When first discovering the world of staffing analytics, medical practices typically focus on historical data, such as trends in call distribution and holiday assignments. Yet, while the importance of historical data to staffing analytics cannot be understated, it can only offer half the story – the past.
To make informed decisions about immediate staff concerns, as well as to keep current on provider flow and forecast the impact of proposed scheduling changes, real-time data are needed.
Let’s return to Jackie, who needed to send a nurse home early without adding to her department’s overtime costs.
Jackie* needed to know precisely how many hours each nurse had worked, as well as how many hours they were scheduled to work for the remainder of the current pay period. To be accurate, she needed to account for schedule updates and last-minute changes.
Historical data cannot provide that; Jackie needed real-time data.
That is the only way she would have known exactly how each nurse ranked in terms of hours worked and is the only way she would have been able to know exactly who to send home early.
Had Jackie had access to real-time data, there would have been no guessing, and there would have been no surprises at the next departmental meeting.
Luis, who was underutilized after being inappropriately assigned to a cardiac case, would also have benefitted from real-time data.
One frequent cause of inappropriate staffing is lack of reliable data on provider flow. Although a historical list of all providers on staff is useful, it is not as useful as knowing precisely who is at which facility when, and what their certifications are.
In addition, shift swaps and requests for time off mean provider flow information can change at any time. That is why having access to that information in real time is so useful.
Had Luis’ practice used real-time data, it would have been easier to find out which provider with cardiac certification was available to take over the case. Delays could have been reduced or avoided altogether.
Moreover, had the cause of the inappropriate assignment been that Luis’ schedule simply had not been updated after a change in caseload, real-time data would have still made all the difference. If his schedule had been updated in real time, Luis would have known he had been reassigned and an appropriately certified colleague would have arrived on time for the procedure.
In the case of Ling, whose colleagues complained at length about unfair call schedules, real-time data would have been a game-changer.
For Ling to have distributed the call shifts evenly, she would have required access to both historical and real-time data. The real-time data would have picked up where the historical data left off and would have made it possible to forecast how much call each person would have worked by the end of the period.
Real-time data would also have allowed Ling to test different call distribution scenarios during the build process. This would have removed the guesswork from determining which scenario would result in the best schedule, and would have made the process fully transparent.
Real-time data are indispensable for any medical practice wanting to base staffing decisions on the most up-to-date information. It allows practices to keep control of overtime costs and provider flow, and makes it possible to forecast the impact of different scheduling models.
*Names, situations, and example data presented throughout this post are meant to serve as fictional examples only. They have been created as composites representing common situations, but do not reflect specific individuals or organizations.