Incorporating Social Data into Risk

Stratification Models to Improve Health

Equity and Demonstrate Value

PCA HCCN Conference

San Diego, CA

November 18, 2019

© 2019. National Association of Community Health Centers, Inc., Association of Asian Pacific Community Health Organizations, Oregon Primary Care Association. PRAPARE and its resources are proprietary

information of NACHC and its partners, intended for use by NACHC, its partners, and authorized recipients. Do not publish, copy, or distribute this information.

Michelle ProserDirector of Research

National Association of Community Health Centersmproser@nachc.org

Performance Accountability

76% of health centers could receive financial incentives for achieving certain clinical care targets

58% of health centers participating in a financial incentive program use an SDOH screening tool*

Commonwealth Fund 2018 National Survey of Federally Qualified Health Centers,

https://www.commonwealthfund.org/publications/surveys/2019/apr/2018-national-survey-federally-qualified-health-centers

*Preliminary finding based on NACHC analysis

Positioning Health Centers For Sustainability

• Funders and stakeholders hold health centers accountable

• More shifts towards value-based payment

• Greater demands for evidence of impact

• Growing competition

• Health centers’ unique model of care of care positions them to address the SDH

• Need for tools to

• Stratify patients by social risks to address these risks

• Document patient complexity and demonstrate value

What Is PRAPARE?

A national standardized patient risk assessment protocol built into

the EHR designed to engage patients in assessing and addressing

social determinants of health

Assess Needs →At the Patient and Population Level

Customizable Implementation and Action Approach

Respond to Needs

© 2019. National Association of Community Health Centers, Inc., Association of Asian Pacific Community Health Organizations, Oregon Primary Care Association.

From Patient to Policy Level

for insured and uninsured patients

Incorporating Social Data into Risk Stratification Models to

Improve Health Equity and Demonstrate Value

© 2019. National Association of Community Health Centers, Inc., Association of Asian Pacific Community Health Organizations, Oregon Primary Care Association. PRAPARE and its resources are proprietary

information of NACHC and its partners, intended for use by NACHC, its partners, and authorized recipients. Do not publish, copy, or distribute this information.

Rosy Chang Weir, Director of Research

Association of Asian Pacific Community Health Organizations

PCA/HCCN Conference

November 2019

Definitions

Risk stratification: process or tool for identifying—and predicting—which patients are at high risk—or likely to be at high risk—and prioritizing the management of their care in order to prevent worse outcomes (care team, clinic level)

Risk adjustment: method to offset the cost of providing health insurance for individuals who represent a relatively high risk to insurers (policy, payment level)

“Risk Stratification is an intentional, planned and proactive process carried out at the practice-level to effectively target clinic services to patients.”

Agenda

Slide from Hardin, Trumbo & Murray Presentation - November 2019

National Academy of Medicine’s Vital Directions for Health and Health Care (Blumenthal et al., 2016)

“It is clear that effective tools, care models, and policies must extend beyond strictly medical approaches to address social and behavioral factors…

Payers and health systems…need to divide patients into groups that have common needs so that specific complex care-management interventions can be targeted to the people who are most likely to benefit.”

Addressing clinical needs alone will not improve outcomes or cost:

Why Risk Stratification?

Identify high-risk, high cost patients

Understand reasons why patients are high-

risk, high-cost

Match patients to appropriate

interventions to provide higher quality

care

Inform how to allocate needed resources to care teams, including

prioritizing team workloads

Inform types of staff training needed for

managing care

Identify manageable panel sizes for care

managers/teams

Learning Collaborative Objectives

1. Help participants and national PRAPARE team better understand and assess potential risk stratification strategies and possible pathways toward a common national standardized approach with options for localized methodologies

2. Help the national PRAPARE team understand how organizations apply and use risk stratification as a strategy for improving population health

3. Help participants identify best practices and lessons learned as well as resources to improve organizations’ capacity to apply risk stratification methodologies

Learning Collaborative Participants—Thank You!

CPCA w/ CCALAC, CHP

and NEVC (California)

Iowa PCA/Siouxland

Community Health

Community HealthNet, Inc.

(Indiana)

Charles B. Wang CHC (New York)

Missouri PCAWaianae Coast

Comp CHC (Hawaii)

Compass Community

Health (Ohio)

Callen-Lorde CHC (New

York)

STRIDE CHC (Colorado)

Learning Collaborative Anticipated Outcomes

• Learn from organizational best practice risk stratification methods

• A core national standardized risk stratification method (with optional measures/methods that can be tested with PRAPARE data

• A plan to test the risk stratification method + potential future proposal concept

• Development of national webinar/publication on best practices and lessons learned

Learning Collaborative Meetings

Session TopicSession 1: Overview of Risk Stratification Using SDH Data

Session 2: Approaches to National PRAPARE Risk Stratification

Session 3: Stakeholder Input on PRAPARE Risk Stratification Model

Sessions 4 and 5: Results of PDSAs of PRAPARE Risk Stratification Model

InPerson Meeting: Review, reach consensus on, and finalize the PRAPARE risk

stratification national model incorporating social determinants of health data,

particularly the measures and metrics within each component

IN-PERSON RISK STRATIFICATION MEETING

Meeting Outcomes:

• A stakeholder-vetted national PRAPARE risk stratification model with local options

• Use cases for the national model and local options

• List of targeted interventions by risk group

• Recommendations for reporting and visualization of the risk stratification model

• Considerations for next steps for health centers nationally

As You Think Towards a National Model, Consider…

1-2 Core Principles for Risk Stratification That We Should Lift Up

• Use multiple sources of data• Balanced model that includes separate buckets for SDH, clinical, behavioral, utilization,

that culminates in overall risk score• Automated Process

• The need for reliable data (HIE, SDH data collection, etc.)

• Importance of buy-in from care team• Flexibility to allow for provider/care team input to adjust level of patient risk• Flexibility in using model at clinic level based on resources and staff

• Perfection is the enemy of good. Move forward so have starting point with majority consensus and can always modify over time.

• Use metrics that apply to every person regardless of gender.

RISK STRATIFICATION CROSSWALKLearning Collaborative Team by Component

Learning Collaborative Team

National

Model

Component

Callen-

Lorde

CHC

(New

York)

Charles B.

Wang CHC

(New York)

Community

HealthNet,

Inc.

(Indiana)

Compass

Community

Health

(Ohio)

CPCA w/

CCALAC,

CHP and

NEVC

(California)

Iowa

PCA/Siouxl

and

Community

Health

Missouri

PCA

STRIDE

CHC

(Colorado)

Waianae

Coast

Comp CHC

(Hawaii)

Clinical X X X X X X X X

Mental Health/

Substance

Abuse

X X X X X X

SDH X X X X X X X X

UDS

demographic

X X X X X X X X

Utilization X X X

Lab Results X X

PRAPARE Learning Collaborative Risk Stratification Model, Draft 4

Target population Complex patients based on general population of adult patients

Top Risk Stratification

Goals

1. Identify complex patients to facilitate appropriate interventions primarily for clinic use (clinical/community)

2. Demonstrate the complexity of patients (policy)

Data sources

including PRAPARE

SDH data used (see

detail slide)

Predictor Variables

1. Clinical

2. Behavioral Health

3. SDH

4. Demographics

5. Utilization

Outcome Variables

4. Cost

5. Medications

Risk Stratification

Process/Steps

1. Compile data from active patients having a visit in the past one year

2. Assign a score for each data component, using most recent 1-yr patient data

3. Combine and calculate total risk scores for each data component

4. Sort by total risk score and stratify patients into risk groups using standard deviations

5. Clinic team huddles to validate the risk groups (e.g., Did patients fall into expected groups?),

accounting for clinic/community characteristics (e.g. capacity for interventions, strong community

interventions) and patient characteristics (e.g., ability to manage risk, benefit, acceptability)

6. Target interventions based on the risk groups

Risk stratification

groups

1. Urgent Risk (2 Standard Deviations above Mean Risk Score)

2. High Risk (Between 1 Standard Deviation and 2 Standard Deviations above Mean Risk Score)3. Average Risk

4. Low Risk

Resources provided to

risk groups

1. Intensive care coordination

2. Community health worker intervention (community referrals) with closed loop follow-up

3. Community referrals without closed loop follow-up

Clinical (5 max)

•Number of total chronic conditions that fall in 17 UDS and CCC high-risk clinical conditions

Mental Health/SubAb (5 max)

•Number of total mental health/substance abuse conditions that fall in 7 UDS and CCC high-risk conditions

SDH (5 max)

•Number of SDH Risks

Demographics (5 Max)

•Number of UDS demographic risks

Utilization (5 Max)

Cost High Risk Medications

21

NATIONAL PRAPARE RISK STRATIFICATION, DRAFT 4

Measure

Specs(see also

risk

calculator

spread-

sheet)

Source: Most recent UDS and CCC

model (see Calculator for detailed

codes): 1.Cancer (codes for Metastatic

Cancer and Acute Leukemia, Lung and

Other Severe Cancers, Lymphoma

and Other Cancers, Colorectal,

Bladder, and Other Cancers, Breast,

Prostate, and Other Cancers and

Tumors) 2.Heart

Disease/Cardiovascular Disease

(CVD) 3.Exposure to Heat or Cold

4.Hepatitis B 5.Hepatitis C 6.HIV

7.Lack of Expected Normal

Physiological Development 8.Otisis

Media and Eustachian Tube Disorders

9.Contact Dermatitis and Other

Eczema 10.Syphilis and Other

Sexually Transmitted Diseases

11.Tuberculosis (TB) 12.Abnormal

Cervical Findings 13.Abnormal Breast

Findings 14.Chronic Lower Respiratory

Diseases and Asthma 15.Diabetes

16.Hypertension 17.Obesity

1 condition = 1

2-3 condition = 2

4-5 conditions = 3

6-7 conditions = 4

8+ conditions = 5

Source: Most recent UDS and CCC

model (see Calculator for detailed

codes)

- Depression and other mood

disorders

- Anxiety disorders including PTSD

- Attention Deficit and Disruptive

Behavior Disorders

- Other mental disorders, excluding

drug or alcohol dependence

- Alcohol Related Disorders

- Tobacco use disorder

- Other substance related disorders

(excluding tobacco use disorders)

1 condition = 1

2 condition = 2

3 conditions = 3

4 conditions = 4

5+ conditions = 5

Source: National PRAPARE

General Population Data (excluding

UDS demographic categories of

race, ethnicity, veteran status,

farmworker status, federal poverty

level, and insurance). See the

"PRAPARE Risk Responses" tab in

the excel risk calculator for the

positive or "high risk" responses.),

1 SDH risks = 1,

2 SDH risks = 2,

3 SDH risks = 3,

4 SDH risks = 4,

5+ SDH risks = 5

Source: Most recent UDS

Number of demographic risks from

UDS data (race, ethnicity, veteran

status, farmworker status, federal

poverty level, insurance). See the

"PRAPARE Risk Responses" tab for

the positive or "high risk" responses.

1 demographic risk = 1,

2 demographic risks = 2,

3 demographic risks = 3,

4 demographic risks = 4,

5-6 demographic risks = 5,

Source: MO Medicaid

1 ER visit or inpatient

hospital stay = 1

2 ER visits or inpatient

hospital stays = 2

3 ER visits or inpatient

hospital stays = 3

4 ER visits or inpatient

hospital stays = 4

5+ ER visits or

inpatient hospital stays

= 5

LOCAL

OPTION

Number of total chronic

conditions as prioritized by

clinic

Number of total mental

health/substance abuse

conditions as prioritized by

clinic

Number of SDH risks as

prioritized by clinic

Number of UDS

demographic risks as

prioritized by clinic

Number of ER

visits or inpatient

hospital stays as

prioritized by clinic

PROS Uses UDS + NACHC

national standards

Uses UDS national

standards

Uses current national data Uses current national data Uses current ACO

standards

CONS Does not include all

conditions to identify risk

for all patients, only

UDS/CCC

Does not include all

conditions to identify risk for

all patients, only UDS

Relies on comprehensive

PRAPARE administration

Relies on comprehensive

administration of UDS

demographic questions

Data not widely

available

Predictors: Clinical + Mental Health/Substance Abuse + SDH + Demographics + Utilization Outcomes

See Next Slide

•Number of ER

Visits OR

Inpatient stays

22

Clinical Component

Number of total chronic conditions that fall in 17 UDS and CCC high-risk clinical conditions

Measure

Specs

(Detailed codes in

Risk Calculator)

(Reference:

NEVHC)

Source: Most recent UDS, ICD10, Chronic Condition Count (CCC) model1. Cancer (codes for Metastatic Cancer and Acute Leukemia, Lung and Other Severe Cancers, Lymphoma and

Other Cancers, Colorectal, Bladder, and Other Cancers, Breast, Prostate, and Other Cancers and Tumors)

2. Heart Disease/Cardiovascular Disease (CVD)

3. Exposure to Heat or Cold

4. Hepatitis B

5. Hepatitis C

6. HIV

7. Lack of Expected Normal Physiological Development

8. Otisis Media and Eustachian Tube Disorders

9. Contact Dermatitis and Other Eczema

10. Syphilis and Other Sexually Transmitted Diseases

11. Tuberculosis (TB)

12. Abnormal Cervical Findings

13. Abnormal Breast Findings

14. Chronic Lower Respiratory Diseases and Asthma

15. Diabetes

16. Hypertension

17. Obesity

Scoring

(Max score = 5)

1 condition = 1

2-3 condition = 2

4-5 conditions = 3

6-7 conditions = 4

8+ conditions = 5

LOCAL OPTION Number of total chronic conditions as prioritized by clinic

23

Measure

Specs

(Detailed codes in Risk Calculator)

(Reference: NEVHC)

Source: Most recent UDS, ICD10, Chronic Condition Count (CCC) model

1. Depression and other mood disorders

2. Anxiety disorders including PTSD

3. Attention Deficit and Disruptive Behavior Disorders

4. Other mental disorders, excluding drug or alcohol dependence

5. Alcohol Related Disorders

6. Tobacco use disorder

7. Other substance related disorders (excluding tobacco use disorders)

Scoring

(Max score = 5)

1 condition = 1

2 condition = 2

3 conditions = 3

4 conditions = 4

5+ conditions = 5

LOCAL OPTION Number of total mental health/substance abuse conditions as prioritized by clinic

Mental Health/Substance Abuse Component

•Number of total mental health/substance abuse conditions that fall in 7 UDS and CCC high-risk conditions

24

Measure

Specs

(Detailed codes in Risk Calculator)

Source: PRAPARE General Population Data (excluding UDS demographic categories of race, ethnicity, veteran status, farmworker

status, federal poverty level, and insurance)Limited English proficiency

Housing status

Housing stability

Education

Employment

Insurance status

Income as a percentage of Federal Poverty Level

Food security

Utilities security

Childcare security

Clothing security

Phone security

Medicine or health care security

Other material security needs

Transportation for medical needs

Transportation for non-medical needs

Social integration/isolation

Stress

Scoring

(Max score = 5)

See the "PRAPARE Risk Responses"

tab in the PRAPARE Risk Calculator

1 SDH risks = 1,

2 SDH risks = 2,

3 SDH risks = 3,

4 SDH risks = 4,

5+ SDH risks = 5

LOCAL OPTION Number of SDH risks as prioritized by clinic

Social Determinants of Health (SDH) Component

•Number of PRAPARE SDH Risks

How is PRAPARE SDH risk scored?

Response Categories for PRAPARE Core Measures PRAPARE Tally Points by Response Category

Housing Situation: What is your housing situation today? (maximum of 1 tally)

I have housing 0

I do not have housing 1

Housing Stability: Are you worried about losing your housing? (maximum of 1 tally)

Yes (unstable housing) 1

No (stable housing) 0

Education: What is the highest level of school that you have finished? (maximum of 1 tally)

Less than high school degree 1

High school diploma or GED 0

More than high school 0

Employment: What is your current work situation? (maximum of 1 tally)

Unemployed and seeking work 1

Part-time work 1

Full-time work 0

Otherwise unemployed but not seeking work 0

Material Security: In the past year, have you or any family members you live with been unable to get any of the following when it was really needed? (Check all that apply.) (maximum of 7 tallies)

Food 1

Clothing 1

Utilities 1

Child care 1

Medicine or health care 1

Phone 1

Other (enter written answer) 1

No unmet needs 0

Examples – More detail can be found in PRAPARE National Model Risk Calculator

26

Measure

Specs

(Detailed codes in Risk Calculator)

Source: Most recent UDS

Number of demographic risks from UDS data:

Race

Ethnicity

Veteran status

Farmworker status

Federal poverty level

Insurance

Scoring

(Max score = 5)

1 demographic risk = 1

2 demographic risks = 2

3 demographic risks = 3

4 demographic risks = 4

5-6 demographic risks = 5

LOCAL OPTION Number of UDS demographic risks as prioritized by clinic

Demographics Component

•Number of UDS demographic risks

27

Measure

Specs

(Detailed codes in Risk Calculator)

Reference: MO PCA Medicaid

ACA Section 2703 Health Home

Initiative

Source: Payer Data

Emergency CPT codes - 99283, 99284, 99285, 99281, 99282

Hospital Inpatient CPT Code range 99221- 99239

Scoring

(Max score = 5)

1 ER visit or inpatient hospital stay = 1

2 ER visits or inpatient hospital stays = 2

3 ER visits or inpatient hospital stays = 3

4 ER visits or inpatient hospital stays = 4

5+ ER visits or inpatient hospital stays = 5

LOCAL OPTION Number of ED visits or inpatient hospital stays as prioritized by clinic

Emergency Department Utilization Component

•Number of ED Visits OR Inpatient stays

Cost (5 max)

• If or not the patient is among the top 5% in terms of total cost of care

High-risk Medications (5 max)

• Number of high-risk medications

28

NATIONAL PRAPARE RISK STRATIFICATION, DRAFT 4, CONTINUED

Measure

Specs

(see also risk

calculator

spreadsheet)

Source: Payer Data (Source: Pharmacy Data, if the patient is taking 5 or more high risk

medications as indicated with ICD10 code Z79- Long term (current) drug

therapy by daily)

LOCAL OPTION If or not patient is among top 5% of clinic’s priority costs of care If the patient is taking 5 or more high-risk medications as prioritized by

clinic

PROS Priority indicator/outcome for complex patients Priority indicator/outcome for complex patients

CONS Data not widely available, may be duplicative of ER utilization Data not widely available, experimental, duplicative

Outcomes (used for validation of risk model): Cost and/or Medications

Calculation of Components and Total Risk Score

Component Score Range Weight

Clinical 0-5 20%

Mental Health / Substance Abuse 0-5 20%

SDH 0-5 20%

Demographic 0-5 20%

ED Utilization 0-5 20%

Total Risk Score 0-25 100%

Risk Groups

1. Urgent Risk = Higher than 2 Standard Deviations above Mean Risk Score

2. High Risk = Between 1 Standard Deviation and 2 Standard Deviations above the Mean Risk Score

3. Moderate Risk = Between the Mean and 1 Standard Deviation above Mean Risk Score

4. Low Risk = Lower than Mean Total Risk Score

COMPARISON OF POPULATIONS WITH DIFFERENT MEAN TOTAL SCORES AND STANDARD DEVIATIONS

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Pro

ba

bili

ty

Total Score

Example 1: Distribution of A Population with Mean Score = 10, SD = 7

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Pro

ba

bili

tyTotal Score

Example 2: Distribution of A Population with Mean Score = 13, SD = 3

High Risk:17-24

HighRisk:

16-19

UrgentRisk:>24

Urgent Risk:>19

Mean Score= 10

Mean Score= 13

SD = 7 SD = 7

SD=3

SD=3

Moderate Risk:10-16

ModerateRisk:

13-15

Risk Stratification Process

Compile data from active patients having a visit in the

past one year

Assign a score for each data component, using most recent

1-yr patient data

Combine and calculate total risk scores for each data

component for each patient

Sort by total risk score and stratify patients into risk groups using standard

deviations

Clinic team huddles to validate the risk groups (e.g., Did patients fall into

expected groups?), accounting for clinic/community characteristics (e.g.

capacity for interventions, strong community interventions) and patient characteristics (e.g., ability to manage

risk, benefit, acceptability)

Target interventions based on the risk groups

Interventions for Risk Groups

• Still TBD

Higher Risk Average Risk Lower Risk

• Intensive care coordination• Closed loop referrals with follow-

up• Different staff involved• Patient Navigation• Case management• Home visitation• Longer visit time?• More hands-on care?

• Interventions for unhealthy behaviors• Health education• Shared medical visits for self-

management

• Preventive screening• Group health education• Links to community resources

without closed loop follow-up

What if you don’t have complete data?

• Participants should ideally use comprehensive data for all components where possible for best prediction.

• If data is unavailable for specific data component, risk can still be calculated but may not be as precise. Since risk score is based on mean and standard deviation, risk score is relative to your own patient population data.

• What about incomplete PRAPARE data?

Potential Strategies to Handle Incomplete PRAPARE Responses

Strategy Consider incomplete response as “no

risk” (score=0)

Consider incomplete response as the

average of the rest of the scores (score =

mean of the rest)

Example: 6 risks out

of 21, with 3 left

blank

Score of blank questions = 0;

Total score = 6

Score of blank questions = 6/18 = 0.33;

Total score = 6 + 0.33*3 = 6.99 ≈ 7

Pro Simple, no need for additional calculation Able to amend incomplete responses

Con SDH risk of patients with incomplete

responses would be underestimated;

Potential bias towards patients with

complete responses

Additional calculation required;

Potential bias towards questions where

blank mostly means “no risk” such as

migrant farm worker or veteran

**Participants should ideally use comprehensive PRAPARE responses where possible for best prediction.

If data incomplete, participants can use a combination of the two strategies above: Based on your patient

population, identify the questions where a blank response mostly means “no risk”, set blank responses for these

questions to 0, then use the second strategy on the rest of the questions.

PRAPARE National RISK Stratification Model CALCULATOR

PRAPARE Learning Collaborative, Draft 2B PDSA Results

TeamTotal number of

testing sample

Number identified as

"High Risk" or

"Urgent Risk" by the

algorithm

Number validated as

correct

% of High or Urgent

Risk % of correct

1 6 6 6 100% 100%

2 18 13 8 72% 62%

3 6 4 4 67% 100%

4 10 9 7 90% 78%

5 10 10 10 100% 100%

6 30 18 11 60% 61%Total N/

Average % 80 60 46 75% 77%

How would this look in clinic systems?

Automated Scores from Clinic

High Risk Patient Prediction

Risk Component

Patient's Risk

Score in Each

Component Notes

Clinical Score 3

Mental Health / Substance Abuse Score 2

PRAPARE SDH Score 3

Demographics Score 2

ER Utilization Score 3

Patient's Total Risk Score 13

PRAPARE Risk Stratification Example Visualization

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Non-High Risk Group Average

High Risk Group Average

Urgent Risk Group Average

Comparative Groups Risk

Scores

Patient's Total Risk Score

Patient Total Risk Score

Urgent Risk Average Score

High Risk Average Score

Non-High Risk Average Score

Modeled after Framingham Risk Visualization

Principles for Risk Stratification Model

Intended to inform but not replace clinical judgment

Encourage use of a hybrid approach using quantitative data for the risk algorithm and qualitative data from clinical staff judgment

Simple, easy to use Low-tech

Risk factors organized in categories to better

understand aspects of each component for each

patient

Uses a point system to make the complex model useful at the point of care

a.Higher score = higher risk patient

b.Range is 0 to 25, 5 for each component

Uses standard deviation to define risk tiers to account for risk scores relative to all other patients in the population/denominator

Why a local option?

• Vary SDH risk model based on local situations

• Primary difference from National Model: Vary the criteria of components based on local situations

• Asian American network prioritize Hepatitis B conditions as opposed to all conditions in Clinical Component

• Local area without good housing resources prioritize homeless in SDH component

• Local area with large opioid population weigh mental health component higher

• Various local options for consideration:1. Vary weights of components and subcomponents2. Vary cutoffs of high risk of PRAPARE SDH3. Vary other component cutoffs for urgent and high risk based on resources or

interventions/enabling services available in health center/community

AAPCHO DATA COLLECTION PROTOCOL:THE ENABLING SERVICES ACCOUNTABILITY PROJECT

Enabling Services Accountability Project

(ESAP)

The ONLY standardized data system to track

and document non-clinical enabling

services that help patients access care.

Enabling Service Categories Code

Social Services Assessment SS001

Case Management CM001

Referral- Health RF001

Referral- Social Services RF002

Financial Counseling/Eligibility Assistance FC001

Health Education- Individual (one-on-one) HE001

Health Education- Small Group (2-12) HE002

Health Education- Large Group (13 or more) HE003

Supportive Counseling SC001

Interpretation IN001

Outreach OR001

Inreach IR001

Transportation- Health TR001

Transportation- Social Services TR002

Other OT001

Data Collection Protocol, Handbook, and other resources at:http://enablingservices.aapcho.org

http://EnablingServices.aapcho.org

• Needs Assessment

• Readiness

Assessment

• Workflows

• EHR Integration

• Database Strategy

• Training Guidelines

• Report Cards

ESDC Implementation Companion

Benefits of using the National PRAPARE Risk Stratification Model - LC Perspectives

Improve Care Management and Interventions

• Meet the needs of patients in various risk tiers

• Prioritize and address care management needs that can ensure high-quality and timely care

• Make important decisions including interventions offered based on level of patient risk

• Correctly assign limited staff/resources for the highest risk patients

• Standardize our current care management practice to provide a systematic way to identify patients who need extra attention from the care team

• Define interventions for specific risk tiers to inform how resources should be used

Standardization and Systematic Approach

• Appreciate having a standard format to compare with others nationally

• Having a systematic approach to gather as much data as necessary to increase confidence in interventions by key stakeholders and staff

• Encourage uniform metrics and common methods/source of collecting the data

• Use of a universal framework with national aggregated data will allow great knowledge sharing & communication

• Easier to troubleshoot as unanticipated problems arise if all sites use the same model

Demonstrate Complexity of Patients

• Illustrates the complexity of our patients’ biopsychosocial conditions

• Build a fuller picture of intervention target needs for our most complex patients

Benefits of using the National PRAPARE Risk Stratification Model (continued)

Inform Value-based Care and Cost Savings

• Prepare us nationally for value-based payments

• Decrease total cost of care and improve outcomes for patients by focusing efforts on highest utilizers

• Develop low touch interventions to meet the needs of those patients identified as low risk tier patients

• Advance value-based care through cross-sector collaboration to improve health outcomes

Qualify for PCMH and Quality Incentives

Inform Payment and Policy

• Opportunity to work with Medicaid regarding most successful strategies to reduce health care costs and improve health

• Inform risk adjustment of social factors that will be key to payment and delivery reform we’re working towards in Missouri

• Development of a network-wide risk stratification methodology across all centers will inform the development of more robust and refined care team and care coordination models as well as provide a set of data to take to payors to inform risk and resource allocation to support care coordination at the health center level

Challenges and Solutions

Challenge SolutionAdequate staffing for implementation of

model

Risk stratification is intended to improve staff resource

allocation including training needs for care management teams

Resources to act on info As with PRAPARE, resources can be improved with increasing

data on high risk patient profile and needs

Data availability, especially for utilization and

comprehensive SDH data

Participants agreed that even with data availability issues, we

should plan our national model based on data being more

widely available in the near future

Complexity of national algorithm when clinics

do not have automated systems

All algorithms are challenging without automation. We

developed a risk calculator in excel, though automation is

highly encouraged.

Obtaining consensus among teams who had

different risk stratification approaches

Teams agreed perfection is the enemy of good. We can always

test and revise. Teams could also use the local model option.

Different clinics/locations may have different

barriers, priorities

Teams can choose to use the local model option

© 2019. National Association of Community Health Centers, Inc., Association of Asian Pacific Community Health Organizations, Oregon Primary Care Association.

Next Steps

• In-person Risk Stratification Meeting to review, reach consensus on, and finalize the PRAPARE risk stratification national model

• PRAPARE national analysis strategy with a plan to test the nationally developed risk stratification model with large patient-level dataset

• Development and dissemination of national webinar/publication on best practices and lessons learned

Save-the-Date!

“Enabling Services Data Collection –

For PCAs” Training by AAPCHO

April 15-16, 2020

Omaha, NE

Hosted by: Health Center Association of

Nebraska (HCAN) and Health Outreach Partners

(HOP)

Questions and Resources for More Information

Visit

www.nachc.org/prapare

Join our listserv!

Email prapare@nachc.org

Resources Available to Support Health Centers, PCAs, & HCCNs

✓ PRAPARE Implementation and Action Toolkit

✓ Free EHR templates for Cerner, eCW, Epic, GE Centricity, Greenway, NextGen

✓ 10 translations of PRAPARE including Spanish, Somali, Arabic, Chinese, Tagalog, Korean, Vietnamese, and more! 10 more coming soon!

✓ PRAPARE Readiness Assessments for CHCs & PCAs

✓ Recorded Webinars on PRAPARE, Workflows, EHR Templates, etc.

✓ PCA/HCCN Case Studies Highlighting Successful Strategies

http://EnablingServices.aapcho.org

Incorporating Social Data into Risk Stratification Models

to Improve Health Equity and Demonstrate Value:

California’s PerspectiveLucy Saenz, MPH

Assistant Director of Data InformaticsCalifornia Primary Care Association

California Team

Why Risk Stratification in California?

Improved Provider

Experience

Improved Patient

Experience

Lower Costs

Improved Outcomes

Quadruple Aim Standardized Data

Standardized Data

How PRAPARE positions California for Risk Stratification

• Standardized data at the state, regional and community and patient level

• Comprehensive Assessment with Different Domains

• ResourcesCalifornia Health Centers (Organizations)

PRAPARE Implementation

What CA hoped to achieve out of participating on the learning collaborative?

1. Involve California members in developing, refining, and piloting a collaborative risk stratification process for social needs

2. Support members improve outcomes for patients3. Gain knowledge to spread to all clinics in the state

What CA did to position us for the achievements?

1. Included consortium and health center partners2. Collaborated with California partners to provide input

on the model3. PDSA cycles 4. Health Center feedback is key to the development

Application and Use of the Risk Stratification Model in California

• Benchmarking: National, State and Regional Comparisons

• Community Health Center Patients

• Targeted Patient Care – Meeting Patients where they are

• Spread across the state

Recommendations on How to get Started and Why:

• Collaboration and partnerships are crucial!

• Support health centers with standardizing SDOH data (e.g. PRAPARE)

• Start small and conduct PDSA’s that will help you test the model

For More Information:

Lucy Saenz, MPH

Assistant Director of Data Informatics

lsaenz@cpca.org

Social Determinants of Health Policy and Practice: the Iowa Experience

2019 Tennessee PCA Annual Conference – October 3, 2019

Our Vision and Interest

• Provide better care to patients• Collect more robust data about other factors impacting

health

• Begin to match identified issues with solutions with the health center

• Use data to establish or grow partnerships with other community resources

• Leverage data and accompanying interventions to provide evidence to payors and policymakers about the needs of patients, a broader definition of patient risk, and to ensure adequate reimbursement for safety net providers

• Iowa has 13 health centers and one migrant/farmworker health center

• Two health centers were PRAPARE pilots (Sioux City & Waterloo) and started using the tool in 2014

• Goal is to achieve 100% adoption of PRAPARE across our clinically integrated network (CIN), IowaHealth+

• Six health centers have implemented PRAPARE and four health centers are actively working to implement PRAPARE

• Last CIN member owner health center is migrating to new EMR• Three other health centers are not part of our CIN so outreach from

PCA staff has not occurred

PRAPARE Implementation Update

Sample Tableau Visualizations

1. Incorporating preventive measures/visits and hospital utilization into the methodologya) Preventive screenings not completedb) Patients not regularly coming in for care (another way to look at utilization)c) ED/inpatient visits

2. Social Determinants of Health – transportation as top concern3. Integration of behavioral health

a) More than three BH medicationsb) Medication adherence (should apply on the medical side too)c) Weigh pts. with chronic diseases AND co-morbid BH diagnoses more heavily

4. Weighting of controlled vs. uncontrolled diseases 5. Patient activation/engagement – readiness to change, confidence, literacy 6. What diagnoses would automatically put a patient in a high-risk tier and how do we incorporate provider/care team

discretion?a) Heart failure, kidney failure, diabetes, coronary artery disease, COPD, chronic hypertension, certain BH

diagnoses, Hep C, Sickle Cell7. Incorporation of claims data, including ED utilization for oral health issues, and identifying patients who used the ER

and were admitted to the inpatient setting8. Centers will want to see how different weighting structures influence the patient risk tier mix

Parking Lot – Risk Stratification

1. Risk scoring needs to be automated2. Without universal PRAPARE screening, scoring will be inaccurate3. PRAPARE data needs to be captured accurately4. PRAPARE questions need to be accurately weighted (education, stress, social

isolation, employment)5. Medications for high-risk conditions should be used as a predictive variable to

properly weight medical and behavioral health conditions, not as an outcome variable. ICD-10 should not be used to determine high-risk medications.

6. Total score determines who will benefit from care coordination, individual predictive variables determine which care team member will provide the care coordination.

7. Flexibility based on health center resources, what score and who to manage?

Top Recommendations for National Model

• Appointment length based on risk score

• Provider empanelment

• Provider burnout

• Insurance contracting

Administration/Leadership Considerations

Sarah Dixon, MPA

Chief Strategy Officer

sdixon@iowapca.org

Questions?

@iowapca www.iowapca.org

Hearing from You:

HOW IS RISK STRATIFICATION HELPFUL?

Care managementPopulation health/QI

Community health/partnership

Payment reform, risk adjustment

Clinic Full spectrum patient care System

Identifying individual

patient needs to better

develop treatment plans

Determining populations of

greatest risk for care

coordination

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