prapare risk stratification model overview
TRANSCRIPT
PowerPoint PresentationPRAPARE Risk Stratification Model
Overview
© 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 Do not publish, copy, or distribute this information in part of whole without written consent from NACHC.
For Learning Collaborative Participant Review
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.
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
RISK STRATIFICATION CROSSWALK Learning 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)
Iowa PCA/Siouxl and Community Health
Missouri PCA
Clinical X X X X X X X X
Mental Health/ Substance Abuse
SDH X X X X X X X X
UDS demographic
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. Moderate 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)
Utilization (5 Max)
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 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
See Next Slide
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 subcomponents 2. Vary cutoffs of high risk of PRAPARE SDH 3. Vary other component cutoffs for urgent and high risk based on resources or
interventions/enabling services available in health center/community
8
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) model 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. Otitis 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
9
(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
10
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
Phone 1 Other (enter written answer) 1
No unmet needs 0
Examples – More detail can be found in PRAPARE National Model Risk Calculator
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
1
Housing Stability: Are you worried about losing your housing? (maximum of 1 tally)
Yes (unstable housing)
0
Education: What is the highest level of school that you have finished? (maximum of 1 tally)
Less than high school degree
1
0
0
Employment: What is your current work situation? (maximum of 1 tally)
Unemployed and 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
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.
13
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
14
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
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
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%
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
Pr ob
ab ilit
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
Pr ob
ab ilit
y Total Score
Example 2: Distribution of A Population with Mean Score = 13, SD = 3
High Risk: 17-24
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
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)
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.
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
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.
Slide Number 1
Slide Number 2
Slide Number 3
Slide Number 4
Slide Number 5
Slide Number 7
Slide Number 8
Slide Number 9
Slide Number 10
Slide Number 11
Slide Number 12
Slide Number 13
Slide Number 14
Slide Number 16
Slide Number 17
Slide Number 18
Slide Number 19
Slide Number 20
Slide Number 21
Slide Number 22
© 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 in part of whole without written consent from NACHC.
For Learning Collaborative Participant Review
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.
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
RISK STRATIFICATION CROSSWALK Learning 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)
Iowa PCA/Siouxl and Community Health
Missouri PCA
Clinical X X X X X X X X
Mental Health/ Substance Abuse
SDH X X X X X X X X
UDS demographic
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. Moderate 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)
Utilization (5 Max)
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 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
See Next Slide
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 subcomponents 2. Vary cutoffs of high risk of PRAPARE SDH 3. Vary other component cutoffs for urgent and high risk based on resources or
interventions/enabling services available in health center/community
8
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) model 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. Otitis 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
9
(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
10
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
Phone 1 Other (enter written answer) 1
No unmet needs 0
Examples – More detail can be found in PRAPARE National Model Risk Calculator
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
1
Housing Stability: Are you worried about losing your housing? (maximum of 1 tally)
Yes (unstable housing)
0
Education: What is the highest level of school that you have finished? (maximum of 1 tally)
Less than high school degree
1
0
0
Employment: What is your current work situation? (maximum of 1 tally)
Unemployed and 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
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.
13
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
14
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
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
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%
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
Pr ob
ab ilit
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
Pr ob
ab ilit
y Total Score
Example 2: Distribution of A Population with Mean Score = 13, SD = 3
High Risk: 17-24
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
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)
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.
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
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.
Slide Number 1
Slide Number 2
Slide Number 3
Slide Number 4
Slide Number 5
Slide Number 7
Slide Number 8
Slide Number 9
Slide Number 10
Slide Number 11
Slide Number 12
Slide Number 13
Slide Number 14
Slide Number 16
Slide Number 17
Slide Number 18
Slide Number 19
Slide Number 20
Slide Number 21
Slide Number 22