Why Be Normal?
There are many times when it’s important to be unique and stand out. For example, when attending a job interview, showing off your costume for a Halloween competition, or when you’re auditioning for American Idol. The creation of healthcare data, however, is not one of them.
Unfortunately, unique is often the default state of imaging and other health data as it is generated across modalities, systems, departments, and facilities – where the presence of diverse vendors and local policies result in bespoke data management practices and attribute values such as procedure or study names, series descriptors, disease characteristics, and in which format, tag, or sequence data is stored. Such inconsistency in data structure and content leads to a number of workflow and operational challenges, and significantly reduces the value of underlying health data, for instance:
- It complicates the creation and maintenance of reliable and consistent hanging protocols that are required for efficient reading workflow, forcing a never ending and hard to manage set of rules that require a complex set of rules to maintain consistent hanging protocols
- It limits the ability to effectively curate and analyze data for clinical and business improvement purposes
- It inhibits effective artificial intelligence (AI) and machine learning algorithm training
- It results in difficult and costly migration implications when considering system retirement or replacement
Data normalization – the process of defining standards and applying them to the structure and content of health data – overcomes these challenges by ensuring incoming data arrives in a consistent and predictable manner. The resulting clean, standardized data can be leveraged to:
- Inform continuous improvement initiatives to improve workflow efficiency, quality, and cost
- Better support interoperability between existing applications, and simplifies implementation and integration of new enterprise imaging systems
- Reduce the cost and complexity of future data migration projects
- Allow data to be more easily be inspected and mined to unlock valuable insights at departmental, organizational, and population levels
The value of being normal(ized)
Whether undertaken as part of a larger Enterprise Imaging initiative, or a standalone project, data normalization has the potential to yield a huge return on investment. Not only can it realize measurable improvements in the quality and efficiency of clinical workflow, stakeholder satisfaction, and your bottom line, it can also unlock the untapped value of what could prove to be one of your organization’s biggest assets – your data.
You may be wondering – this all sounds good in theory, but how exactly can data be normalized and what are the practical business and clinical applications? To address these questions, we will be posting a series of articles that will explore the methods of data normalization and dive deeper into the clinical, operational, and fiscal use cases and benefits for data normalization at the enterprise, departmental, and modality level.
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