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Leveraging advanced clinical phenotyping to enhance problem lists and support value-based healthcare
Award Information
Agency: Department of Health and Human Services
Branch: N/A
Contract: 1R43LM012357-01A1
Agency Tracking Number: R43LM012357
Amount:
$222,977.00
Phase:
Phase I
Program:
SBIR
Solicitation Topic Code:
NLM
Solicitation Number:
PA15-269
Timeline
Solicitation Year:
2016
Award Year:
2016
Award Start Date (Proposal Award Date):
2016-07-01
Award End Date (Contract End Date):
2017-06-30
Small Business Information
113 BARKSDALE PROFESSIONAL CTR, Newark, DE, 19711-3258
DUNS:
827623245
HUBZone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Principal Investigator
Name: DANIEL RISKIN
Phone: (650) 777-7978
Email: grants@vmt.com
Phone: (650) 777-7978
Email: grants@vmt.com
Business Contact
Name: DANIEL RISKIN
Phone: (650) 777-7978
Email: grants@vmt.com
Phone: (650) 777-7978
Email: grants@vmt.com
Research Institution
N/A
Abstract
Project Summary
As United States healthcare seeks to address inconsistent quality and overwhelming cost data and
technology have become central to all suggested approaches With newly available electronic health
data and massive growth in processing power the hardest challenges in using clinical data are becoming
clear
Big data holds the potential to enable personalized patient care population health management and
value based payment models However it also creates challenges in discriminating accurate data from
inaccurate or incomplete information One of the greatest areas of data inaccuracy is the patient
phenotype or clinical description of the patient Every clinical decision support tool population health
management system and payment reform product relies on accurate electronic patient descriptions as
its source data
But the descriptions are not accurate most notably in terms of completeness and granularity Recall
often falls below in describing a patient s medical conditions such as heart failure and cancer
Detailed descriptions such as low ejection fraction heart failure or stage III breast cancer needed for
downstream analytics are lacking in the discrete record Poor data puts care delivery payment reform
and population health efforts in peril The time is right for technology to proactively define the clinical
phenotype from source data without reliance on current manual approaches This will necessitate
overcoming challenges in harmonizing discrepant narrative and discrete data inferring when a
characteristic such as cough is a primary condition versus symptom of another condition and screening
noise from signal in robust narrative text
This Small Business Innovation Research SBIR Phase I project will include the following specific aims
Create the components required to define an accurate and comprehensive clinical phenotype
including i extract problem medication procedure and lab features from clinical data using
natural language processing NLP and ontologic mapping ii build a large knowledge database
of associated clinical conditions and iii assess extracted features against the knowledge
database to accurately distinguish symptoms from diseases and surface relevant active diseases
in a candidate problem list
Validate the clinical phenotyping components using de identified longitudinal clinical data for
patients
The goal dependent on Phase I success is to create an automated accurate and robust clinical
phenotyping engine to enable personalized patient care population health management and value
based payment models Project Narrative
Individual and global care improvement demands accurate phenotypes This type of clinical
phenotyping is extremely challenging requiring full clinical data and advanced semantic technologies to
develop a longitudinal patient map The approach if successful offers an opportunity to empower
national efforts to improve outcomes and reduce costs * Information listed above is at the time of submission. *