You hear a lot in the news about Artificial Intelligence (AI) and Machine Learning (ML) applied to the healthcare industry. And there is no doubt that this technology trend has significant potential to change healthcare delivery. And it is not just care management and delivery. There is significant potential for AI/ML contribution to billing management, claims, cost management and control, staffing, and inventory management.
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.
Let me start by making sure that you, the reader, know that the purpose of this blog post is NOT to debate the concepts of patient versus customer, and the perils of confusing the two.
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.
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.
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?