Healthcare Data and Exchange Standards

Hersh, William R.

doi:10.1007/978-3-030-93765-2_13

Cited by 1 publication

(4 citation statements)

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“…The healthcare industry has witnessed a remarkable surge in the volume of healthcare data in recent times, primarily driven by the widespread adoption of electronic health record (EHR) systems worldwide [3]. In addition, there has been an unprecedented growth in other types of healthcare data, such as genome sequencing and other biological structures [4]. The analysis of this clinical data is commonly referred to as analytics or healthcare data analytics, which falls under the category of secondary use of clinical data.…”

Section: Healthcare Data Analyticsmentioning

confidence: 99%

“…The analysis of this clinical data is commonly referred to as analytics or healthcare data analytics, which falls under the category of secondary use of clinical data. While the term data analytics is extensively used in and outside of healthcare [ 3 ], our focus in this study is on its application in the healthcare industry.…”

Section: Introductionmentioning

confidence: 99%

“…All three levels of healthcare data analytics are of paramount importance. However, predictive analytics has gained more attention in the current healthcare landscape [ 3 ], as medical experts seek to predict various clinical-related variables in healthcare data to enhance healthcare delivery services and optimize health and financial outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…To summarize, data analytics has become a pivotal aspect of current healthcare settings, a core requirement for both the industry and its experts [4]. Moreover, the future of healthcare holds tremendous promise when it comes to data analytics.…”

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confidence: 99%

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Transforming Healthcare Analytics with FHIR: A Framework for Standardizing and Analyzing Clinical Data

et al. 2023

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In this study, we discussed our contribution to building a data analytic framework that supports clinical statistics and analysis by leveraging a scalable standards-based data model named Fast Healthcare Interoperability Resource (FHIR). We developed an intelligent algorithm that is used to facilitate the clinical data analytics process on FHIR-based data. We designed several workflows for patient clinical data used in two hospital information systems, namely patient registration and laboratory information systems. These workflows exploit various FHIR Application programming interface (APIs) to facilitate patient-centered and cohort-based interactive analyses. We developed an FHIR database implementation that utilizes FHIR APIs and a range of operations to facilitate descriptive data analytics (DDA) and patient cohort selection. A prototype user interface for DDA was developed with support for visualizing healthcare data analysis results in various forms. Healthcare professionals and researchers would use the developed framework to perform analytics on clinical data used in healthcare settings. Our experimental results demonstrate the proposed framework’s ability to generate various analytics from clinical data represented in the FHIR resources.

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Section: Healthcare Data Analyticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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