Using data to improve healthcare
A recent Freakanomics podcast episode asked the question: “How do we really know what works in healthcare?”
As usual, the focus on the argument is a little off the beaten path, not focusing on efficiency in the insurance system or other more obvious areas, but exploring the lack of RCT (randomized clinical trials) in healthcare:
RCTs are far too rare in healthcare delivery — which is a shame, for the link between healthcare and poverty is strong:
FINKELSTEIN: We take a rather broad view of poverty alleviation. And so anything that improves the efficiency of healthcare delivery, I think is important for the public for two reasons. First, you know, the poor are disproportionately unhealthy and therefore have the burden of healthcare relative to less poor people. Also, given that healthcare spending is currently about a fifth of public-sector budgets at the state and federal level, anything one can do to improve the efficiency of healthcare delivery frees up more money to spend on other programs as well. Or to spend on, you know, getting even better health.
And yet, in those rare circumstances where data becomes available that can approximate an RCT, the application of even the most basic data intelligence, can lead to very interesting results, such as in the Oregon healthcare lottery:
the finding that Medicaid led to a rise, not the suspected fall, in ER visits
And in one of the few actual RCTs being done, in Camden, NJ:
we learned that 1 percent of the patients is 30 percent of the payments to the hospitals, and that 5 percent of the patients is about 50 percent of the payments to the hospital. So a very small sliver of patients are driving all of the revenues to the system. … And you know, the question really is this the fault of the patients or is this a system failure?
So what are some of the data-driven outcomes?
Camden provides some of the most encouraging ideas – branching out from what is traditional healthcare provided in doctor’s offices and hospitals, to a holistic approach of working with people in the community to make sure medicines are available and taken properly. This has led to some great progress in overall community health.
And after all, what are we really after – healthcare or health?
The implications are obvious – data-driven decisions can lead to overall systemic improvements. We know that companies that don’t leverage their data, don’t thrive over time. Healthcare is sensitive, because lives depend on it, and many organizations and individuals don’t like to talk about efficiency. The theory is that we should only consider what is best for the patient.
But why should that exclude studying data to improve treatment?
The Camden example is a perfect example. A smart organization, led by smart people, looked at the data. They looked at how the patterns of utilization connected to people, and how those people fit into a group (cluster analysis – can you envision the graph in your mind?), and what commonalities that group might have. Using that foundational understanding, they understood that some of the root causes for high system utilization stemmed from environmental or situational factors, such as difficult transportation. They then moved some services closer to the patients.
Who wins? Everyone!
- The patient: Gets better care
- The doctors: See healthier patients
- The community: Has higher health overall
- The system: Sees lower unnecessary utilization
- The taxpayer: Pays a lower bill
Photo credit: http://www.flickr.com/photos/23307937@N04/5517839272, pop life