SAS® Analytics simulation debunks myth that shorter NICU stays lower costs

Study by SAS and Duke University Health System uses discrete event simulation model to disprove assumption that longer stays in a neonatal intensive care unit sap hospital resources

Per capita health care spending in the United States is more than double that of other developed nations. Yet chronic disease and mortality averages show US health outcomes sorely lagging. Hospital administrators across the country aiming to cut costs and improve outcomes have long assumed shorter hospital stays equate to cost savings. Right?

Actually, no.

At least not for newborns in the neonatal intensive care unit (NICU), says a new study led by SAS and Duke University Health System. Published today in the Journal of Perinatology, the research applied a discrete event simulation model of the Duke NICU to predict outcomes and costs using pre-existing study data from National Institutes of Health Neonatal Research Network (NRN).

“Using this model, we debunked what has been a pervasive tenet in health care – the belief that if you relentlessly drive down length of stay, you will universally decrease costs,” said one of the study’s lead authors, Chris DeRienzo, MD, MPP, a neonatologist and Chief Quality Officer at Mission Health System. DeRienzo first became involved with the project during his neonatology fellowship at Duke.

“Our evidence shows that’s just not true,” he said. “We found that in a composite NICU with the best possible outcomes, the length of stay actually averages three days longer than in a unit with poor outcomes. However, comparable annual costs are $3 million less.”

Longer lengths of stay means lower costs and better clinical outcomes

Key findings of the study show that, in the composite best virtual NICU:

• Overall average length of stay (ALOS) was three days longer (27 days versus 24 days). ALOS was 20 days longer (86 days versus 66 days) for infants of 28 weeks or less gestational age.
• Average cost per patient was actually lower ($16,400 versus $19,700 overall and $56,800 versus $76,700 for infants of 28 weeks or less gestational age).
• Mortality was more than 75 percent less.
• Related disorders of prematurity were dramatically lower:

o   Incidence of necrotizing enterocolitis (a rare but devastating intestinal disease among premature babies) were 91 percent lower.

o   Cases of sepsis (a life-threatening bloodstream infection) were nearly 97 percent fewer.

o   Incidence of intraventricular hemorrhage (a bleed inside the brain) were 59 percent lower, hinting at even greater lifetime cost savings for this patient population based on known long-term neurodevelopmental impacts of even low-grade IVH.

“The findings suggest that, being single-mindedly focused on this one measure [average length of stay], executives might actually be missing the boat in reducing costs and improving outcomes,” said study co-lead author, David Tanaka, MD, a neonatologist at Duke Children’s Hospital. “It’s more critically important to focus on quality outcomes – not just because it’s the right thing to do, but also because this is tangible evidence to the CFO that it’s financially the right thing to do.”

Tanaka, the impetus behind Duke’s NICU event simulation model’s development, first approached global analytics leader SAS about his interest in simulation modeling in 2012. SAS’ Emily Lada, a Principal Operations Research Specialist, later built the model free of charge as part of a research project using SAS^® Simulation Studio 14.1. The model was validated earlier this year in a study published by the Health Informatics Journal. That study used the simulation tool to predict and plan for NICU staffing needs.

For the current study, researchers replaced the model’s standard probability distributions with composite distributions representing the best and worst neonatal outcomes published by the NRN.

The future of simulation analytics in health care

Discrete event simulation modeling with advanced analytics gives organizations a fast, effective and non-intrusive means to perform “what-if” experiments without disrupting their real-world systems. Workflow-oriented industries like manufacturing, retail and finance have used discrete event modeling for decades to gauge how different scenarios might affect business operations. Now, a growing number of health systems are adopting the technology, which DeRienzo calls “the Rosetta stone” for performance and quality improvement in health care settings.

“I think health care in 10 years will necessarily look very different from health care now, and we’re right in the middle of that transition,” said DeRienzo. “This project is just one example of how we can use innovative analytics tools to improve not only the ways we provide care but the actual care we provide. Analytics has a tremendous role to play in facilitating that transformation – not just in the NICU but across the clinical spectrum.”

Duke’s current NICU simulation tool requires a data scientist to run the models. However, a general user interface currently under development at SAS will make the tool independently accessible to everyday users like medical directors, nurse managers, and hospital administrators. Future iterations of the study’s discrete event simulation model will also be applicable to any NICU in the US.