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As mobile health applications continue to proliferate without clear regulation, the need for app evaluation frameworks to offer guidance to patients and clinicians also expands. However, this expanding number of app evaluation frameworks itself can be a source of confusion and often contradictory recommendations. In pursuit of better frameworks that offer innovation for app evaluation, we present 4 challenges that app evaluation frameworks must overcome as well as examples from [...]

Raw audit logs provide a comprehensive record of clinicians' activities on an electronic health record (EHR) and have considerable potential for studying clinician behaviors. However, research using raw audit logs is limited because they lack context for clinical tasks, leading to difficulties in interpretation. We describe a novel unsupervised approach using the comparison and visualization of EHR action embeddings to learn context and structure from raw audit log activities. Using [...]

To develop an unbiased objective for learning automatic coding algorithms from clinical records annotated with only partial relevant International Classification of Diseases codes, as annotation noise in undercoded clinical records used as training data can mislead the learning process of deep neural networks.

The aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used.

Progression of HIV disease, the transmission of the disease, and premature deaths among persons living with HIV (PLWH) have been attributed foremost to poor adherence to HIV medications. mHealth tools can be used to improve antiretroviral therapy (ART) adherence in PLWH and have the potential to improve therapeutic success.

Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development [...]

A previous study, PheMAP, combined independent, online resources to enable high-throughput phenotyping (HTP) using electronic health records (EHRs). However, online resources offer distinct quality descriptions of diseases which may affect phenotyping performance. We aimed to evaluate the phenotyping performance of single resource-based PheMAPs and investigate an optimized strategy for HTP.

Many genetic variants are classified, but many more are variants of uncertain significance (VUS). Clinical observations of patients and their families may provide sufficient evidence to classify VUS. Understanding how long it takes to accumulate sufficient patient data to classify VUS can inform decisions in data sharing, disease management, and functional assay development.