Search AMIA.org

To address the gap between artificial intelligence (AI) model development and clinical implementation by developing a machine-learning model to predict cochlear implant (CI) eligibility based on routine hearing tests, while illustrating 3 practical lessons: reformulation for clinical utility, selection of problem-specific metrics, and handling input data variability.

Patients often receive health care from multiple organizations. Privacy Preserving Record Linkage (PPRL) is a technology for linking patient records without releasing personally identifiable information. We compared a commercial PPRL tool that uses the XGBoost machine learning algorithm with Care Everywhere (CE), a widely used rule-based patient linkage module.

To develop and validate a prespecified logistic model for detecting mild cognitive impairment (MCI) using MyCog Mobile, a self-administered smartphone-based screening application, and to evaluate a structured simplification that reduces patient burden while maintaining diagnostic accuracy.

Predictive models of suicide risk have focused on features extracted from structured data found in electronic health records, with limited consideration of predisposing life events (LE) expressed in unstructured clinical text such as housing instability and marital troubles. This study aims to expand upon previous research, demonstrating how high-performance computing (HPC) and machine learning methodologies can be used to extract and annotate 8 LE across all Veterans Health Administration (VHA) [...]

Medical product safety surveillance efforts, whether using electronic health record (EHR) or claims data, typically rely on structured codes. Utilizing unstructured EHR data, particularly information extracted from clinical text through natural language processing (NLP), enriches information available for data mining, phenotyping, and surveillance. To assess overlapping and distinct information across structured and unstructured EHR data, we mapped both to a common vocabulary (Medical Dictionary for Regulatory Activities, MedDRA). We assess [...]

This study evaluates the completeness of diagnostic information captured in electronic health records (EHRs) compared to administrative claims data across multiple U.S. healthcare organizations between 2010 and 2022.

To evaluate CigStopper, a machine learning algorithm designed to predict billing eligibility for tobacco cessation counseling (CPT 99406/99407), addressing persistent underbilling and documentation gaps in health systems.

This study examined the use of machine learning (ML) and domain-specific enrichment in patient-generated health data, in the form of free-text meal logs, to classify meals on alignment with different nutritional goals.

Rheumatoid and Psoriatic Arthritis (RA and PsA) are autoimmune diseases that cause debilitating joint pain. Disease-modifying antirheumatic drugs (DMARDs) are recommended for the treatment of both conditions. However, real-world evidence studies would help characterize compliance with these recommendations. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standardizes electronic health record (EHR) data, allowing for research that incorporates multiple data sources. We are interested in determining whether OMOP CDM [...]

We report on using electronic health records (EHRs) and other health information technology (IT) (eg, REDCap, Excel, and population-health tools) for tracking patients and managing interventions to improve colorectal screening (CRC) among primary care practices who participated in the National Cancer Institute's Accelerating Colorectal Cancer Screening and Follow-up through Implementation Science (ACCSIS) program.