When a census crisis flares up at a hospital, its sources are
fairly easy to identify or guess at. Such developments as prospective
payment, insurance companies’ copayment requirements, and
ambulatory care alternatives contribute to current declines in inpatient population.
It’s harder to understand why lab workload may not drop at the
same time. That’s what puzzled administrators at our 400-bed
university medical center. During the first half of fiscal 1984, census
was off by 6 per cent–not a critical slide, but seemingly significant
enough to slow down activity in the lab. Yet we were as busy as ever.
We decided to take a close look at what affects workload.
Administrators believed our laboratory should be only as busy as the
census is high. But what about seasonal effects? What about our status
as a teaching institution with a house staff that ranges from first-year
medical school graduates to sixth-year residents? Did they
indiscriminately order laboratory tests? What were the real influences
First, I needed an adequate measurement of workload. The CAP
workload recording method wouldn’t do for this purpose. We have
employed the method for 10 years, but it contains discrepancies that
limit its usefulness. With each year’s edition of the CAP manual,
time values assigned to a number of procedures change. The values
usually decrease, often not because of new techniques or instruments but
because of new time study data. As a consequence, a laboratory section
may appear to suffer a drop in workload even though the amount of work
is the same.
I also decided against using our records of total number of tests
performed. Ways of counting vary among sections and are subject to
change as new instruments are purchased. A prime example occurred a
year and a half ago when we acquired a new chemistry analyzer.
Electrolytes, calcium, creatinine, BUN, and glucose were combined to
form a profile that now counted as one procedure.
I finally turned to the hospital’s accounting department for
help. They supplied monthly totals of individual tests that patients are
billed for. After all, this was the bottom line: The data translated
directly into money.
Using the number of billed procedures per month as an index of our
workload and as the dependent variable, I tested how closely the figures
paralleled various census categories–by comparing total tests with
total patient days and with total admissions, for example, and pediatric tests with pediatric patient days. Census data came from monthly
medical records on patient days, admissions, visits, and discharges by
service. My statistical tools were correlation and regression analyses.
I reviewed 40 months of data covering October 1980 to February 1984
(data for October 1981) were unavailable). Figure I shows the
correlation coefficients for 11 independent census variables. Nothing
tied in very well with billed procedures. The coefficients ranged from
-0.066 for ER visits per patient to 0.406 for admissions per month. By
service, obstetrics and pediatric patient days correlated best, but
their coefficients were low–0.348 and 0.317, respectively.
Then I applied stepwise regression, combining data for several of
the variables through the hospital’s statistical computer program
(Music/Statpack, McGill University, Montreal). The highest correlation
achieved, joining nine variables, was 0.67. That was an improvement,
though still not high enough. Besides, the formula was complex and
The discovery that no aspect of hospital census correlates well
with our workload in the lab was interesting and useful. For one thing,
we learned that the medical staff at our teaching hospital does not
order lab tests just because the patient is in the hospital. If that
were the case, average length of stay would correlate better with billed
procedures than 0.119.
The laboratory also could rebut administration’s claim that
census controls workload. This made my work easier.
But what does control workload? I noticed when looking at the raw
data that December was unusual. Census drops, but the number of billed
procedures increases. Why? Patients who can be discharged are sent
home to enjoy the holidays. Those who remain hospitalized are usually
very ill, and they require laboratory tests. Perhaps it was not the
number of patients but the kind of patients that determines how busy the
laboratory is. I now knew the data I wanted to look at.
Our nursing service bases its staffing on a computerized work
index. The index is derived from a system of classifying patients into
categories, depending on how much care they need. The software was
prepared by Medicus Systems (Evanston, Ill.).
There are four categories of patients: type 1 requires 0-2 nursing
hours per 24 hours; type 2, 2-4 nursing hours; type 3,4-10 hours; and
type 4, 10-24 hours. The categories cover the time spent on taking
vital signs, giving medications, assessment and development of care
plans, assessment and evaluation of plans, and certain other nursing
functions. The sicker the patient, the more nursing time that is
required. In a nursing unit or for nursing as a whole, the average
severity of illness in terms of workload is called patient acuity.
To calculate a work index, each patient category is given a
weighted factor. The factor for type 1 is 0.5; type 2, 1.0; type 3,
2.5; and type 4, 5.0. The work index is a simple total of all
patients’ weighted factors. It isn’t the number of FTEs that
will be needed in a nursing unit, but it leads to a staffing estimate.
Acuity equals the work index divided by census or patient days.
Each nursing unit does these calculations. A total work index and
average acuity for the hospital are computed monthly. I decided to
perform correlation and regression studies using the monthly nursing
work index and acuity levels as the independent variables. Nineteen
months of data were available for the study, from September 1982 to
April 1984 (data for June 1983 were unobtainable).
The results were exciting. The monthly work indexes correlated
0.796 with laboratory billings. Acuity levels correlated 0.734. Again
using the Statpak program on the hospital computer, I applied stepwise
regression with the index and acuity in combination. The adjusted
multiple regression coefficient was 0.805. Wonderful! This meant that
more than 64 per cent (0.805 squared) of the variance from perfect
correlation could be explained.
Since the nursing work index and acuity variables correlate well
with the number of billed procedures, they are much better predictors of
laboratory workload than is the patient census. Census is bound to
affect the laboratory to some extent, of course.
Figure II shows the regression model used by the computer to
compare the work index and acuity level with the number of billed lab
procedures. From this model, we get the numerical constants in the
following formula for predicting our laboratory workload: Y = 944 +
37(X.sub.1.) + 7437(X.sub.2.)
Y is the total number of laboratory procedures. The constants
correlate work index and acuity with known laboratory billings. X.sub.1
is the work index and X.sub.2 is the acuity level, supplied to us by the
nursing service. As you can see in Figure III, the equation estimates
laboratory volumes for May, June, and July 1984 that are very close to
actual billed procedures.
I believe this approach could easily be adapted to laboratories in
other institutions as well as to other departments in a hospital. Our
hospital’s administration, looking at the data in Figure III, no
understands why the lab has remained busy despite a decline in patient
census. As a result, I have been asked to apply the work index and
acuity system to the pharmacy and respiratory therapy departments. Both
departments have experienced a revenue decrease this past year without a
corresponding decrease in workload.
Work index and acuity data, along with such other yardsticks as the
CAP workload recording method, are helping us to monitor the impact of
health care changes and to manage our workload. Sometimes you have to
develop your own tools.