Leveraging AI to Tackle Inequities through actionable insights:

Once the data is collected, the learnings are captured, reports can be quickly pulled to demonstrate this data inequity across geographies and regions. This can be tagged at government program levels or provider level to bring a comprehensive strategy for tackling the issues. 

With AI, historical datasets can be compared and classified to identify specific inequities amongst the group of people. It can be further leveraged to write algorithms that can predict and prevent inequities. Below we have laid down some of the tools that are leveraged to make a data driven world. 

Data which is present form of structured, unstructured or semi structured data can be ingested to data lake hosted on cloud like Azure or AWS. Once the data is captured, all sort of cleaning activities can be taken up to transform the structureless datasets into structured data using any of the native data cleaning services i.e.

Azure Databricks or AWS Databricks available on Azure or AWS 

From the insight’s standpoint, we can simply leverage the machine learning/deep learning techniques using Python, R or Scala to derive deeper insights from the data present.   

From reporting standpoint, we can simply perform various root cause and raw data analysis by using tools like Power BI or can simply take the insights from Databricks to non-relational or relational databases before pulling them on dashboards over web or mobile apps. 

To summarize, whether you are a provider, payer or pharma company, for building an equal world data driven digitization and democratization has a significant role to play. Lot of initiatives we are observing are towards making a strong collaboration between these different enterprises through innovative usage of technologies.