Using Text Analytics to Accurately Segment Workers Compensation Injuries

(1)

Using Text Analytics to Accurately Segment

Workers’ Compensation Injuries

Presented by: Amir Biagi

Associate, Predictive Modeling March 7, 2012

(2)

Insurance is a data driven industry, and only

structured data is usually used for analyses.

(3)

Key information captured in text notes is

typically overlooked.

(4)

Text analytics can be used to draw insights from

unstructured data to enrich traditional methods.

(5)

We will review the case study in three steps.

Results

Background

(6)

Results

Background

Solution

(7)

A small percentage of injuries are responsible

for the majority of workers’ compensation loss.

Best 20% Worst 20%

1% 3%

6%

14%

77%

Percentage of Loss Costs

(8)

Insurers can utilize predictive models to identify

high-cost injuries early-on.

(9)

A predictive model can use claimant attributes

to segment injuries based on outcome potential.

(10)

A predictive model can use claimant attributes

to segment injuries based on outcome potential.

(11)

A predictive model can use claimant attributes

to segment injuries based on outcome potential.

1 2 3

w₁(Age) + w₂( Gender) + w₃(Injury) + w₄(Co-morbidities) + ...

Scoring Equation

(12)

A predictive model can use claimant attributes

to segment injuries based on outcome potential.

1 2 3

(13)

A predictive model can use claimant attributes

to segment injuries based on outcome potential.

1 2 3

(14)

Injured workers’ secondary conditions (i.e.,

co-morbidities) can influence the overall outcome.

(15)

Co-morbidities are traditionally captured in claim

notes.

Clmt indicated that next month, he will have large piece of abdomen removed, cosmetic in nature because clmt is large person. Clmt

indicated that he is a slow healer, not diabetic

and does not have high blood pressure. Clmt

indicated he is doing dietary adjustment to try to impact weight. Weight has mushroomed

because of inactivity. Clmt is considering gastric bypass to work with weight also.

(16)

There can be multiple notes per injured worker.

(17)

Our task is to develop co-morbidity indicators

based on claim notes.

Clmt indicated that next month, he will have large piece of abdomen removed, cosmetic in nature because clmt is large person. Clmt indicated that he is a slow healer, not diabetic

and does not have high blood pressure. Clmt indicated he is doing dietary adjustment to try to impact weight. Weight has mushroomed because of inactivity. Clmt is considering gastric bypass to work with weight also.

Clmt indicated that next month, he will have large piece of abdomen removed, cosmetic in nature because clmt is large person. Clmt indicated that he is a slow healer, not diabetic and does not have high blood pressure. Clmt indicated he is doing dietary adjustment to try to impact weight. Weight has mushroomed because of inactivity. Clmt is considering gastric bypass to work with weight also.

(18)

We will review the case study in three steps.

Results

Background

(19)

We will outline the solution in six steps.

1 IdenFfy the co-‐morbidiFes that _{are relevant.}

2 Work with clinicians to deﬁne _{synonyms for each co-‐morbidity.}

3 Determine how false posiFves _{will be handled.}

4 Apply text analyFcs algorithm to _{all notes.}

5 Develop note based indicators.

(20)

Step 1:

Work with the clinical team to identify

the co-morbidities that are relevant.

(21)

Step 1:

Work with the clinical team to identify

the co-morbidities that are relevant.

(22)

Step 2:

Work with clinicians to define synonyms

for each co-morbidity.

(23)

Step 2:

Work with clinicians to define synonyms

for each co-morbidity.

Diabetes Diabetic Neuropathy Insulin Blood Sugar Mellitus Diabetes Mellitus Glucose Type 2 Diabetes Actos Glucophage Hypoglycemia Iddm Lantus A1C Blood Glucose

(24)

Step 3:

Determine how false positives will be

handled.

indicated that he is a slow healer, not diabetic

and does not have high blood pressure. Clmt indicated he is doing dietary adjustment to try to impact weight. Weight has mushroomed

(25)

Step 4:

Apply text analytics algorithm to all

notes.

(26)

Step 5:

Develop note based indicators.

Claim

A

B

C

Note ID

1

2

3

2

1

2 Diabetes

0

1

0

1

0

(27)

Step 6:

Develop claim level indicators.

Claim

A

B

C

Diabetes

1

0

(28)

We will review the case study in three steps.

Results

Background

(29)

The presence of diabetes is strongly related to

overall outcome.

Key Word Reference % Average Cost

No References 96.5% $23,000

False Positives 0.9% $38,000

Diabetes Identified 2.6% $101,000

Average Cost per Claimant

$23,000 No References $38,000 False Positive $101,000 Diabetes Identified

(30)