“Stone Soup” Data Gathering

Data is the raw material that powers the growing industry of hospital analytics.  However, many well-meaning quality improvement and operational efficiency projects die simply because they cannot get the necessary data.  Given that most hospital IT departments have far more on their plate than hours in the day, overcoming this barrier has become a skill unto itself when Hospital IQ engages with clients.

We start by sharing our published data specification.   But “real-life” hospital data is rarely presented in that clean tabular normalized format.  Furthermore, because our specification is general-purpose for all hospitals we work with, it can be overly-detailed in some areas, while missing important subtlety in others.  We see the specification not as a demand, but simply the start of a collaboration between a hospital’s IT department and Hospital IQ.

This collaborative process often makes me think of the story of “Stone Soup.”  This refers to the tale of a traveler who shows up in a village with nothing more than a cooking pot.  The villagers are reluctant to share any of their food, so the traveler declares he has a soup recipe that requires nothing more than a stone.  The curious villagers gather round while the traveler heats the stone in a pot of water, all the while talking about how tasty the soup will be.  At one point, the traveler says the soup would really be helped by some seasoning, so one adventurous villager parts with a small satchel of spice.  The spice does indeed make the broth smell appetizing.  The traveler then asks for a carrot or two to give the soup some heft.  Another villager steps forward.  This goes on and the traveler eventually persuades the villagers to contribute more vegetables and even some meat.  Eventually the traveler cooks a soup that indeed lives up to its expectations and the village celebrates.

In Hospital IQ’s effort to gather data to help a client solve its operational challenges, the stone is the demo and the recipe are mathematical tools from modern operations research.  We start by showing our capabilities using anonymized data from another fictitious hospital.  That data arouses curiosity, but data from another hospital can’t necessarily help new and potential clients make better decisions about their own hospitals and healthcare systems.  However, the demo is usually enough to pique interest in importing some of their own “easier to get” data into our platform such as perioperative in/out times and a block schedule.  This allows us to show some more basic analytics around perioperative flow and demonstrate the speed, fidelity, and verifiability of our platform.  This first view is the “satchel of spice.”

While presenting this aromatic first pass of data, someone will typically ask “Can your platform show how perioperative flow impacts inpatient bed demand?” or “How I should arrange my ICU beds to make sure I can handle peak flow from both the PACU and ED?” 

To this we answer, “We can absolutely do this, but we will need more data”.  Now that we’ve shown value and gained credibility, barriers to data quickly fall.  Phone calls are made and we’re put in touch with the right people who can provide the necessary patient movement and visit data for these more sophisticated analysis and simulation modeling.

This iterative cycle goes on where we
(1)   Show value,
(2)   Questions are asked,
(3)   More data is obtained. 

This data often grows to include extracts including block releases, staff rosters, vacation schedules, clinic locations, medical and surgical scheduling, equipment requirements, anticipated discharges, anticipated post-surgical nursing requirements, ICD codes, and more.  Furthermore, multi-site hospitals start asking about our ability to model consolidation and centers of excellence scenarios, which means gathering data from peer facilities.  Where we encountered resistance when we first arrived, we are now welcomed.

Some people think of Stone Soup as a story about a clever traveler tricking the village into handing over food.  We see it as a skilled chef finding a way to demonstrate his talent to a skeptical audience.  At the end of the story the village enjoys a well-prepared communal soup they would not have otherwise enjoyed.  The hospital industry faces a similar challenge – sharing the ingredients and best practices they already have to make a data soup far more appetizing than would have happened if each individual prepared their meal separately.  This aligns with our mission to create tools that allow hospitals to benefit from modern management science in the same way as every other 21st century industry.

Hospitals have the data they need to get powerful predictive analytics that inform key decisions about resource allocation.  We realize that predictive analytics are new to most hospitals — but the “Stone Soup” approach to data gathering shows one way for hospitals and third parties like Hospital IQ can work with IT to launch more projects that drive better operations.  IT departments are often overworked and understaffed so projects that show the most potential will naturally move to the front of the line.  Incrementally gathering the “easiest” data and showing results has been effective in building momentum and eventually drilling for the more elusive data.

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