By Rom Eizenberg

Today, we can walk into a FedEx shop and drop a parcel. That parcel will go through numerous trucks and regional, national, and international distribution centers. The parcel will move through multiple airports and several airplanes, and somehow miraculously it appears at the desired destination. Even though drivers might be sick and trucks might break, the parcel will show up – potentially on the other side of the planet – in about 24 hours.

Compared with the often slow-moving and inefficient journey of the average patient – cumbersome forms, insurance nuances, triage, staffing delays, and lengthy wait times – this leads to some uncomfortable questions for health systems. Why can't we do this in healthcare? Why do healthcare leaders feel that they are continuously coping with crisis after crisis? Why do they feel that they are incapable of predicting the future and aligning resources?

These questions concern the factors behind many of the inefficiencies that contribute to the incredible cost of delivering care across the U.S. As an industry, health systems are struggling not with structural challenges, but with modernizing the process of managing health systems, aligning resources around care delivery, and catching up so a hospital can be as predictable, manageable, and optimal as delivering a parcel across the globe in 24 hours.

What prevents this from happening? Two words: data silos. It is almost impossible today for a health system leader to look at the inpatient journey end-to-end, from admission to discharge. Each part of the process of delivering care is contained within a different legacy system, with siloed data, often held by different vendors, and, quite frankly, no one has the big picture. As a result, while today’s hospitals generate an enormous amount of data by second from their care operations, much of the data remains untapped, representing a significant missed opportunity to create value.

Using machine learning (ML) and artificial intelligence (AI), we can dive deep into the data of patient journeys to uncover previously inaccessible insights and opportunities, and ultimately improve hospital operations and patient experiences.

Begin by exploring “known unknowns” and then “unknown unknowns.” In other words, start by finding answers to the questions you know; then evolve into new answers that you aren’t even aware of today.

Examples of “known unknowns” include:
● The discrepancy between planned patient discharge time and actual discharge time

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