Sequoia Project shares its new guide on data usability

Health and Wellness Informatics News

Sequoia Project published its implementation guide on data usability. The guide instructs the way to improve data sharing between providers and public health agencies.

It enables the ability to interpret the sent and received data, which will help to improve clinical workflows.

Sequoia Project released the final version of its implementation guide on data usability last week. However, the aim is to improve the way clinicians perceive the data and help them improve clinical workflow.

The guide is a collaborative effort of 360 members of Sequoia’s public Data Usability Workgroup. They mainly focus on six main categories of data usage. The categories are data provenance and traceability of changes; reducing the impact of duplicates; effective use of codes data integrity and trust; and effective use of narrative for data usability, data tagging, and searchability.

The stakeholders build the guide with the help of 120 comments from various experts and stakeholders. It also provides useful insight into what hospitals can improve in implementers, governance frameworks, networks, health information exchange vendors, and testing programs.

CEO of The Sequoia Project, Mariann Yeager, says that the guide contains solution-oriented real-life examples. It will also help to understand and enhance the data usability. The guide emphasizes the importance of health data sharing between public health, providers and patients.

The Data Usability Workgroup came into existence in 2020 to serve the purpose of the development of semantic interoperability. The participants of the organization need to build an implementation guide for clinical content. 

The aim was to provide necessary instructions to healthcare workers on the proper use of data usability. They have also been developing guides since then, and Sequoia Project is the latest in the line. 

Sequoia Projects’ data usability implementation guide is an advanced guide with updated information. It also enables semantic interoperability in the receiving and sending systems. Furthermore, it incorporates the data directly into the system. It makes the work easier for clinicians and helps to improve overall accuracy. 

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