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
Great!
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.
“paymentperperson”
Why?
How?Functions: IF, OR, ISBLANK, DIVIDE
“diffyear”
Why?“malyear” and “origyear” both had low significance
How?Functions: IF, ISBLANK, AVERAGE, MROUND
“yearexperience”
Why?“grad” had low significance
How?Functions: IF, ISBLANK, AVERAGE, MROUND
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
THANK YOU!
Q & A
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