Systems and methods for the detection of short and long samples

1. A method for identifying a specimen as a short sample, a long sample, or an acceptable sample, the method comprising:

obtaining sample data collected from the analysis of the specimen, the sample data defining a value for one or more measurable characteristics of the specimen;

obtaining reference data, the reference data comprising:

at least one parameter that defines a portion of a distribution for the one or more measurable characteristics identified by the sample data; and

relationship data defining a relationship between the at least one parameter and the sample data that assists in identifying the specimen as a short sample, a long sample, or an acceptable sample; and

thereby determining the probability of the specimen being a short sample, a long sample, or an acceptable sample; and

outputting or storing the determined probability, thereby identifying the specimen as a short sample, a long sample, or an acceptable sample.

2. A method as in claim 1 , further comprising comparing the determined probability to a user defined acceptable probability value and outputting the result to a user.

3. A method as recited in claim 1 , further comprising determining an overall probability of the specimen being a short sample, a long sample, or an acceptable sample for two or more of the measurable characteristics.

4. A method as recited in claim 3 , further comprising:

determining the probability that the specimen is a short sample for each of the measurable characteristics; and

calculating the probabilities that the specimen is a short sample for each of the two or more measurable characteristics.

5. A method as recited in claim 3 , further comprising:

determining the probability that the specimen is a long sample for each of the measurable characteristics; and

calculating the probabilities that the specimen is a long sample for each of the two or more measurable characteristics.

6. A method as in claim 3 , further comprising:

determining the short sample probability for each measurable characteristic in a plurality of representative samples;

calculating the short sample probabilities for each measurable characteristic in each representative sample to obtain a short sample probability for each representative sample;

creating a distribution spread of the short sample probabilities for the plurality of representative samples; and

selecting a threshold probability value that corresponds to a cumulative probability frequency, wherein the threshold probability value indicates a high probability that the specimen is a short sample.

7. A method as recited in claim 3 , wherein obtaining reference data further comprises accessing reference data stored in a data module.

8. A method as recited in claim 3 , wherein obtaining reference data comprises:

creating a distribution spread defining an occurrence frequency for the measured characteristics defined by the sample data, the distribution spread comprising a distribution maximum value and a distribution minimum value;

defining the distribution maximum value and all values of the measured characteristic greater than the measured characteristic value associated with the distribution maximum value to have a short sample probability of zero percent and the distribution minimum value to have the short sample probability of 100 percent;

identifying one or more parameters that define the distribution spread bounded by the distribution maximum value and the distribution minimum value; and

defining relationship data that defines a relationship between the one or more parameters and the sample data, the relationship being used to determine the probability of the specimen being a short sample.

9. The method as recited in claim 8 , wherein said distribution minimum value corresponds to a point on the distribution spread where the sample data value occurrence frequency is close to or equal to zero.

10. The method as recited in claim 9 , wherein said distribution maximum value corresponds to the maximum measured characteristic value occurrence frequency.

11. The method as recited in claim 9 , further comprising using a statistically significant data set obtained from a representative population of samples to create the distribution spread.

12. A method as recited in claim 1 , wherein obtaining reference data comprises:

creating a distribution spread defining an occurrence frequency for the measured characteristic defined by the sample data, the distribution spread comprising a distribution maximum value and a distribution minimum value;

defining the distribution maximum value and all values of the measured characteristic lesser than the measured characteristic value associated with the distribution maximum value to have a long sample probability of zero percent and the distribution minimum value that has a measured characteristic value greater than the measured characteristic value associated with the distribution maximum value to have a long sample probability of 100 percent;

identifying one or more parameters that define the distribution spread bounded by the distribution maximum value and the distribution minimum value;

defining relationship data that defines a relationship between the one or more parameters and the sample data, the relationship being used to determine the probability of the specimen being a long sample.

13. The method as recited in claim 12 , wherein said distribution minimum value corresponds to a point on the distribution spread where the sample data value occurrence frequency is close to or equal to zero.

14. The method as recited in claim 12 , wherein said distribution maximum value corresponds to the maximum measurable characteristic value occurrence frequency.

15. The method as recited in claim 12 , further comprising using a statistically significant data set obtained from a representative population of samples to create the distribution spread.

16. A method as recited in claim 1 , wherein the one or more sample datum are selected from the group consisting of: albumin, alkaline phosphotase, alanine aminotransferase, amylase, aspartate amino transferase, total bilirubin, blood urea nitrogen, calcium, cholesterol, creatinine, glucose, total protein, total proteinlalbumin, blood urea nitrogen/creatinine, creatine kinase, carbon dioxide, sodium, potassium, chlorine, and osmolarity.

17. A method as recited in claim 1 , wherein the at least one measurable characteristic is obtained from an immuno assay.

18. A method as recited in claim 1 , wherein the at least one measurable characteristic is obtained from a genomic assay.

19. A method as recited in claim 1 , wherein the at least one measurable characteristic is any measurable characteristic of water.

20. A method for determining whether a test sample is a short sample, the method comprising:

obtaining sample data associated with the test sample, the sample data defining one or more values for one or more measurable characteristics of the test sample;

obtaining reference data associated with the sample data, the reference data comprising:

at least one parameter that defines a portion of a standard distribution for the one or more measurable characteristics identified by the sample data; and

relationship data defining a relationship between the at least one parameter and the sample data that assists in identifying the test sample as a short sample; and

thereby determining the probability of the specimen being a short sample; and

outputting or storing the determined probability, thereby determining whether the test sample is a short sample.

21. A method as recited in claim 20 , further comprising determining an overall probability that the test sample is a short sample for two or more of the measurable characteristics.

22. A method as recited in claim 21 , further comprising:

determining the probability that the test sample is a short sample for each of the measurable characteristics; and

calculating the probabilities that the test sample is a short sample for each of the two or more measurable characteristics.

23. A method as in claim 21 , further comprising:

determining the short sample probability for each measurable characteristic in a plurality of representative samples;

calculating the short sample probabilities for each measurable characteristic in each representative sample to obtain a short sample probability for each representative sample;

creating a distribution spread of the short sample probabilities for the plurality of representative samples; and

selecting a threshold probability value that corresponds to a cumulative probability frequency, wherein the threshold probability value indicates a high probability of the test sample being a short sample.

24. A method as recited in claim 20 , wherein obtaining reference data comprises:

creating a distribution spread defining an occurrence frequency for the measured characteristics defined by the sample data, the distribution spread comprising a distribution maximum value and a distribution minimum value;

defining the distribution maximum value and all values of the measured characteristic greater than the measured characteristic value associated with the distribution maximum value to have a short sample probability of zero percent and the distribution minimum value to have the short sample probability of 100 percent;

identifying one or more parameters that define the distribution spread bounded by the distribution maximum value and the distribution minimum value; and

defining relationship data that defines a relationship between the one or more parameters and the sample data, the relationship being used to determine the probability that the test sample is a short sample.

25. The method as recited in claim 24 , wherein said distribution minimum value corresponds to a point on the distribution spread where the sample data value occurrence frequency is close to or equal to zero.

26. The method as recited in claim 24 , wherein said distribution maximum value corresponds to the maximum measured characteristic value occurrence frequency.

27. The method as recited in claim 24 , further comprising using a statistically significant data set obtained from a representative population of samples to create the distribution spread.

28. A computer program product for implementing, in a system that includes at least one processor and is configured to determine whether a specimen is a short sample, a method for determining whether a specimen is a short sample, the computer program product comprising:

a computer readable medium carrying computer executable instructions for implementing the method, wherein the computer readable medium is not a wireless medium, the computer executable instructions, when executed, performing:

receiving sample data collected from analysis of a specimen and indicative of at least one sample characteristic;

receiving reference data associated with the at least one sample characteristic, the reference data comprising:

at least one parameter that defines a standard distribution of a sample characteristic defined by the sample data; and

relationship data defining a relationship between the at least one parameter and the sample data to result in a determination of a probability that the specimen is a short sample; and

thereby determining the probability of the specimen being a short sample.

29. A computer program product as defined in claim 28 , wherein the computer readable medium further carries computer executable instructions for comparing the determined probability to a user defined acceptable probability value and outputting the result to a user.

30. A computer program product as defined in claim 28 , wherein the computer readable medium further carries computer executable instructions for determining an overall probability that the specimen is a short sample for two or more of the measurable characteristics.

31. A computer program product as defined in claim 30 , wherein the computer readable medium further carries computer executable instructions for:

determining the probability that the specimen is a short sample for each of the measurable characteristics; and

calculating the probabilities that the specimen is a short sample for each of the two or more measurable characteristics.

32. A computer program product as defined in claim 30 , wherein the computer readable medium further carries computer executable instructions for:

determining the probability that the specimen is a long sample for each of the measurable characteristics; and

calculating the probabilities that the specimen is a long sample for each of the two or more measurable characteristics.

33. A computer program product as defined in claim 30 , wherein the computer readable medium further carries computer executable instructions for:

determining the short sample probability for each measurable characteristic in a plurality of representative samples;

calculating the short sample probabilities for each measurable characteristic in each representative sample to obtain a short sample probability for each representative sample;

creating a distribution spread of the short sample probabilities for the plurality of representative samples; and

selecting a threshold probability value that corresponds to a cumulative probability frequency, wherein the threshold probability value indicates a high probability that the specimen is a short sample.

34. A computer program product as defined in claim 28 , wherein the computer readable medium further carries computer executable instructions for:

creating a distribution spread defining an occurrence frequency for the measured characteristics defined by the sample data, the distribution spread comprising a distribution maximum value and a distribution minimum value;

defining the distribution maximum value and all values of the measured characteristic greater than the measured characteristic value associated with the distribution maximum value to have a short sample probability of zero percent and the distribution minimum value to have the short sample probability of 100 percent;

identifying one or more parameters that define the distribution spread bounded by the distribution maximum value and the distribution minimum value; and

defining relationship data that defines a relationship between the one or more parameters and the sample data, the relationship being used to determine the probability of the specimen being a short sample, a long sample, or an acceptable sample.

35. A computer program product as defined in claim 34 , wherein said distribution minimum value corresponds to a point on the distribution spread where the sample data value occurrence frequency is close to or equal to zero.

36. A computer program product as defined in claim 34 , wherein said distribution maximum value corresponds to the maximum measured characteristic value occurrence frequency.

37. A computer program product as defined in claim 34 , wherein the computer readable medium further carries computer executable instructions for using a statistically significant data set obtained from a representative population of samples to create the distribution spread.