In cancer patients undergoing RT, a machine learning model identified patients at risk of 30-day hospitalization. Predictive analytics may be a key tool to help providers identify high-risk patients and optimize interventions, while improving quality and value of care.
Oncora and Northwell abstract accepted to ASTRO 2021, looking retrospectively to measure and characterize the impact of Covid-19 pandemic on radiation therapy treatments.
This project demonstrates the ability to extract and analyze RWD to identify patients matching eligibility criteria to four historical clinical trials in metastatic castration-resistant prostate cancer (mCRPC), and calculate outcome measures.
Oncora and MD Anderson teamed up to create a predictive model for unplanned hospitalization. Results were accepted for publication in the International Journal of Radiation Oncology. Together the team developed a ML model to identify GI patients at high risk of unplanned hospitalizations.
Abstract accepted for publication in Clinical Lung Cancer Journal, looking into the National Cancer Database (NCDB) to assess patterns-of-care and overall survival (OS) among patients with MPM by gender.
Oncora and Northwell Health machine learning research selected for a special session poster presentation during ASTRO 2020.
Oncology providers and Oncora Medical discuss big data and informatics playing an increasingly important role in radiation oncology clinical practice and research. Highlighted at the NCI cancer workshop.
Oncora and MD Anderson abstract was accepted as both press and oral presentation during the 2019 American Society of Radiation Oncology conference (ASTRO). The abstract focused on applying a machine learning techniques to predict acute radiation toxicities for head and neck cancer patients.
The department of Radiation Oncology at MD Anderson and Oncora abstract accepted as oral presentation at the 2019 ASCO Quality of Care Symposium in San Diego, CA. In addition to being one of the twelve abstracts accepted, this research also received a Merit Award from Conquer Cancer ASCO Foundation!
Oncora traveled to Washington DC to highlight the success of building a pediatric oncology research database with Oncora's point-of-care electronic data capture and automated data extraction software for the department of radiation oncology at MD Anderson. The 3 day symposium will bring together leaders to discuss the current issues and opportunities in childhood cancer research that can be alleviated through data initiatives.
Oncora data science team traveled to the NIH campus in Bethesda, MD to discuss current applications of AI to real world data in radiation oncology. The NCI State of Data Science in Radiation Oncology workshop brought together leaders from MD Anderson, Penn Medicine, Johns Hopkins, Yale Medicine, Sidney Kimmel Cancer Center and more
Industry leaders from Yale, MD Anderson, OHSU, UCSF and Oncora consider implications of AI in radiation oncology for patients, providers, and the entire healthcare system.
Published in the Journal of Clinical Oncology, Brocade [before Oncora Patient Care] proves to be a reliable Web-based EDC tool that improves clinical documentation without disrupting clinical workflow. Moreover, Brocade has the advantage of capturing data in a structured manner that facilitates real-time analytics and outcome reporting.
Oncora heads to San Antonio, TX showcasing a successful application of a machine learning-based, precision oncology approach to accurately predict acute RT toxicities. Presentation will be delivered by Dr. Jay Reddy, a radiation oncologists at MD Anderson Department of Radiation Oncology.
An abstract authored by MD Anderson was accepted for publication and presentation at ASTRO's annual meeting. The study emphasizes a prospective electronic data capture tool built in collaboration between Oncora and MD Anderson to improve workflow and research.
Oncora will present a second abstract at the 2018 annual AAPM conference, exploring radiotherapy outcomes using deep learning. This research was done in collaboration with the University of Pennsylvania and the University or Maryland School of Medicine
Oncora will travel to Nashville, TN to present two accepted abstracts at the annual AAPM conference! The first abstract explored predictors of unplanned hospitalizations after radiotherapy.
Accepted in Lung Cancer journal, Oncora's research discusses the postoperative outcomes and overall survival for malignant pleural mesothelioma by facility volume.
Oncora manuscript accepted in Journal of Thoracic Oncology focusing on Pneumonectomy versus Lung-Sparing Surgery for Malignant Pleural Mesothelioma.
Oncora Medical recently showcased new research at the annual ASTRO conference in San Diego, CA discussing the potential impact of predictive analytics in radiation oncology.
Oncora Medical traveled to Denver, CO to present a second accepted abstract at the annual American Association of Physicists in Medicine, exploring which clinical variables contribute the most to the prediction of different radiotherapy outcomes and practical uses for radiation oncologists.
Oncora Medical traveled to Denver, CO to present one of two accepted abstracts at the annual American Association of Physicists in Medicine, exploring which clinical variables contribute the most to the prediction of different radiotherapy outcomes and practical uses for radiation oncologists.
Radiation oncology software, Brocade (now Oncora Patient Care), improves workflow at MD Anderson Cancer Center by reducing documentation time for breast radiation oncologists by 68%. Leaders in the radiation oncology field further address the need for improvements in HIT to improve clinical efficiency, reliability, cost effectiveness, provider satisfaction and patient safety to ultimately lead to better patient outcomes.
Oncora Medical was recently in Boston to present new research comparing the predictive power of treatment site-specific and treatment site-agnostic models at the 58th Annual Meeting of American Society for Radiation Oncology (ASTRO). The abstract is published in ASTRO's official journal.
Oncora traveled to the NIH campus in Bethesda, MD to present research evaluating predictive models built from automatically extracted EMR data. With collaborators from Penn Medicine, Oncora compared the reliability of models employing ensemble methods against those using logistic regression.
An abstract authored by Oncora and Penn Medicine was accepted for publication at the 2015 Annual Meeting of the American Association of Physicists in Medicine.