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HELPeR: Interface Design Decision and Evaluation.
Khushboo Thaker1, Behnam Rahadari1, Vivian Hui2
1School of Computing and Information, University of Pittsburgh, USA.
Studies in Health Technology and Informatics
|July 25, 2024
Summary
Health recommender systems can help ovarian cancer patients find crucial information. HELPeR, a new system, addresses information access challenges for these patients, improving health equity.
Area of Science:
- Health Informatics
- Oncology
- Information Science
Background:
- Health information access inequities exacerbate health outcome disparities.
- Health recommender systems offer a potential solution for information retrieval.
- Limited research exists on recommender systems for cancer patient support.
Purpose of the Study:
- To introduce HELPeR, a novel recommender system tailored for ovarian cancer patients.
- To address the challenge of providing relevant information to cancer patients with limited prior data (cold-start problem).
- To explore the utility of health expert input and patient forum data in system design.
Main Methods:
- Designed HELPeR incorporating insights from health experts and ovarian cancer patient forum discussions.
- Focused on mitigating cold-start issues inherent in recommender systems.
- Conducted an evaluation of HELPeR with nurse practitioners in a cold-start setting.
Main Results:
- The evaluation highlighted the potential benefits of HELPeR for ovarian cancer patients.
- Identified specific areas for future development and enhancement of the system.
- Demonstrated the feasibility of using expert and patient-generated data for recommender system design.
Conclusions:
- HELPeR shows promise in assisting ovarian cancer patients with their information needs.
- Addressing cold-start challenges is critical for effective health recommender systems.
- Further research and development can optimize HELPeR for improved patient support and health equity.
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