Prediction of Medical Malpractice Payment Claims Gopher 6 Consulting Group

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Prediction of Medical Malpractice Payment Claims

Gopher 6 Consulting Group

Meian Yu
I can do this part if you guys want me to.
Mike Qi

AgendaAbstractSoftwareModel SummaryFuture Investigation

Abstract The goal was to create a model predicting medical

malpractice payments. All of our models were created using the Cognalysis

MultiRate software provided by Gross Consulting. In our process of creating the model, using data from the

National Practitioner Data Bank, we modified our variables, adjusted data, and created new fields.

Han Yong Wunrow
Michael do you mind doing the Abstract slide? Meian isn't coming tomorrow...
Mike Qi
Sure, I can do that
Han Yong Wunrow
Awesome. Thanks
Mike Qi
How long does it take for you to finish all sildes?
Mike Qi
I can come to coffman tomorrow morning
Han Yong Wunrow
Awesome. I haven't practiced the speech so I don't know. I'm guessing around 15 minutes
Mike Qi
So far, it looks like it will take more than 15 mins...any other sections you want me to take care of?
Han Yong Wunrow
Yeah... Maybe I'll get rid of some sections
Han Yong Wunrow
Let's see could you do slides: 2, 3, 4, 5, and 20?
Mike Qi
We can go over all this sections in detail tomorrow morning. Could you let me know when you finished writing all the slides? I'll start making it look fancier..
Han Yong Wunrow
I finished writing all the slides except summary, could you add animations for each bullet point?

Cognalysis MultiRateGross Consulting’s inhouse predictive

modeling softwareEasy to useComplex Math Concepts

Simple and visible

How to Run AnalysisImport DataSelect FieldsChoose Credibility and IterationFilter

Train and testRun!

Result Inspection Panel

Result Inspection PanelRaw Factor

factor without accounting for other variablesAdjusted Factor

factor after accounting for other variablesModel Factor

Takes adjusted factor and credibility

Compare Analysis

EvaluationAverage Absolute Error

average distance between actual and model

Our Model!Variables ModificationData AdjustmentsNew FieldsChange Credibility

Variable ModificationEliminated VariablesGrouped vs Generic Characteristic

Eliminating VariablesReasons

Same Value

Different Value for each recordNot very many data points

Grouped vs. Generic CharacteristicGrouped

groups numeric data into bins

i.e, Age, Years

Generic CharacteristicEach distinct value will

be treated independently

i.e., Male and Female, Field of License

Data AdjustmentsErroneous Data

Found with Pivot Tables

New Fields“paymentperperson”

The payment divided by the number of people who were paid

“diffyear”The number of years from when the malpractice took

place and when the claim was filed“yearexperience”

The number of years between the practitioner’s graduation and the time the malpractice took place.



How?Functions: IF, OR, ISBLANK, DIVIDE


Why?“malyear” and “origyear” both had low significance



Why?“grad” had low significance


Change Credibilityexposure

More credibility when you have more records

t-statisticBad scores

SummaryBest submission

Used the fields with larger R^2Used Fields with higher significance and high effective

ratioRemoved fields with large average absolute error

(result inspection panel)Use exposure 10, 100 iterations

Future Investigation interaction effectrandom variablesfields we created but did not useunsubmitted model


Q & A