Using Social Determinants of Health To Power Risk Contracts · 2014 Patient103 2 2 FALSE FALSE...

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Using Social Determinants of Health To Power Risk Contracts

Session 4, February 20, 2017

Klaus Koenigshausen, CEO, MediQuireJennifer Daley, MD, Market Medical Executive, Cigna

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Speaker Introduction

Klaus KoenigshausenChief Executive OfficerMediQuire

Jennifer Daley, MDMarket Medical ExecutiveCigna

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Conflict of Interest

Klaus Koenigshausen

CEO of MediQuire, a for-profit data analytics vendor

Jennifer Daley, MD

Clinical advisor to MediQuire

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Agenda

• The World’s Shortest Course in Risk Adjustment

• Personalized Care Management & Risk Contracts

• Companion Diagnostic Research Results

• Essential Conversation

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Learning Objectives

1. Recognize how traditional methods of patient risk stratification (e.g. prior

claims, diagnoses codes, provider intuition) miss care opportunities

2. Discuss how usage of social determinants data can help not only identify at

risk patients but also determine what care programs are most effective

3. Analyze how payers and providers can utilize unstructured data in their EHR

and care management software to drive better patient prioritization decisions

4. Summarize how systematic and comprehensive incorporation of social

determinants into care drives superior risk contract outcomes

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STEPS™ Category Graphics

Treatment Savings Population

Management

Patient

Satisfaction

• Disease Prevention

• Personalized Care

Electronic Data

• Care Management

Efficiency

• Avoidable Medical

Costs

• Conversion of

unstructured SDOH*

• Non-clinical data set

combination

• Patient Adherence • Better personalized

care

* SDOH = Social Determinants of Health

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The World’s Shortest Course on Risk Adjustment (TWSCRA)

1. Risk of what?

2. Model of how risk is related to outcome?

3. Limitations of data sources?

4. How well does the model predict the outcome?

5. Association vs. causality?

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TWSCRA: Risk of what?

1. Change in health status (e.g., mortality,

morbidity, functional status)

2. Healthcare utilization/cost (e.g., readmission,

future utilization/cost)

3. Clinical outcomes (e.g., onset of disease,

intermediate outcomes, cure)

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This morning the outcome of interest we’re going to focus on is….

…. Clinical Outcomes ….

as a proxy and leading indicator

for health status and healthcare utilization

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TWSCRA: Model of how risk is related to outcome?

1. Basic science

2. Clinical literature

3. Social sciences

4. Health services research

5. Other

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Year PatientId fromSP toSP Transition Prediction ConfidenceSex Age Medicaid ScoreCE

2015 Patient135 2 1 FALSE FALSE 0.998769529F 62 0 2.423



2015 Patient156 2 2 FALSE FALSE 0.999999966M 52 1 1.969



2012 Patient15 2 3 FALSE TRUE 0.184487812F 60 0 1.696



2014 Patient111 2 12 TRUE TRUE 0.890190297M 62 0 1.661







2013 Patient57 2 2 FALSE TRUE 0.863674863M 61 0 1.345








2015 Patient146 2 11 TRUE TRUE 0.064451305F 51 0 1.156


















2013 Patient44 2 12 TRUE FALSE 0.999560402F 53 1 0.879

2014 Patient129 2 2 FALSE FALSE 1 F 63 0 0.878
















2012 Patient19 2 12 TRUE FALSE 0.999999768M 52 0 0.781







2012 Patient13 2 1 FALSE FALSE 1 M 63 1 0.732




2015 Patient154 2 2 FALSE FALSE 0.9998256 F 44 1 0.691
































































































Model for Today

Patient

Risk Domains

Demo-

graphics

Severity

& Comor-

bidities

Disease

SDOH*BH*

Clinical Outcomes

for Diabetes

Hypertension

Obesity

Asthma

* SDOH = Social Determinants of Health; BH = Behavioral Health

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TWSCRA: Limitations of Data Sources

1. Claims data and other secondary data

2. Medical record data (e.g., EHRs, coded and

unstructured data, presence of SDOH)

3. Personalized medicine data (e.g., genomics)

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TWSCRA: Examples of Limitations

Claims Based Diagnosis Based

32%

21%

11%

Disease A Disease B Disease C

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TWSCRA: How well does the model predict the outcome of interest?

1. Face validity to audiences (e.g., clinical, social

scientists, health services researchers)

2. Predictive validity (i.e., how accurately does

the model predict the outcome of interest in

other populations?)

3. Generalizability

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TWSCRA: CAUTION! Association does not imply causality

1. Observational datasets

2. Large datasets with many variables may have

statistically significant associations that are

spurious

3. It’s tempting to attribute causality to the

independent variables where there is none

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What’s Missing And Why?

Source: www.weforum.org/agenda/2016/06/transforming-healthcare-for-the-low-income

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Companion Diagnostic: Introduction (1)

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Companion Diagnostic: Introduction (2)

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Why Are SDOH Important for Risk Contracting?

Example Risk Contracts Example CDx Use Cases

• Population Capitation

(e.g. ACOs)

• Bundled Payments/ Episode

of Care Payments

• Shared Savings Contracts

• Emergent risk patients

• Smarter episode triggers

• Prospective contracting/ pricing

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Our ObjectiveBuild an algorithm that identifies risk factors for specific clinical outcomes in the

low income population using EHR and other external data

EHR Data

External Data

Claims Data

+ Predict

Clinical Outcomes

Interventions

Utilization

Prescribe

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Year PatientId fromSP toSP Transition Prediction ConfidenceSex Age Medicaid ScoreCE












































2014 Patient129 2 2 FALSE FALSE 1 F 63 0 0.878



























2015 Patient154 2 2 FALSE FALSE 0.9998256 F 44 1 0.691
































































































Approach

Patient

Domains

Demo-

graphics

Severity

& Comor-

bidities

Disease

SDOH*BH*

NLP

~1,000,000 Patients

urban & rural, FQHCs

across 15 states

6 EHR vendors

Database

* SDOH = Social Determinants of Health; BH = Behavioral Health

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Value of Expanded Domains: Severity

HC

C R

isk S

co

res

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Value of Expanded Domains: SDOH & BH

HC

C R

isk S

co

res

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0

0.5

1

1.5

2

2012 2012.5 2013 2013.5 2014 2014.5 2015 2015.5 2016

HCCRiskScoresatYear-end Patient1

Patient2

Example: Tale of Two Patients

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Example: Patient Divergence

Patient 1 Patient 2

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Patient 1 Patient 2

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Patient 1 Patient 2

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Patient 1 Patient 2

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Patient 1 Patient 2

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Challenges & Next Steps

• Presence of relevant data > Personalized PoC Collection

• Applicability to different patient population

• Non-EHR data sets often highly expensive

• Combine with claims data to model cost outcomes

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What is the likely prevalence of unstructured socio-behavioral data in EHRs?

Question 1

Option A: 5%

Option B: 10%

Option C: 25%

Option D: 50%

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EHRs contain hidden SDOH/ BH data. Each factor has a small prevalence but overall they account for ~25% of patients

16.5% 12.7% 1.7% 1.6% 1.2% 0.7% 0.7% 0.4% 0.1% 0.1%0%

5%

10%

15%

20%

Anxiety Stress SubstanceAbuse

Non Smoker inSmoking HH

Soc-EconCirumstance

BehavioralHealth

DomesticAbuse

Loss and Grief Emotional Incarceration

% of patients with selected SDOH & BH factors in un-structured data

Insight

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EHRs contain hidden SDOH/ BH data. Each factor has a small prevalence but overall they account for ~25% of patients

16.5% 12.7% 1.7% 1.6% 1.2% 0.7% 0.7% 0.4% 0.1% 0.1%0%

5%

10%

15%

20%

Anxiety Stress SubstanceAbuse

Non Smoker inSmoking HH

Soc-EconCirumstance

BehavioralHealth

DomesticAbuse

Loss and Grief Emotional Incarceration

% of patients with selected SDOH & BH factors in un-structured data

Insight

Example terms: ptsd, stress, gender

identity, death, violence, reduce

tension, assault, accident, feeling

fear or nervous, fear safe, threat

and any misspellings thereof

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Adoption and higher usage of EHRs apparently improved documentation of SDOH/ BH in unstructured EHR data

0%

5%

10%

15%

20%

25%

30%

2012 2013 2014 2015 2016

% of patients with at least 1 SDOH & BH factor in EHR

1.7% 2.1%

7.9%

24.3% 25.2%Unstructured Data Examples

Structured Data Examples

Domestic Abuse

Anxiety (very few examples)

Incarceration

Domestic Abuse

Substance Abuse

Emotional Status

Death & Grief

Stress

Anxiety

Insight

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Out of all the patient information available to a provider or care manager at the point of care, what percent derives from SDOH & BH?

Question 2

Option A: 5%

Option B: 10%

Option C: 25%

Option D: 40%

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SDOH/ BH data represents a disproportionate share of what we know about a patient before disease onset but the richness of SDOH/ BH data is better after disease onset

Insight

After Disease Onset

Before Disease Onset

Demographics & Dx Codes Pharmacy Data

Disease Severity (Lab/ Vitals Data) SDOH &BH

Percent of total patient data available to provider by data domain

33% 18% 13% 37%

36% 20% 21% 24%

*

*

* Diabetes patients as example; defined as A1c > 8%

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What accuracy do you expect from a predictive risk model, e.g. Diabetic clinical outcomes?

Question 3

Positive Predictive Value (PPV)

Definition: Percent of patients accurately predicted

to have a worsening clinical outcome

Option A: 25%

Option B: 40%

Option C: 60%

Option D: 85%

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What accuracy do you expect from a predictive risk model, e.g. Diabetic clinical outcome?

Question 4

Negative Predictive Value (NPV)

Definition: Percent of patients accurately predicted

NOT to have a worsening clinical outcome

Option A: 30%

Option B: 60%

Option C: 70%

Option D: 80%

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Insight

Broad prediction models tend to have lower PPV around 30% but high NPV around 80-90%. More narrowly focused prediction models have higher PPVs around 60%.

Outcome Measurement Type of input PPV NPV

Prostate Cancer Dx PSA Physical sample 30% 86%

Prostate Cancer Dx MRI Imaging 77% 92%

Breast Cancer Dx MRI Imaging 57% 78%

30-day readmission Multivariate EHR record 29% 93%

30-day readmission # discharge meds EHR record 25% 83%

COPD readmission Multivariate EHR record 70% 63%

Sources: Adhyam, Surg Oncol (2012); Wang, Clin Cancer Res. (2017); Yang, PLoS ONE (2016); Low, PLoS ONE(2016); Picker,

BMC Health Services Research (2015); Amalakuhan, J Community Hosp Intern Med Perspect. (2012)

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Disease severity, SDOH and BH can add 20% incremental model performance for narrow, already risky populations

Insight

39%

81%

0%

20%

40%

60%

80%

100%

NPV

62%

96%

0%

20%

40%

60%

80%

100%

PPV

74%

48%

3%

19%

23%32%

Emergent Diabetes Risk Patients

(undiagnosed)

Emergent Diabetes Risk Patients

(diagnosed)PPV Model Performance By Data Type

Demo &

Dx Codes

Pharmacy

SDOH, BH,Severity Data

NPVPPV 39% PPV 62% PPV

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While there is significant under-coding, it is uniform across populations thereby not impacting our conclusions

Insight

Emergent Diabetes Risk Patients (diagnosed)Emergent Diabetes Risk Patients (undiagnosed)

Non-

Diabetic

A1C

> 8%

A1C

< 8%

Diabetic

A1C

> 8%

A1C

< 8%

0.2 0.6

HCC Score(Coded)

HCC Score (Coded& Undercoded)

0.4 1.0

HCC Score(Coded)


0.2 0.6

HCC Score(Coded)


0.4 0.8

HCC Score(Coded)


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Diabetes subpopulation identification: Asthma subpopulation identification:

3%

22%

Non-Smoker SmokingHousehold

Smoker No CessationCounseling

2%

16%

PHQ9 > 9 Anxiety/ Stress

SDOH/ BH EHR data can be useful to match patients to interventions and improve care management cost-effectiveness

Insight

Emergent Risk Diabetics (100%) Emergent Risk Asthma (100%)

Intervention: Behavioral Health Provider Intervention: Smoking Cessation Programs

Non-Smoker Smoking Household

Smoker (no Dx Code)No Cessation Counseling

PHQ>5 but no Dx Code or BH provider

Stress & Anxietyno Dx Code or BH provider

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(1) EHR data holds a wealth of social-behavioral insights that providers can’t use effectively today

(2) Disease severity, SDOH and BH data can make prediction models more accurate and is useful to ‘characterize’ populations for intervention

(3) Application of this data can result in better risk contract pricing, better care management resource allocation and personalized patient care management

Summary

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STEPS™ Category Graphics

Treatment Savings Population

Management

Patient

Satisfaction

• Disease Prevention

• Personalized Care

Electronic Data

• Care Management

Efficiency

• Avoidable Medical

Cost Savings

• Conversion

unstructured SDOH*

• Non-clinical data set

combination

• Patient Compliance • Better, more

personalized care

* SDOH = Social Determinants of Health

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Questions & Answers

Contact Details:

Klaus Koenigshausen: [email protected]

@kkoenigshausen

Jennifer Daley: [email protected]

Please remember to complete online session evaluation

mailto:[email protected]

mailto:[email protected]

Using Social Determinants of Health To Power Risk Contracts · 2014 Patient103 2 2 FALSE FALSE...

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