Background Information technology has shifted paper-based documentation in the health
care sector into a digital form, in which patient information is transferred electronically from …

Abstract Objectives The HL7® fast healthcare interoperability resources (FHIR®)
specification has emerged as the leading interoperability standard for the exchange of …

Background: Qualitative methods analyze contextualized, unstructured data. These methods
are time and cost intensive, often resulting in small sample sizes and yielding findings that …

Background Machine learning–enabled clinical information systems (ML-CISs) have the
potential to drive health care delivery and research. The Fast Healthcare Interoperability …

As data size increases drastically, its variety also increases. Investigating such
heterogeneous data is one of the most challenging tasks in information management and …

Background Assessing patient-reported outcomes (PROs) through interviews or
conversations during clinical encounters provides insightful information about survivorship …

Advancing health data interoperability can significantly benefit research, including
phenotyping, clinical trial support, and public health surveillance. Federal agencies such as …

PURPOSE Knowing the treatments administered to patients with cancer is important for
treatment planning and correlating treatment patterns with outcomes for personalized …

Background Increasing digitalization in the medical domain gives rise to large amounts of
health care data, which has the potential to expand clinical knowledge and transform patient …

Abstract Background Knowledge graphs (KGs) play a key role to enable explainable
artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs …