Building Bridges for Federated Learning in Healthcare: Review on Approaches for Common Data Model Development.

Common data models provide a standardized way to represent data used in federated learning tasks. The aim of this review was to explore the development and use of common data models to harmonize electronic health record data in health research. The data search yielded 724 records, of which 19 were included for this study.

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.

A Privacy-Preserving Audit and Feedback System for the Antibiotic Prescribing of General Practitioners: Survey Study.

Background: Antibiotic resistance is a worldwide public health problem that is accelerated by the misuse and overuse of antibiotics. Studies have shown that audits and feedback enable clinicians to compare their personal clinical performance with that of their peers and are effective in reducing the inappropriate prescribing of antibiotics. However, privacy concerns make audits and feedback hard to implement in clinical settings.

Clinical Notes De-Identification: Scoping Recent Benchmarks for n2c2 Datasets.

Publicly shared repositories play an important role in advancing performance benchmarks for some of the most important tasks in natural language processing (NLP) and healthcare in general. This study reviews most recent benchmarks based on the 2014 n2c2 de-identification dataset. Pre-processing challenges were uncovered, and attention brought to the discrepancies in reported number of Protected Health Information (PHI) entities among the studies.

De-Identifying Swedish EHR Text Using Public Resources in the General Domain.

Sensitive data is normally required to develop rule-based or train machine learning-based models for de-identifying electronic health record (EHR) clinical notes; and this presents important problems for patient privacy. In this study, we add non-sensitive public datasets to EHR training data; (i) scientific medical text and (ii) Wikipedia word vectors. The data, all in Swedish, is used to train a deep learning model using recurrent neural networks.

Serious Game Co-Design for Children with Type 1 Diabetes.

Co-design or participatory design has emerged as a useful concept where stakeholders and end-users have a greater stake in designing the end product. To date, few accounts exist of the use of the concept in serious game design, especially for children with chronic diseases. We report initial steps in serious game co-design for children with type 1 diabetes.

Play and Learn: Developing a Social Game for Children with Diabetes.

While serious games in healthcare have gained much attention in recent years, the pedagogical, social or behavioural frameworks tied to the game elements are still poorly understood. We report the prototyping effort as work-in-progress for a serious social gaming framework for children with diabetes. Motivation theories were combined with child education literature to evaluate potential elements of the framework.

Effectiveness of an Internet Community for Severely Obese Women.

While Internet communities have become thriving sources of support, little is yet known about their effectiveness. We retrospectively sampled morbidly obese (Body Mass Index, BMI > 40) women who were active for at least a year in an Internet community. We compared self-reported weight changes between women who had high online participation levels (n = 71) versus those with low participation levels as control (n = 69).

Persuasive attributes of medication adherence interventions for older adults: a systematic review.

Background: Low adherence to prescribed medications leads to serious negative health consequences in older adults. Effective interventions that improve adherence are often labor-intensive and complex. However, most studies do not analyze the separate effects of the components.

Designing a diabetes mobile application with social network support.

Although mobile applications and social media have emerged as important facets of the Internet, their role in healthcare is still not well-understood. We present design artefacts, inspired by persuasive technology concepts, from a study of social media as part of a diabetes mHealth application. We used the design science approach for mobile application design, and real-life user testing and focus group meetings to test the application over a 12-week period with 7 participants.

Surgical telementoring in knowledge translation--clinical outcomes and educational benefits: a comprehensive review.

Background: Surgical telementoring has been reported for decades. However, there exists limited evidence of clinical outcome and educational benefits.

Objective: To perform a comprehensive review of surgical telementoring surveys published in the past 2 decades.

Mobile health applications to assist patients with diabetes: lessons learned and design implications.

Self-management is critical to achieving diabetes treatment goals. Mobile phones and Bluetooth® can supportself-management and lifestyle changes for chronic diseases such as diabetes. A mobile health (mHealth) research platform--the Few Touch Application (FTA)--is a tool designed to support the self-management of diabetes.

Exploring the community structure of a diabetes forum.

A health forum is a kind of social network where users share information for specific topics they create. The purpose of this study was the identification of the key actors and the user communities in such a network. We used the publicly available data from a diabetes forum to create the corresponding network and explore several algorithms for the detection of user communities.

Features of mobile diabetes applications: review of the literature and analysis of current applications compared against evidence-based guidelines.

Background: Interest in mobile health (mHealth) applications for self-management of diabetes is growing. In July 2009, we found 60 diabetes applications on iTunes for iPhone; by February 2011 the number had increased by more than 400% to 260. Other mobile platforms reflect a similar trend.

Mobile peer support in diabetes.

As in other domains, there has been unprecedented growth in diabetesrelated social media in the past decade. Although there is not yet enough evidence for the clinical benefits of patient-to-patient dialogue using emergent social media, patient empowerment through easier access to information has been proven to foster healthy lifestyles, and to delay or even prevent progression of secondary illnesses. In the design of diabetes-related social media, we need access to personal health data for modelling the core disease-related characteristics of the user.