The Business Problem

Automated Triggers with 72 hour delay:

  1. Cases in which medical expenditures and/or reserves each reach $3,000.00
  2. Anticipated or actual inpatient or outpatient surgery of any type
  3. Anticipated or actual hospitalization
  4. All back injuries
  5. All repetitive motion/cumulative trauma disorder/injuries
  6. All claims with prior injury history to same or related body part, and/or current open claim
  7. Restricted duty over 2 weeks
  8. Any injury with anticipated/actual off work days greater than five days.
  9. All shoulder injuries

 

Basic Rule Modeling

Identify the Business Decision(s) to be made

Should this claim be referred?

Collect and Review Rules needed for each decision

To make this decision we will need some rules.

Here are some  of the sample rules provided by

 

  • All repetitive motion/cumulative trauma disorder/injuries
    Part Target Code is one of: 3400,3900 AND Either the Cause Code is one of:  6001,6003,9700  OR the Nature Code is one of:7800,800

  • Cases in which medical expenditures and/or reserves each reach $3,000.00
    Medical Incurred >= 3000

  • All back injuries
    Part Target Code is one of: 2000,2100,2102,2200,2300,2400,2500,4100,4101,4200,4300,4500,4700,6300
    AND Either the Cause Code is one of:  2500,2600,2700,2800,2900,3000,3100,3101,3200,3300,5000,5300,5400,5500,5600,5700,5800,6000,6001,6002,6003,6004,9700,9700
    OR   the Nature Code is one of: 1600,2800,3400,4900,5200

 

We will walk through the entire process of modeling these rules, including the discovery of errors, ambiguities and incompleteness using Corticon Studio.

We’ll find that even these two apparently simple rules contain problems which Corticon will help us discover and resolve

Identify Business Objects (Entities)

By referring to the rule statements we can deduce the existence of objects such as these:

Case  – the entity that is being considered for referral

Create a Corticon Vocabulary

This will be modeled in the Corticon vocabulary:

Figure 1 Client Business Object

Create Sample Data

This helps to make sure the data model makes sense

This can be done in the Corticon Testing tool which is built into Corticon.

We can also set up some expected results to verify that our rules are producing the answers we expect.

We’ll see during the demo how Corticon will automatically compare the actual and expected results and flag any variations once the rules have filled in the output column

  

Figure 2 Sample Test Data

 

Decide how the rules need to be grouped into Rule sheets

There are no hard and fast rules about how to divide up the rules into groups, but a good way to start is to group the rules according to the main attribute that they are determining.

The book “The Decision Model” has some good guidelines on how to organize rules.

Here are some of the components in this problem

 

These are the rule statements that would be on the rule sheet. The rule statements are always the starting point for rule modeling.

Figure 3 Rule Statements

 

Model Rules

Now we are ready to model the rules.

Within this phase of rule modeling there are a number of steps

 

For each rule statement create a rule column that connects the conditions to the actions

Here’s what the rules might look like

Figure 4 Rules Modeled As Specified

Write Natural Language Statements if Desired

Note that it’s also possible to toggle this view to a natural language view:

Figure 5 Natural Language

 

Run Test Cases

We already have some test data and expected results so we can run our rules:

This is what we will get:

Figure 6 A Test Case

Other Rulesheets

Figure 7 Repetitive Motion

 

Figure 8 Back Injuries

Note that the various codes are assigned BEFORE the conditions are evaluated. These codes could also be looked up from a database

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