One of the biggest problems in healthcare is timely access to the most expensive assets. For example, if you need an MRI, the scheduler may tell you that the first available slot is a week from today. In reality, most MRIs are only used 60% of the available time, meaning there are plenty of 20-minute slots available to perform your scan before next week. As healthcare systems face mounting cost pressures, machine learning is providing administrators with new tools to optimize the usage of their most expensive assets. As a result, the adoption of machine learning in healthcare is skyrocketing.

Changes in healthcare reform and an aging population means the demand for healthcare services will continue to rise. Meanwhile, pressure from payers to improve efficiency means that providers cannot increase capacity indefinitely by building new facilities or hiring additional staff. Instead, they will need to use sophisticated optimization methods to balance the growing demand for healthcare services with the existing operational constraints.

One company experiencing fast adoption of their machine learning software in healthcare is LeanTaas.  The Santa Clara, CA analytics provider recently received $40 million in series C3 funding from Goldman Sachs,  bringing the total amount raised within the last 24 months to $101 million.

Their machine learning algorithms are combined with predictive analytics to continuously compare the expected versus the actual operational performance in order to adapt to the consumer demand.   As a result, healthcare facilities are able to digitally transform their core operational processes to increase patient access, decrease wait times, and reduce healthcare delivery costs.  The initial areas experiencing success include operating rooms, infusion chairs and ambulatory clinics.

The following chart illustrates a 35% decrease in consumer wait time and a resulting 25% decrease in overtime costs at one facility.

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