Automatic selection of statistical quality control procedures

• US 5,937,364 A
• Filed: 05/07/1996
• Issued: 08/10/1999
• Est. Priority Date: 05/07/1996
• Status: Expired due to Term

• Current Assignee: Bio-Rad Laboratories Incorporated
• Original Assignee: Westgard Quality Corporation
• Inventors: Westgard, James O., Stein, Bernard
• Primary Examiner(s): MacDonald, Allen R.
• Assistant Examiner(s): KANOF, PEDRO R
• Application Number:
  US08/643,993
• Time in Patent Office: 1,190 Days
• Field of Search: 705/7, 705/21, 436/8, 364/151, 364/468.15, 364/468.16, 364/468.17, 702/81, 702/82, 702/83, 702/84
• US Class Current: 702/83
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06395 Quality analysis or management
• CPC Class Codes: G06Q 20/202 Interconnection or interaction of plural electronic cash registers [ECR] or to host computer, e.g. network details, transfer of information from host to ECR or from ECR to ECR

Abstract

Disclosed is a computer implemented method for automating the selection of an analytical statistical quality control (QC) procedure. Inputs of a quality requirement and observed performance characteristics of a method of measurement are initially provided, together with a database of theoretical operating characteristics of statistical QC procedures. An appropriate quality-planning algorithm (either analytical or clinical) which relates the quality requirement, the observed performance characteristics of the method of measurement, and the theoretical operating characteristics of statistical QC procedures is also provided. Using predetermined QC selection criteria and selection logic, a computer is programmed to automatically choosing, based on the quality requirement, observed method performance, and operating characteristic of candidate QC procedures, a subset of control rules and numbers of control measurements which satisfy selection criteria and selection logic. Also disclosed is a dynamic QC process for automatically changing the statistical QC procedure when there are changes in performance of a method of measurement. In this embodiment, real-time statistical estimates of the performance characteristics of a method are provided through on-line sampling with an analytical instrument and this data is used to automatically select a QC procedure.

Claims

1. A computer implemented method for automating the selection of an analytical statistical quality control (QC) procedure, the method comprising:

a) providing inputs of a quality requirement and observed performance characteristics of a method of measurement;

b) providing a database of theoretical operating characteristics of statistical QC procedures;

c) providing an analytical quality-planning algorithm which relates the quality requirement, the observed performance characteristics of the method of measurement, and the theoretical operating characteristics of statistical QC procedures;

d) providing QC selection criteria and selection logic;

e) automatically choosing, based on the quality requirement, observed method performance, and operating characteristic of candidate QC procedures, a subset of control rules and numbers of control measurements which satisfy selection criteria and selection logic.

2. The method of claim 1 wherein the statistical QC procedure can be characterized by a control rule, control statistic, or control chart whose operating characteristics can be described in terms of probabilities of acceptance or rejection as a function of the size of systematic and random errors.

3. The method of claim 2 wherein the control statistic represents the average of normal patient test results used to monitor errors or changes.

4. The method of claim 1 wherein the quality specification is an analytical quality requirement for the allowable total error, TEa.

5. The method of claim 1 wherein the input parameters necessary for the analytical quality-planning algorithm are the stable imprecision observed (S_meas);

stable inaccuracy (bias_meas);

matrix inaccuracy (bias_matx);

number of replicate samples (n_samp); and

z-value for maximum defect rate (z).

6. The method of claim 1 further comprising automatically selecting from among the subset of control rules in step e) the control rule which best satisfies the selection criteria and selection logic for type of control rule or control chart, total numbers of control measurements per analytical run, error detection rate, and false rejection rate.

7. The method of claim 6 wherein the database of operating characteristics of statistical QC procedures comprises types of control rules and control charts selected from the group consisting of single-rule constant limits, single-rule variable limits, multi-rule with specified maximum number of runs, and mean/range rules.

8. The method of claim 6 wherein the selection criteria and selection logic for the total number of control measurements per analytical run (N) includes 1, 2, 3, 4, 6, and 8 control measurements per run.

9. The method of claim 6 wherein the selection criteria and selection logic for the rate of error detection are 90% or more, 50% or more, or 25% or more.

10. The method of claim 9 wherein the selection criteria and selection logic for error detection depend on the input for the observed instability of the measurement procedure.

11. The method of claim 6 wherein the selection criteria and selection logic for the rate of false rejection are 10% or less, 5% or less, or 1% or less.

12. A computer implemented method for automating the selection of a clinical statistical quality control (QC) procedure, the method comprising:

a) providing inputs of a quality requirement and observed performance characteristics of a method of measurement;

b) providing a database of theoretical operating characteristics of statistical QC procedures;

c) providing a clinical quality-planning algorithm which relates the quality requirement, the observed performance characteristics of the method of measurement, and the theoretical operating characteristics of statistical QC procedures;

d) providing QC selection criteria and selection logic;

e) automatically choosing, based on the quality requirement, observed method performance, and operating characteristic of candidate QC procedures, a subset of control rules and numbers of control measurements which satisfy selection criteria and selection logic.

13. The method of claim 12 wherein the statistical QC procedure can be characterized by a control rule, control statistic, or control chart whose operating characteristics can be described in terms of probabilities of acceptance or rejection as a function of the size of systematic and random errors.

14. The method of claim 13 wherein the control statistic represents the average of normal patient test results used to monitor errors or changes.

15. The method of claim 12 wherein the quality specification is a clinical quality requirement for a medically important change in the form of a decision interval, DInt.

16. The method of claim 12 wherein the input parameters necessary for the clinical quality-planning algorithm are the stable imprecision observed (S_meas);

stable inaccuracy (bias_meas);

matrix inaccuracy (bias_matx);

specimen bias (bias_spec);

within-subject biological variation (S_wsub);

sampling variation (S_spec);

number of tests (n_test);

number of specimens (n_spec);

number of replicate samples (n_samp); and

z-value for maximum defect rate (z).

17. The method of claim 12 further comprising automatically selecting from among the subset of control rules in step e) the control rule which best satisfies the selection criteria and selection logic for type of control rule or control chart, total number of control measurements per analytical run, error detection rate, and false rejection rate.

18. The method of claim 17 wherein the database of operating characteristics of statistical QC procedures comprises types of control rules and control charts selected from the group consisting of single-rule constant limits, single-rule variable limits, multi-rule with specified maximum number of runs, and mean/range rules.

19. The method of claim 17 wherein the selection criteria and selection logic for the total number of control measurements per analytical run (N) includes 1, 2, 3, 4, 6, and 8 control measurements per run.

20. The method of claim 19 wherein the selection criteria and selection logic for the rate of error detection are 90% or more, 50% or more, or 25% or more.

21. The method of claim 20 wherein the selection criteria and selection logic for error detection depends on the input for the observed instability of the measurement procedure.

22. The method of claim 16 wherein the selection criteria for the rate of false rejection are 10% or less, 5% or less, or 1% or less.

23. A dynamic QC process for automatically changing the statistical QC procedure when there are changes in performance of the method of measurement, the method comprising:

a) providing inputs of a quality requirement and initial observed performance characteristics of a method of measurement;

b) providing a database of theoretical operating characteristics of statistical QC procedures;

c) providing quality-planning algorithms which relate the quality requirement, the observed performance characteristics of the method of measurement, and the theoretical operating characteristics of statistical QC procedures;

d) providing QC selection criteria and selection logic;

e) automatically choosing, based on the quality requirement, observed method performance, and operating characteristic of candidate QC procedures, a subset of control rules and numbers of control measurements which satisfy selection criteria and selection logic;

f) providing real time statistical estimates of method performance characteristics through on-line sampling with an analytical instrument;

g) repeating steps b)-f) above for a sequence of runs thereby automatically selecting a higher number of control measurements or more sensitive control rules when method performance deteriorates and decreasing the number of control measurements or changing to less sensitive control rules when method performance improves;

h) implementing the selected statistical QC procedure through calculations and control charts;

i) providing an assessment of the state of control and a decision about the acceptance or rejection of individual analytical runs based on the selected statistical QC procedure.