Developing Innovative Testing Strategies for Healthcare Data and Analytics

Your team is deep into a project to develop a repository of healthcare data for use by the business to perform analytics, trend analysis, complex metrics, and ad hoc reporting. Given the wide range of potential use cases, you want to make sure that the data is ‘good’ before you make it available.  You checked the source data. You checked your integration work. You checked the master data for patient and provider. Your basic test reports all seem to be fine.  But you still aren’t sleeping. And there is a good reason for that.

Healthcare Analytics and the Challenge of Imagination

The strategic importance of becoming a data driven organization in healthcare cannot be overemphasized.  Making the transition from opportunistic and transactional information sharing to a proactive data centric business can present challenges for most health systems.

Predictive Algorithms: The importance of reliable data

There is little doubt that one of the hottest trends in healthcare information management is predictive analytics. Some of the most common research topics are predicting the likelihood of sepsis in a hospital patient, understanding who is most likely to readmit and getting insight into co-morbidities and their likely progression for certain chronic conditions.

A New Perspective on your Healthcare Data

Not a day goes by when I don’t receive an email or see an article about the importance of good data at the point of care. As a proponent of maximizing the value of data this is a hard point to argue against. While this represents a trend in terms of thinking about healthcare data, it isn’t clear if there is a payoff for most practitioners, even if it were more than just an idea or goal.  Why?