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Identification of patients with diabetes at high risk for treatment failure

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41DK105612-01
Agency Tracking Number: R41DK105612
Amount: $223,336.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: 200
Solicitation Number: PA14-072
Solicitation Year: 2015
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-09-01
Award End Date (Contract End Date): 2016-08-31
Small Business Information
Brookline, MA 02446-2338
United States
DUNS: 876836870
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 (617) 553-8373
Business Contact
Phone: (617) 259-0197
Research Institution
BOSTON, MA 02115-6110
United States

 Domestic Nonprofit Research Organization

DESCRIPTION provided by applicant The overall goal of this project is to improve quality of care of patients with diabetes by identifying patients who may have particular difficulty reaching treatment targets and who could therefore potentially benefit from additional resources Investigators propose to achieve this goal by developing a technology to accurately identify patients at high risk for not reaching blood glucose target to enable cost effective implementation of resource intensive interventions that improve glycemic control in high risk individuals Improving blood glucose control in patients with diabetes could both improve their quality of life by reducing diabetes complications and decrease costs Some patients that face particularly high barriers to glucose control may benefit from additional resources that are too expensive to apply to broad patient populations However it is not currently possible to identify patients at high risk not being able to reach blood glucose targets with sufficient accuracy to make these interventions cost effective One reason for this is that a large fraction of critical information about the patientsandapos functional status social circumstances and other important factors that may present barriers to glucose control is only found in narrative documents from which it is difficult to extract Furthermore the amount of information about any single patient i the medical record is enormous and is impossible to process efficiently using standard analytical algorithms such as regression analysis In the proposed project investigators will combine two novel technologies natural language processing of electronic provider notes and artificial intelligence technology Dynamic Logic to help circumvent these challenges to build a high accuracy model of risk of not being able to reach blood glucose targets in patients with diabetes Natural language processing will identify key concepts documented in the provider notes and will help translate their text into a compendium of facts about the patient Dynamic Logic makes use of a limited number of iterative approximations to reduce the complexity of a problem with multiple predictor variables from exponential to approximately linear Utilization of Dynamic Logic will therefore allow to greatly increasing the number of factors variables that can be considered for the models for prediction of failure to reach glucose control and ultimately improve their accuracy Consequently this translational multidisciplinary project will both advance our understanding of the risk factors for not being able to reach glucose control for patients with diabetes and assist in real life implementation of interventions that can help lower their blood glucose decrease the rate of diabetes complications improve the patientsandapos quality of life and help control the rising costs of healthcare

PUBLIC HEALTH RELEVANCE Lowering blood glucose of patients with diabetes could both improve their quality of life and reduce healthcare costs but may be more difficult for some patients than others Patients who have particularly high barriers to blood glucose control could benefit from extra resources but it can be challenging to identify them in advance In this projec the investigators will combine advanced computational technologies including artificial intelligence and natural language processing to identify patients with diabetes at high risk for difficulty lowering blood glucose to help make commitment of additional resources cost effective

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

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