The shift of the healthcare industry towards the Value-Based Care (VBC) model has been the biggest contributor in the progression of healthcare analytics.

This change in the healthcare delivery model translates to a higher element of risk for the provider organizations. As a result, there will be more emphasis on population risk evaluation, performance monitoring, cost optimization, patient satisfaction, etc. with an aim to balance quality and profitability.

Analytics platforms will play a crucial role in enabling this transition to value-based care while ensuring the associated risks are managed.

From identifying care gaps to network gaps, from preventing readmission to providing wellness care, from compliance monitoring to remote patient monitoring, the analytics platform can cover the whole nine yards with the power of data science.

The healthcare ecosystem comprising providers, payers, ACOs and regulatory bodies can all benefit from the advantages, provided the analytics platforms are implemented correctly.

The benefits of using the analytics platform to the healthcare community and the patients are clear; however, an improper implementation or a badly-designed platform can pose several challenges in realizing the true benefits.

The impediment to insight

One key challenge we come across often is badly-designed platforms without any thought given to scalability. For example, certain platforms are designed with batch data loads, which are available at periodic intervals. This lack of flexibility hampers the ability to effectively integrate and use real-time data, which is so crucial for making clinical decisions.

One other key issue is an over-reliance on developers to support the ongoing operations. For example, the ability to create a custom measure or a custom report should be made available to the business users, otherwise, it severely impacts your ability to turn around a new requirement.

The same is true in dealing with data quality issues. Can your analytic platform resolve data quality issues on the fly and allow the data load operation to continue from that point without causing data integrity issues? We have seen many cases, where the data load needs to start all over again.

How about monitoring the progress of your complex ETL cycle? Do you have visual dashboards that can accurately pinpoint issues?

The very essence of analytics solutions lies in their ability to generate quick and accurate insights from mountains of data, at the speed of light.

With the future looking to be driven by data and analytics, how can healthcare analytics organizations address these challenges sustainably, scale up and meet the industry’s demands?

The story of analytics transformation

Recently, we had an experience with one of our analytics platform customers– a large Population Health Management vendor primarily using the adjudicated claims data for analytics and reporting.

The customer’s solution was impeding their ability to quickly on-board new clients, with severe data quality issues. Most importantly, their platform was not flexible enough to scale up to use the real-time data.

Besides, the data refresh in the system process took over 21 days and there was also no data quality system in place to ensure the accuracy of predictions…

Curious to know what happened next? Click HERE to read the rest of this HealthAsyst Case Study.

Bhupesh Nadkarni

Bhupesh Nadkarni brings with him over 20 years of rich experience in IT services industry with expertise in Healthcare and Manufacturing domains.
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