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Ensuring Patient Safety through Digital Health Technologies
(Reykjavík University, 2025-04-22) Jóhannsdóttir, Lilja Guðrún; Islind, Anna Sigríður; Óskarsdóttir, María; Department of Computer Science
Patient safety is a core objective in healthcare, aiming to minimize risks, prevent avoidable harm, reduce errors, and mitigate the impact of adverse events. While some errors are easily identifiable, others require retrospective analysis to determine their consequences for specific patient groups. Patient safety is often discussed alongside quality of care, as both involve reducing harm and improving healthcare outcomes. Beyond being a fundamental goal, patient safety is a discipline that applies safety science to healthcare delivery. Safety science integrates engineering, psychology, human factors, risk management, and organizational behavior to improve systems, technologies, and human behavior, ultimately enhancing safety and reducing hazards. This thesis explores how digital health technologies can enhance patient safety, focusing on the National Hospital of Iceland’s digital health platform. The platform includes web and mobile applications for healthcare professionals and patients, integrating third-party solutions to modernize healthcare, streamline workflows, and improve access to patient data. A co-design approach involving healthcare professionals and patients has been used in its development, ensuring user needs are addressed. The effectiveness of medical treatments relies heavily on human behavior. The decision-making of both healthcare professionals and patients can be influenced by their environments, and even minor changes in how information is presented can significantly affect these user groups’ behavior. The concept, known as “choice architecture,” includes the use of “nudges” to guide decisions without restricting options. In healthcare, framing user interface design choices, such as physician recommendations for vaccines or default settings in electronic health records for prescriptions, can greatly impact treatment choices. As healthcare becomes increasingly digital, digital nudging offers a valuable tool for improving patient safety. This thesis examines digital nudging from the perspective of digital health platform designers working closely with healthcare professionals. While prior research has highlighted how digital nudges benefit patient care, there is limited exploration of how these nudges are designed and developed to enhance patient safety. The research is based on five papers that analyze different aspects of digital health technologies. Paper P1 investigates how the introduction of a mobile app for bedside documentation supports patient safety. Paper P2 examines how data collection through digital tools can improve quality of care, particularly for patients with atrial fibrillation before and after surgery. Paper P3 shifts focus to patients with inflammatory bowel diseases, exploring how a digital health program influences health outcomes and literacy. Paper P4 systematically reviews digital nudges in healthcare, assessing their impact on treatment and patient management. Paper P5 explores user perceptions of digital nudges and develops design guidelines for their ethical implementation. To study the various aspects of digital health technologies incorporated into the National Hospital digital health platform and how they contribute to patient safety we adopt both qualitative and quantitative methods. For our research design, we have strategized to use empirical data from the National Hospital to answer the research question. We apply standard usability questionnaires to measure the views of the healthcare professionals, and to measure the impacts of the design features we compare usage of the healthcare professionals before and after the features were deployed. The thesis (i) examines five themes that can contribute to the design and development of DHTs that ensure patient safety (ii) presents six dimensions that can contribute to patient safety in the design of DHTs, and (iii) presents a model that conceptualizes socio-technical key elements of patient safety. Together, the findings contribute to the discourse of digital health platforms in the information system literature with the conceptualization of the examination of a continuous co-design process.
Verk
Antimicrobial resistance in the Nordics : Mapping existing surveillance systems and assessing the impact of COVID-19 using regression models
(2025-12) Tran, Tam T.; Krolicka, Adriana; Tiwari, Ananda; Pitkänen, Tarja; Lood, Rolf; Ásmundsdóttir, Ásta Margrét; Wikmark, Odd Gunnar; Faculty of Natural Resource Sciences
Background: Coronavirus disease 2019 (COVID-19) pandemic constituted the largest global health crisis in recent generations. It may also have disrupted the pattern of antimicrobial use (AMU) and subsequently affected the development of antimicrobial resistance (AMR) – a grave human health concern. This study aimed to give an overview of existing AMR surveillance systems and evaluate the impact of COVID-19 on AMU and AMR in the Nordics using data from these systems. Methods: Nordic AMU data (2017–2022) were extracted from national annual reports (for both humans and animals) and the European Surveillance System (TESSy) (for humans only). For humans, AMU was expressed in defined daily dose (DDD) per 1000 inhabitants per day; for animals, it was expressed in kilogram (kg). Nordic human AMR data (2017–2022) were extracted from TESSy. Multilevel linear regression and negative binomial regression models were used to fit the TESSy data. Data between 2017 and 2019 were categorised as the pre-COVID-19 time, while data between 2020 and 2022 were the per-COVID-19 time. Results: Denmark had a remarkably greater AMU in animals (about 10 times greater) than Norway, Sweden, and Finland. Iceland had the highest human AMU, while Sweden had the lowest. Drug categories, countries, and sectors were significant predictors in the model used to fit human AMU. Bacterial species and drug categories were significant predictors in the models used to fit human resistant Gram-negative and Gram-positive bacteria. The COVID-19 time was not a significant predictor in these models. Among the Nordics, Iceland had the lowest number of resistant isolates; however, high human AMU remains a great concern for Iceland. Conclusions: The study provided insight into current existing AMR surveillance systems in the Nordics. It also showed that the COVID-19 pandemic had very little impact on AMU and AMR in theses countries. This implied that strict regulations on AMU as well as well-coordinated national AMR surveillance systems in the Nordics mitigated the development of AMR crisis also during COVID-19 pandemic. However, the Nordics would still benefit further from a standardized AMR surveillance at regional level, which ultimately facilitate timely information sharing and improve our preparedness for and response to future pandemics and/or large-scale outbreaks.
Verk
Theory of non-Markovian dynamics in Fluorescence spectra
(2014-12) Kumar, Abhishek; Erlingsson, Sigurður Ingi; Manolescu, Andrei
Robust quantum coherence is an important prerequisite for any system that may be used to perform quantum information processing tasks. For systems that can be probed optically, the fluorescence spectrum may provide indirect evidence of coherence times when supplemented with an appropriate model of the decay process. A common approach is to assume a Markovian system, resulting in exponential decay of correlation functions and Lorentzian features in the associated spectrum. For physical systems with strongly history-dependent (non-Markovian) dynamics, there is currently limited systematic theoretical approach to establish the associated spectrum. In this thesis, we present a detailed theoretical method to obtain the fluorescence spectrum for a general system undergoing non-Markovian dynamics. This procedure can be used to systematically account for features in fluorescence spectra due to genuine non-Markovian dynamics. We apply this method to study the non-Markovian behaviour in the side peak of the Mollow-triplet and absorption spectrum of coherent population trapping systems.
Verk
Applications of latent variable methods in primary aluminium production
(2025) Manolescu, Petre; Sævarsdóttir, Guðrún Arnbjörg; Duchesne, Carl; Department of Engineering
The aluminum industry is currently facing challenges on multiple fronts. One of them is the raw material quality and their impact on process stability. In modern aluminum smelter plants, there are increasing environmental and cost efficiency demands while raw material quality decreases. The digitalization of the manufacturing industry, dubbed Industry 4.0, opens up new avenues of exploration to meet the demands of the 21st century aluminum production. In this research, big data analytics methods were employed to determine the stability of a potline. An anomaly detection tool was built using principal component analysis. The prototype showed that drifts in process operations can be correctly identified earlier than with existing univariate statistical process control tools. The multivariate prototype was later implemented in production at Alcoa Fjardaal. Using the principal component analysis model, a ranking system was developed as the prioritization method of the problematic reduction cells. The variables’ contributions plots were used to help identify the issues in the problem cells. The model performance was assessed with the help of operators attending to the pots on the shop floor. A comparison with existing plant tools showed the potential of advanced notice of the onset of process anomalies. Through an anode tracking system, smelter data was collected to estimate the net carbon consumption (NCC) of individual anodes using latent variable methods. The properties of individual anodes were thus linked to their corresponding reduction cell performance. A multivariate statistical analysis of the linked carbon and pot operation databases, was compared to the existing net carbon consumption formula developed by R&D Carbon. The comparison demonstrated that the prediction for net carbon consumption, for individual anodes, significantly increased using the anode tracking system and latent variable methods. The anode tracking data was split into several blocks determined by manufacturing steps sequence. The impact of each block onto the net carbon consumption was determined using the sequential multi-block partial least squares algorithm. This methodology helped establish a link between green anode properties and the net carbon consumption of individual anodes.
Verk
Technological Change in Wealth Management : The development of a conceptual CRM model for the HNWI client interaction under the consideration of AI
(2025-03) Zimmermann, Fabian; Durst, Susanne; Department of Business and Economics
The constantly developing landscape of financial technology significantly shapes the financial sector and how cooperation and individuals interact with each other in a financial setting. While the developments incrementally unfold, the magnitude of their impact is difficult to comprehend and not addressed in wealth management. Many of today’s management models, which are used in academia and business alike, fall short of incorporating the latest developments and are on the brink of becoming outdated or obsolete in the eye of artificial developments. One particular field of interest within financial services, is the wealth management area. The development in this specific field is of particular interest, as it is a human-driven field of finance in which the human aspect (relationship and trust) plays a crucial role in the client interaction. Several models exist which conceptually address the client relationship management, to grasp their complexity. These commonly used models become increasingly impacted and jeopardized by artificial intelligence which impacts the composition of their factors leading to incomplete and outdated models. This doctoral study addresses the issue by analyzing existing models, extracting the factors which hold their validity despite technological developments and incorporating new factors. Thus, proposing a contemporary and conceptual client relationship model (CRM) model enhanced by AI factors. Thereby, this research broadens the understanding of the current developments in the wealth management sector and identifies key areas of development. It offers a conceptual and contemporary CRM model factoring in AI considerations to enable practitioners and academics alike to identify areas of developments and foster a better understanding of the interconnection between the factors in the client interaction. Thereby it provides considerations on AI and uncovers the increasing interconnectivity of CRM factor models.

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