Streamlining blood counts with a microcomputer.
Changing reimbursement incentives, however, have pressured the medical establishment to scrutinize costs and benefits of every aspect of patient care, blood counts included. In the current setting, we're unlikely to find funds for more and better testing; in fact, we may be able to add new procedures only by deleting others. The blood count, with its multiple components, offers potential savings without a reduction in quality of service.
In our hematology labs, a microcomputer helps us identify and eliminate unnecessary blood film reviews. The computer "reads" data directly from our automated hematology analyzer, flags specimens that need review, and cues technologists on possible causes for the abnormality. Here's how we developed the system.
We began with a re-examination of the basics: Why do physicians order blood counts, and what do they hope to learn from the results? Is a rapid normal or abnormal result sufficient when the test is used as a screening procedure? We expect the blood count to indicate certain basic conditions in peripheral blood cells, as shown in Figure 1. If these expectations are reasonable, then we must determine how our sophisticated instruments can help examine specimens most efficiently.
Optimal instrument use rests on the principle that a specimen is "normal" it selected quantitative and qualitative parameters lie within a prescribed set of ranges, indicating no need for additional studies. These studies, such as the stained blood film review and differential leukocyte count, cost considerable time, labor, and money. By eliminating them when not clinically indicated, we can trim costs and make better use of technologist time. The decision sapling in Figure II--the algorithm is too simple to qualify as a decision tree--illustrates this line of reasoning.
On that basis, we laid the scientific groundwork for our study. Hematology instruments produce whole sets of quantitative and qualitative data on a single blood specimen within one minute after sampling. By qualitative data, we mean the histograms automatically generated by all larger instrments. Quantitative data analysis used to employ strict statistical methods, such as determining normal ranges by the mean, plus or minus 2 standard deviations. As the inadequacies of this simple method became clear, percentile determinations came into wider use.
We have pursued a somewhat different tack in our studies over the last few years, by attempting to set clinically useful limits for blood film reviews. We evaluated various arbitrary limits on quantitative CBC data to insure that no significant findings from the automated analysis would be missed. By comparing individual determinations to each patient's entire set of test results, we arrived at the flagging limits shown in Figure III. As we'll see, the personal computer handles the task of flagging out-of-range results.
Two other parameters have proved quite sensitive to hematologic abnormalities. The first, the multivariate reference range, originated in clinical chemistry as a statistical technique designed to simplity analysis of large amounts of data, such as chemistry panels.
This method merges all eight quantitative CBC results and determines their relative normality by the size of the resulting number. The multivariate statistic is far more sensitive than histogram analysis to the presence of circulating normoblasts, for instance, and has flagged such specimens almost unerringly.
The second parameter is the histogram distance, or HD. So far we have worked primarily with the white cell histogram because of its greater importance. We ran 250 normal blood specimens and stored their white cell histograms on the microcomputer. Next, the computer determined the mean and variability of all these curves and calculated a Chi square distance at four carefully selected points--shown in Figure IV, using the formula: [sigma] (observed -- expected).sup.2./expected
To obtain the HD, the computer compares the patient's histogram to the reference curve at all four points. Specimens that differ significantly are flagged for further study.
Our system was designed to maximize the capabilities of laboratory staff members as well as instruments. In addition to extending our automation, it frees skilled technologists from the tedium of routine blood film evaluations, allowing them to concentrate on those specimens that really need attention. For this reason, we have avoided attempts to computerize blood evaluation beyond the parameters of the CBC. Now let's take a closer look at how our computer puts theory into action.
We interfaced a multichannel automated hematology analyzer, the Ortho ELT-8/ds with Data Handler, and an Apple IIe personal computer, including disk drive and CRT, and equipped with a serial input/output card. The interfacing process required some real detective work since the complexity of the instrument's software made access difficult. Finally, after much research and effort, we were able to capture all relevant data and display it on the CRT in real time. Figure V depicts how the two systems interconnect. With expert advice, other multichannel instruments and microcomputers can probably be interfaced and programmed in a similar manner, although our experience is limited to this system.
When a specimen's index parameters on the automated analyzer fall outside any of the predetermined limits, the computer flags the variant value with a flashing arrow. These single and double flags produce a footnote-like CRT display of appropriate prompts (Figure VI). These prompts give the operator useful information for blood film examination, and can be custom-programmed for any laboratory's patient population.
Of course, few if any laboratory tests are 100 per cent sensitive and 100 per cent specific. We have deliberately set the flagging limits to point out all abnormal specimens. As a result, the computer includes some false-positive specimens for review, but we also lower the risk of missing a significant abnormality. Our latest review shows a specificity of 76 per cent, with a predictive value of 86 per cent for positive screens.
We tabulated the sensitivity of the individual flags--that is, how often each flag yields an abnormal result (Figure VII). The more complex statistics, like histogram distance, are abnormal in about half of all specimens. Out-of-range hemoglobin, on the other hand, is flagged in a far lower percentage of cases, but this and other less frequent flags often provide the most direct clues to abnormalities.
In a review of 3,500 cases, we
identified clear flagging patterns associated with various clinical conditions, also shown in Figure VII. We determined possible causes based on observation and experience. The next phase of our study confirmed the correlation of non-flagged ABCs with the clinical condition on a case-by-case basis.
At this point, it's difficult to measure how the system has affected the laboratory's workload. Orders for differential counts have decreased by some 50 per cent, but some of this drop may be due to a change in ordering protocols that allows physicians to order an automated blood count alone, without differential. In any case, an increasing proportion of CBCs are being ordered as screening counts only, with the option for further review left up to the laboratory.
Our system now functions in three sections of the hematology lab system. We were careful to introduce and develop the new method in an evolutionary rather than a revolutionary way. Technologists have accepted it well, once they realize that the computer helps them use their time and skills most effectively.
Most important, our personal computer allows us to cut unnecessary testing without compromising patient care. In light of prospective payment, that's a strategy with considerable implications for the future.
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|Title Annotation:||Duke University Medical Center case study|
|Author:||Koepke, John A.; Dotson, Mary Ann; Shifman, Mark A.; Boyarsky, M. William|
|Publication:||Medical Laboratory Observer|
|Date:||Nov 1, 1984|
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