Structuring EMR Data For Analytics

2015 Annual Conference

HDWA 2015 – Grand Rapids, Michigan October 13 – 15

UNLOCKING THE POWER of DATA to TRANSFORM HEALTHCARE

Sponsored by Spectrum Health

Photos courtesy of ExperienceGR.com and Pure Michigan

Structuring EMR Data For Analytics:Engineering Features from Repeated Clinical Measurements

Brandon StangeData Scientist, Trinity Health

HDWA 2015Grand Rapids, MichiganSponsored by Spectrum Health Photos courtesy of ExperienceGR.com

Agenda

• General Data Formatting for Analytics• Transforming Repeated Clinical Measurements– Standardize length of time-series– Cluster common trends together

• Scaling and other options• Questions

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Institution Profile

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Data Governance and Research

• 13 members (4 managers)• Data scientists (3), data governance analysts

(4), clinical/business intelligence analysts (6)• Dozens of data sources• 30k daily reports from Unified Data

Warehouse

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Traditional BI vs. “Data Science”

OpenMRS.org

EncounterID DRG LOS BloodGlucoseResult CreatinineResult1 870 8 80 2.32 281 3 170 0.83 313 5 100 0.6

Relational Database Flat, Tabular Data

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How can we flatten repeated measurements?

EncounterID ResultTime ResultValue1 11/18/14 8:00 1.71 11/19/14 10:30 2.81 11/20/14 8:25 1.12 2/26/15 19:15 0.83 9/14/15 11:27 0.563 9/15/15 7:40 0.513 9/16/15 7:27 0.523 9/17/15 9:38 0.543 9/18/15 8:15 0.593 9/19/15 9:20 0.51

• Avg(x)• Max(x)• Last(x)

Reducing repeated measurements down to one number leaves out substantial, clinically relevant information!

Transactional Flat, Tabular Data

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Another Approach

EncounterID R1 R2 R3 R41 0.8 0.7 0.72 0.673 0.5 0.61 0.62 0.624 0.6 0.8 0.7

EncounterID S1 S2 S3 S41 0.8 0.73 0.7 0.72 0.67 0.67 0.67 0.673 0.5 0.61 0.62 0.624 0.6 0.73 0.77 0.7

Standardize to length m• Choose a value to meet needs

(3-5 seems to work well)• For long series, smooth to m• For short series, impute to m

Standardize the Length of Jagged Time-Series

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Cluster Patients with Similar Trends

Clustering Method• K-Means (efficient)• K-Medoids• Choosing an appropriate k

Scale Each encounter to mean• Log difference• Allows clusters to represent

trends

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Specific Clusters

“Flat” Cluster “Slight Increase” Cluster

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Additional Considerations

• Store cluster centers for easy reference• If scaled well, many types of measurements can be

clustered together• Allows for standard “Common Trends”• The trends can be named more descriptively

• Can be used in other areas• Tremendous importance in Pop Health• Clinical text (rounding observations, disease

progression)

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New Data Structure

EncounterID CreatMean CreatClus GlucMean GlucClus1 0.55 C3 93 C12 0.91 C1 156 C43 0.76 C5 72 C2

EncounterID Measure Value1 CreatMean 0.551 CreatClus C31 GlucMean 931 GlucClus C12 CreatMean 0.912 CreatClus C12 GlucMean 1562 GlucClus C43 CreatMean 0.763 CreatClus C53 GlucMean 723 GlucClus C2

Storing as a flat table is an option

Storing in a “long” format is ideal for some applications• Easily pivot in R, Python• Flexible model• Converts easily to sparse

matrix

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Alternative Approaches

• Single Value (mean, max, last, etc.)• Generative models• Constant + linear trend + quadratic

• Principle Components or other Matrix Decomposition

• Other Kernel Methods

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Summary

• The needs of advanced analytics are different than those of traditional BI

• A variety of methods exist for flattening complex health care data

• Applying more simple methods can allow for rapid model generation while maintaining interpretability

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Presenter Contact Information

Brandon [email protected]

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Structuring EMR Data For Analytics

Data & Analytics

Transcript of Structuring EMR Data For Analytics