These product teams consist of data scientists, physicians, and software engineers, just to name a few. And this understanding happens only through careful engineering.Īt Penn Medicine, we've created integrated product teams around our AI applications. This linked understanding requires the same amount of rigor that AI initiatives need in order to be successful, safe and effective. This linking of patients to interventions and, finally, to outcomes, requires that BI teams spend much more time deep in the details. There's often too many confounding factors: which intervention actually worked? The problem, however, is that this top-down approach often misses the intricacies of interventions in the middle. Healthcare BI teams spend time segmenting the patient population, then tracking the outcomes of these segments of patients.
In healthcare, however, BI is typically done at the highest level only: the number of lives saved, dollars spent. In the business world, BI is typically done around a product: how many downloads, how many times did a user click the red button, etc. These BI groups are called upon when a health system is facing a particularly thorny challenge such as a new shared-risk payer contract in place that requires careful risk mitigation, among other possible examples. Its purpose is often both broad and vague, something like, "increase revenue and patient safety while complying with all regulations and standards." There's a business intelligence group in every health system.
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