uniting lab and claims data for next gen predictive analytics...3 session objectives •gain an...

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Uniting Lab and Claims Data for Next Gen Predictive Analytics

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Our Speakers Today

Frank JacksonExecutive VP - Payers

Prognos

Roxanna CrossAVP, Product Management

BHI

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Session Objectives

• Gain an understanding of the predictive value of clinical lab data

• Learn about approaches used by Prognos that help overcome the challenges of acquiring electronic lab results data

• Understand a use case where combining lab and claims data improved predictive performance

BHI and Prognos share experiences combining lab and claims data in the development of predictive analytics and machine learning models

Why clinical lab data?

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Lab Data Drives Key Decisions But Highly Underutilized

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70% of Treatment Decisions are Based on Lab Results

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Augmenting Claims with Disease Severity / Progression Creates New Insights

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The Power of Prognos AI and Clinical Data

Significant Curation of Lab Results Required to Make Smart Data

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How Prognos Converts “Raw Data” into “Smart Data”

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Lab Results are Clinically Deciphered

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Clinical rules and medical guidelines are applied to normalized lab results to make actionable

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Lab Relationships for Member Level

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Prognos' Application of Machine Learning

Underwriting/Member Risk Predictor AI Models to Predict Group & Member 12 month Costs

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Risk Adjustment

• Predicts gaps in diagnosis coding

• Models for high volume / high revenue impact HCCs that are industry ‘problem areas’

Capture more hard to detect understated risk with

significantly less wasteful medical record reviews

Cost & Risk Predictions

• Total Cost of Care

• HiCCs (>250K, >100K, other thresholds)

• Potentially Avoidable Events

• High Risk Events

An ensemble of models for rare, difficult to predict,

reoccurring and emerging high cost members

Intervene earlier to mitigate avoidable events& progression to higher cost

Automated Personas & Prescribed Actions

• Predicts and clusters members into groups to surface and prioritize opportunities for cost mitigation and improved outcomes

• Identifies medical, socioeconomic, andbehavioral areas of impact with prescribed actions

BHI’s Application of Machine Learning

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BHI’s algorithms are proven accurate and transparent with more precise targeting to significantly reduce wasteful reviews

We continually measure to improve

Individual risk scores drive payment

▪ Diagnosis codes on claims notoriously understated

Hundreds of thousands of medical record reviews to close gaps

▪ Significant # of wasteful reviews

Risk Adjusted Payment Systems: The Business Problem

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Measure and ImproveA Comparison of Past & Current Performance

• The Baseline (to improve from)• A large Medicare Advantage population

• 2016 results of 500K+ MMRs

• Charts reviewed for 90%+ of MA members

• gaps identified by a prior vendor’s algorithms

A retrospective comparison performed by BHI in collaboration with a Plan

• The Comparison

Prior Algorithms

BHI Algorithms

Top High-Volume Condition Groups

Volumeof MRRs

Captured Comparable

RevenueAccuracy

Significantly Less %of MRRs

Cardiovascular 88K 103% 18%

Psychiatric Disorders

43K 85% 34%

Neoplasms 57K 106% 16%

Endocrine 63K 80% 35%

Vascular 62K 95% 28%

Prior Algorithms

BHI Algorithms

MRR Targets 500,382 116,447

MRR Targets Closed

10,535 11,026

Closure Rate 2% 10%

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Measure and ImproveA Comparison of Past & Current Performance

• The Baseline (to improve from)• A large Medicare Advantage population

• 2016 results of 500K+ MMRs

• Charts reviewed for 90%+ of MA members

• gaps identified by a prior vendor’s algorithms

A retrospective comparison performed by BHI in collaboration with a Plan

Prior Algorithms

BHI Algorithms

MRR Targets 500,382 116,447

MRR Targets Closed

10,535 11,026

Closure Rate 2% 10%

• The Comparison

Even if ROI stronger to chase less, Plans are cautious about reducing reviews when understated risk accuracy is at stake

There were more complex patterns out there to be discovered

Total Revenue $$ Captured

from doing 500K+ MRRs

$$Not Targeted $$

Targeted

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Why a Machine Learning Model?

• Machine learning excels at finding insightful patterns from data. It can rapidly find patterns to shortcut the laborious process of algorithm development.

• Important to expand to include clinical data to increase predictive power. Lab data is messy and algorithm development for LOINC codes and other clinical observations is laborious requiring many hours of clinical review.

• Augment existing algorithms with a machine learning model to capture more hard to detect understated risk with significantly less wasteful medical record reviews

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• HCC 18 – Diabetes with chronic complications

• HCC 108 – Vascular disease

• HCC 22 – Morbid obesity

• HCC 111 – COPD

• HCC 85 – CHF

• HCC 161 – Chronic ulcer of skin,except pressure

Models to Compliment Existing Gap Identification Algorithms

Models trained for selectHierarchical Condition Categories (HCCs)

➢ High Volume

➢ High Revenue Impact

➢ Plans typically experience low closure

➢ Plans are finding a high degreeof revenue closed not targeted

Gradient Boosting Models with hundreds of input variables:

• 1,789 with lab variables

• 1,276 without lab variables

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Diabetes with Chronic Complications

Machine Learning + Algorithms outperformed traditional methods

What was measuredBaseline

YearBHI

AlgorithmsBHI Algorithms + Machine Learning

Targeted Gaps 100%41%

of baseline targets

60%of baseline

targets

Closure Rate 10% 23% 21%

Revenue Captured and Targeted 78% 72% 94%

• Prediction threshold 10% and greater

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Vascular Disease


Baseline Year

BHIAlgorithms

BHI Algorithms + Machine Learning

Targeted GapsBaseline

(100.0%)

29%of baseline

targets

67%of baseline

targets




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COPD


BaselineYear

BHIAlgorithms



(100.0%)

31%of baseline

targets

49%of baseline

targets




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CHF

BaselineYear

BHIAlgorithms



(100.0%)

32%of baseline

targets

40%of baseline

targets





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Morbid Obesity


BaselineYear

BHIAlgorithms



(100.0%)

44%of baseline

targets

63%of baseline

targets




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Chronic Ulcer of Skin


BaselineYear

BHIAlgorithms



(100.0%)

30%of baseline

targets

34%of baseline

targets




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Overall Impact for the Six HCC Condition Models

Machine Learning + Traditional Algorithms outperformed all

Baseline Year

BHIAlgorithms

BHI Machine Learning

BHI Algorithms+ Machine Learning


(100.0%)

33%of baseline

targets

41%of baseline

targets

55%of baseline

targets

Closure Rate 5% 13% 14% 11%

Revenue Captured and Targeted 59% 58% 71% 80%

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Impact of Adding Lab Results Data

Machine Learning with lab data performed better than without lab data

• BHI’s experience adding electronic lab results to methods

• A 10% improvement in revenue capture potential for select conditions

• Even a 1% improvement can result in millions of dollars in enhanced revenue accuracy (depending on size of health plan)

• BHI believes even greater increase is achievable through more complete acquisition of lab results data

• Prognos’ experience adding electronic lab results to claims only methods

• A 15% improvement in revenue capture potential

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Wrap Up

• Large datasets are essential to build high performing predictive models

• Combining claims data with clinical data increases predictive power

– 70% of Treatment Decisions are Based on Lab Results

• BHI and Prognos are committed to partnerships that expand value to Blue Plans by combining areas of specialization

uniting lab and claims data for next gen predictive analytics...3 session objectives •gain an...

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