No More Crystal Balls


Over the centuries, many people have believed it was possible to predict the future by reading tea leaves, consulting mystics, or looking into a crystal ball.  (Some still do, but that’s a topic for a different post.)

These days, most of us trust more scientific approaches for making predictions, and most mature industries rely heavily on science and technology to help guide decision making.  But not all: despite all of the technological capabilities available at our fingertips, some organizations still rely on a hodgepodge of disconnected data, institutional memory, and intuition for making decisions. 

Healthcare is one example of an industry that has lagged behind other industries in using well-established scientific and data-driven approaches to guide management decisions.  Contributing factors have included 1) lack of pressure to be efficient due to historically high reimbursement rates, 2) lack of formal training in operations science for leaders who rise through the ranks from clinical backgrounds, and 3) lack of access to timely and reliable data.  These are changing, but there is still significant opportunity to improve organizational efficiency as well as patient care by incorporating these approaches in hospitals and other healthcare organizations.

For example, many hospitals find patient admissions and census peaks to be unpredictable and therefore deal with them reactively, leading to delays between the times that additional resources are needed versus when they are actually added.  This puts stress on hospital staff and other resources, and makes care less safe for patients. Most experienced hospital managers know intuitively that census is higher on certain days of the week and times of day, but often don’t accurately anticipate severe peaks far enough in advance to take proactive action ahead of time.  This is something with which current science and technology can help, enhancing managers’ instincts and intuition with timely and actionable data.  

With Hospital IQ’s data science, predictive analytics, and powerful simulation capabilities, it is possible to predict patient census and discharge dates with an actionable degree of accuracy, giving hospitals a modern “crystal ball” to anticipate the future and take appropriate action.  For example, if predicted census for tomorrow goes above a threshold that would trigger a hospital’s “surge plan,” the hospital could take proactive steps to be ready to implement the plan quickly if the prediction turns out to be true.  These steps could include notifying on-call staff that they are likely to be needed tomorrow, and/or proactively staffing additional beds to accommodate the higher-than-usual number of patients.   In this way, hospitals are empowered to mitigate problems before they occur and to create a better environment for patients and staff.