SBIR Phase I: Software for Developing Consumer-Driven Health Care Solutions

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1647616
Agency Tracking Number: 1647616
Amount: $225,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: SH
Solicitation Number: N/A
Timeline
Solicitation Year: 2016
Award Year: 2017
Award Start Date (Proposal Award Date): 2016-12-15
Award End Date (Contract End Date): 2017-08-31
Small Business Information
5317 NW Bluff Way, Kansas City, MO, 64152-3473
DUNS: 079407584
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 MaryKay O'Connor
 (816) 866-0363
 maryk.oconnor@patientsvoices.net
Business Contact
 MaryKay O'Connor
Phone: (816) 866-0363
Email: maryk.oconnor@patientsvoices.net
Research Institution
N/A
Abstract
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of software that automatically identifies and labels problems that patients encounter when they receive care. This information tells health care providers exactly what they need to fix to improve patient care. Applications of the software include: a) the developing different software versions for different health care situations, the technology can be used to improve the patient experience in clinics, emergency departments, primary care settings, outpatient centers, etc.; b) Early interventions. Healthcare leaders are asking for an early warning system. By recording patient conversations with nurses and doctors while they are still in the hospital, this feedback can be used to identify and resolve problems before the patient is discharged; c) New health care delivery models. One hospital wants to reduce the length of stay for heart patients; doctors want to be sure that patients are ready to go home. The software is analyzing the stories of heart patients who did well versus patients who had problems when they left the hospital early. Their feedback will help keep patients out of the hospital and if they are admitted, improve their care before and after they leave; d) Following the doctor's orders. When chronic care patients don't follow through on the treatment plan recommended by their doctor, their health problems often get worse. This software can be used to collect feedback from chronically ill patients and identify patterns in why these patients are not following doctor's orders solutions that motivate and engage patients can be implemented; and e) Other industries. This software can be used to analyze patient feedback during clinical trials. The resulting information will make it easier to recruit and retain patients in clinical trials as well as improve clinical outcomes. Ultimately, results from these analyses could be used to inform FDA decision making within the pharmaceutical industry. The proposed project develops software that uses advanced Natural Language Processing (NLP) techniques to analyze patients? responses about their health care experiences in interviews and open response survey questions in order to provide hospitals with concrete, actionable information on how to improve care and patient outcomes. To date, hospitals have relied primarily on surveys to inform their attempts to improve patient experiences. This research will develop an NLP application for mining patient feedback across health care settings. Data where patients freely tell their "stories" provides a clearer and more precise view of the patient's experience than a standard survey with ratings on predefined questions. The challenge is that the variability of expression and experiences requires sophisticated techniques to be able to classify the information, determine the sentiment, and extract essential details that can provide actionable recommendations to hospitals. The team proposes a combination of data annotation, pattern matching, and machine learning techniques for classification and information extraction of core concepts like problem root causes from unstructured patient feedback. Since interviews comprise much of the most informative data, we will also evaluate which speech recognition technologies can best convert audio to text, for subsequent classification and information extraction.

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

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