# Efficient and privacy-enhancing consent management for health informatics data sharing

> **NIH NIH R43** · DATA SECURITY TECHNOLOGIES, LLC · 2021 · $252,931

## Abstract

Project Abstract:
To reduce costs and enhance health outcomes, it is of critical importance that patient data are systematically
gathered, cleaned and analyzed, thereby allowing us to build more accurate, timely and reliable models for
diagnosing, managing and treating diseases. One example application domain is asthma control and prediction.
In USA alone, about 40 million people suffered from lifetime asthma (13% of the USA population) and 26 million
people (8%) suffered from current asthma. Developing better predictive models for asthma attacks, can result
in enhancing preventive strategies, improving patient outcomes, and significantly reducing healthcare costs due
to reduced emergency care need. One of the key factors obstructing such models for health care management
is the integration of patient data that are scattered across multiple organizations. This fundamental challenge is
particularly acute for chronic diseases such as asthma where patients often receive care at multiple institutions
within a region. Furthermore, single site studies may provide inaccurate picture due to data inaccuracies. For
example, due to certain selection biases, number of patients from certain race group maybe underrepresented
in one location. In addition, severity information of diseases may not be complete if all the emergency care visits
are not recorded. Without proper record linkage and data duplication, many of the disease specific conditions
may be over-represented. For instance, it is reported that after cross-institution deduplication, number of records
related to diabetes reduced 24.0%, asthma reduced 28.0%, and myocardial infarction reduced 10.9%.
Therefore, it is of paramount importance to merge records in a manner that mitigates duplication, as well as
fragmentation, of an individual’s information. Although there have been efforts to implement health information
exchanges to facilitate data integration and exchange, linking patient records across multiple health care
organizations create significant security and privacy challenges. At the same time, as the usage of healthcare
analytics and the data sharing increases, patient trust in the overall data analytics pipeline must be ensured by
asking patients to make a “consent decision”. This consent decision concerns the sharing and accessing of the
patient’s health data for treatment, payment, and health care operations purposes. As a result, our healthcare
analytics research nowadays is at utmost need of a product that can manage patient consent while allowing
secure and privacy-preserving linkage of health care data across multiple institutions.
To address these challenges, we will develop a privacy-preserving solution that can 1) efficiently capture
consent, use the captured consent information to gather patient data distributed across resources within a certain
health organization efficiently and 2) link the data hosted by different users across disparate health organizations
while protecting patie...

## Key facts

- **NIH application ID:** 10385293
- **Project number:** 1R43LM013960-01
- **Recipient organization:** DATA SECURITY TECHNOLOGIES, LLC
- **Principal Investigator:** Fahad Shaon
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $252,931
- **Award type:** 1
- **Project period:** 2021-09-15 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10385293

## Citation

> US National Institutes of Health, RePORTER application 10385293, Efficient and privacy-enhancing consent management for health informatics data sharing (1R43LM013960-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10385293. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*