Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study.

Background: Physicians spend approximately half of their time on administrative tasks, which is one of the leading causes of physician burnout and decreased work satisfaction. The implementation of natural language processing-assisted clinical documentation tools may provide a solution.

Objective: This study investigates the impact of a commercially available Dutch digital scribe system on clinical documentation efficiency and quality.

Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care: A Systematic Review.

Importance: The aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow.

Objectives: To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle.

Applying natural language processing to patient messages to identify depression concerns in cancer patients.

Objective: This study aims to explore and develop tools for early identification of depression concerns among cancer patients by leveraging the novel data source of messages sent through a secure patient portal.

Materials And Methods: We developed classifiers based on logistic regression (LR), support vector machines (SVMs), and 2 Bidirectional Encoder Representations from Transformers (BERT) models (original and Reddit-pretrained) on 6600 patient messages from a cancer center (2009-2022), annotated by a panel of healthcare professionals. Performance was compared using AUROC scores, and model fairness and explainability were examined.

Predicting Depression Risk in Patients With Cancer Using Multimodal Data: Algorithm Development Study.

Background: Patients with cancer starting systemic treatment programs, such as chemotherapy, often develop depression. A prediction model may assist physicians and health care workers in the early identification of these vulnerable patients.

Objective: This study aimed to develop a prediction model for depression risk within the first month of cancer treatment.

[Making a diagnosis with the aid of the internet in times of large language models].

The internet is an excellent aid in making diagnoses. One can retrieve diagnostic information from a reliable source such as a continuously updated textbook or search specifically for a diagnosis in bibliographic databases such as PubMed. Entry of a patient summary in a general search engine or a large language model such as ChatGPT can suggest differential-diagnoses to the expert user,but one must be conscious of the limitations of current large language models.

Natural Language Processing Methods to Identify Oncology Patients at High Risk for Acute Care with Clinical Notes.

Clinical notes are an essential component of a health record. This paper evaluates how natural language processing (NLP) can be used to identify the risk of acute care use (ACU) in oncology patients, once chemotherapy starts. Risk prediction using structured health data (SHD) is now standard, but predictions using free-text formats are complex.

Predicting Depression Risk in Patients with Cancer Using Multimodal Data.

When patients with cancer develop depression, it is often left untreated. We developed a prediction model for depression risk within the first month after starting cancer treatment using machine learning and Natural Language Processing (NLP) models. The LASSO logistic regression model based on structured data performed well, whereas the NLP model based on only clinician notes did poorly.

Diagnosis Classification in the Emergency Room Using Natural Language Processing.

Diagnosis classification in the emergency room (ER) is a complex task. We developed several natural language processing classification models, looking both at the full classification task of 132 diagnostic categories and at several clinically applicable samples consisting of two diagnoses that are hard to distinguish.

Picture a data scientist: a call to action for increasing diversity, equity, and inclusion in the age of AI.

The lack of diversity, equity, and inclusion continues to hamper the artificial intelligence (AI) field and is especially problematic for healthcare applications. In this article, we expand on the need for diversity, equity, and inclusion, specifically focusing on the composition of AI teams. We call to action leaders at all levels to make team inclusivity and diversity the centerpieces of AI development, not the afterthought.

Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM).

Background: Evaluating patients' experiences is essential when incorporating the patients' perspective in improving healthcare. Experiences are mainly collected using closed-ended questions, although the value of open-ended questions is widely recognized. Natural language processing (NLP) can automate the analysis of open-ended questions for an efficient approach to patient-centeredness.

The digital scribe in clinical practice: a scoping review and research agenda.

The number of clinician burnouts is increasing and has been linked to a high administrative burden. Automatic speech recognition (ASR) and natural language processing (NLP) techniques may address this issue by creating the possibility of automating clinical documentation with a "digital scribe". We reviewed the current status of the digital scribe in development towards clinical practice and present a scope for future research.

Multitracer model for staging cortical amyloid deposition using PET imaging.

Objective: To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability.

Methods: Three thousand twenty-seven individuals (1,763 cognitively unimpaired [CU], 658 impaired, 467 with Alzheimer disease [AD] dementia, 111 with non-AD dementia, and 28 with missing diagnosis) from 6 cohorts (European Medical Information Framework for AD, Alzheimer's and Family, Alzheimer's Biomarkers in Daily Practice, Amsterdam Dementia Cohort, Open Access Series of Imaging Studies [OASIS]-3, Alzheimer's Disease Neuroimaging Initiative [ADNI]) who underwent amyloid PET were retrospectively included; 1,049 individuals had follow-up scans. With application of dataset-specific cutoffs to global standard uptake value ratio (SUVr) values from 27 regions, single-tracer and pooled multitracer regional rankings were constructed from the frequency of abnormality across 400 CU individuals (100 per tracer).

Development and Usability of ADappt: Web-Based Tool to Support Clinicians, Patients, and Caregivers in the Diagnosis of Mild Cognitive Impairment and Alzheimer Disease.

Background: As a result of advances in diagnostic testing in the field of Alzheimer disease (AD), patients are diagnosed in earlier stages of the disease, for example, in the stage of mild cognitive impairment (MCI). This poses novel challenges for a clinician during the diagnostic workup with regard to diagnostic testing itself, namely, which tests are to be performed, but also on how to engage patients in this decision and how to communicate test results. As a result, tools to support decision making and improve risk communication could be valuable for clinicians and patients.

Disclosure of amyloid positron emission tomography results to individuals without dementia: a systematic review.

Background: Disclosure of amyloid positron emission tomography (PET) results to individuals without dementia has become standard practice in secondary prevention trials and also increasingly occurs in clinical practice. However, this is controversial given the current lack of understanding of the predictive value of a PET result at the individual level and absence of disease-modifying treatments. In this study, we systematically reviewed the literature on the disclosure of amyloid PET in cognitively normal (CN) individuals and patients with mild cognitive impairment (MCI) in both research and clinical settings.

Association of Amyloid Positron Emission Tomography With Changes in Diagnosis and Patient Treatment in an Unselected Memory Clinic Cohort: The ABIDE Project.

Importance: Previous studies have evaluated the diagnostic effect of amyloid positron emission tomography (PET) in selected research cohorts. However, these research populations do not reflect daily practice, thus hampering clinical implementation of amyloid imaging.

Objective: To evaluate the association of amyloid PET with changes in diagnosis, diagnostic confidence, treatment, and patients' experiences in an unselected memory clinic cohort.