Leveraging Biomedical Big Data: A Hybrid Solution
Innovate Digital Services To Accelerate Business Growth and Opportunities
Data Analytics: New Edge for Success
Turning Big Data into Big Money
Finding Talent is a Challenge
Max Mortensen, CIO, Norwegian American Hospital
Leveraging the Power of the Enterprise to Streamline and Secure DoD's IT
Terry Halvorsen, CIO, US Department of Defense
Our Calling and Time
Vincent A. Marin, CIO, Sidley Austin LLP
ERP: A New Age of Innovation
William R. Dyer, CIO, Cincom Systems, Inc
Future Predictive Analytics Solutions Need the Right Level of Regulatory Governance
By Frank Wang, System Vice President, IDN Decision Support Analytics, Health First
1. Share the purpose and motivation for using a specific algorithm to help building the trust and understanding of the analytical insights when adopting an analytics solution. To reduce the “black box” barrier on the end user side will help to utilize the business users’ expertise in the process improvement and improve the buy-in status from the end users. The impacted individuals need to be aware of why and how an analytics solution is used in the decision-making process.
As a provider of analytics solutions or a data science team in an organization, it is important to take proactive approach to adopt the best practices in the development life cycle
3. Make it transparent of what data is used in the analytics solution and how the data is collected and used. To explain to the consumers of an analytics solution how a data set is used will help to build trust and reach the outcome as expected. This is crucial for building the coordination between the impacted entities.
4. Authorized agencies need to help and assess the fundamental crucial features and core performance measures of an analytics system. For example, FDA need to work with the industries to provide the clinical decision support analytics guidelines.
5. Standardize the analytics system trial and validation process. The feedback loop for analytics solutions is relatively long, especially for healthcare predictive analytics solutions. It will take very long time to validate the predicted clinical outcomes in healthcare industry. A standard trial protocol needs to be agreed and followed for developing predictive analytics solutions.
6. Publish and share the performance data for major commercial analytics solutions. The authorized agencies need to maintain and share the standard performance measures for the analytics solutions to help the uses to assess the predictive modeling methodologies and pick the proper solutions.
7. Set up a center for testing the commercial analytics solution, guided by the authorized agencies. To share and public the standard performance by the center will help the users to select the right solutions as addition to their analytics tool kit.
As a provider of analytics solutions or a data science team in an organization, it is important to take proactive approach to adopt the best practices in the development life cycle to be prepared in the seven areas discussed. We will have a more detail discussion in a separate article.