Quality Management

Quality

-Degree of Excellence

– Conformance to the requirements of users or customers; satisfaction to the needs of the customer

Cost

-Related to Quality

-Quality Increase, Cost Decrease

 

Types of Quality Costs

1) Cost of conformance- anything spent to eliminate waste and improve productivity

2) Cost of non-conformance- anything spenf because of wastes or wasteful practices

Total Quality Management

[TQM]

A management philosophy for organizational development as well as a management process for improving quality in all aspects [satisfy customer]
TQM involves what?
Processes in the lab
5Q Framework

1)QLP- general procedures, policies, practices

2) *QC- emphasis on stats control procedures

3)QA- broad monitoring of all lab processes

4)QI- id’s problems and offers solutions

5)QP- are problems solved and requirements met?;

Controlling Variables

1)Preanalytical Variables

2) Analytical Variables

Preanalytical Variables

Must be carefully controlled for (for all Qs);

-Factors that affect specimen before they get to the lab

Analytical Variables

Must be controlled for to ensure accurate measurements (for QA;QC)

-Affect specimen while still in the lab

-Stat procedures monitor these

Statistical Quality Control (QC)

Monitors performance of analytical methods thru use of stat analysis of:

1) QC procedures and materials

2) Control Charts

3)Westgards multi-rules

4)Sending specimen to other labs to check accuracy

How to check on QC procedures and materials:
Use stimulated serum and test to see if it falls in interval.
Control Charts Include:

1)Levey-Jennings Charts: x-y plot that uses the average and SD of control values

2) Cumulative sum chart: examines scatter of all values around the mean

;

Examined for trends and analyzes stat.

Westgard’s Multi-Rules

Criteria that detect subtle change in control data

-Improves quality and decreases subjectivity in data analysis

-Includes rejection criteria

-1st check analytical method!

WEST Rules

  1. 12s: 1 control value exceeds the mean +/-2s
  2. 13s: 1 control value exceeds the mean +/-3s
  3. 22s: 2 consective controls exceed the mean +/-2s
  4. 41s: 4 consecutive controls exceed the mean +/-1s
  5. R4s:1 control observations exceeds the mean +2s and other exceeds -2s
  6. 10x: 10 consecutive controls exceed the mean +/-1s

 

Patient Data:

Non-Statisitical QC

-Most direct form of QC (Correlation of test results with other patient data)

 

Patient Data: Procedures

1) Average of normals: statistically assesing all patient values to assess stability

2) Delta check: Compares one specimen results with previous results from same patient

3) Pattern Recognition: Special checks that detect unlikely combinations of test values.

Instrument Maintenance
Records of all maintenance procedures must be kept for all equipment
QUALITY ASSURANCE

QA is practice of assuring that everything related to the lab meets quality standars

-products to customers

-Control values to agencies

-procedure manuals to the lab itself

QA apart of ? required by ? and ? Score

Quality control procedures are a statistical part of QA, required by CLIA ’88
A score of 80% must be attained on three consecutive external proficiency tests for a lab to continue patient testing

Proficiency Testing (PT)

Specimens sent to laboratories by non-profit organizations that evaluate the adequacy of lab performance

 

PT validates internal QC programs; PT is also called “external QC”

 

Sanctions are severe for cheating, failing to get an 80%, failing to participate, failure to return results on time

Important Aspects of PT

Accuracy:  the closeness of the agreement between a measured value to the “true” value;

Error is used to assess accuracy

 

Error:  deviation from what is correct; caused by the introduction of “variables”
Westgard rules determine what type of error has occurred

2 Types of Error

1) Random Error: affects precision (repeated measurements) and is the basis of the varying differences of repeat measurements.

2) Systematic Error: arises from factors that contribute to a constant diff or trend to a value

Random Error

No trend; cant predict when it will happen; chance experiences

-Cause include pipette errors, poor transfer; temp changes; poor sample prep

Systematic Error

affects the estimate of the mean
causes include poorly made reagents, bad calibration, failing instruments, poorly written procedures, interferences in samples
is considered to be a measure of the agreement between the measured quantity and the true value

Kinds of Systematic Error

Constant systematic error:  stays the same distance from the mean even as the analyte concentration changes

 

Proportional systematic error:  changes in relation to the concentration of the analyte

Westgard Rules and Error Type

12s – warning only (probably random)

13s – detection of random error

22s – detection of systematic error

41s – detection of systematic error

R4s – detection of random error

10 – detection of systematic error

Precision

ability of a method to produce the same value for many measurements of the same sample (also called “reproducibility”)
many kinds of precision checked when new methods or equipment are used

Analytical Range

range of analyte concentratons that a method can measure and still remain linear

Analytical Sensitivity

ability of a method to produce a change in signal for change in quantity; this will detect small changes in concentration of an analyte

Analytical specificity 

related to accuracy; ability of a method to determine only the analyte it’s supposed to without being subject to interferences

Important Aspects of QA: Clinical Sensitivity 

the ability of a specific test to diagnose a specific disease…it is stated as the proportion of individuals with a disease that test positively for it

How to calculate CSens:

How to calculate:  in patients with the disease

  # with a positive result = true positives (TP)

  # with a negative results = false negatives (FN)

Clinical Sensitivity % = [TP / (TP+FN)] X 100

Clinical Specificity 

the ability of a test to correctly predict the number of individuals without a specific disease

How to calculate CSpec:

 

How to calculate:  in patients without the disease

  # with a positive result = false positive (FP)

  # with a negative result = true negative (TN)

 

Clinical Specificity % = [TN / (FP+TN)] X 100

Inspections by outside agencies:

Inspections by outside agencies
JCAOH
CAP
HCFA
ASCP
Inspectors assess recordkeeping, QC, comments of customers

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