Search AMIA.org

The objectives of this study are to synthesize findings from recent research of retrieval-augmented generation (RAG) and large language models (LLMs) in biomedicine and provide clinical development guidelines to improve effectiveness.

To determine if natural language processing (NLP) and machine learning (ML) techniques accurately identify interview-based psychological stress and meaning/purpose data in child/adolescent cancer survivors.

We conducted a scoping review to identify barriers to telehealth use and uptake from the perspective of patient, provider, and system that were documented in the literature. In addition to identifying and categorizing the barriers, we aimed to assess how barriers differed for studies conducted during the COVID-19 pandemic, as well as how barriers differed between the United States vs internationally based studies.

Accurate, complete allergy histories are critical for decision-making and medication prescription. However, allergy information is often spread across the electronic health record (EHR); thus, allergy lists are often inaccurate or incomplete. Discrepant allergy information can lead to suboptimal or unsafe clinical care and contribute to alert fatigue. We developed an allergy reconciliation module within Mass General Brigham (MGB)'s EHR to support accurate and intuitive reconciliation of discrepancies in the allergy [...]

To identify distinct patterns in consumer willingness to share health data with various stakeholders and analyze characteristics across consumer groups.

To assess the capacity of a bespoke artificial intelligence (AI) process to help medical writers efficiently generate quality plain language summary abstracts (PLSAs).

Sepsis recognition among infants in the Neonatal Intensive Care Unit (NICU) is challenging and delays in recognition can result in devastating consequences. Although predictive models may improve sepsis outcomes, clinical adoption has been limited. Our focus was to align model behavior with clinician information needs by developing a machine learning (ML) pipeline with two components: (1) a model to predict baseline sepsis risk and (2) a model to detect evolving [...]

Heightened muscular effort and breathlessness (dyspnea) are disabling sensory experiences. We sought to improve the current approach of assessing these symptoms only at the maximal effort to new paradigms based on their continuous quantification throughout cardiopulmonary exercise testing (CPET).

 Not all patients at risk for opioid overdose are prescribed naloxone when discharged from the emergency department or hospital. Clinical decision support (CDS) can be used to promote clinical best practices, such as naloxone prescribing; however, it may be ignored due to knowledge deficiencies or alert fatigue.

 When implemented in both electronic health records (EHRs) and pharmacy management software, CancelRx communicates medication discontinuation in the EHR to pharmacies, which has been demonstrated to reduce medication discrepancies at pharmacies and the risk of medication dispensing after e-prescription discontinuation. Provider workflows for making medication changes and the configuration of CancelRx will affect whether a medication change (e.g., dose adjustment) is communicated to a pharmacy.