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. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention’s generalizability and interoperability with existing systems, as well as the inner settings’ data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting.

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care.

Background. Type-1 diabetes is a condition caused by the lack of insulin hormone, which leads to an excessive increase in blood glucose level. The glucose kinetics process is difficult to control due to its complex and nonlinear nature and with state variables that are difficult to measure. Methods. This paper proposes a method for automatically calculating the basal and bolus insulin doses for patients with type-1 diabetes using reinforcement learning with feedforward controller. The algorithm is designed to keep the blood glucose stable and directly compensate for the external events such as food intake. Its performance was assessed using simulation on a blood glucose model. The usage of the Kalman filter with the controller was demonstrated to estimate unmeasurable state variables. Results. Comparison simulations between the proposed controller with the optimal reinforcement learning and the proportional-integral-derivative controller show that the proposed methodology has the best performance in regulating the fluctuation of the blood glucose. The proposed controller also improved the blood glucose responses and prevented hypoglycemia condition. Simulation of the control system in different uncertain conditions provided insights on how the inaccuracies of carbohydrate counting and meal-time reporting affect the performance of the control system. Conclusion. The proposed controller is an effective tool for reducing postmeal blood glucose rise and for countering the effects of external known events such as meal intake and maintaining blood glucose at a healthy level under uncertainties.

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. To date, much of what we know regarding these tools comes from lab-based evaluations, which often fail to account for real-world complexity and variability in clinical text. Objective: This study aims to investigate whether an AI tool developed by by Norwegian Centre for E-health Research at the University Hospital of North Norway, Easy-ICD (International Classification of Diseases), can enhance clinical coding practices by reducing coding time and improving data quality in a realistic setting. We specifically examined whether improvements differ between long and short clinical notes, defined by word count. Methods: An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a 1:1 crossover randomized controlled trial conducted in Sweden and Norway. Participants were randomly assigned to 2 groups (Sequence AB or BA), and crossed over between coding longer texts (Period 1; mean 307, SD 90; words) versus shorter texts (Period 2; mean 166, SD 55; words), while using our tool versus not using our tool. This was a purely web-based trial, where participants were recruited through email. Coding time and accuracy were logged and analyzed using Mann-Whitney U tests for each of the 2 periods independently, due to differing text lengths in each period. Results: The trial had 17 participants enrolled, but only data from 15 participants (300 coded notes) were analyzed, excluding 2 incomplete records. Based on the Mann-Whitney U test, the median coding time difference for longer clinical text sequences was 123 seconds (P<.001, 95% CI 81-164), representing a 46% reduction in median coding time when our tool was used. For shorter clinical notes, the median time difference of 11 seconds was not significant (P=.25, 95% CI −34 to 8). Coding accuracy improved with Easy-ICD for both longer (62% vs 67%) and shorter clinical notes (60% vs 70%), but these differences were not statistically significant (P=.50and P=.17, respectively). User satisfaction ratings (submitted for 37% of cases) showed slightly higher approval for the tool’s suggestions on longer clinical notes. Conclusions: This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for clinical coding tasks with more demanding longer text sequences. Further studies within hospital workflows are required before these presumed impacts can be more clearly understood.

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. To date, there have only been a few user studies; therefore, our understanding is still limited regarding the role CAC systems can play in reducing the burden of coding and improving the overall quality of coding. Objective: The objective of the user study is to generate both qualitative and quantitative data for measuring the usefulness of a CAC system, Easy-ICD, that was developed for recommending ICD-10 codes. Specifically, our goal is to assess whether our tool can reduce the burden on clinical coders and also improve coding quality. Methods: The user study is based on a crossover randomized controlled trial study design, where we measure the performance of clinical coders when they use our CAC tool versus when they do not. Performance is measured by the time it takes them to assign codes to both simple and complex clinical texts as well as the coding quality, that is, the accuracy of code assignment. Results: We expect the study to provide us with a measurement of the effectiveness of the CAC system compared to manual coding processes, both in terms of time use and coding quality. Positive outcomes from this study will imply that CAC tools hold the potential to reduce the burden on health care staff and will have major implications for the adoption of artificial intelligence–based CAC innovations to improve coding practice. Expected results to be published summer 2024. Conclusions: The planned user study promises a greater understanding of the impact CAC systems might have on clinical coding in real-life settings, especially with regard to coding time and quality. Further, the study may add new insights on how to meaningfully exploit current clinical text mining capabilities, with a view to reducing the burden on clinical coders, thus lowering the barriers and paving a more sustainable path to the adoption of modern coding systems, such as the new ICD-11. Trial Registration: clinicaltrials.gov NCT06286865; https://clinicaltrials.gov/study/NCT06286865 International Registered Report Identifier (IRRID): DERR1-10.2196/54593

Background. Since physical activity has a high impact on patients with type 1 diabetes and the risk of hypoglycemia (low blood glucose levels) is significantly higher during and after physical activities, an automatic method to provide a personalized recommendation is needed to improve the blood glucose management and harness the benefits of physical activities. This paper aims to reduce the risk of hypoglycemia and hyperglycemia (high blood glucose levels), and empowers type 1 diabetes patients to make decisions regarding food choices connected with physical activities. Methods. Traditional and Bayesian feedforward neural network models are developed to provide accurate predictions of the blood glucose outcome and the risks of hyperglycemia and hypoglycemia with uncertainty information. Using the proposed models, safe actions that minimize the risk of both hypoglycemia and hyperglycemia are provided as food recommendations to the patient. Results. The predicted blood glucose responses to the optimal and safe food recommendations are significantly better and safer than by taking random food. Conclusions. Simulations conducted on the state-of-the-art UVA/Padova simulator combined with Brenton’s physical activity model show that the proposed methodology is safe and effective in managing blood glucose during and after physical activities.