Browsing by Author "Taramasco, Carla"

Now showing 1 - 5 of 5
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    Co-design of a Mobile App for Engaging Breast Cancer Patients in Reporting Health Experiences: Qualitative Case Study
    (2023) Taramasco, Carla; Rimassa, Carla; Noel, Rene; Storm, Maria Loreto Bravo; Sanchez, Cesar
    Background: The World Health Organization recommends incorporating patient-reported experience measures and patient-reported outcome measures to ensure care processes. New technologies, such as mobile apps, could help report and monitor patients' adverse effects and doubts during treatment. However, engaging patients in the daily use of mobile apps is a challenge that must be addressed in accordance with the needs of people.
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    Design of an Electronic Health Record for Treating and Monitoring Oncology Patients in Chile
    (2023) Taramasco, Carla; Rivera, Diego; Guerrero, Camilo; Marquez, Gaston
    Identifying the clinical needs to evaluate and manage the treatment and monitoring of cancer patients is a multidimensional challenge in healthcare institutions. In this regard, electronic health records (EHRs) are beneficial for managing clinical information; however, EHRs focused exclusively on patients with cancer have not been sufficiently adopted. In Chile, the need for oncology EHR has only been briefly addressed, resulting in insufficient updated and systematized information on oncology patients. In this paper, we propose the design of an oncology EHR that manages critical variables and processes for the treatment and monitoring of patients with cancer in Chile. We used a systematic methodology to design a software architecture oriented to focus groups and interviews to elicit the requirements and needs of stakeholders. We created and described an EHR design that considers four modules that group and manage the main variables and processes that are critical for treating and monitoring oncology patients. Enabling and designing a treatment and monitoring registry for cancer patients in Chile is essential because it allows for the evaluation of strategic clinical decisions in favor of patients.
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    Differential Effects of a Telemonitoring Platform in the Development of Chemotherapy-Associated Toxicity: A Randomized Trial Protocol
    (2024) Martínez, Felipe; Taramasco, Carla; Espinoza Sepúlveda, Manuel Antonio; Acevedo, Johanna; Goic Boroevic, Carolina; Nervi Nattero, Bruno
    Chemotherapy requires careful monitoring, but traditional follow-up approaches face significant challenges that were highlighted by the COVID-19 pandemic. Hence, exploration into telemonitoring as an alternative emerged. The objective is to assess the impact of a telemonitoring platform that provides clinical data to physicians overseeing solid tumor patients, aiming to enhance the care experience. The methodology outlines a parallel-group randomized clinical trial involving recently diagnosed patients with solid carcinomas preparing for curative intent chemotherapy. Eligible adult patients diagnosed with specific carcinoma types and proficient in Spanish, possessing smartphones, will be invited to participate. They will be randomized using concealed allocation sequences into two groups: one utilizing a specialized smartphone application called Contigo for monitoring chemotherapy toxicity symptoms and accessing educational content, while the other receives standard care. Primary outcome assessment involves patient experience during chemotherapy using a standardized questionnaire. Secondary outcomes include evaluating severe chemotherapy-associated toxicity, assessing quality of life, and determining user satisfaction with the application. The research will adhere to intention-to-treat principles. This study has been registered at ClinicalTrials.gov (NCT06077123)
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    Machine learning analysis of a Chilean breast cancer registry
    (2021) Acevedo, Francisco; Causa, Leonardo; Bravo, Sebastián; García, Pablo; Cuevas, Ricardo; Bravo, Maria Loreto; Avellaira, Carla; Muñiz, Sabrina; Petric, Militza; Martinez, Raúl; Guerra, Constanza; Navarro, Marisel; Taramasco, Carla; Sanchez, Cesar
    In recent years, artificial intelligence (AI) and machine learning (a form of AI) have offered valuable tools for medicine by applying and training algorithms in order to make predictions. Herein, we applied a machine learning algorithm to analyze data from a >20 year breast cancer (BC) registry elaborated in two Chilean health institutions (a public hospital and a private center) that includes a total of 4838 patients and their basic clinicalpathological characteristics. Preliminary results suggest that this cohort of patients can be subdivided into five clusters according to key variables that also correlate with overall survival and disease-free survival rates. To our knowledge this is the first Latin American report of its kind. Our laboratory is currently expanding these analyses.
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    ML models for severity classification and length-of-stay forecasting in emergency units
    (2023) Moya-Carvajal, Jonathan; Perez-Galarce, Francisco; Taramasco, Carla; Astudillo, Cesar A.; Candia-Vejar, Alfredo
    Length-of-stay (LoS) prediction and severity classification for patients in emergency units in a clinic or hospital are crucial problems for public and private health networks. An accurate estimation of these parameters is essential for better planning resources, which are usually scarce. Although it is possible to find several works that propose traditional Machine Learning (ML) models to face these challenges, few works have exploited advances in Natural Language Processing (NLP) on Spanish raw-text vector representations. Consequently, we take advantage of those advances, incorporating sentence embeddings in traditional ML models to improve predictions. Moreover, we apply a strategy based on SHapley Additive exPlanations (SHAP) values to provide explanations for these predictions. The results of our case study demonstrate an increase in the accuracy of the predictions using raw text with a minimum preprocessing. The precision increased by up to 2% in the classification of the patient's post-care destination and by up to 8% in the prediction of LoS in the hospital. This evidence encourages practitioners to use available text to anticipate the patient's need for hospitalization more accurately at the earliest stage of the care process.