The key to predicting laboratory workload - University of South Alabama Medical Center case study - 1984 MLO Article Awards Contest prize winner

Medical Laboratory Observer, Nov, 1984 by Shannon S. Harper

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 on workload?

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 unwieldy.

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.