The actionable intelligence research (AIR) process involves the transformation of data into action-driven information in the business environment. In healthcare, the process enables administrators to compile data from different sources for efficient report generation and presentation. As a process, AIR involves six major steps available for data transformation as follows.
First, data managers collect as well as organize reinforcement data for ease of management. Next, they convert the data into information using available reporting tools. Third, it would be essential to analyze the information for knowledge extraction. Fourth, the organization them uses the knowledge for prediction purposes. Furthermore, given the new intelligence, managers have the ability of identifying potential decisions. The final stage involves taking action based on the actionable intelligence under insight (Allelunas, 2014).
Now, Allelunas (2014) identifies Universal Data Management (UDM) as a notable framework that can be applicable in healthcare environments. Allelunas (2014) describes this framework as an ultimate tool for linking pharmaceutical representatives to physicians. The UDM recognizes the importance of effective management given the complexity of their practice environment. Thus, both physicians and sales reps can use the UDM to collect clean, quality, and time-conscious information for appropriate connections to their respective services.
Finally, some of the tools identified for transforming data into AI are as follows. First, Microsoft SQL and Excel exist as the two most common resources for information consolidation as required in business intelligence. These tools help hospitals customize reports to help solve a series of health-care related issues. Other tools such as Medline Plus Connect API, Upmize, IBM Insight Analytics and Metrics Insight serve similar purposes in the collection and aggregation of organizational metric data. The systems then perform statistical analyses for alert delivery to users. Lastly, they allow users to conduct a comparison to events as well as view projections to such actions for the ultimate pick (Bohle, 2013).
References
Allelunas, M. (2014, December 15). Transforming Data into Actionable Intelligence. Retrieved from Healthcare Executive Insight: http://healthcare-executive-insight.advanceweb.com/Features/Articles/Transforming-Data-into-Actionable-Intelligence.aspx
Bohle, S. (2013, July 10). Open Data Tools: Turning Data into ‘Actionable Intelligence’. Retrieved from SciLogs: http://www.scilogs.com/scientific_and_medical_libraries/open-data-tools-turning-data-into-actionable-intelligence/
