Percorrer por autor "Oliveira, Mariana"
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- Managing Data in Screening Programs: Challenges and SolutionsPublication . Monteiro, Hugo; Oliveira, Mariana; Martinho, Ricardo; Martins, CarlosPopulation-based screening programs are vital public health initiatives that enable the early detection of diseases, significantly reducing both morbidity and healthcare costs. As these programs expand, the management of the extensive data they generate becomes increasingly complex, highlighting the need for structured digital solutions. This narrative review article presents a pragmatic framework aimed at clarifying big data analytics tailored to the needs and practices of healthcare professionals and administrators, focusing on effective integration into routine screening workflows. To achieve effective data utilization, the process begins with systematic archiving, which involves cloud-based storage solutions capable of securely maintaining various data formats in compliance with regulatory standards, thus ensuring long-term accessibility and continuity. Subsequent real-time processing of screening data facilitates rapid decision-making and patient management by providing immediate validation and analysis, essential for maintaining the responsiveness of screening services. Transformation processes play a critical role in converting diverse data inputs into standardized, consistent formats, enabling seamless communication and exchange among multiple healthcare systems. Integration further builds upon this standardization, merging data from different healthcare providers and diagnostic centers into centralized analytical platforms. This unified approach enables comprehensive patient monitoring and supports predictive modeling for early identification of at-risk individuals. Advanced analytics, particularly process mining and predictive techniques, reveal inefficiencies within screening workflows, highlighting areas needing improvement. These methods help healthcare managers to streamline operations, optimize resources, and enhance overall program performance. Real-time visualization tools provide administrators with continuous, practical insights into operational dynamics, despite existing challenges related to data governance and system interoperability. This article illustrates these concepts through concrete examples from the colorectal cancer screening program in Northern Portugal and the response to the COVID-19 pandemic. The colorectal cancer screening scenario demonstrates how structured data management significantly boosts operational efficiency and healthcare accessibility. Meanwhile, the COVID-19 experience highlights the importance of having flexible digital infrastructures capable of quickly adapting to unexpected crises. Finally, ongoing investments in digital infrastructure, professional training, and comprehensive data governance are crucial for sustaining these improvements. This review provides clear, actionable knowledge to support healthcare professionals in adopting big data analytics effectively within preventive healthcare programs.
- Population-Based Cancer Screening analysis in Northern Portugal Using Process MiningPublication . Monteiro, Hugo; Oliveira, Mariana; Martinho, Ricardo; Reis, João; Tavares, Fernando; Felgueiras, Óscar; Martins, CarlosBackground This study focuses on the Colorectal Cancer Screening Program in Northern Portugal, aiming to evaluate the disruption effects on its performance and efficiency. Methods We conducted an observational analyses of 271 637 administrative records from 2020 to 2022. Administrative timestamps were converted into a step-by-step dataset of screening activities (an “event log”) and analysed using process mining and comparative performance analysis across time periods and ACeS (primary care administrative clusters). Results Consultation‑to‑colonoscopy time lengthened by 53 %, rising from a median 58 days (IQR 29–92) in early 2020 to 89 days (IQR 53–127) in 2021, before improving to 73 days in 2022. Conversely, referral‑to‑consultation time fell from 110 days to 26 days (−76 %), reflecting targeted backlog clearance. Screening volumes declined in 2020 but recovered above baseline levels by 2022. Performance differences across primary care administrative clusters were significant (p < 0.001), with some units outperforming regional median transition times. Early adoption of automated electronic referrals and flexible consultation scheduling may have contributed to improved programme performance during the recovery period following pandemic-related disruptions. Substantial heterogeneity across units was observed for key transitions, indicating uneven disruption and recovery patterns across administrative units. Conclusion Process Mining techniques revealed critical vulnerabilities in the screening program during the initial stages of the period in analysis (matching the pandemic). These findings support targeted monitoring and prioritisation of operational improvements to reduce avoidable delays and strengthen continuity of population-based screening. Policy summary Policies aimed at strengthening healthcare service continuity and operational capacity benefit from analytical methods like process mining. Key recommendations include standardizing workflows, enhancing coordination between primary care and hospital services, and investing in digital monitoring systems to mitigate disruptions and ensure continuity in cancer screening programs during periods of system stress.