Leading payers, providers & life sciences organizations from the health care industry gathered recently for the kickoff of IIA’s Health Care and Life Sciences Analytics Research Council. The goal of the project is to uncover the leading analytical techniques being used within health care.
There were some fairly esoteric issues discussed, such as the role of patient registries, the possibility of post-market drug surveillance in hospitals, and doing analytics on genomic and proteomic data. On the call for providers (hospitals, medical practices, and home health organizations), however, some very basic and important questions were raised.
One was simply, “What should we use analytics to predict?” I refer to this issue as the targets for analytical activity, and it’s important for every organization—but it’s particularly important for health care providers. Many of them are just now implementing electronic medical record (EMR) systems, and they’re trying to lay the foundation for analytics. For what should they be planning? There are many options for “what to predict,” including prediction of patients:
who are likely to acquire particular diseases;
who are likely to require emergency care soon;
who are likely to be rehospitalized;
who are likely not to pay their bills;
who are likely to purchase lucrative optional services.
And those aren’t the only choices; prediction can also be applied to clinicians, treatment protocols, bed occupancy levels, and utility bills. Which predictions should a provider endeavor to implement?
Of course, I know the answer…but there’s no room in a blog post to present it. Actually, of course, there is no one answer. But one of the key questions we’ll try to answer for provider organizations is what sorts of predictions make sense under particular strategic, organizational, and technical circumstances. I predict that it will be useful information for those who provide health care.