The lights blink, the bells ring. It seems like there are hundreds of things wrong with this patient. Your best team is working. The result of test after test comes in. But what is the diagnosis? You have no theory. So far, you’re just treating the symptoms.
That is the state of health of the typical hospital today. HHS collects hundreds of measurements of hospital health, but most of them catch the symptoms of broken processes. How many expensive tests do we have to run to find out that our processes are broken? How many of these tests are actually cost effective in driving change?
On the other hand, very few tests measure the overall health of the hospital — which ultimately shows up in how much healthier it makes its patients. Better would be to measure some the actual outcomes patients achieve: if someone replaces their hip so they can walk up the stairs without pain, can they walk up the stairs without pain after a year?
Then, for those outcomes that are poor, actually diagnose the problem in the processes, not just the symptoms. For this we need to measure the change in symptoms given intervenions. We need longitudinal data that correlates with operations: who was on duty, what sorts of patients were present, etc. when certain types of results or failures occurred. We need robust analytics and personel who get direct feedback and can think about the data themselves, at every level.