BIG DATA MEANS BIG POTENTIAL, CHALLENGES FOR NURSE EXECS

ANALYSIS

BIG DATA MEANS BIG POTENTIAL, CHALLENGES FOR NURSE EXECS

BY JENNIFER THEW RN  |   APRIL 19, 2016

TOPICS

Leadership

Business

Management

Technology

To ensure big data is used to influence outcomes that are meaningful to the nursing profession, nurse executives need to act as data visionaries and architects.

Have you ever found yourself poring over stacks of data, feeling more like a statistician than a nurse? If you have, welcome to the world of big data.

“You have all of these different data sources coming at you on a weekly, monthly, quarterly basis. The CFO has a stack of data for you, your productivity-management engineer people have a stack of data for you, HR has a stack of data for you, and then your quality director, your clinical folks, have a stack of data for you,” says Jane Englebright, RN, PhD, CEBP, FAAN, chief nursing executive and senior vice president at Nashville, Tennessee-based HCA.

“And your job is to sort through all that data and synthesize it in some way and come up with brilliant conclusions about how to run the organization.”

Big data “typically refers to a large complex data set that yields substantially more information when analyzed as a fully integrated data set as compared to the outputs achieved with smaller sets of the same data that are not integrated,” according to the  Online Journal of Nursing Informatics .

Top of Form

GET THE LATEST ON HEALTHCARE LEADERSHIP IN YOUR INBOX.

Bottom of Form

Dealing with big data can understandably be challenging for chief nurse executives.

During a session titled, “The CNE Role in the Big Data Revolution,” at the  American Organization of Nurse Executives  last month in Fort Worth, TX, Englebright and healthcare management consultant Barbara Caspers, RN, MS, PHN, discussed importance of shared strategies to help CNEs ready their organizations for the “big data revolution.”

Drowning in Data

When a CNE is analyzing and synthesizing data, it’s typically done manually and is a very time- and labor- intensive process, in part, because technology systems have traditionally been built in silos. “Often they don’t even call the units the same thing. They don’t name them the same thing. They don’t necessarily define them the same way,” Englebright says.

For example, the definition of a day may vary from system to system and the way a month is calculated in the finance systems may differ from how it is calculated in the payroll system.  

Trying to “figure out how to keep up with your agency hours and what the cost of your agency is in the finance system versus the scheduling system,” Englebright says, is “just a nightmare, trying to make all of these different things sync.”

The lack of data standardization can also make it challenging for a CNE to assess how the organization or a particular unit is performing and to make well-informed decisions about what to change. Having good data is key to making effective changes.

“For those of us who grew-up studying the biological sciences, we understand that we have taken a very linear, Newtonian-approach to data over something that’s really much more like a biological system,” she explains. “When you perturb one part of our system… it has ripple effects throughout the entire organization.”

Failure to recognize how this data interacts throughout the system has been a limitation in the types of data analytics that have been put forth.

“The frustration that we often have as nurse leaders in looking at this data, is [that] some of the variables we care about the most, aren’t even in the data,” Englebright says. “We don’t have something that measures nursing competence, for example. We don’t have something that measures how committed the nurses are. We don’t have something that measures if the patient really [is] going to do the stuff we just invested all this time in teaching them to do.”

Because of this, CNEs end up having to advocate for the things they care about in a person-on-person debate, than being able to make a persuasive business case based on data, she says.

Pages

· 1

· 2

· next ›

· last »