Modern Data Management and Visualization in Healthcare
There is a growing demand for better healthcare IT innovations and analytics to drive better patient care services and reduce care cost. Healthcare IT analytics requires managed data platform as a foundation for business intelligence, data mining, and predictive analytics with modern data visualization.
In IT, we were dealing with many various data requests from all kind of users for different purposes and we were providing data services without understanding how the data is being used and analyzed. This was due to a gap between IT and business units led to inefficiency in data delivery and reporting services; and impacted business decisions.
The key focus area of data governance should include data inventory, quality, integrity, consistency and security, and establishing processes to ensure effective data management throughout the enterprise
Few years ago, we decided to re-draw the line between IT and business units to bridge the gap and developed an IT/ business partnership. Our strategy was to develop a modern data management platform and build better innovative analytical program to introduce new technologies that help solving complex business issues. This would create a better business analysis outcome and improve business workflow processes to provide high quality care services and enhance the patient and care team experience.
Data Management is not about storing clinical and business data and make it available to business users. There are many challenges facing us when it comes to how to manage data and present it as integrated, consistent and transformed into meaningful information. This is key in developing a data hub as a core source for all kind of innovations and advanced analytics.
One of the key aspect of data management is data governance which can be defined in different ways, but the key focus area of data governance should include data inventory, quality, integrity, consistency and security, and establishing processes to ensure effective data management throughout the enterprise.
As we are dealing with massive amount of data growth which becomes more challenging every day; data inventory is an important phase in data management. We need to identify, catalog and document every data stores and data elements in order to better manage and allow business users to discover what data is available and in what format.
We developed a data classification program by categorizing, cataloging and labeling all data stores and data elements to help any users to find the accurate answers to their questions. Our goal is to provide a discovery feature to allow the users specify the data elements they need for their analysis to build the data models that satisfy their needs. We are working on developing a searching tool so the users can describe their type of analysis and what they are doingso we can deliver the required data model without having the user to figure out which database or data warehouse they need access to.
This searching tool will provide many benefits:
1. Allow IT capturing what the users are requesting and granting the correct access
2. Build a relationship between the users analysis works and requested datasets so we can suggest other datasets based on other users usage
3. Sharing users codes and analysis with other users
4. Better data discovery process
Sometimes the user request access to specific dataset for certain analysis i.e. medications, lab and/or orders related analysis without realizing that it could be incomplete data analysis because of missing additional access to other datasets that the user is not aware of. It is very challenging for the users to keep track of all data stores to make sure they have access to the required datasets in order to deliver the complete analysis job. This presented the need to develop clinical and business data models that integrate data from different sources and apply business rules to answer questions by modeling different business subject areas i.e. financial model, operational model, ambulatory model, or specific like opioid model.
As IT is partnering and worked closely with business users, we were able to develop multiple data models addressing different business needs and make it available for users to deliver quick reporting turnaround and enforce accuracy and comply with business rules and policies.
Building a modern data management platform allows IT and Business units to focusing more on business intelligence and advanced analytics using machine learning (ML) and artificial intelligence (AI) and deliver better data visualization.
Data visualization is a presentation layer to transform the data into information and bring more insights to business users by summarizing data and using graphs to understand business performance, volume and financial trending to explain what happened and why did it happen.
Modern data visualization tools are integrated with ML and AI which provides insights and makes sense of data to comprehend large amount of data fast and easy for better decision making. It makes complex data more accessible, understandable, and usable.
Modern data visualization is an art and science, it displays the information as what happened and why did it happen and can provide advanced insight as what will happen and how we can make it happen.
In order to deliver a rich well informative dashboards and reports, you need a well-defined data management strategy to maintain a high quality data and manage data models that can answer any business questions accurately and provide more insights.