# Development and Implementation of a Health e-Librarian with Personalized Recommender (HELPeR)

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $322,690

## Abstract

Abstract
As patients increasingly play more active roles in their health care, the Internet has become a prominent source
of health information to guide their decision-making and self-management activities. Despite the great potential
of the Internet, many patients who sought health information on the web reported feeling overwhelmed by the
vast amount of unfiltered information and unqualified to determine the quality, veracity, and relevance of the
information. Ninety-one percent of online health information seekers indicate they either need or want
navigational support in locating appropriate health information that adapts to their changing needs and
knowledge across the disease trajectory. However, current strategies to improve patients’ ability to find reliable
and relevant information online are limited by static, time and resource intensity, and not personalized.
Recommender systems, information filtering systems that integrate user profiles and online activity (e.g., search
history), can efficiently determine what information is the most relevant to an individual user, but such systems
have not been used to provide health information to patients. The overall goal of this proposal is to build and
implement a “Health E-Librarian with Personalized Recommendations (HELPeR)” - a personalized information
access system with a hybrid recommender engine that adapts to different aspects of the patient. This would be
the first implementation of a patient-centered system that can serve as a virtual health librarian. The HELPeR
recommender engine is innovative in its capacity to integrate three dimensions of an individual patient (i.e.,
information needs based on the user’s profile, the user’s unique expressed information interests, and the level
of user’s disease-related knowledge) to direct patients to highly personalized sets of information, that are high
quality, trustworthy, and appropriate for each patient’s knowledge level. We have selected ovarian cancer (OvCa)
as our initial population as it represents a complex disease with multiple tumor types and a range of prognoses,
requiring personalized treatments and supportive care needs that evolve over time. HELPeR will be housed on
a standalone website linked to the online health community (OHC) of the National Ovarian Cancer Coalition, a
national OvCa advocacy organization. In order to attain our goal of implementing HELPeR, the aims of this
proposal are: (1) Define user needs, preferences, and expectations for personalized health information, (2)
Develop and evaluate the HELPeR system that is able to adapt to three types of individual user characteristics
across the disease trajectory evolving information needs, personal information preferences, and progressive
cancer-related knowledge, and (3) Conduct a field trial with OvCa patients to determine the acceptability and
value of HELPeR in a real-world setting. HELPeR can be easily adapted to filter information for other cancers
and chronic conditi...

## Key facts

- **NIH application ID:** 10451704
- **Project number:** 5R01LM013038-04
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Daqing He
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $322,690
- **Award type:** 5
- **Project period:** 2019-09-05 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10451704

## Citation

> US National Institutes of Health, RePORTER application 10451704, Development and Implementation of a Health e-Librarian with Personalized Recommender (HELPeR) (5R01LM013038-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10451704. Licensed CC0.

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