Driving Healthcare Operations with Data Science

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Driving Healthcare Operations with Data Science

Transcript of Driving Healthcare Operations with Data Science

Page 1: Driving Healthcare Operations with Data Science

Driving Healthcare Operations with Data

Science

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"literally a health insurance company"

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"Operations"Clinical Operations

● Close member "gaps in care"○ Not taking their meds○ Not seeing their doctors○ Not getting tested

● Document conditions

Insurance Operations

● Approve / deny claims● Approve / deny authorizations● Catch fraud

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"Operations"Clinical Operations

● Close member "gaps in care"○ Not taking their meds○ Not seeing their doctors○ Not getting tested

● Document conditions

Insurance Operations

● Approve / deny claims● Approve / deny authorizations● Catch fraud

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E.g.

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Talk to them about consequences of not

doing so?

Knock on the doors of most

non-adherent members?

Ask members politely? Use different messages for rich and

poor members?

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Enter

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Data ScienceEnter

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Data ScienceWhat should we do?

For whom?

Did it work?

Enter

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Case Study: Whom to Call for Home Visits?

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Can we predict which of our diabetic members will have complications in the

next 6 months?

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Time

Observation Interval Prediction Interval

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Time

Observation Interval Prediction Interval

Demographic info, lab tests, medications,

other diagnoses

Diagnosed with diabetes

complications?

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Features Labels

Member Age Hypertension hba1c

CP001 65 Yes 6.5

CP002 77 No 8.3

CP002 84 Yes 7.4

Diagnosed with Complication in 6-month Interval

Yes

No

Yes

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Challenge: High Class Imbalance● Historically, only 8% of diabetic members have been diagnosed with

complications over a 6-month period.

● Easy to get "high" accuracy, but hard to get decent precision/recall tradeoff.

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Approach: High Class Imbalance● Evaluate using area under ROC curve.

● Empirically, tree ensemble models appear to handle the imbalance better than logistic regression.

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Challenge: Missing Data● Glycated hemoglobin clearly an important feature… but we only have

measurements for ~60% of members.

● Whether we have a measurement correlates with both:○ Diabetes complications.○ How well a model trained without the lab measurement performs.

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Approach: Missing Data● Simply hardcode all missing values to something outside the measurement

range.○ In our case, 0.0.

● This way, tree models can split on "have a measurement" vs. "don't have a measurement".

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Final Model: Gradient Boosting Tree Ensemble

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Evaluation

AUROC: 0.8

Precision: 24%

Recall: 66%

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Most Predictive Features

Glycated Hemoglobin

Age

Hypertension

Takes Insulin

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Did it work?

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Do we catch more complications if we make calls using the model?

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Control Group Treatment Group

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Control Group Treatment Group

Call Group(Chosen at Random)

Call Group(Chosen by Model)

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Found Complications Didn't Find Complications

Control Group 8 92

Treatment Group 24 76

FAKE RESULTS

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Found Complications Didn't Find Complications

Control Group 8 92

Treatment Group 24 76

FAKE RESULTS

Chi-Squared Test