Healthcare App Design in Low-Resource Contexts: Challenges, Practices, and Opportunities
- URL: http://arxiv.org/abs/2604.04669v1
- Date: Mon, 06 Apr 2026 13:28:05 GMT
- Title: Healthcare App Design in Low-Resource Contexts: Challenges, Practices, and Opportunities
- Abstract summary: This Birds of a Feather session aims to bring together researchers, designers, and practitioners interested in healthcare application design in low-resource contexts.<n>The session will provide an informal forum for discussing challenges encountered in the design and deployment of digital health technologies in underserved settings.
- Score: 1.1470070927586014
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Digital health technologies are increasingly used to improve healthcare access and delivery worldwide. However, many healthcare applications are designed for environments with stable infrastructure, high digital literacy, and strong institutional support. These assumptions often do not hold in low-resource contexts where healthcare delivery often depends on community health workers, caregivers, and informal care networks. Designing effective healthcare applications for such environments requires attention to infrastructural constraints, cultural contexts, language diversity, and usability challenges. This Birds of a Feather session aims to bring together researchers, designers, and practitioners interested in healthcare application design in low-resource contexts. The session will provide an informal forum for discussing challenges encountered in the design and deployment of digital health technologies in underserved settings, sharing field experiences, and identifying opportunities for collaboration within the Interactive Health (IH) community.
Related papers
- Artificial Intelligence in Rural Healthcare Delivery: Bridging Gaps and Enhancing Equity through Innovation [3.192479877329154]
Rural healthcare faces persistent challenges, including inadequate infrastructure, workforce shortages, and socioeconomic disparities.<n>We systematically reviewed 109 studies published between 2019 and 2024 from PubMed, Embase, Web of Science, IEEE Xplore, and Scopus.<n>The findings reveal significant promise for AI applications, such as predictive analytics, telemedicine platforms, and automated diagnostic tools.
arXiv Detail & Related papers (2025-08-15T17:08:10Z) - "I Want My Chart to Be Just for Me": Community-Engaged Design to Support Outpatient Healthcare for Resettled Communities [4.300666368635324]
We conducted two community-based participatory design workshops with 30 Hmong community members in a large metropolitan area in the US.<n>We identified four types of assets the community has gradually developed, including intergenerational support for health management and storytelling-based communication practices that facilitate relatable and culturally grounded interactions.<n>We show how participatory design workshops can foster asset-based approaches, and discuss design implications for technologies that leverage patients' existing strengths to support their health management during outpatient visits.
arXiv Detail & Related papers (2025-08-14T15:38:04Z) - Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [58.61680631581921]
Mental health disorders create profound personal and societal burdens, yet conventional diagnostics are resource-intensive and limit accessibility.<n>This paper examines these challenges and proposes solutions, including anonymization, synthetic data, and privacy-preserving training.<n>It aims to advance reliable, privacy-aware AI tools that support clinical decision-making and improve mental health outcomes.
arXiv Detail & Related papers (2025-02-01T15:10:02Z) - Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis [68.06621490069428]
Geographic health disparities pose a pressing global challenge, particularly in underserved regions of low- and middle-income nations.<n>We propose a novel cross-silo federated learning framework, named FedHelp, aimed at alleviating geographic health disparities and fortifying the diagnostic capabilities of underserved regions.
arXiv Detail & Related papers (2024-12-27T13:59:58Z) - Implementing a Nordic-Baltic Federated Health Data Network: a case
report [56.96209893909196]
We formed an interdisciplinary consortium to develop a feder-ated health data network, comprised of six institutions across five countries.
The objective of this report is to offer early insights into our experiences developing this network.
arXiv Detail & Related papers (2024-09-26T14:15:54Z) - Data-Driven Approach to assess and identify gaps in healthcare set up in South Asia [1.9058065795191268]
South Asian countries are working to improve their primary healthcare system.
Data-driven surveillance approaches are required.
We propose to harness current breakthroughs in Earth-observation technology.
arXiv Detail & Related papers (2024-09-21T16:50:16Z) - The Design and Implementation of a National AI Platform for Public
Healthcare in Italy: Implications for Semantics and Interoperability [62.997667081978825]
The Italian National Health Service is adopting Artificial Intelligence through its technical agencies.
Such a vast programme requires special care in formalising the knowledge domain.
Questions have been raised about the impact that AI could have on patients, practitioners, and health systems.
arXiv Detail & Related papers (2023-04-24T08:00:02Z) - Sustainable Development Through a Mobile Application for a Community
Clinic [0.0]
This paper investigates the question: How can technology and training interventions enable clinicians to offer care that addresses the socioeconomic limitations of their patients?
This paper observes the implementation of a mobile app designed to offer people who cannot access health resources in Omaha, Nebraska, a city in the Midwestern United States.
arXiv Detail & Related papers (2021-08-22T16:40:55Z) - Internet-of-Things Devices and Assistive Technologies for Healthcare:
Applications, Challenges, and Opportunities [5.8497361730688695]
Hospitals and clinics no longer have the ability to accommodate large numbers of incoming patients.
It is clear that the current state of the health industry needs to improve its valuable and limited resources.
The evolution of the Internet of Things (IoT) devices along with assistive technologies can alleviate the problem in healthcare.
arXiv Detail & Related papers (2021-07-11T12:18:12Z) - The Medkit-Learn(ing) Environment: Medical Decision Modelling through
Simulation [81.72197368690031]
We present a new benchmarking suite designed specifically for medical sequential decision making.
The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
arXiv Detail & Related papers (2021-06-08T10:38:09Z) - Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state.
The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
arXiv Detail & Related papers (2020-09-21T03:45:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.