CHANGING FACE of NURSING - School of Nursing - University of ...

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CHANGING FACE of NURSING - School of Nursing - University of ...

Data-Based

Modeling

For hospital administrators, it’s the

next best thing to a crystal ball.

BY NANCY GIGUERE

Many thanks to Mercy Hospital,

Iowa City, Iowa, for providing information

about data-based modeling and simulation.

Modern hospitals are complex systems

of interwoven relationships and social

networks. Changes in one hospital process,

such as the introduction of new technology,

can impact the entire system.

“Decision-making is difficult because

outcomes are impacted by numerous

variables in the hospital environment,”

says SoN Clinical Professor Tom Clancy,

PhD, MBA, RN. “These variables include the

people who work there, the patients and

their response to treatment, the availability

and functioning of equipment, and the use

of different protocols.”

COSTLY DECISIONS

A poor decision is costly is terms of dollars

and staff morale. It can also lower the

quality of patient care. For example, the use

of an electronic health record may appear,

on the surface, to be efficient and costeffective.

But the success of the new system

is influenced by a complex set of variables.

The transition from a paper to an

electronic record has a dramatic effect on

the nurses’ workflow. In addition, computer

terminals must be placed within easy reach

but not in the way of staff and equipment,

and protocols need to be established about

when and how data will be entered into

the system and who will do it. And, of course,

not everyone can type.

“A hospital can spend multi-millions of

dollars on an electronic health record, but the

expenditure will be a waste of money if the

staff refuses to use the system,” says Clancy,

an experienced hospital administrator.

ANALYZING ALTERNATIVES

What if health care systems and hospital

administrators had a crystal ball that

allowed them to see the results—both

intended and unintended—of their decisions

before they made an investment of time,

effort, and dollars?

Thanks to the emerging field of

complexity, they now have the next best

thing: the ability to make predictions

using data-based models that simulate the

interaction of multiple variables.

Let’s say that the hospital administration

wants to modify the workflow so that

patients in the emergency department will

have a shorter wait. Before making any

changes, department managers consult with

the staff and map out the current workflow.

Then they create alternate maps or

flow charts and analyze how changes

would affect emergency department staff,

patients, and other areas of the hospital.

This process is known as scenario analysis,

and in the past, it was done on paper.

CREATING VALID MODELS

“Today using computers, we can create

models that are far more complex, run

various scenarios, and see how the system

reacts to changes over time,” Clancy says.

Although the models look simple, the

underlying statistical analysis is based on

complex mathematical formulas. Once

created, the models must be validated. This

is done by entering existing data into the

model—length of wait, day and time of

arrival, staffing patterns, admissions criteria,

and so on—and comparing the results with

the observable, real-life situation.

Once the model is validated, new

values can be substituted for existing data,

and the results analyzed. Sometimes the

results are unexpected: A new policy that

benefits patients by reducing waiting time

14 minnesota nursing

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