Oct 17, 2021

Challenges of Big Data Analytics in Healthcare

  • By Ricky M.,
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Challenges of Big Data Analytics in Healthcare

The healthcare organizations are working on improving the data analytics which is increasing by the time and managing the datasets and turning those assets into data insights for providing better healthcare to patients through these datasets. This task is not easy for organizations as it depends on many aspects. Some of the challenges that organizations are facing are as follows:

Capturing

The data capturing is the first step in managing the datasets and governance of the data. It is a difficult task to assemble data in an accurate, clean and complete format to use the datasets in multiple systems.

Cleaning

Cleaning data makes the data accurate, correct, consistent, relevant, and protects from getting corrupted. It is also called as cleansing or scrubbing. Uncleansed data can derail any project-based on data analytics.

Storage

The increasing volume of healthcare data has made healthcare providers think about the storage of data securely and at an affordable cost. Where 90% of organizations are opting cloud storage with more reliability and low cost, some are also going for on-premises data storage with much security, access and easy- handling of data but expensive than cloud storage.

Security

Security of the data is a very big concern for the healthcare system due to the cyber-attacks seen in past.

The HIPAA security rule has created the guidelines and a list of technical safeguards for organizations who are organizing and storing PHI (Protected Health Information).

Stewardship

Healthcare data is used by many in a healthcare system as it has a long shelf life (accessible for around 6 years). So, Governance of data is also required such as when the data was created, by whom, the purpose of creating data. And who has used that data after creating the data, why, when and how. The data steward can make sure of all these tasks and make the data more useful.

Querying

The stewardship process makes it easier to query the organizations’ data and get the expected results. The organizations use SQL (Standard Query Language) to overcome the data silos and their interoperability problems. The SQL (Standard Query Language) make the large datasets and databases to identify and return the correct information, but it is only be trusted depending on the data present at hand(which should be accurate, complete, and standardized).

Reporting

Reporting is necessary to ensure that the information generated should be correct and is the one that is needed which is generated by the database administrators.

Visualization

Data Visualization makes it easier for the clinician to understand the data and use it appropriately. The data presented in a clean and engaging form that it should be easier for making an immediate response from the data.

Updating

Updating the huge amount of data is quite a difficult task but it is necessary to update the datasets because the decisions or response of clinicians are based on these datasets maintaining the quality and integrity of the dataset.

Sharing

The data sharing is quite a challenge as the patients do not seek care at a single place and this results in sharing the data with external patients.

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