Two of the driving tenets of the Affordable Care Act are the push for better patient outcomes and reduction of costs. Since labor spend accounts for the largest percentage of acute care expense, it is a prime target for cost cutting.
This presents a conundrum for the hospital. How to cut labor costs while improving the quality of care (decreased re-admission rates). A corollary to this dilemma has been the “just-in-time” staffing models the industry has used for years. The difference is while a manufacturer can judge work in process by the amount of incoming orders, the healthcare industry has not been able to predict the number of orders (average length of stay) in a reliable manner.
Peter Drucker, foremost business analyst declared just before his death in 2005 that “increasing the productivity of knowledge workers was the most important contribution management needs to make in the 21st century.” The question here is how can data be used to transform the healthcare industry to both decrease costs and improve quality?
The answer is equal parts simple and complex. There is enough historical data from millions of hospital admissions across the country to fine-tune a census model that provides extremely accurate daily figures for a unit-by-unit count. Several firms have been working on the analysis of these figures for years. In the same way the electronic medical records have grudgingly made their way into our healthcare system, strategic census models will soon be the norm.
When this data becomes granular for hospital systems, the current models for large scale census predictors will become extinct. This new model for just-in-time staffing will finally provide a day-to-day, week-to-week picture of labor needs. Fine tuning the levels of FTE’s will become better, and non-fixed-cost labor will boom.
