When a census crisis flares up at a hospital, its sources arefairly easy to identify or guess at. Such developments as prospectivepayment, insurance companies’ copayment requirements, andambulatory care alternatives contribute to current declines in inpatient population. It’s harder to understand why lab workload may not drop at thesame time.
That’s what puzzled administrators at our 400-beduniversity medical center. During the first half of fiscal 1984, censuswas off by 6 per cent–not a critical slide, but seemingly significantenough 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 thecensus is high. But what about seasonal effects? What about our statusas a teaching institution with a house staff that ranges from first-yearmedical school graduates to sixth-year residents? Did theyindiscriminately order laboratory tests? What were the real influenceson workload? First, I needed an adequate measurement of workload. The CAPworkload recording method wouldn’t do for this purpose. We haveemployed the method for 10 years, but it contains discrepancies thatlimit its usefulness. With each year’s edition of the CAP manual,time values assigned to a number of procedures change. The valuesusually decrease, often not because of new techniques or instruments butbecause of new time study data.
As a consequence, a laboratory sectionmay appear to suffer a drop in workload even though the amount of workis the same. I also decided against using our records of total number of testsperformed. Ways of counting vary among sections and are subject tochange as new instruments are purchased. A prime example occurred ayear and a half ago when we acquired a new chemistry analyzer.Electrolytes, calcium, creatinine, BUN, and glucose were combined toform a profile that now counted as one procedure. I finally turned to the hospital’s accounting department forhelp. They supplied monthly totals of individual tests that patients arebilled for. After all, this was the bottom line: The data translateddirectly into money.
Using the number of billed procedures per month as an index of ourworkload and as the dependent variable, I tested how closely the figuresparalleled various census categories–by comparing total tests withtotal patient days and with total admissions, for example, and pediatric tests with pediatric patient days. Census data came from monthlymedical records on patient days, admissions, visits, and discharges byservice. 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 thecorrelation coefficients for 11 independent census variables.
Nothingtied in very well with billed procedures. The coefficients ranged from-0.066 for ER visits per patient to 0.406 for admissions per month. Byservice, obstetrics and pediatric patient days correlated best, buttheir coefficients were low–0.348 and 0.317, respectively. Then I applied stepwise regression, combining data for several ofthe variables through the hospital’s statistical computer program(Music/Statpack, McGill University, Montreal).
The highest correlationachieved, joining nine variables, was 0.67. That was an improvement,though still not high enough. Besides, the formula was complex andunwieldy.
The discovery that no aspect of hospital census correlates wellwith our workload in the lab was interesting and useful. For one thing,we learned that the medical staff at our teaching hospital does notorder lab tests just because the patient is in the hospital. If thatwere the case, average length of stay would correlate better with billedprocedures than 0.119.
The laboratory also could rebut administration’s claim thatcensus controls workload. This made my work easier. But what does control workload? I noticed when looking at the rawdata that December was unusual. Census drops, but the number of billedprocedures increases. Why? Patients who can be discharged are senthome to enjoy the holidays. Those who remain hospitalized are usuallyvery ill, and they require laboratory tests. Perhaps it was not thenumber of patients but the kind of patients that determines how busy thelaboratory is. I now knew the data I wanted to look at.
Our nursing service bases its staffing on a computerized workindex. The index is derived from a system of classifying patients intocategories, depending on how much care they need. The software wasprepared by Medicus Systems (Evanston, Ill.). There are four categories of patients: type 1 requires 0-2 nursinghours per 24 hours; type 2, 2-4 nursing hours; type 3,4-10 hours; andtype 4, 10-24 hours.
The categories cover the time spent on takingvital signs, giving medications, assessment and development of careplans, assessment and evaluation of plans, and certain other nursingfunctions. The sicker the patient, the more nursing time that isrequired. In a nursing unit or for nursing as a whole, the averageseverity of illness in terms of workload is called patient acuity.
To calculate a work index, each patient category is given aweighted 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 allpatients’ weighted factors. It isn’t the number of FTEs thatwill 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 andaverage acuity for the hospital are computed monthly. I decided toperform correlation and regression studies using the monthly nursingwork index and acuity levels as the independent variables. Nineteenmonths of data were available for the study, from September 1982 toApril 1984 (data for June 1983 were unobtainable). The results were exciting. The monthly work indexes correlated0.796 with laboratory billings. Acuity levels correlated 0.
734. Againusing the Statpak program on the hospital computer, I applied stepwiseregression with the index and acuity in combination. The adjustedmultiple regression coefficient was 0.805. Wonderful! This meant thatmore than 64 per cent (0.805 squared) of the variance from perfectcorrelation could be explained.
Since the nursing work index and acuity variables correlate wellwith the number of billed procedures, they are much better predictors oflaboratory workload than is the patient census. Census is bound toaffect the laboratory to some extent, of course. Figure II shows the regression model used by the computer tocompare the work index and acuity level with the number of billed labprocedures. From this model, we get the numerical constants in thefollowing 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 constantscorrelate work index and acuity with known laboratory billings. X.sub.
1is the work index and X.sub.2 is the acuity level, supplied to us by thenursing service. As you can see in Figure III, the equation estimateslaboratory volumes for May, June, and July 1984 that are very close toactual billed procedures. I believe this approach could easily be adapted to laboratories inother institutions as well as to other departments in a hospital. Ourhospital’s administration, looking at the data in Figure III, nounderstands why the lab has remained busy despite a decline in patientcensus. As a result, I have been asked to apply the work index andacuity system to the pharmacy and respiratory therapy departments.
Bothdepartments have experienced a revenue decrease this past year without acorresponding decrease in workload. Work index and acuity data, along with such other yardsticks as theCAP workload recording method, are helping us to monitor the impact ofhealth care changes and to manage our workload. Sometimes you have todevelop your own tools.