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Cancer is a leading cause of death, but much of the diagnostic information is stored as unstructured data in pathology reports. We aim to improve uncertainty estimates of machine learning-based pathology parsers and evaluate performance in low data settings.

The electronic health record is a rising resource for quantifying medical practice, discovering the adverse effects of drugs, and studying comparative effectiveness. One of the challenges of applying these methods to health care data is the high dimensionality of the health record. Methods to discover the effects of drugs in health data must account for tens of thousands of potentially relevant confounders. Our goal in this work is to reduce [...]

In the last decade, expanding use of health information technology (IT) across the United States has created opportunities for use of electronic health data for health services and biomedical research, but efforts may be hampered by limited data access, data quality, and system functionality. We identify five opportunities to advance the use of health IT for health services and biomedical research, which informed a federal government-led, collaborative effort to develop [...]

Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients' (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine [...]

Managing participants and their data are fundamental for the success of a clinical trial. Our review identifies and describes processes that deal with management of trial participants and highlights information technology (IT) assistance for clinical research in the context of participant management.

The "Bow-tie" optimal pathway discovery analysis uses large clinical event datasets to map clinical pathways and to visualize risks (improvement opportunities) before, and outcomes after, a specific clinical event. This proof-of-concept study assesses the use of NHS Hospital Episode Statistics (HES) in England as a potential clinical event dataset for this pathway discovery analysis approach.

To advance use of real-world data (RWD) for pharmacovigilance, we sought to integrate a high-sensitivity natural language processing (NLP) pipeline for detecting potential adverse drug events (ADEs) with easily interpretable output for high-efficiency human review and adjudication of true ADEs.

Referring patients to specialty care is an inefficient and error-prone process. Gaps in the referral process lead to delays in patients' access to care, negative patient experience, worse health outcomes, and increased operational costs. While implementation of standards-based electronic referral options can alleviate some of these inefficiencies, many referrals to tertiary and quaternary care centers continue to be sent via fax.

Growing numbers of academic medical centers offer patient cohort discovery tools to their researchers, yet the performance of systems for this use case is not well understood. The objective of this research was to assess patient-level information retrieval methods using electronic health records for different types of cohort definition retrieval.

Electronic health record (EHR) data linked with address-based metrics using geographic information systems (GIS) are emerging data sources in population health studies. This study examined this approach through a case study on the associations between changes in ejection fraction (EF) and the built environment among heart failure (HF) patients.