Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1390&sort=funder_divisions
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The current pandemic and mental health crisis have affected society deeply. In a 2021 survey, McKinsey found that 85% of frontline employees and managers do not find meaning at work. The US Bureau of Labor Statistics reported 4.3 million people, or 2.9% of the entire workforce, quit their jobs in August 2021. These statistics demonstrate the urgent need to address the issue of workplace dissatisfaction and support individuals in finding meaning and purpose in their lives. Since 2017, the team has successfully taught over 150 adult students to achieve their potential by studying behavioral science research and applying it to their lives. Over 50% of these students, despite having accomplished careers, are dissatisfied with their lives and seek more meaning. The team has found that adopting an empowering mindset is the most effective resilient action for these students to achieve more and find deeper meaning in life. The Resilience Gym uses technology to provide a scalable solution to improve the emotional health and prosperity of working adults.<br/><br/>The team's innovation is a mobile and web app subscription service that delivers a step-by-step Resilience Gym process and guides users to adopt new, empowering mindsets. The product is based on decades of behavioral science research and uses virtual reality, artificial intelligence and mobile nudges to provide a scalable solution that is personalized the individual user. Based on real-time progress, users may adopt new mindsets. The team will also incorporate neuroscience research to enrich the solution. To develop the design, the team uses the 5-step Stanford d.school design thinking approach (empathize, define, ideate, prototype, and test) for which the team has deep expertise. The approach is complimented with the agile methodology to have short milestones, scrum meetings, and backlog tracking to ensure new learnings can be adapted from users and delivered on schedule and within the planned budget. Combining the expertise of technology startups and university researchers, the team develops scientifically driven products that achieve both significant impact and commercial success.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "13049", "attributes": { "award_id": "2112093", "title": "SBIR Phase I: An intelligent mental health care companion for kids", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 806, "first_name": "Alastair", "last_name": "Monk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-15", "end_date": null, "award_amount": 256000, "principal_investigator": { "id": 29050, "first_name": "Beth", "last_name": "Carls", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2222, "ror": "", "name": "ONESEVENTEEN MEDIA, PBC", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to proactively identify and resolve mental health issues for children ages 10 - 18 before treatments and consequences become more acute and significantly more costly. This project innovation proposes bringing care to children at the onset of need using chatbot companions tailored deliberately and precisely to each child’s mental healthcare needs. Optionally, a child can be paired with live chat therapists and coaches no matter where the child is — at home, school, or when on their own. The system leverages proven mobile device-accessed Cognitive Behavioral Therapies in tandem with machine learning (ML)-enabled technologies that learn from a variety of interactions with the child to detect and digitally triage their experiences of loneliness, anger, anxiety, and depression and to alert adults when intervention and treatment are deemed necessary. Without appropriate care, these symptoms frequently increase in severity over time and become more difficult to treat. Intervening early can help slow or halt mental illness, reducing for parents, schools, and society the practical and financial burdens associated with reactive, generalized mental healthcare treatments while nurturing children into happier, healthier adults.<br/><br/>This Small Business Innovation Research (SBIR) Phase I project seeks to build a system to determine the nature and severity of a child’s mental health needs through real-time, early detection. Research reveals children face more barriers than adults when obtaining mental healthcare, especially in rural, marginalized, and low-socioeconomic-status communities. Due to a severe shortage of child behavioral health practitioners across the country, children frequently wait up to a decade between the onset of mental health symptoms and treatment. This project seeks to eliminate the delay between when children first experience mental or emotional needs and when they receive appropriate care, ensuring they flourish—not flounder—during those crucial developmental years. Leveraging ML algorithms and proprietary question weighting, the project focuses on: 1) algorithm development to determine the most effective combination of chatbot and live chat counselor engagements to understand a child’s immediate issues and provide resolutions for the child and their parent(s); 2) improvements to ensure the responses are fit for purpose, recognizing and flagging appropriate conversations for human interaction; and 3) refining the frequency and content of notifications; such as, adaptive motivational messages and recommendations to counselors for tailoring interactions.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "13217", "attributes": { "award_id": "2129148", "title": "SBIR Phase I: A personalized consumer health guidance platform", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 806, "first_name": "Alastair", "last_name": "Monk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-04-01", "end_date": null, "award_amount": 256000, "principal_investigator": { "id": 29266, "first_name": "Kelton", "last_name": "Shockey", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2252, "ror": "", "name": "RIFT VALLEY HEALTH COMPANY", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project addresses poor long-term public health outcomes stemming from lifestyle choices through an intuitive and effective tool to analyze and optimize their sleep, exercise, nutrition, and mental health. The technology will guide users through an individualized health and well-being analysis based on real-time data that presents an individualized baseline and heat map that helps individuals determine which lifestyle behaviors are most beneficial or detrimental towards their holistic health. This personalized approach seeks to establish new habits, interventions, medications, diets, etc. This tool may help people to live longer and more fulfilled lives with fewer health concerns and costs.<br/><br/>This Small Business Innovation Research (SBIR) Phase I project determines the viability of objective measurement of health metrics (heart rate, blood pressure, sleep cycles, and exercise) through the integration of already-existent technologies including, but not limited to, wearables and “smart” technologies such as Smart Scales and Smart Blood Pressure cuffs. Additionally, this project will determine the viability of using subjective inputs surrounding diet, mood, and exercise and the ability to draw meaningful correlations between both subjective and objective inputs. This project will develop and validate methods to integrate these two data streams into a health scoring system that may be used to improve physical and mental health and fitness.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "11030", "attributes": { "award_id": "2125909", "title": "SBIR Phase I: A Platform for Health Care Data Integration Using Blockchain and Artificial Intelligence", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 806, "first_name": "Alastair", "last_name": "Monk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-02-15", "end_date": "2023-01-31", "award_amount": 254091, "principal_investigator": { "id": 26998, "first_name": "Irene", "last_name": "Woerner", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1980, "ror": "", "name": "EMTRUTH, INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project aims to improve health outcomes while managing costs. The proposed technology enables the integration and retrieval of data from many places and formats across a distributed ecosystem secured by blockchain. Healthcare, a $2.9 B market segment, is targeted because the need is great and the US healthcare market is highly fragmented. Making healthcare data faster to interoperate and share while maintaining data integrity, security and privacy is key to potentially improving healthcare outcomes. \n\nThis Small Business Innovation Research (SBIR) Phase I project is advancing foundational technology for searching and retrieving heterogeneous data secured in blockchain across a distributed data platform. Multiple data sources with different formats and data models will be transformed into more granular data blocks in blockchain. In addition to normalizing blockchain data into more granular data blocks for sharing and re-use in different applications (i.e., simplifying data integration and interoperability), research will use natural language processing (NLP) to assist in automatically generating metadata tags to facilitate searching across blockchains. To improve the accuracy of data returned in the search, a human expert-curated healthcare dictionary and thesaurus will be created and used in concert with NLP assistance. This combined approach should improve the accuracy of data retrieval by non-IT, healthcare, users across a secure, peer-to-peer data platform where data owners retain full ownership and control of their data. The proposed research will also validate through performance testing new blockchain search capabilies will meet the responsiveness and scale required by healthcare enterprises, a key need for commercialization. In particular, the project will establish benchmarks for speed and latency across a geographically dispersed network.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "11050", "attributes": { "award_id": "2224172", "title": "SBIR Phase I: A Versatile Nucleic Acid Collection and Purification Technology for Wastewater-Based Epidemiology", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 936, "first_name": "Henry", "last_name": "Ahn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-03-01", "end_date": "2024-02-29", "award_amount": 275000, "principal_investigator": { "id": 27020, "first_name": "Kyle", "last_name": "King", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1982, "ror": "", "name": "FRONTLINE BIOTECHNOLOGIES INC.", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide private and public stakeholders with new technological tools to better monitor and predict pandemics. This project is expected to modernize the tools used today by private and public laboratories to collect and purify pathogens, particularly human coronaviruses from community wastewater, for testing and diagnostic purposes. The new technological tools are expected to simplify the workflow, reduce costs and time, and enable the prediction of COVID-19 outbreaks and other pandemics several weeks before observing clinical cases. Such early prediction would provide the public and government agencies with important data and sufficient time to take preventive measures. The technological products of this project are expected to empower the growing number of companies and laboratories offering wastewater-based epidemiology (WBE) services and help establish WBE as a routine, cost-effective and reliable tool for public health monitoring.\n\nThis Small Business Innovation Research (SBIR) Phase I project will address a major technological barrier for the detection of viruses such as SARS-CoV-2 in wastewater. Commercially available nucleic acid collection and purification kits are designed for clinical samples with small volumes. These kits are not generally used for large wastewater volumes where the virus is present at low concentrations. As a result, current processes are time-consuming, and result in the recovery of less than 30% of viruses and nucleic acids, significantly reducing the sensitivity. The goal of this SBIR Phase I project is to demonstrate the feasibility of a novel virus and nucleic acid collection and purification technology from wastewater. Specifically, the project tasks aim at enhancing understanding of virus properties, particularly human coronaviruses in wastewater and their interactions with filter media. The project’s innovative approach is to design a streamlined workflow that includes all the steps from sample collection to detection, in a single disposable cartridge containing novel filters with high affinity to viruses. This development is expected to enhance viral and nucleic acid recovery in wastewater to over 90%, while reducing costs. The developed tools will be independently tested and evaluated by third-party laboratories to confirm their performance.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "11169", "attributes": { "award_id": "2232502", "title": "SBIR Phase I: A novel method to scaling mentoring and career development in Institutes of Higher Education", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 1782, "first_name": "Rajesh", "last_name": "Mehta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-04-01", "end_date": "2023-09-30", "award_amount": 275000, "principal_investigator": { "id": 27177, "first_name": "Anita", "last_name": "Balaraman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1993, "ror": "", "name": "EPIXEGO INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to increase post-secondary student success via academic and career guidance. A large body of research on career navigation has studied how post-secondary education, career readiness (understanding viable career paths at graduation), and its interconnectedness are important for a growing number of first-generation, low-income, and underrepresented students. With increasing undergraduate degree program offerings in response to an evolving future of work and student-to-counselor ratios of 1: 1,800 in public colleges, career guidance, and academic navigation risk being unavailable. As a result, during the pandemic, Institutes of Higher Education (IHEs) that serve students of color and students from low-income backgrounds saw declines in enrollment that far outpaced their predominantly White peer institutions. The proposed platform intends to increase the visibility, accessibility, and discoverability of competencies to potential career and academic paths for students at IHEs. The platform envisions doing this via near-peer role models who are similar in their dimensions of self-efficacy. With more than 85 million jobs that could go unfilled by 2030, the proposed platform may help alleviate part of that shortage by widening the talent aperture.\n\nThe intellectual merit of this project is in the company’s patented technology of a unified, multi-dimensional, data representation model that creates a ‘competency fingerprint’ for each user. The data representation method enables better machine learning models to ‘infer’ competency from unstructured data of a student’s traditional and non-traditional learning experience, rather than degrees, majors, grade point averages (GPAs), or test scores. The platform uses a consistent, scalable, competency nomenclature for hard and soft skills gained via traditional academic and outside-of-the-classroom experiences to discover academic-career paths where the students’ learning competencies may be in demand. There is a significant technical challenge in adopting this technology for the inter/cross-disciplinary jobs of the future: such a platform requires a robust, larger data set to evaluate the relevance of matching, in a discipline-agnostic context. Reduction of this variability is the key technical risk to be overcome by the proposed research and development.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "11269", "attributes": { "award_id": "2232908", "title": "SBIR Phase I: Machine to fabricate a bioinspired insulation material: The Concatenator", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 1782, "first_name": "Rajesh", "last_name": "Mehta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-06-01", "end_date": "2024-05-31", "award_amount": 275000, "principal_investigator": { "id": 27287, "first_name": "Chris", "last_name": "Kratzer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2005, "ror": "", "name": "OWLFLY", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in the development of a novel, and more benign than fiberglass, thermal insulation technology for use in homes across the United States. Improvements in insulation technology have the potential to reduce energy use nationwide, along with all carbon emissions associated with the production and transmission of that energy. According to the Energy Information Administration, 51% of all residential energy in the United States is used for heating and cooling living spaces, which amounts to about 11% of the total energy consumption of the country. This project aims to use the principles of biomimicry to develop a more effective batt insulation. Unlike other insulation materials, unprotected exposure by the insultation installers will not aggravate respiratory issues, which is increasingly important for homeowners and working people who suffer from the long-term effects of COVID-19. This project seeks to push the potential and affordability of this new technology while creating new American jobs. \n\nThe project is inspired by the nests of yellowjacket wasps that live in pockets of permafrost high above the Arctic circle. The nests are protected from extreme temperatures by the hollow wall structure surrounding the nest’s interior. This structure can be adapted to create insulation panels that are highly efficient, lightweight, water-resistant, non-combustible, non-toxic, non-dusting, and irritant-free. The project focuses on the development of such a thermal insulation material in an efficient way to keep its price point competitive with the current products. This involves designing a manufacturing machine capable of producing the new insulation quickly and consistently. To further push the thermal performance of the material, the company will also develop a complementary machine that can scan insect specimens in museum collections to assess biological structures that are highly reflective in infrared wavelengths and use the data to address management of radiative heat transfer. The team will apply a six-sigma approach to quality control for improving the insulation manufacturing process.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "11276", "attributes": { "award_id": "2232826", "title": "SBIR Phase I: A Student Learning Dashboard", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 1782, "first_name": "Rajesh", "last_name": "Mehta", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-05-01", "end_date": "2024-04-30", "award_amount": 274471, "principal_investigator": { "id": 27292, "first_name": "Patrick", "last_name": "Hong", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2007, "ror": "", "name": "PRENOSTIK, LLC", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in improving retention in higher education and increasing graduation rates. Currently, the average U.S. college dropout rate is 40%. Moreover, underserved Science, Technology, Engineering and Mathematics (STEM) student populations are more likely to leave school without a degree. Due to the COVID-19 pandemic, increased financial insecurity and mental health challenges have negatively impacted student learning. This project aims to develop a student learning dashboard platform that acts as a co-pilot during students' higher education learning journey by delivering targeted, personalized, and real-time actionable assistance. The solution holistically identifies each student's unique learning motivation challenges (e.g., subject difficulty, relevance to career goals, social and economic constraints, etc.) and provides specific recommendations to overcome barriers. Coaching students to learn how to learn more effectively based on their own context fosters a growth mindset, grit, and agency to help them become successful lifelong learners. The application also significantly improves diversity, equity, and inclusion in higher education, especially in STEM, and thus increases effective workforce training. \n\nThis Small Business Innovation Research (SBIR) Phase I project uses machine learning to understand each student's unique learning challenges, map how barriers affect learning motivation, and influences coursework engagement. Machine learning is applied to analyze qualitative and quantitative learning motivation and behavior data to identify gaps so real-time, targeted, and relevant guidance can be delivered while the students are still progressing through the courses rather than waiting until it might be too late for intervention. This project provides descriptive, predictive, and prescriptive recommendations to simulate one-on-one, personalized advising at scale and at a lower cost. The technology also acts as an early detection system when students show the first sign of academic and non-academic struggles affecting their mental state of readiness to learn. When in-person human intervention is required, instructors, academic advising, and/or relevant on-campus student support services can be alerted. This project can be used by any educational institution or private company providing in-person, flipped/hybrid, remote, synchronous, or asynchronous instruction formats.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10766", "attributes": { "award_id": "2112208", "title": "SBIR Phase I: Artificial Intelligence for Competency-Based Medical Training", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 806, "first_name": "Alastair", "last_name": "Monk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-08-15", "end_date": "2022-11-30", "award_amount": 256000, "principal_investigator": { "id": 26833, "first_name": "Carisa", "last_name": "Cooney", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1964, "ror": "", "name": "EDUMD, LLC", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to train physicians efficiently, assure high-quality patient care, and provide the United States with a robustly competent physician workforce. Current assessment practices require attending physicians and surgeons to review tens-to-hundreds of data points, removing them from clinical activities. Integrating a machine learning model in an existing resident assessment system to predict performance can address trainees’ learning needs and identify excelling, competent, and struggling residents months earlier. This is vital to patient care: earlier identification of trainee performance can benefit patient care faster than the current human-based, semiannual process. Improved tracking and documentation of competence may be of interest to multiple stakeholders including patients, hospitals, third-party payers such as insurance companies or the Centers for Medicare and Medicaid Services, and the residency accreditation entity, the Accreditation Council for Graduate Medical Education. Improved, automated assessment models using existing trainee data help training programs facing increasing documentation burden, as well as hospitals and third-party payers interested in reducing adverse health events for the patients they serve.\n\nThis Small Business Innovation Research (SBIR) Phase I project will integrate an artificial intelligence model to support resident physician training programs in customizing training based on individual learners’ needs. Starting with plastic and reconstructive surgery and one of the four training programs in the United States engaged in time-variable training is an efficient way to create, test, and assess the model’s efficacy. The created machine learning model will be assessed for its predictive ability at different points during resident physicians’ training and compared with attending physicians’ assessments of trainees’ skills. Such models make time-variable training feasible enabling adaptive, needs-based scheduling of various educational rotations. This has the added advantages of keeping residents fully engaged in their training and returning faculty physicians to clinical care faster, improving job satisfaction and reducing risk of burnout. Ultimately, time-variable training and use of their associated machine learning models will reduce the direct and indirect costs of graduate medical education; accelerate the entry of new, fully competent physicians into the workforce; and retain valuable physician educators in the workforce.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "11301", "attributes": { "award_id": "2205111", "title": "SBIR Phase I: Rapid Single-Use, Point-Of-Care, Disposable Lateral Flow Device for Detection Of Kennel Cough", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 936, "first_name": "Henry", "last_name": "Ahn", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-01", "end_date": "2023-05-31", "award_amount": 256000, "principal_investigator": { "id": 27335, "first_name": "Paula", "last_name": "Walker", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2010, "ror": "", "name": "CONTROLPOINT INC.", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve the health and well-being of animals, particularly respiratory infections in dogs commonly known as kennel cough. More than 5 million veterinarian visits a year in the United States alone are due to kennel cough. In 90% of those cases, dogs are treated based on empirical data, which means veterinarians take an educated guess, and fewer than 10% are tested. Dogs are more likely to develop clinical signs of kennel cough the longer they are in a group-housing environment. The 5 million cases in the United States are underestimated and do not typically include cases from kennels, shelters, and/or boarding facilities. With the current procedures, veterinarians may send samples to central laboratories that take many days to provide results. The proposed project will develop a diagnostic test that will provide veterinarians with a cost-effective, easy-to-use single test that can detect a wide range of organisms. The test will provide results within minutes of sample collection. \n\nThis Small Business Innovation Research (SBIR) Phase I will demonstrate the initial feasibility of detecting several pathogens in a single test device. The selected pathogens will be a combination of bacteria and viruses that have been identified to cause kennel cough in dogs, a common but debilitating ailment. The proposed work will allow test validation, prototype manufacturing, clinical sample evaluation, and procedures to report results to both veterinarians and pet owners. The innovation will help fill the extensive gaps in understanding the disease ecology, prevalence, incidence, and geographic distribution of kennel cough, facilitating faster and more efficient disease management decisions. The ability to rapidly identify the contagious pathogens involved in an occurrence will permit a rapid response to limit the dimensions of a possible outbreak.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1390, "pages": 1419, "count": 14184 } } }