Referring now to FIGS. 3A and 3B, a flow diagram of an exemplary embodiment of the spot-checking method of the present invention is depicted. For each clinical diagnostic processes with which the spot checking system and method of the present invention is to be used, a laboratory must initially define several process parameters as follows:

NUMREP—is the number of replicates at each quality control (QC) concentration level to be used to estimate the size of the out-of-control condition. Typically NUMREP will preferably be in the range of 2 to 4, although values outside of that range may be appropriate depending on the specific clinical diagnostic processes and/or specific analytes.

TE_a—is the allowable total error for the clinical diagnostic process. The value of TE_ais typically determined by the operator of the laboratory, preferably in consideration of the technical capability and the clinical utility of the analyte. TE_ais preferably based as a percentage or based on a function that assigns a value for TE_abased on a concentration of the analyte.

MAXNI—is the maximum number of predicted incorrect results for spot checking. MAXNI is preferably selected based on the number of patient specimens that are evaluated between quality control (QC) events. As will be explained in more detail below, if the predicted number of incorrect results exceeds MAXNI, then spot-checking may not be suitable and other recovery procedures may be used.

PILO—is the lowest probability of an incorrect result to be considered when determining which specimens need to be spot checked due to their proximity to a medical decision limit.

PIMIN—is the minimum probability of an incorrect result to be considered when determining which specimens need to be spot checked due to a high probability of being incorrect.

Preferably, a laboratory operator will determine default or initial values of NUMREP, TE_a, MAXNI, PILO, and PIMIN based on the laboratory's history and knowledge of the various clinical diagnostic processes being implemented and the type of analytes being processed. Of course, as the laboratory's knowledge base increases, adjustments or changes to the initial or default values may be implemented as necessary within the scope of the present invention.

With the initial parameters for a particular clinical diagnostic process defined and set-up as just described, the general process steps for implementing the spot checking method of the present invention are depicted in the flow diagram of FIGS. 3A and 3B. Each step will first be described generally; a more detailed description of each step in the process is set forth below.

Looking to FIGS. 3A and 3B, when an out-of-control condition is identified, spot checking of the condition begins at block 200 where an estimate of the magnitude of the out-of-control condition is made across the concentration range of the patient results.

At block 202, for every specimen evaluated since the last good quality control evaluation, the probability of that evaluation being incorrect is estimated.

At block 204, the expected number of incorrect results (N_I) evaluated since the last good quality control evaluation is computed.

At block 206, if the calculated expected number of incorrect results N_Iis greater than the predefined threshold (MAXNI) for spot checking, then another recovery method should be used and the spot checking process terminates at block 208. As discussed above, the spot checking process of the present invention is suitable for use with small out-of-control conditions. If the out-of-control condition is larger than the predetermined threshold, MAXNI, then another method of recovering from the out-of-control condition will likely be more suitable. Other methods of recovery, such as simply re-testing every specimen since the last good QC event, will be apparent to those skilled in the art and are discussed above.

If the out-of-control condition does not exceed the MAXNI threshold, then the process continues at block 210, where it is determined if the estimated number of incorrect results N_Iis less than 1. If so, then no spot checking is necessary (because there are no, or less than one, incorrect results) and the spot checking process terminates at block 212, at which point regular testing can be resumed.

If the estimated number of incorrect results (N_I) is not less than 1, then the spot checking process continues at block 214 where the out of control condition is resolved. It will be apparent to one skilled in the art that the potential causes of out-of-control conditions are numerous and the required actions to resolve the malfunction are varied.

With the out-of-control condition resolved, at block 216 a list of patient specimens to be spot checked is computed. The list includes patient specimens: (1) with a high probability of being incorrect or (2) with a significant chance of crossing a medical decision limit due to the out-of-control condition.

At block 218 the patient specimens identified in the list are retrieved and reevaluated, generating new results for each of those specimens.

At block 220 it is determined whether the initial results for the patient specimens identified in the list were already reported or published.

If the results were not reported, then at block 222 the old results are replaced with the new results.

If the results were already reported, then at block 224 a correction list is generated having the results of any specimen where: (1) the new result is on a different side of a medical decision limit from the old result (e.g., the new result has changed the outcome of the medical decision) or (2) the new result differs from the old result by more than the allowable total error TE_a(e.g., the new result varies from the old result by more than an allowable amount).

Thus, the spot-checking process of the present invention allows identification of the specific specimens that need to be re-evaluated upon the occurrence of an out-of-control condition. With the process set forth generally, each of the above-described steps will now be described in more detail, still with reference to FIGS. 3A and 3B.

At block 200, the magnitude of the out-of-control condition is evaluated as follows:

First, a selected number of quality control (QC) specimens are evaluated for each QC level currently being tested by the clinical diagnostic process. Preferably, the selected number of quality control specimens is NUMREP, as initially set-up as described previously.

Next, using the results of the QC evaluation just performed, a bias function is constructed for each concentration x of QC being considered as:

bias(x)=MEAN(QC result)−MEAN (QC target level)

It should be understood that this calculation should include the values of QC results from the QC event result that failed.

For concentrations x that fall between QC levels, the bias is calculated by linearly interpolating between those QC levels. For concentrations above or below QC levels the bias is truncated such that for concentrations above the highest level of QC, the bias of the highest level of QC is used and for concentrations below the lowest level of QC, the bias level of the lowest level of QC is used.