Pareto's result is applied to a wide variety of behavior over rnanysysterns. It is sornetirnes referred to as the "80-20 Rule." A cereal rnanufacturer rnay find that rnost of the packaging errors are due to on1y a few causes. A student rnight think that 80% of the work on a group project was done by on1y 20% of血etearn rnernbers.百leuse of a Pareto diagrarn can also irnprove cornrnunication with ernployees or rnan-agernent and within production tearns・Exarnple1.5 illustrates the Pareto principle applied to a problern in a health insurance cornpany.

1.3 Graphs to Describe Categorical Variables 35

Errors in Health Care Claims Processing Table 1.6

FREQUENCY

40

9 2

6

17

37

ERROR TYPE

Procedural and diagnostic codes

Piovider information

Patient informatiori

Pricing schedules

Contractual applications

Provider adjus加 ents

CATEGORY

1

3

4

5

7 6

4 Others 7

Errurs in Health Care Claims Processing {Pareto Diagraml Figure 1 ~7

Pareto Chart: Errors in Health Care Claims Processing

日

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100

80

60

40

20

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4

3.3

100.0

100

50

Defect 叫川

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HuaMJ1

0

C

明

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C

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We can see in Figure 1.7 that， as the defect percentages for the types of error are added (from left to right)， the increase泊 thecumulative frequency line indicates the relative improvement that would result from correcting each of the most fre-quent prQblems. From the Pareto diagram the analysts saw that error 1 (Procedural and Diagnostic Codes) and error 5 (Contractual Applications) were the major causes of error. The combination of errors 1，5， and 4 (Pricing Schedules) result吋in nearly 80% of the errors. By examining the Pareto diagram in Figure 1.7， 血eanalysts can quickly determine which causes should receive most of the prob-lem correction effort. Pareto analysis separated the "vital few" causes from the "trivial many."

Armed with this information， the team made a number of recommendations to reduce errors and bring the process under control.

Using Graphs to Describe Data Ti

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tTもPA

a

'n c

36