Publicação
Data-driven decision-making: Drivers of Year-over-Year Patient Return in a Healthcare Clinic
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Albuquerque, Carina Isabel Andrade | |
| dc.contributor.advisor | Bernardo, Ivo Samuel Pereira | |
| dc.contributor.author | Alves, Paula Margarida da Silva | |
| dc.date.accessioned | 2025-11-17T12:07:42Z | |
| dc.date.available | 2025-11-17T12:07:42Z | |
| dc.date.issued | 2025-10-31 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence | pt_PT |
| dc.description.abstract | Machine-Learning (ML) is transforming data-driven decision-making across industries. However, Small and Medium Enterprises (SMEs), which are dominant both in Portuguese and European economies, are still struggling to adopt the tools necessary to drive managerial decisions and extract value from data. In the healthcare sector in particular, medical practitioners are already using data and predictive analytics to improve disease diagnosis and prediction; however, management teams often rely too heavily on intuition when making decisions. To address the challenges of rapid technological advancements, an aging population, increased patient demands, and scarcer human resources, healthcare organizations must effectively leverage data as a strategic asset. Data-driven insights can enhance business performance, with patient retention strategies serving as a critical example. This study examines a real-world private clinic in Portugal, demonstrating how predictive analytics can optimize strategic decision-making by integrating transactional data with ML techniques. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, we identified the variables most strongly associated with year-on-year patient recurrence. We then developed and evaluated binary classification models to assess how effectively these features could predict patient loyalty, both overall and within two key medical specialties. The shortlisted features informed actionable recommendations to support data-driven management and long-term organizational sustainability. Although the primary dataset analyzed was moderately imbalanced, the balancing strategies used were unable to improve predictive performance. The top-performing model, based on a Random Forest classifier, achieved ROC scores exceeding 0.75. The 10 most predictive features excluded categorical variables and comprised Recency, Frequency, Monetary, and Clumpiness. The proposed methodology used in this empirical study offers a replicable framework for other SMEs and sectors seeking to enhance customer loyalty through datadriven decision-making. | pt_PT |
| dc.identifier.tid | 204070872 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190848 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Machine-Learning | pt_PT |
| dc.subject | Healthcare | pt_PT |
| dc.subject | Management | pt_PT |
| dc.subject | Decision-making | pt_PT |
| dc.subject | SDG 3 - Good health and well-being | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.subject | SDG 11 - Sustainable cities and communities | pt_PT |
| dc.title | Data-driven decision-making: Drivers of Year-over-Year Patient Return in a Healthcare Clinic | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Business Intelligence | pt_PT |