Anaeko Blog

Data Integration Solutions for Inpatient Waiting Times

09 Dec 2019 by Emil Indricau 4 minute read

 

Inpatient Waiting Times

In Northern Ireland inpatient waiting times are increasing and becoming longer compared with other parts of the UK.

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Automated Data Classification for Healthcare Security

02 Dec 2019 by Emma Foster 2 minute read

Providers in the healthcare industry must take every precaution to ensure the data they are handling is protected with levels of data security and privacy.

To ensure healthcare companies follow security and privacy, regulations are outlined in the Health Insurance Portability and Accountability Act (HIPAA), including policies and procedures so all healthcare providers can establish compliance. One of the challenges in becoming HIPAA compliant is the large amount of data and unstructured data that organisations need to handle, track and classify to eliminate risk of exposure. This is were data classification can help give a view of compliance and assess data to make sure it is handled in the correct way.

 

Automated Data Classification 

Data classification involves the application of a classification for a particular document, file, image, piece of data or message by a pre-defined rule set and is usually done manually by an individual classifying the documents they have created, modified or touched. Automated data classification and data discovery can solve the problems of analysing huge amounts of data and reduce time on human resources by introducing machine learning to process data and achieve a high level of precision in classifying data and ensuring compliance.

 

An example from Axis Technical explains how a financial services holding company used machine learning technology and data classification to analyse a back log of existing paper based Health Claims Forms, transform these into contextual data and process 4.5 million claims forms annually. The transfer of claims data across providers, doctors and patients had to be HIPAA compliant so with machine learning processes in place data could be tagged and reliability was greatly improved.

 

"The financial services holding company was able to cut overall claims processing costs by 65% through a combination of automated data extraction and auto-adjudication functionality."

 

Providing streamlined processes and data optimisation solutions for the healthcare sector means they can effectively maintain regulatory compliance and focus on their core mission of patient care and improved services. 

 

Anaeko Data Classification solutions for Compliance

We deliver solutions that scan "dark data" using machine learning to deep-inspect files and objects in order to identify risk and compliance. Our metadata management solutions leverage AI to intelligently classify data, extract, redact and curate files in line with data governance and privacy policy regulations.

Anaeko performed a data discovery within a Central Government Department to analyse asset data and catalogue entities representing facilities, networks, devices, users, permissions, applications and patches across multiple lines of business. Read more on our Data Optimisation Services as a Trusted Data Partner in our latest Datasheet:

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Why Data Discovery using Machine Learning has huge potential for Healthcare

18 Nov 2019 by Emma Foster 5 minute read

Healthcare systems generate huge amounts of data and this opens the opportunity for data discovery, artificial intelligence and machine learning to provide insight, synchronise data between silos, optimise innovation, enhance decision making and increase efficiency of research.

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The Two Main Problems of Artificial Intelligence In Healthcare

27 Feb 2019 by Anaeko 2 minute read

 

Artificial Intelligence In Healthcare

There are few (if any) industries that can be transformed by artificial intelligence to the degree that healthcare can. As initiatives around the world seek to digitize healthcare data, there are huge opportunities for game-changing tools and platforms. However, with potentially a yottabyte (1024 gigabytes) of healthcare data in the United States alone, we are no closer to this utopian, data-driven world of healthcare. Data in existence is not standardized, highly fragmented and is stored in incompatible legacy platforms. The technology exists, so why is healthcare so far behind other sectors in utilizing existing technology and what needs to be done to catch up?

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