Why Hospitals Need Better Data Science

Airlines are arguably more operationally complex, asset-intensive, and regulated than hospitals, yet the best performers are doing a better job by far than most hospitals at keeping costs low and make a decent profit while delivering what their customers expect. Southwest Airlines, for example, has figured out how to do well the two operational things that matter most: Keep more planes in the sky more often, and fill each of them up more, and more often, than anyone else. Similarly, winners in other complex, asset-intensive, service-based industries — Amazon, well-run airports, UPS, and FedEx — have figured out how to over-deliver on their promise while staying streamlined and affordable.

These examples are relevant to health care for two reasons.

First, hospital operations are in many ways like airline and airport operations and transportation
services. There are many steps in the service operation (check-in, baggage, the security line, gates), high variability at each step (weather delays, congestion, mechanical issues), multiple connected segments in the user journey — and all these operations involve people, not just machines. In mathematical terms, hospital operations, like airlines and transportation, consist of hundreds of mini-processes, each of which is more stochastic and less deterministic than, say, the steps in assembling a car.

And second, hospitals today face the same cost and revenue pressure that retail, transportation, and airlines have faced for years. As Southwest, Amazon, FedEx, and UPS have demonstrated, to remain viable, industries that are asset-intensive and service-based must streamline operations and do more with less. Health care providers can’t keep spending their way out of trouble by investing in more and more infrastructure; instead, they must optimize their use of the assets currently in place.

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