Domain-Specific Pretraining of NorDeClin-Bidirectional Encoder Representations From Transformers for Code Prediction in Norwegian Clinical Texts: Model Development and Evaluation Study.

Background: Accurately assigning ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes is critical for clinical documentation, reimbursement processes, epidemiological studies, and health care planning. Manual coding is time-consuming, labor-intensive, and prone to errors, underscoring the need for automated solutions within the Norwegian health care system. Recent advances in natural language processing (NLP) and transformer-based language models have shown promising results in automating ICD (International Classification of Diseases) coding in several languages.

Artificial Intelligence to Improve Clinical Coding Practice in Scandinavia: Crossover Randomized Controlled Trial.

Background: Clinical coding is critical for hospital reimbursement, quality assessment, and health care planning. In Scandinavia, however, coding is often done by junior doctors or medical secretaries, leading to high rates of coding errors. Artificial intelligence (AI) tools, particularly semiautomatic computer-assisted coding tools, have the potential to reduce the excessive burden of administrative and clinical documentation.

Exploring Pain Reduction through Physical Activity: A Case Study of Seven Fibromyalgia Patients.

Fibromyalgia is a chronic disease that affects a considerable fraction of the global population, primarily women. Physical activity is often recommended as a tool to manage the symptoms. In this study, we tried to replicate a positive result of pain reduction through physical activity.

Telehealth development in the WHO European region: Results from a quantitative survey and insights from Norway.

Background: The COVID-19 pandemic sent shock waves through societies, economies, and health systems of Member States in the WHO European Region and beyond. During the pandemic, most countries transitioned from a slow to a rapid adoption of telehealth solutions, to accommodate the public health and social measures introduced to mitigate the spread of the disease. As countries shift to a post-pandemic world, the question remains whether telehealth's importance as a mode of care provision in Europe continues to be significant.

Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial.

Background: Computer-assisted clinical coding (CAC) tools are designed to help clinical coders assign standardized codes, such as the ICD-10 (International Statistical Classification of Diseases, Tenth Revision), to clinical texts, such as discharge summaries. Maintaining the integrity of these standardized codes is important both for the functioning of health systems and for ensuring data used for secondary purposes are of high quality. Clinical coding is an error-prone cumbersome task, and the complexity of modern classification systems such as the ICD-11 (International Classification of Diseases, Eleventh Revision) presents significant barriers to implementation.

A multinational study on artificial intelligence adoption: Clinical implementers' perspectives.

Background: Despite substantial progress in AI research for healthcare, translating research achievements to AI systems in clinical settings is challenging and, in many cases, unsatisfactory. As a result, many AI investments have stalled at the prototype level, never reaching clinical settings.

Objective: To improve the chances of future AI implementation projects succeeding, we analyzed the experiences of clinical AI system implementers to better understand the challenges and success factors in their implementations.

De-identifying Norwegian Clinical Text using Resources from Swedish and Danish.

The lack of relevant annotated datasets represents one key limitation in the application of Natural Language Processing techniques in a broad number of tasks, among them Protected Health Information (PHI) identification in Norwegian clinical text. In this work, the possibility of exploiting resources from Swedish, a very closely related language, to Norwegian is explored. The Swedish dataset is annotated with PHI information.

Using a Large Open Clinical Corpus for Improved ICD-10 Diagnosis Coding.

With the recent advances in natural language processing and deep learning, the development of tools that can assist medical coders in ICD-10 diagnosis coding and increase their efficiency in coding discharge summaries is significantly more viable than before. To that end, one important component in the development of these models is the datasets used to train them. In this study, such datasets are presented, and it is shown that one of them can be used to develop a BERT-based language model that can consistently perform well in assigning ICD-10 codes to discharge summaries written in Swedish.

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators.

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021.