6 Considerations For Creating A Robust Healthcare Data Analytics Program

Healthcare organizations are increasingly turning to data analytics to solve their toughest business challenges. As information becomes less siloed, and technology systems do a better job of both generating and sharing data, organizations are seeing the need to more precisely identify patterns to predict patient needs, anticipate resources and improve processes. There is now as much emphasis on making the data work as there was on collecting data, so it can affect change. Unfortunately, entities often struggle to realize a solid ROI with regards to data analytics technology, especially as they try to anticipate the future of healthcare data use and how tools like artificial intelligence (AI) will factor into their success. Although data analytics solutions are designed to help organizations make sense of data and prompt action to improve business and clinical outcomes, healthcare entities must establish the appropriate context and processes for the technology, like identifying and standardizing data submission requirements across the organization, or they increase the chances of less-than-optimal results.