Karol Wykrota

Large-scale administrative hospital discharge data is a key resource for secondary health systems research, yet reuse is constrained by barriers of access, quality, interoperability, and semantic comparability. This paper presents and validates a reproducible, declarative, loss-aware harmonization framework for public discharge data that avoids full migration to a comprehensive common data model. The framework comprises a lightweight 14-field canonical model, versioned JSON crosswalks, a shared execution engine, a resilient file reader, schema validation, idempotency tests, value-domain checks, and an information-loss map. It was evaluated on public record-level discharge data from five jurisdictions on three continents (Korea, Brazil, Mexico, Chile, and New York State), comprising 561,966,231 harmonized records from 2001 to 2025. Validation demonstrated conformance to the declared source profiles for 82 of 99 files and full canonical conformance for 39, idempotency across all 99 files, 99.99% conformance with permitted value domains under an explicitly stated aggregation, and detection of source-level defects such as truncated files, malformed rows, and completeness anomalies. A marker-condition query for ischemic stroke (ICD-10 I63) showed that a single case definition executes consistently on the four sources retaining raw ICD-10 codes. The results show that, for heterogeneous administrative data, the key value lies not in scale alone but in the auditability of transformations, explicit loss documentation, and reproducibility of the harmonization process.

The article presents the Parrot AI smart system, which supports physicians specialising in internal medicine and pediatrics. The solution is designed to automate time-consuming administrative processes and assist physicians in clinical decision-making. The main purpose of the system is to relieve medical staff of administrative tasks, such as manually filling out medical records, which translates into increased work efficiency and more time to focus on direct contact with patients. Parrot AI uses advanced natural language processing methods and generative language models to enable automated semantic analysis of medical interviews. Based on this, the system generates the content necessary to complete the electronic medical records and assists the physician by suggesting preliminary diagnoses and treatment options. Interaction with the patient can take place both at the initial medical interview stage (via chatbot or voicebot) and during a personal appointment at the clinic. Functional tests have demonstrated that the solution is highly effective. All tested components of the system achieved results well above the 80% effectiveness threshold, confirming the validity of further implementing the system into everyday clinical practice. Parrot AI has the potential not only to streamline physicians’ work, but also to improve the quality and consistency of medical records.

Multi-class and multi-label classification of medical dialogues remains a challenging task due to high linguistic variability and transcription noise. This study proposes an ensemble approach based on three fine-tuned Polish T5 (Text-to-Text Transfer Transformer) models trained on partially overlapping clinical dialogue datasets. The models are evaluated exclusively on low-quality, highly noisy, automatically transcribed conversations to assess real-world robustness. The results demonstrate that the ensemble of models improves classification stability and outperforms the best single model, increasing the F1-score by 21.8% for internal medicine dialogues and by 44.9% for paediatric interviews. The proposed method shows potential for practical deployment in clinical decision support and automated medical documentation systems.

This article presents an ethical simulation of a phishing attack as a research method for analysing users' susceptibility to such threats. The study involved conducting an experiment in which emails mimicking authentic messages from popular services such as Google, Spotify, and InPost were sent. A total of 50 participants were involved in the experiment, divided into five age groups, allowing for an assessment of the impact of age on phishing attack susceptibility. The aim of the study was to determine the effectiveness of various social engineering techniques and to analyse users' reactions to the fraudulent messages. The experiment utilized dedicated, proprietary software that enabled monitoring of recipient activity, including reading messages, opening URLs, and filling out forms. The results showed that most users opened the phishing emails; however, only a small percentage took further actions that could potentially lead to data disclosure. The analysis of the results confirmed the crucial role of educational factors and user awareness in mitigating the effectiveness of phishing attacks. A particularly significant aspect was users’ prior experience with similar attacks and their active participation in informational campaigns, which significantly reduced their susceptibility to manipulation. In conclusion, recommendations were formulated emphasizing the need for systematic training campaigns and the implementation of advanced email filtering systems, which are crucial in counteracting the threats discussed in this study.