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Top 6 Challenges of Data Analytics in Healthcare

Most sensible 6 Demanding situations of Knowledge Analytics in Healthcare

Data analytics is turning out to be one of the most challenging undertakings in recent memory for the healthcare industry.

Healthcare providers who are having a hard time putting data into their Electronic Health Records (EHR) are now being asked to extract actionable insights out of them – and implement those learnings to complex initiatives that influence their reimbursement rates. In the absence of proper technology and other resources, extracting insights and integrating these data-driven insights into their clinical and operational processes has become difficult than ever. Providers, thereby, are compelled to deal with issues such as poor security, lack of visualization, and compromised data integrity.

One must identify issues related to data analytics to achieve their data-driven clinical and financial goals. In this blog post, you’ll find what major data analytics challenges are and how they can be resolved.

The Rocky Road to Healthcare Data Analytics


In the ideal case, the data being collected for the analysis ought to be clean, complete, and accurate. However, in the real-world scenario, data is often skewed which, in turn, makes it difficult to be used in multiple systems. In order to resolve this critical issue, health care firms need to carve out a new approach for data collection as well as data prioritization. Not to mention, they need to make sure that the clinicians are trained enough to recognize the value of relevant information.


Traditional on-premises data warehouses are not equipped to serve the purpose when the volume of healthcare data reaches beyond a certain limit. These poorly-sized warehouses become one of the major reasons for compromising cost, security, and performance. To deal with this problem organizations can use cloud storage technology. With its added capacities of information storage, healthcare providers can store a huge amount of data at one go.


Manual data cleaning process tools are prone to errors or inaccuracies. They consume a lot of time to boot. When data is not properly cleansed, organizations are likely to face issues such as operational inefficiencies, missed opportunities, and revenue losses.

To solve this problem, organizations can use ETL tools that offer advanced data cleansing capabilities along with premium-grade data transformation functionality to address complex data management scenarios.


The repeated incidents of hacking of patient records, high profile data breach, and ransomware etc. are posing credibility threats to data solutions for organizations. Hence, creating an end-to-end encrypted environment for data is necessary. Modern data integration solutions allow organizations maintain the authenticity of healthcare data by providing a secure, end-to-end encrypted environment. Only authenticated users are allowed to access the data, and thus the risk of breaches lowers substantially.


For healthcare providers to interpret information, a clean and engaging data visualization technique must be used. It helps decision-makers analyze data analytics, so they can grasp difficult concepts or identify new patterns even without in-depth expertise in analytics.


Healthcare providers do not function in a vacuum. This means that sharing data with external entities is necessary, especially as the industry moves towards population health management and value-based care. Due to poor data-sharing mechanisms, healthcare organizations face problems of interoperability that can impact their ability to make key decisions, follow up with patients, and more. Organizations can overcome these challenges by using robust data integration technology. By breaking data free of siloes and allowing it to be available wherever and whenever it is needed, healthcare organizations can streamline their overall outcomes.

In short, to allow organizations use data analytics effectively, one needs to take care of the 6 challenges mentioned above.

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