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SBIR Phase I:Tackling Healthcare’s Paradoxes: Quality Patient Care, Provider Workflow, and Data Security

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2233197
Agency Tracking Number: 2233197
Amount: $275,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: DH
Solicitation Number: NSF 22-551
Timeline
Solicitation Year: 2022
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-05-01
Award End Date (Contract End Date): 2024-04-30
Small Business Information
2450 Holcombe Blvd Suite X
Houston, TX 77021
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Omar Mohtar
 (310) 748-1971
 Omar.mohtar@paradocshealth.com
Business Contact
 Omar Mohtar
Phone: (310) 748-1971
Email: Omar.mohtar@paradocshealth.com
Research Institution
N/A
Abstract

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide a new tool for physicians to potentially automate the preparation of insurance documentation and facilitate claim building which may help to lower provider costs and increase patient access to and quality of care. Physicians can spend up to 50% of their time performing non-clinical tasks which have also been associated with physician burnout, a psychological condition known to result in medical errors, lower quality of care, higher costs, and overall poorer patient outcomes. The proposed innovation is a proprietary algorithm that leverages data to automate the completion of insurance form documentation. This new technology aims to resolve workflow bottlenecks and complement existing clinical workflows by delivering a simpler provider experience by streamlining the preparation of medical form documentation._x000D_
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This Small Business Innovation Research (SBIR) Phase I project aims to develop a machine learning-enabled electronic medical record access toolset designed to automate and streamline the preparation of insurance form documentation. A major issue in the US healthcare system is the process through which healthcare providers seek reimbursement through health insurance companies. Filing claims and seeking prior authorizations on procedures or tests from insurance companies is a manual process that is slow and error prone, often resulting in delays in treatment or even rejection, jeopardizing patient health, and resulting in higher costs. Designed for physicians, the proposed technology will facilitate claim building using pre-trained natural language models to extract medical text and relationships from various inputs including patient and provider demographic information as well as payer information, clinical taxonomy, functional features, and relations._x000D_
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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.

* Information listed above is at the time of submission. *

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