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Unstructured Data In Healthcare

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There are vast amounts of unstructured data in the healthcare industry. It is no surprise that the healthcare industry is struggling with data overload. During every patient encounter, providers rely on hundreds of discrete signals from lab results and qualitative descriptions. These are used to make decisions about treatment plans for patients. These varied, unstructured, discrete signals make it challenging to easily access healthcare data. Clinicians need easy access, but what is the best way to organize all this unstructured clinical intelligence into innovative insights which can improve patient outcomes?

When healthcare practitioners began storing and managing data digitally, they (like people in most industries) used structured data. This type of information can be clearly recorded with specific ways machines understand like a relational database or spreadsheet that contains names, dates, etc. Alphanumeric characters such as those used in ICD-10 Codes and CPT codes are part of this kind of record keeping system.

Electronic Health Record (EHR) systems are designed to use structured data. The Structured Data Capture (SDC) standards initiative is working toward a goal of interoperability so healthcare organizations can share information for patient care.

Unstructured Data In Healthcare Challenges

In order to process unstructured data to be actionable for clinicians, the healthcare industry needs ways of overcoming numerous challenges, listed below:

Interpreting Handwriting

Handwritten notes, which are a gold mine of information written by patients, nurses, or physicians, have never been easily accessible as unstructured information.

Reading Radiological Images

Reviewing and analyzing X-ray, CAT, MRI, or ultrasound medical images requires the skill of experienced professionals. These professionals may need to access past procedures, stored in unstructured form, to distinguish healthy tissue from abnormalities.

Creating Metadata

There is a way to pre-process unstructured data for use in an EHR or other system that requires structured data. For example, technicians and physicians can describe images using codes and keywords which are then entered into the computing system. This is done by adding information about what was seen through this metadata making them searchable.

Accommodating File Sizes

Unstructured data generally requires more storage than structured. Unstructured information is measured by terabytes and petabytes, not gigabytes. This requires greater capacity storage infrastructure.

Performing Searches

Without metadata, many forms of unstructured information would be impossible to search (e.g., images, or audio from a conference).

Using Streaming Data

Data is becoming more and more dynamic. The Internet of Things (IoT), edge computing, and Artificial Intelligence (AI) are all able to analyze this new type of information in real time.

Managing Massive Data Volumes

Data overload is the bane of modern healthcare professionals. They will need applications that help them prioritize and manage all this new, unstructured data.

Dealing With Existing Records

There are huge stores of unstructured healthcare data that could potentially aid better treatment decisions. These data are inaccessible to the EHR systems. AI and ML are changing the way healthcare providers store, analyze, and share data. These technologies are making it possible to have a more efficient patient care experience now by utilizing unstructured information that was previously inaccessible in EHRs. These will be needed tomorrow too as we continue moving forward with time-sensitive health initiatives. A good example is personalized medicine or precision oncology treatments, which require ever increasing amounts of precise knowledge.

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