Evidence-based medicine, rooted in randomized controlled trials, offers treatment estimates for the average patient but struggles to guide individualized care. This challenge is amplified in complex conditions like congenital heart disease due to disease variability and limited trial applicability. To address this, medicine-based evidence was proposed to synthesize information for personalized care. In their recent article, Li et al. introduced the patient similarity network “CHDmap”, which represents a promising technical rendition of the medicine-based evidence concept. Leveraging comprehens ..read more

Background: Pseudonymization has become a best practice to securely manage the identities of patients and study participants in medical research projects and data sharing initiatives. This method offers the advantage of not requiring to directly identify data to support various research processes, while still allowing for advanced processing activities such as data linkage. Often, pseudonymization and related functionalities are bundled in specific technical and organization units, so-called Trusted Third Parties (TTPs). However, pseudonymization can significantly increase the complexity of da ..read more

Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less att ..read more

Background: In the evolving field of health care, multimodal generative artificial intelligence (AI) systems, such as ChatGPT-4 with vision (ChatGPT-4V), represent a significant advancement, as they integrate visual data with text data. This integration has the potential to revolutionize clinical diagnostics by offering more comprehensive analysis capabilities. However, the impact on diagnostic accuracy of using image data to augment ChatGPT-4 remains unclear. Objective: This study aims to assess the impact of adding image data on ChatGPT-4’s diagnostic accuracy and provide insights into how i ..read more

Background: Large language models (LLMs) have shown remarkable capabilities in natural language processing (NLP), especially in domains where labeled data are scarce or expensive, such as the clinical domain. However, to unlock the clinical knowledge hidden in these LLMs, we need to design effective prompts that can guide them to perform specific clinical NLP tasks without any task-specific training data. This is known as in-context learning, which is an art and science that requires understanding the strengths and weaknesses of different LLMs and prompt engineering approaches. Objective: The ..read more

Background: Despite the potential of routine health information systems in tackling persistent maternal deaths stemming from poor service quality at health facilities during and around childbirth, research has demonstrated their suboptimal performance, evident from the incomplete and inaccurate data unfit for practical use. There is a consensus that nonfinancial incentives can enhance health care providers’ commitment toward achieving the desired health care quality. However, there is limited evidence regarding the effectiveness of nonfinancial incentives in improving the data quality of insti ..read more

Background: Biomedical natural language processing tasks are best performed with English models, and translation tools have undergone major improvements. On the other hand, building annotated biomedical datasets remains a challenge. Objective: The aim of our study is to determine whether the use of English tools to extract and normalize French medical concepts on translations provides comparable performance to that of French models trained on a set of annotated French clinical notes. Methods: We compare two methods: one involving French-language models and one involving English-language models ..read more

Background: With the capability to render pre-diagnosis, consumer wearables have the potential to affect subsequent diagnosis and the level of care in the healthcare delivery setting. Despite this, post-market surveillance of consumer wearables has been hindered by the lack of codified terms in EHR to capture wearable use. Objective: We sought to develop a weak supervision-based approach to demonstrate the feasibility and efficacy of EHR-based post-market surveillance on consumer wearables that render AF pre-diagnosis. Methods: We applied data programming where labeling heuristics are expresse ..read more

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