SaludConectaMX: Lessons Learned from Deploying a Cooperative Mobile Health System for Pediatric Cancer Care in Mexico
- URL: http://arxiv.org/abs/2408.00881v1
- Date: Thu, 1 Aug 2024 19:13:49 GMT
- Title: SaludConectaMX: Lessons Learned from Deploying a Cooperative Mobile Health System for Pediatric Cancer Care in Mexico
- Authors: Jennifer J. Schnur, Angélica Garcia-Martínez, Patrick Soga, Karla Badillo-Urquiola, Alejandra J. Botello, Ana Calderon Raisbeck, Sugana Chawla, Josef Ernst, William Gentry, Richard P. Johnson, Michael Kennel, Jesús Robles, Madison Wagner, Elizabeth Medina, Juan Garduño Espinosa, Horacio Márquez-González, Victor Olivar-López, Luis E. Juárez-Villegas, Martha Avilés-Robles, Elisa Dorantes-Acosta, Viridia Avila, Gina Chapa-Koloffon, Elizabeth Cruz, Leticia Luis, Clara Quezada, Emanuel Orozco, Edson Serván-Mori, Martha Cordero, Rubén Martín Payo, Nitesh V. Chawla,
- Abstract summary: SaludConectaMX is a comprehensive system to track and understand the determinants of complications throughout chemotherapy treatment for children with cancer in Mexico.
The system is composed of a web application (for hospital staff) and a mobile application (for family caregivers)
This paper presents the system's preliminary design and usability evaluation results from a 1.5-year pilot study.
- Score: 33.91720564325487
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We developed SaludConectaMX as a comprehensive system to track and understand the determinants of complications throughout chemotherapy treatment for children with cancer in Mexico. SaludConectaMX is unique in that it integrates patient clinical indicators with social determinants and caregiver mental health, forming a social-clinical perspective of the patient's evolving health trajectory. The system is composed of a web application (for hospital staff) and a mobile application (for family caregivers), providing the opportunity for cooperative patient monitoring in both hospital and home settings. This paper presents the system's preliminary design and usability evaluation results from a 1.5-year pilot study. Our findings indicate that while the hospital web app demonstrates high completion rates and user satisfaction, the family mobile app requires additional improvements for optimal accessibility; statistical and qualitative data analysis illuminate pathways for system improvement. Based on this evidence, we formalize suggestions for health system development in LMICs, which HCI researchers may leverage in future work.
Related papers
- Harnessing the Digital Revolution: A Comprehensive Review of mHealth Applications for Remote Monitoring in Transforming Healthcare Delivery [1.03590082373586]
The review highlights various types of mHealth applications used for remote monitoring, such as telemedicine platforms, mobile apps for chronic disease management, and wearable devices.
The benefits of these applications include improved patient outcomes, increased access to healthcare, reduced healthcare costs, and addressing healthcare disparities.
However, challenges and limitations, such as privacy and security concerns, lack of technical infrastructure, regulatory is-sues, data accuracy, user adherence, and the digital divide, need to be addressed.
arXiv Detail & Related papers (2024-08-26T11:32:43Z) - Health-LLM: Personalized Retrieval-Augmented Disease Prediction System [43.91623010448573]
We propose an innovative framework, Heath-LLM, which combines large-scale feature extraction and medical knowledge trade-off scoring.
Compared to traditional health management applications, our system has three main advantages.
arXiv Detail & Related papers (2024-02-01T16:40:32Z) - Large Language Models for Healthcare Data Augmentation: An Example on
Patient-Trial Matching [49.78442796596806]
We propose an innovative privacy-aware data augmentation approach for patient-trial matching (LLM-PTM)
Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%.
arXiv Detail & Related papers (2023-03-24T03:14:00Z) - Internet of Things and Health Care in Pandemic COVID-19: System
Requirements Evaluation [0.0]
This paper aims to find the important requirements for a remote monitoring system for patients with COVID-19.
The requirements and the value are determined for the proposed system, which integrates a smart bracelet that helps to signal patient vital signs.
arXiv Detail & Related papers (2022-05-05T12:07:00Z) - MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence
using Federated Evaluation [110.31526448744096]
We argue that unlocking this potential requires a systematic way to measure the performance of medical AI models on large-scale heterogeneous data.
We are building MedPerf, an open framework for benchmarking machine learning in the medical domain.
arXiv Detail & Related papers (2021-09-29T18:09:41Z) - 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) - Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration [55.88616573143478]
Outcome prediction from clinical text can prevent doctors from overlooking possible risks.
Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets.
We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
arXiv Detail & Related papers (2021-02-08T10:26:44Z) - CPAS: the UK's National Machine Learning-based Hospital Capacity
Planning System for COVID-19 [111.69190108272133]
The coronavirus disease 2019 poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources.
We developed the COVID-19 Capacity Planning and Analysis System (CPAS) - a machine learning-based system for hospital resource planning.
CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic.
arXiv Detail & Related papers (2020-07-27T19:39:13Z) - SSHealth: Toward Secure, Blockchain-Enabled Healthcare Systems [13.035267999201935]
This paper presents a novel smart and secure Healthcare system (ssHealth), which permits epidemics discovering, remote monitoring, and fast emergency response.
We develop a blockchain-based architecture and enable a flexible configuration thereof, which optimize medical data sharing between different health entities.
We highlight the benefits of the proposed ssHealth system and possible directions for future research.
arXiv Detail & Related papers (2020-06-18T20:34:56Z)
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.