonin reuptake inhibitors (SSRI) for patients with anxiety and/or depression by using medical record review.We identified 1685 patients who had at least 1 visit with a diagnosis of anxiety and/or depression in a large primary care network and were prescribed an SSRI by a network PCP. We randomly selected 110 for chart review. We reviewed the visit when the SSRI was first prescribed (medication visit), immediately previous visit, and immediately subsequent visit. We abstracted rationale for prescribing medication, subspecialist involvement, referral for psychotherapy, and medication monitoring practices.At the medication visit, in 82% (n = 90) of cases, PCPs documented reasons for starting an SSRI, most commonly clinical change (57%, n = 63). Thirty percent (n = 33) of patients had documented involvement of developmental-behavioral pediatrics or psychiatry subspecialists at 1 of the 3 visits reviewed. Thirty-three percent (n = 37) were referred to unspecified psychotherapy; 4% (n = 4) were referred specifically for cognitive behavioral therapy. Of 69 patients with a subsequent visit, 48% (n = 33) had documentation of monitoring for side effects.When prescribing SSRIs for children with anxiety and/or depression, PCPs in this network documented appropriate indications for starting medication and prescribed without subspecialist involvement. Continuing medical education for PCPs who care for children with these conditions should include information about evidence-based psychotherapy and strategies for monitoring potential side effects.

  View details for DOI 10.1542/peds.2022-058846

  View details for PubMedID 37066669

• Primary Care Pediatricians Prescribing Selective Serotonin Reuptake Inhibitors for Children with Anxiety and Depression Lester, T., Herrmann, J., Bannett, Y., Gardner, R., Feldman, H., Huffman, L. LIPPINCOTT WILLIAMS & WILKINS. 2023: E149

  View details for Web of Science ID 001002197500009

• Lessons from Developing Multimedia Learning Materials for the Digital Generation Biomedical Engineering Education Herrmann, J. E., Spielman, S., Venook, R., Yock, P., Denend, L. 2023

  View details for DOI 10.1007/s43683-023-00110-w

• Large-Scale Production of Wholly-Cellular Bioinks via the Optimization of Human Induced Pluripotent Stem Cell Aggregate Culture in Automated Bioreactors. Advanced healthcare materials Ho, D. L., Lee, S., Du, J., Weiss, J. D., Tam, T., Sinha, S., Klinger, D., Devine, S., Hamfeldt, A., Leng, H. T., Herrmann, J. E., He, M., Fradkin, L. G., Tan, T. K., Traul, D., Vicard, Q., Katikireddy, K., Skylar-Scott, M. A. 2022: e2201138

  Abstract

  Combining the sustainable culture of billions of human cells and the bioprinting of wholly-cellular bioinks offers a pathway towards organ-scale tissue engineering. Traditional 2D culture methods are not inherently scalable due to cost, space, and handling constraints. Here, we optimize the suspension culture of human induced pluripotent stem cell-derived aggregates using an automated 250 mL stirred tank bioreactor system. Cell yield, aggregate morphology, and pluripotency marker expression are maintained over three serial passages in two distinct cell lines. Furthermore, we demonstrate that the same optimized parameters can be scaled to an automated 1 L stirred tank bioreactor system. Our 4-day culture resulted in a 16.6- to 20.4-fold expansion of cells, we generate approximately 4 billion cells per vessel, while maintaining > 94% expression of pluripotency markers. The pluripotent aggregates can be subsequently differentiated into derivatives of the three germ layers, including cardiac aggregates, and vascular, cortical and intestinal organoids. Finally, the aggregates are compacted into a wholly-cellular bioink for rheological characterization and 3D bioprinting. The printed hAs are subsequently differentiated into neuronal and vascular tissue. This work demonstrates an optimized suspension culture-to-3D bioprinting pipeline that enables a sustainable approach to billion cell-scale organ engineering. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1002/adhm.202201138

  View details for PubMedID 36314397

• Exercise Capacity and Training Programs in Pediatric Fontan Patients: A Systematic Review CJC Pediatric and Congenital Heart Disease Herrmann, J. E., Tierney, E. 2022; 1 (3)

  View details for DOI 10.1016/j.cjcpc.2022.04.005

• Bioprinting of 3D Convoluted Renal Proximal Tubules on Perfusable Chips SCIENTIFIC REPORTS Homan, K. A., Kolesky, D. B., Skylar-Scott, M. A., Herrmann, J., Obuobi, H., Moisan, A., Lewis, J. A. 2016; 6: 34845

  Abstract

  Three-dimensional models of kidney tissue that recapitulate human responses are needed for drug screening, disease modeling, and, ultimately, kidney organ engineering. Here, we report a bioprinting method for creating 3D human renal proximal tubules in vitro that are fully embedded within an extracellular matrix and housed in perfusable tissue chips, allowing them to be maintained for greater than two months. Their convoluted tubular architecture is circumscribed by proximal tubule epithelial cells and actively perfused through the open lumen. These engineered 3D proximal tubules on chip exhibit significantly enhanced epithelial morphology and functional properties relative to the same cells grown on 2D controls with or without perfusion. Upon introducing the nephrotoxin, Cyclosporine A, the epithelial barrier is disrupted in a dose-dependent manner. Our bioprinting method provides a new route for programmably fabricating advanced human kidney tissue models on demand.

  View details for DOI 10.1038/srep34845

  View details for Web of Science ID 000385242900001

  View details for PubMedID 27725720

  View details for PubMedCentralID PMC5057112