Electronic Health Records (EHR)

Artificial intelligence (AI) tools, particularly those that use natural language processing (NLP), are becoming increasingly prominent in healthcare delivery. For these systems to operate safely and reliably in clinical settings, they require high-quality training datasets drawn from multiple sources, including electronic health record (EHR) data, that closely reflect real-world conditions.

The United States (U.S.) faces urgent health challenges, including poor maternal and childhood health outcomes, childhood obesity, opioid use disorder, cancer, and chronic conditions, which require personalized care tailored to each patient’s unique circumstances.

This paper provides an overview of Health Information Technology (HIT) adoption and utilization in long-term and post-acute care (LTPAC) settings. This study found that LTPAC have adopted electronic health records (EHRs) to support clinical and business needs. Interoperable exchange of health information however is not routine or widely used.

RAND identified use cases on identifying frailty using electronic health record (EHR) data in health systems in the US and examples from other countries, which demonstrate applications in both primary and specialist care. The final EHR implementation guide summarizes the learnings from participants in the EHR Learning Network and the identified use cases.

The purpose of this project was to develop and test a suite of electronic Care Plan (eCP) tools for adults with multiple chronic conditions (MCC), including an eCP implementation guide specifying data standards and value sets for key use case conditions and two open-source eCP apps (one for patients and one for clinicians).

This is the final report of an AHRQ study to address the operational gap between CFIs and EFIs. This project focused on validating an established CFI using linked claims-EHR databases of multiple large health systems. The project provides a systematic approach that health systems can use to examine the quality of the EHR data and prepare it for the application of EFI measures.

This project focused on validating an established claims-based frailty indexes (CFI) using linked claims-EHR databases of multiple large health systems. Additionally, the project assessed and compared the EHR and claims data of these data sources to ensure sufficient data quality for frailty analysis.

Electronic health records (EHRs) have been widely adopted in the United States, but there is growing concern that they have unintentionally burdened clinicians, inhibiting their ability to deliver health care efficiently and effectively.

Background: Social determinants of health (SDH) are increasingly seen as important to understanding patient health and identifying appropriate interventions to improve health outcomes, in what is a complex interplay between health system, community, and individual level factors.