Award Abstract # 1831214
STTR Phase II: Self-Health Management Informatics Platform: Improving Patient Engagement in Care Delivery

NSF Org: TI
Translational Impacts
Recipient: SIPPA SOLUTIONS INC
Initial Amendment Date: September 19, 2018
Latest Amendment Date: August 9, 2022
Award Number: 1831214
Award Instrument: Standard Grant
Program Manager: Alastair Monk
amonk@nsf.gov
 (703)292-4392
TI
 Translational Impacts
TIP
 Directorate for Technology, Innovation, and Partnerships
Start Date: September 15, 2018
End Date: July 31, 2022 (Estimated)
Total Intended Award Amount: $711,189.00
Total Awarded Amount to Date: $927,189.00
Funds Obligated to Date: FY 2018 = $711,189.00
FY 2020 = $66,000.00

FY 2021 = $150,000.00
History of Investigator:
  • Michael Wassil (Principal Investigator)
    mjwassil@aol.com
  • Bon Sy (Co-Principal Investigator)
Recipient Sponsored Research Office: SIPPA Solutions LLC
2838 211TH ST
BAYSIDE
NY  US  11360-2523
(917)797-5480
Sponsor Congressional District: 03
Primary Place of Performance: Queens College/City University of NY
65-30 Kissena Blvd
Flushing
NY  US  11367-1575
Primary Place of Performance
Congressional District:
06
Unique Entity Identifier (UEI): CC78AG36RLK3
Parent UEI:
NSF Program(s): STTR Phase II
Primary Program Source: 01001819DB NSF RESEARCH & RELATED ACTIVIT
01002021DB NSF RESEARCH & RELATED ACTIVIT

01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 1591, 165E, 8018, 8023, 8032, 8042, 8240, 9251
Program Element Code(s): 159100
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.084

ABSTRACT

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project is a patient-centric engagement process that offers the best chance for enabling a scalable approach towards achieving the triple aim of improving care quality and outcomes, controlling health care costs and utilization, and increasing patient satisfaction. Currently the national healthcare cost is estimated at $3.2 trillion. Until patients could be engaged to take ownership of their health, advancement in pharmacological interventions through medications will not be able to achieve optimal care outcomes, and the healthcare cost and utilization will remain high. Engaging patients in self-management activities that are in alignment with the motivation indicators not only improves patient experience and satisfaction, but contributes to collecting big data for linking motivation to behavioral therapy --- resulting in an effect essential to improving population health and advancing personalized precision.

This Small Business Technology Transfer (STTR) Phase II project is a patient-centric engagement process MISA (Measure-Integrate-Share-Act). While well-established behavior models such as the Theory of Planned Behavior (TPB) and Information-Motivation-Behavioral Skill (IMB) have been reported to show clinical efficacy, the novelty of this research is the application of Structure Equation Modeling to develop a quantitative behavior model. Quantitative behavior modeling is significant because it allows a rigorous assessment of the model in terms of goodness of fit and statistical power. Furthermore, such model provides the patented SIPPA analytic engine a basis to predict the software services that are in alignment with the motivation indicators of a patient users in order to effectively engage them in self-health management of chronic conditions, and to promote behavioral change for healthy lifestyle.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

, Bon K. Sy and , Jin Chen and , Rebecca Horowitz "Incorporating Association Patterns into Manifold Clustering for Enabling Predictive Analytics" Title: 2019 International Conference on Computational Science and Computational Intelligence (CSCI) , 2019 https://doi.org/10.1109/CSC149370.2019.00243 Citation Details
, Bon Sy "Linking biometric voice identity with self-monitoring health data as a temporal-spatial event stored in a mobile device" Proceedings of the 7th International Conference on Information Systems Security and Privacy, {ICISSP} 2021, Online Streaming, February 11-13, 2021 , 2021 Citation Details
, Bon Sy and , Magdalen Beiting Parrish and , Connor Brown "Behavioral Predictive Analytics towards Personalization for Self-management" Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, Volume 5: HEALTHINF , 2021 https://doi.org/10.5220/0000145600002865 Citation Details
Sy, Bon and Wassil, Michael and Hassan, Alisha and Chen, Jin "Personalizing self-management via behavioral predictive analytics with health education for improved self-efficacy" Patterns , v.3 , 2022 https://doi.org/10.1016/j.patter.2022.100510 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The intellectual merit of this phase 2 SBIR/STTR project is to develop a health informatics platform ? referred to as SIPPA Health Informatics Platform (SHIP) ? to support patient engagement through an innovative MISA approach. MISA as a patient engagement approach consists of four components ? (self) Monitor to gain health awareness, Integrate relevant health information into one single personal health record in HL7 common standard, Share the unified health record with the care support team, and Act on health activities recommended by health coach/care support team. 

SHIP personalizes actionable health activities based on individuals? behavior readiness. It aims to target personalized actionable health activities recommended by clinicians to improve patients? diabetes self-efficacy and engagement in self-health management. The broader impact of this project is its potential to translate improved patient engagement into better health outcomes and lower costs.

The SIPPA Health Information Platform (SHIP) offers a broad range of digital health tools that engage patients throughout their health care journey. The AI machine learning technology involves a two-way communication capability that identifies individual motivation traits and Social Determinants of Health (SDoH) information that is used to deliver personalized feedback to the users and caregivers. We incorporate behavioral predictive analytics to improve engagement in self-health management --- it dynamically personalizes actionable health activities such as self-monitoring on glucose and health education based on one?s behavior readiness. We have validated the outcomes of the improved engagement and demonstrated the practical feasibility of creating an engagement channel through an individual's mobile device. Through this channel, one can securely share health and social determinants data in a common standard format with biometric encryption for improved patient-physician/payer interaction.

A pilot study approved by CUNY-IRB was conducted. We reported the study result in a peer-reviewed health informatics conference that won the best paper award and were invited to publish in follow-up peer-review journals for broader dissemination (10.1007/s42979-022-01092-2 and 10.1016/j.patter.2022.100510). Various core technologies that form the foundation of SHIP are currently patent pending in a number of countries through WIPO Patent Cooperation Treaty (PCT). SIPPA Health mobile app as a patient engagement and communication tool is available in Google Play Store upon request by emailing to info@sippasolutions.com


 

 


Last Modified: 09/20/2022
Modified by: Michael Wassil

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page