Risk Stratification in Mental Health using Big Data

Copyright © SAS Institute Inc. All rights reserved.
Risk Stratification in Mental Health
Using Big Data and
Artificial Intelligence
Learn how risk stratification tools can help determine the likelihood of future
health care events and increase early intervention and treatment of at risk
patients.

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Imagine two patients/members being
discharged at the same time…
• Your goal?
• Prevent Re-admission
• Your challenges?
• Limited resources for intervention
• Knowing where and how to apply those
resources/interventions for the greatest impact
• The first step?
• Understanding who is most at risk
• Knowing how best to intervene
Why is Risk
Stratification
IMPORTANT?

Imagine two patients/members being
• Imagine two patients/members being
• Sally and Sarah both looks the same from a
“traditional data” perspective – same ages,
similar medical history, etc.
• However, they are not same – one of them is at
a much higher risk of readmission. Which one?
• That is where risk stratification comes
in…
Why is Risk
Stratification
IMPORTANT?

OBJECTIVES Risk Stratification in Mental Health Using Big Data and AI
Risk stratification helps payers optimize spending and group
patients based on health risk. It involves processing large amounts
of data and continuously improving algorithm accuracy.
Mental health parameters are complex and require sophisticated
self-learning algorithms.
• Introduction to risk stratification in mental health
• Implementation strategies
• Benefits

COMPLEXITIES OF MENTAL HEALTH
Behavioral health is the horizontal play for all of chronic disease care.
• About one-third of all chronic disease care expenses are directly attributable to co-occurring
untreated mental illness according to the CDC.
• 68% of patient’s with behavioral health concerns also have one or more chronic conditions.
• Behavioral health conditions coupled with chronic co-morbidities typically results in
healthcare costs that are 75% higher than those without.
• The presence of a behavioral co-morbidity is attributed to poor adherence to treatment
programs greater reoccurrences, and readmissions.
Mental health parameters are complex and require sophisticated self-learning
algorithms.

Treating Mental/Behavioral Health
“Improving behavioral health requires a solution that is individualized,
scalable, patient-centric and science-based, and that empowers patients and
practitioners to work together to address, broadly, the underlying biological
systems that have to be balanced to remediate disease and promote
optimal wellness,” says
Cary Sennett, M.D., Ph.D.,
President of Medical Education and Research
Institute for Functional Medicine
“Technology will be essential to that.”

COMPLEXITIES OF MENTAL HEALTH
Patients with behavioral health issues are among the most difficult and
expensive to manage.
• In the US alone, the cost to treat behavioral conditions is forecasted to grow to $280 Billion
by 2020 according to the Substance Abuse & Mental Health Services Administration
(“SAMHSA”).
• Depression alone is the sixth most costly disease in the US.
• Beyond the direct cost of treatment, depression results in 200 million lost workdays a year
and approximately $44 Billion in lost productivity according to the CDC.
According to the American Psychiatric Association, between $26.3 and $48.3 billion in savings
could be realized in annual healthcare costs by integrating early detection and preventative care
options for mental health conditions.

SOME FACTS ABOUT BEAHVIOURAL/MENTAL HEALTH…
23%
Of years of lost to disability are
caused by the combination of
mental illness and substance
abuse
Mental Disorders
Are considered important risk
factors for other illnesses, as well
as unintentional and intentional
injury
1 in 5
American adults will have a
diagnosable mental health
condition in any given year
50%
Of all lifetime cases of mental
illness begin by age 14 and 75%
begin by age 24
Of cocaine is used by individuals
who have experienced a mental
illness at some point in their lives
Approximately
800,000 people commit suicide
every year
84%
Source: http://www.who.int/features/factfiles/mental_health/mental_health_facts/en/index9.html

RISK
STRATIFICATION
Risk Stratification in Mental Health Using Big Data and AI
Risk stratification scoring assists in
• Developing personalized care plans for at-risk patient
• Creating financially efficient population management
• Prioritizing clinical workflow
• Reducing system waste
Risk stratification plays an essential role in allocating
healthcare resources and managing population health.

HOW TO IDENTIFY RISK
Population Data
Raw Data from EHR/EMR/other data source
Determine Risk Level
Calculate High or Low Risk based on an algorithm
Calculate Risk
Population data and risk level
Stratify
Analyze data and stratify population by risk
Data Risk Level
Stratify by
Risk
Risk Calculation

RISK INDICATORS
Diagnosis
Hospitalization
History
Social
Determinates
of Health
Emergency
Department
Utilization History
Medical
Comorbidities

RISK INDICATORS
Diagnosis
Hospitalization
History
Social
Determinates
of Health
Emergency
Department
Utilization History
Medical
Comorbidities
• Depression
• Addiction
• Life Style

HOW BIG DATA AND AI POWERED RISK STRATIFICATION WORKS
INTELLIGENCE PLATFORM
Aggregate data from
across the enterprise.
RISK SCORING
Sort patients based on comorbidity
scores & clinical indicators
DATA SOURCE RISK STRATIFICATION
Create registries to identify
patients with risk factors.
CARE MANAGEMENT
Refer for enrollment
consideration in a care
management program.

OVERVIEW OF RISK STRATIFICATION METHODS
Several different methods are available for stratifying a population by risk:
Charlson Comorbidity Measure – The Charlson
Model
Hierarchical Condition categories (HCCs) - CMS
Medicine Advantage Program
Adjust Clinical Groups (AGC) – Johns Hopkins
University
Elder Risk assessment (ERA)
Chronic Comorbidity Count (CCC) – Clinical Classification
software from AHRQ
Minnesota Tiering (MN) – Major Extended
Diagnostic Groups (MEDCs)

CASE STUDY: OPIOID RISK STRATIFICATION MODEL
Initial Screening
1. CURES
2. UDS
3. Screening Tools
Substance Use Risk Factors
Addiction Evaluation
Psychology Evaluation
Psychological Risk Factors
Psychology Evaluation
“Low Risk”
Opioid Agreement
Initial Treatment
Integrate Information to
Determine Pathway
Active Substance Abuse
Disorder
Hold treatment until Stable on
Addiction Treatment
At Risk
Customized treatment
plan on specific risk
factors Identified
Reclassify as
“Low Risk”
Low Risk
Transfer to Primary Care
AberrantBehavior
2 Months
1. UDS
2. CURES
3. 4 As
3 Months
1. UDS
2. CURES
3. 4 As
Reclassify as “At Risk”
4As, analgesia, activity, adverse effects, and aberrant behavior; CURES, Controlled Substance Utilization Review and Evaluation; UDS, Urine drug screen

INTELLIPAYER REFERENCE DATA ARCHITECTURE
EMR
Billing
MPI
Provider Master
Coding
Members
Claims
Payers
Health System
Capture
Patient
Integration and Transformation
Encounter
Claim
Reference
Provider
Location
Other
Master
Data
DataIntegration&Transformation
Patient Analytics
DataAccess-Navigation&Security
Targeted Populations
& Outcomes
Accountability Models
Financial Data
Population Health
Management
Consumption
Dashboards &
Analytic Views
Reports
Contract Measures
Performance
Summary
Baseline Expenditure
Provider Profile

DATA FLOW MODEL FOR MENTAL HEALTH

BIG DATA IN RISK STRATIFICATION
BIG DATA
Stores large amounts of data
Handles structured and unstructured
data
Transacts large amounts of data
Supports multiple views of same
data set
Rapidly integrate new data source

AI IN MENTAL HEALTH RISK STRATIFICATION
• Develops predictive models based on
• Psychosocial factors
• Comorbidity factors
• Behavioural factors
• Social determinants
• Rapidly integrate new mental health science findings
• Self learning algorithms improves performance with
time

INTELLIPAYER – RISK MODEL
SAS Enterprise Guide

INTELLIPAYER – RISK MODEL OUTPUT
SAS Enterprise Guide Data Modeling

WHAT DOES THIS MEAN?
Tier
Predicted Probability Cut
offs
Patients with
Opioids
Number of
Patients
% Actual
Opioids
Odds Ratio Confidence Interval
High >=0.6 233 293 80% 17.88 (13.273,24.0861)
Medium 0.4<=Pred_Prob<=0.59 133 269 49% 3.72 (2.8861,4.7948)
Low <0.4 433 2903 15% 0.09 (0.0736,0.1101)
• The odds of Opioids in high risk tier are 17.88 times greater than the odds of Opioids in medium or low risk tiers.
• We are 95% confident that the true odds ratio is between 13.273 and 24.0861.
• The null value is 1, and because this confidence interval does not include 1, the result indicates a statistically significant
difference in the odds of Opioids in high risk tier versus other tiers.

INTELLIPAYER - DATA SCORING PROCESS

INTELLIPAYER

RISK STRATIFICATION IMPLEMENTATION STRATEGY
Scoping
• Analyze Past claims
• Analyze and gather target disease / condition information
• Formal / informal interactions with medical experts
• Identify and isolate data element which is of impact
Development
• Develop Model
• Train using training data sets
• Validate
Implement

THE BENEFITS
RISK STRATIFICATION
Prescription drug claims data identifies patients who are at risk of
opioid misuse or overdose.
Alzheimer's (AD-dementia) patients receive earlier intervention resulting
in the delay of cognitive deterioration.
Clinically significant improvements in at-risk patients for depressive
disorder or clinical depression demonstrated.

WIN-WIN
• A set of interventions designed to maintain and improve a
patient’s health across the full continuum of care from low-
risk, healthy individuals to high-risk individuals with one or
more chronic condition for greater Population Health
Outcomes.
• A culture of measurement & problem solving - Clinicians
develop the skills to use Data to inform Care
Provision/Decision Support/ Evidence-based Medicine care
plans.

QUESTION & ANSWER

THANK YOU

Risk Stratification in Mental Health using Big Data

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