1

PRAPARE Risk Stratification Learning Collaborative

© 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 Do not publish, copy, or distribute this information in part of whole without written consent from NACHC.

KICKOFF WEBINAR

May 20, 2019

2

OVERVIEW OF SDH RISK STRATIFICATION MODELS

Rosy Chang Weir

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 type of staff training needed for

managing care

Identify manageable panel sizes for care

managers/teams

3

WHAT WERE THE GOALS FOR SDH RISK STRATIFICATION?

4

WHAT WERE TARGET POPULATIONS USED FOR RISK STRATIFICATION?

Medicare and commercially insured

patients

High-cost (top 1-10 percent), medically complex patients

High-need patients (medically frail)

Adult patients who had two or more hospital

admissions within the preceding 12 months

Ambulatory adult patients continuously enrolled in global risk

Medicare, Medicaid, and commercial contracts

payer claimsPCP and nurse care manager review

of medical, social, and behavioral issues

health expenditures clinical data on acute utilization primary care team assessment

hospital databilled charges demographicspayer sourcecomorbidities

care team assessment patient interviews

administrative data on clinical and service utilization variables of

interest from clinical and financial data warehouse

medical neighborhoodsocial supports

medical complexityself management, coping skills, &

mental health

payer claimssocial needs

substance abusepsychological disorders

unstable housing

5

WHAT OTHER DATA SOURCES WERE INCLUDED IN RISK STRATIFICATION MODELS?

Adjusted Clinical Groups (ACGs)

Hierarchical Condition Categories

(HCCs)

Elder Risk Assessment

Chronic Comorbidity

Count

Charlson Comorbidity

Index

Minnesota Health Care

Home Tiering

6

WHAT EXISTING ALGORITHMS WERE INCLUDED IN THE RISK STRATIFICATION MODELS?

7

CHARACTERISTICS OF HIGH NEED HIGH COST PATIENTS

Example 1 1.Each month, the care team identifies the top 1

percent of high-cost patients for the previous twelve months

2.Evaluate each patient’s needs and assess whether patients will be amenable to care through patient interviews and a review of

medical records

3.A chosen subgroup is admitted to the care program

8

WHAT WAS THE SEGMENTATION PROCESS?

Example 21. Pull EHR and claims data, in addition to patient

hospital admission feeds

2. Use 138 variables across medical and pharmacy utilization, diagnoses, and sociodemographic

factors to predict a patient’s risk of hospitalization within six months

3. Segment high-risk patients into four subgroups

9

WHAT WAS THE SEGMENTATION PROCESS? (CONT’D)

Example 3:1. Risk-stratify patients using the Milliman Adjusted Risk Score, focusing on the top

decile of risk for future cost.

2. Pair the risk-stratification results with an internally developed risk assessment called the “Worry Score,” (diagnoses and control of chronic

conditions, recent acute care utilization, smoking status, age, and socioeconomic risk factors)

3.Select patients with a certain score into complex care management program.

Highly complex

High-risk

Rising-risk

Low-risk

10

RISK STRATIFICATION GROUPINGS(NACHC RISK STRATIFICATION ACTION GUIDE)

Complexity

Planning Monitoring

Communication

Design care models and

target interventions for each risk

group

Whole population risk stratification (e.g., by certain

conditions)

Inclusion of SDH at initial stage?

Inclusion of SDH after initial risk

stratification?

11

DIFFERING METHODS:AT WHICH POINT SHOULD SOCIAL COMPLEXITY BE

INCLUDED IN RISK STRATIFICATION?

Inclusion of SDH at another stage?

• High Risk• Medium Risk • Low Risk

• High Risk• Medium Risk • Low Risk

Should different weights be used?Risk scoring

Should all SDH data be used?Development of clusters

12

OTHER CONSIDERATIONS

Case management that involved primary care–

based care management by a PCP and nurse care

manager

Enhanced primary care and transitional care

Multidimensional patient assessments and care

planning

24/7 access to a multidisciplinary care team

Placement in primary care panelSpecialist disease management

Nurse care managementMental health services

Social support Pharmacy support

13

WHAT RESOURCES WERE PROVIDED TO HIGH RISK PATIENTS?

Importance of multiple sources of data – both quantitative and qualitative

Importance of clinical, patient, and stakeholder engagement to increase clinical relevance

Common targeted outcome: decrease in health utilization (spending) such as hospitalization and ED visits, comorbidities

Huge gap: inclusion of SDH data Effective models must include social and behavioral factors

14

SOME LESSONS LEARNED FROM RISK STRATIFICATION LITERATURE

To be continued…

“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.”

15

NATIONAL ACADEMY OF MEDICINE’S VITAL DIRECTIONS FOR HEALTH AND HEALTH CARE

(BLUMENTHAL ET AL., 2016)

16

OVERVIEW OF LEARNING COLLABORATIVE PRAPARE RISK

STRATIFICATION MODELSRosy Chang Weir

Siouxland Risk Stratification Approach*Medical Complexity

Risk Score:Chronic Disease and

Behavioral Health

High-Risk Conditions (5 or >=10, 2-4=3, 1=1)*

A1c% (9 or >=6, 7.5-8.9=3, 7-7.5=1)

Meds for High-Risk Conditions (8 or >=10, 3-7=3, 1-3=1)

*See second slide

PRAPARE Score Cost Score

ER visits last 12 mo. (3 or >=10, 2=5, 1=2.5)

Hospitalizations last 12 mo. (2 or >=10, 1=5)

PRAPARE responses used to add a SDOH risk score**

Highest SDOH risk score is 46

17*Format credit to RiverStone Health

Care Coordination Strategies

Siouxland’s care team strategy includes using the following care team members with patients in a more informed and targeted manner:-RN Health Coach/Case Manager-mainly medical issues-Clinical Pharmacist- multiple high-risk medications-BH Case Manager- mainly mental health issues-Community Health Worker-mainly SDOH issues-Some patients may require all 3-4 disciplines-High PRAPARE score will proactively be addressed in every circumstance, regardless of medical risk.

WCCHC PRAPARE Risk Stratification Approach

Chronic Disease Score

• Diabetes• Hypertension

PRAPARE Score

Cost Score(Access to actual cost

of care data)

• Primary Care Visits

• Behavioral Health Visits

• Emergency Room Visits

• Hospital Admissions

• Specialist visits

• Medications

• Diagnostics

• Labs

• No Shows, Canceled and

Rescheduled Appointments

• Home Visits and Outreach

• Enabling Services

• PRAPARE SCORE –

weighted scores given for each

positive answer in the

PRAPARE tool

18

Behavioral Health Score

• Behavioral Health Diagnosis• Substance Abuse Diagnosis

Compass Community Health PRAPARE Risk Stratification Approach

PRAPARE Data Elements for Adults

• Migrant Worker• Veteran• Number living in home• Housing Concerns• Neighborhood• School level completed• Employment Status• Insurance• Clothing/Food Insecurity• Transportation • How often interact with others• Stress• Incarceration Hx• Refugee• Feel Safe Where Live• Afraid of Partner

PRAPARE ScoreCommunity

Resource/Close-Loop Referral

• Did the patient and resource

connect (If not CCH will

reconnect with patient to

identify why)

• How often will they be meeting

• Does an internal referral need to

be made – LISW, Psych NP,

AOD facility

• Huddle with medical provider

• Document resource connection

successful and plan for patient

• Document number of No

Shows to appointments in office

and with resources

• PRAPARE Score = 1 point for

each positive answer

• 0-3 = Low Risk, as needed

follow-up/annually

• 4-6 = Moderate Risk, routine

follow-up at next visit (reassess

as needed), complete

community referral if needed

and close-loop on referral

• 7 or more = High Risk, routine

contact, follow-up (reassess at

each visit), complete community

referral and close-loop on

referral19

PRAPARE Data Elements for Pediatrics

• Age• Who lives with you• Where do you sleep• Do you feel safe where you live• What grade they are in at school• Do they have a job before/after

school• How do they get to school and

appointments• Do they have friends, how many,

how often they “hang out” or talk• Who helps you with homework• Do you worry about things at

home or at school• Have you ever gotten in trouble at

school? Have you ever had to speak with the law regarding something you’ve been involved in?

• Are you afraid of someone at school or in your home?

• Do you have clean clothes to wear• How often do you eat? What do

you eat?

FUTURE NEVHC PRAPARE Risk Stratification

Medical Risk Factors (NACHC’s Counting Chronic Conditions)

• Diabetes• Hypertension• Asthma• COPD• IVD

Social Risk Factors (PRAPARE Score)

Health System Utilization

(Cost Score)

• Frequency of visits (MD, Pharmacist,

BH, etc)

• Number of Medications

• Number of referrals

• Number of No Shows

• Number of Medications

• Number of Hospital Admissions

• Number of Emergency Dept. Visits

• PRAPARE SCORE –

weighted points given for

positive answer in the

PRAPARE tool. More weight

given to positive domains

with a higher predictive

impact on health outcomes;

e.g., homelessness.

*Draft scoring method needs to

be validated (included in slide

deck)

20

Behavioral Health Risk Factors

• PHQ-2/9 Score• SBIRT Score

• Abnormal Breast Findings• Abnormal Cervical Findings• Alcohol Related Disorders• Anxiety Disorders Including PTSD• Asthma• Attention Deficit and Disruptive Behavior

Disorders• COPD• Contact Dermatitis and Other Eczema• Dehyrdation• Depression and Other Mood Disorders• DM• Exposure to Heat• Heart Disease• Hep B, Hep C• HIV• HTN• Lack of Expected Normal Physiological

Development• Other Mental Disorders, Excluding Drug

OR Alcohol Dependency• Other Substance related disorders• Otitis media and Eustachian tube disorders• Overweight and obesity• Sexually transmitted infections• Tobacco use disorder• Tuberculosis

Copyright, Starling Advisors LLC, 2017. All rights reserved*Slide deck on Risk Stratification Model here.

Charles B. Wang CHC Risk Stratification Approach

Callen Lorde Risk Stratification Approach

Callen Lorde Risk Stratification Approach

MO PCA:Planned Phase 2 Missouri CHC Risk Stratification

(Population Health Tool Azara DRVS)

Phase 1 Risk Stratification: Chronic

Disease Score

• Diabetes• Hypertension• Asthma• CHF• SMI• Other chronic conditions

Phase 1 Risk Stratification: Other

Factors Score

Phase 2 Risk and Added Criteria Score

(not included in Phase 1 due to data not being

available)

• Number of Hospital Admissions

• Number of ER visits• Claims information to allow

incorporation of cost• Medication possession ratio• SDOH utilizing PRAPARE as

the screening tool• Other criteria will be added as

additional data points become available and selected by clinician committee for inclusion

• Gender• Age

• Number of Medications

• Use of opiates

• Vital Signs (such as BP, BMI)

Phase 1 Risk Stratification: Lab

Values Score

• A1C• GFR• LDL• Etc.

COMMUNITY HEALTHNET RISK STRATIFICATION

Chronic Disease Score Mental Score PRAPARE Score Cost ScoreDiabetes Mental Health

DiagnosisPRAPARE Score-1 point for each positive answer in the PRAPARE Tool

Frequency of Provider visits

HTN Substance Use Diagnosis

Number of referrals

Hyperlipidemia Numbers of no showsCAD Number of cancelled

and rescheduled appointments

Number of ED visitsNumber of Hospital visits

26

STRIDE CHC

Medical + Behavioral Health + SDH (Housing, Uninsured) + Labs & Vitals + Medications

Stride PRAPARE Risk Stratification Approach

Chronic Disease Score

• Diabetes• Hypertension• Hyperlipidemia• ASCVD• CHF• CAD• Ischemic Stroke• Hemorrhagic Stroke • IVD• Afib• Persistent Asthma• COPD• Chronic NonMalignant Pain• Cirrhosis• Other chronic conditions • BMI >= 35 and < 40 (1) • BMI >= 40 and < 45 (3) • BMI >= 45 (5) • BMI Percentile 95+ (Z68.54) (3) • Systolic BP >= 150 (3) • Diastolic BP >= 90 (3)

PRAPARE Score

Cost Score – Work in Progress to ultimately

include ER Utilization/Drug Costs

• Anticoagulant Medications

• Chronic Opioid Therapy

• PRAPARE SCORE – 1 point given for each

positive answer in the PRAPARE tool for

housing insecurity, and uninsured

• We then take those that have completed a

full PRAPARE and identify social needs

within risk categories (i.e. of our high risk

who have completed a PRAPARE who

identified food or transportation

insecurities)

• Work in progress to eventually include the

5 major social areas we find most our

patients need assistance in (food/clothing,

housing, transportation, healthcare costs,

support system insecurity)

1

Mental Score

• Severe Mental Illness • Schizophrenia • Bipolar Disorder • Anxiety • Depression• Dementia & Mental • Autism Spectrum Disorders• ADHD• SED• Tobacco User • SAD/SUD• Illicit Drug Use Disorders

27

OTHER SDH RISK STRATIFICATION MODELS

Rosy Chang Weir

RiverStone Health PRAPARE Risk Stratification Approach

Chronic Disease Score

• Diabetes• Hypertension• Asthma• COPD• IVD

PRAPARE Score Cost Score

• Frequency of visits (MD,

Pharmacist, BH, etc)

• Number of Medications

• Number of referrals

• Number of Labs

• Number of No Shows

• Number of Canceled and

Rescheduled Appointments

• Number of Medications

• Number of Hospital

Admissions

• PRAPARE SCORE – 1 point

given for each positive

answer in the PRAPARE tool

28

Mental Score

• Mental Disease Diagnosis• Substance Abuse Diagnosis

PETALUMA HEALTH RISK MODEL

Inf luenced by AAFP and other r isk models ( l ike HCC condi t ions)

Incorporates r isks, chronic condi t ions, SDOH, medicat ions, ED ut i l izat ion, and admissions

Point Values: Risks = ½pt Chronic condi t ions = 1pt SDOH and ut i l izat ion mixedRisk Level” 0-2 points = Low Risk 3-4 points = Medium Risk >5 points = High Risk

29CENTER FOR CARE INNOVATIONS | 29Source: Raven E-Learning Webinar: Risk Stratification/Population Health, March 2019

SAN MATEO MEDICAL CENTER RISK MODEL (CERNER)

Risk index incorporates member-level data from EHR and other data sources, including:

Demographics

Claims data

Dx mapping using Healthcare Cost and Utilization Project (HCUP)’s Clinical Classification Software (CCS)

Rx mapping using Multum ontology

Labs and vitals

BMI, systolic and diastolic blood pressure, cholesterol values/ratios, and HbA1c

Socioeconomic features

CDC’s Social Vulnerability Index (SVI)

Constructed features

Annual severity Source: Raven E-Learning Webinar: Risk Stratification/Population Health, March 2019

31

FOR REFERENCE ONLY

EXAMPLES OF RISK STRATIFICATION MODELS THAT INCLUDE SDH

CLINICIAN CONSIDERATIONS WHEN SELECTING HIGH-RISK

PATIENTS FOR CARE MANAGEMENT

Author, Program Haime et al.

Target population

Medicare and commercially insured patients 18 years or older participating in a primary care based, nurse-led CMP at Partners HealthCare (not for-profit health care system)

Targeted outcomes

Assist high-risk patients to better manage their health and healthcare utilization.

Segmentation Process

1. Upload prior year of paid claims to Optum Impact Pro software and generate chronic conditions, utilization patterns and a predictive risk score for future total medical expense.

2. Use an internally developed algorithm that incorporates an overall risk score, combinations of specific chronic conditions, and patterns of health care utilization to develop an initial list of the 5 percent of patients identified as high-risk.

3. Sort patients into a PCP specific list for clinical review. 4. PCP and nurse care manager dyad review list and select patients appropriate for CMP.

Subgroups 1.Appropriate for the CMP 2. Not appropriate for the CMP Exclusion criteria: Some interviewees excluded patients whose primary diagnosis was a psychiatric or substance abuse condition because they felt the CMP did not yet have the resources to meet these patients’ needs.

Data sources Hybrid: • Quantitative = billing claims • Qualitative = PCP and nurse care manager review of patients’ needs based on knowledge of patients and their medical, social, and behavioral issues.

Resources provided to subgroups

The CMP involved primary care–based care management by a PCP and nurse care manager. (Description of CMP was not the focus of this article.)

FINDING A MATCH: HOW SUCCESSFUL COMPLEX CARE

PROGRAMS IDENTIFY PATIENTS

Author, Program Hong

Target population

Patients at Cambridge Health Alliance (safety net delivery system)

Targeted outcomes

Identify care sensitive, high-risk patients to include in complex care programs.

Segmentation Process

1. Risk-stratify patients using the Milliman Adjusted Risk Score, focusing on the top decile of risk for future cost.2. Pair the risk-stratification results with an internally developed risk assessment called the“Worry Score.” The score takes into account diagnoses and control of chronic conditions,recent acute care utilization, and a list of modifiers including smoking status, age, and socioeconomic risk factors.3.Select patients with a certain score into their complex care management CCM program.

Subgroups 1.Selected for CCM program. 2.Not selected for CCM program.

Data sources Hybrid: • Quantitative = health expenditures, clinical data on acute utilization • Qualitative = primary care team assessment

Resources provided to subgroups

Authors did not describe specific resources provided to high-risk patients.

THE ECONOMIC IMPACT OF INTENSIVE CARE

MANAGEMENT FOR HIGH-COST MEDICALLY

COMPLEX PATIENTS: AN EVALUATION OF NEW MEXICO’S CARE ONE

PROGRAM

Author, Program Horn et al.

Target population

High-cost (top 1 percent), medically complex patients at University of New Mexico’s Health Sciences Center (public teaching hospital)

Targeted outcomes

Evaluate the economic impact of Care One, an intensive care management program designed to target the most expensive 1 percent of patients in a university health care system.

Segmentation Process

1.Each month, the Care One team identifies the top 1 percent of high-cost patients for the previous twelve months. 2.Evaluate each patient’s needs and assess whether patients will be amenable to care through patient interviews and a review of medical records. 3.A chosen subgroup is admitted to the Care One program.

Subgroups 1.Selected for Care One program. 2. Not selected for Care One Group.

Data sources Hybrid: • Quantitative = HSC’s physician and hospital data, plus patient-specific data including billed charges, demographics, payer source, and comorbidities • Qualitative = care team assessment and patient interviews

Resources provided to subgroups

Patients receive the following services while participating in Care One: • Placement in primary care panel • Specialist disease management • Nurse care management • Mental health services • Social support • Pharmacy support

EFFECTIVE CARE FOR HIGH-NEED PATIENTS: OPPORTUNITIES FOR IMPROVING OUTCOMES, VALUE,

AND HEALTH

Author, Program Long et al.

Target population

High-need patients

Targeted outcomes

Identify key characteristics of high-need patients, a starter taxonomy to target care, and promising care models and attributes to better serve high-need patients

Segmentation Process

The authors did not identify a segmentation process. They did note that when identifying high-need patients, simply looking at cost alone is insufficient. They mentioned that functional limitations are key drivers of need and that because medical care is only a relatively small portion of health, identifying inadequate access to social and behavioural services is critical as well.

Subgroups The authors developed subgroups based on medical characteristics: 1.Children with complex needs 2.Non-elderly disabled 3.Multiple chronic 4.Major complex chronic 5.Frail elderly 6.Advancing illness The authors also include behavioural (for example,. substance abuse or cognitive decline) and social (for example, housing insecurity or community deprivation) factors not as individual segments but as factors that influence the care model or care team composition most likely to benefit high-need patients

Data sources The authors do not identify specific sources of data to use for subgroup segmentation. Based on the authors’ review of two taxonomies (which used claims data and clinical input) and an analysis of Medical Expenditure Panel Survey data to show the importance of behavioural health risk factors, both qualitative and quantitative data could assist in identification of high need patients.

Resources provided to subgroups

The assessment identified four dimensions of focus that constitute a possible analytical framework for identifying successful care models 1. Focus of service setting – Settings include enhanced primary care and transitional care 2. Care attributes – Attributes include multidimensional patient assessments and evidence-based care planning 3. Delivery features – Features include 24/7 access to a multidisciplinary care team 4. Organizational culture – Features of the culture include use of multiple sources of data and leadership across levels

IDENTIFYING SUBGROUPS OF ADULT SUPER UTILIZERS IN AN

URBAN SAFETY-NET SYSTEM USING LATENT CLASS

ANALYSIS

Author, Program Rinehart et al.Target population

Adult patients who had an admission at DH (integrated safety net health care system) in 2014 and two or more admissions within the preceding 12 months

Targeted outcomes

Identify clinically meaningful and distinct subgroups, which can be used by providers to target resources to specific patients and reduce spending.

Segmentation Process

1. Identify super utilizers as adult patients (> 18 years) who had a hospital admission during the study period (January 1, 2014– December 31, 2014) and had two or more admissions within the preceding 12 months of this index admission. 2. Obtain administrative data on clinical and service utilization variables of interest from DH’s clinical and financial data warehouse. 3. Identify individual-level indicator variables that represented medical, mental health/substance use disorders (MH/SUDs), and social conditions influencing overall health to include in the latent class analysis. Also in the analysis are demographic and visit-level data reflecting admissions, outpatient utilization, and total charges. 4. Use the Elixhauser comorbidity software and the Clinical Classification Software system to create validated summary variables that group similar individual International Classification of Diseases (ninth revision) diagnosis codes. 5. Use Mplus 7.1 software to run the LCA with the 30 identified dichotomous indicators and create five high-risk patient subgroups.

Subgroups 1.Class 1: Alcohol/homeless 2.Class 2: Medical, MH/SUDs, homeless 3.Class 3: Medical 4.Class 4: MH/drug use, homeless 5.Class 5: Medical (lower with some MH/SUDs) Exclusion criteria: Small group of patients requiring nearly weekly admissions for emergent dialysis, as these admissions are not preventable through existing clinical financing options.

Data sources Quantitative: Administrative data on clinical and service utilization variables of interest from DH clinical and financial data warehouse

Resources provided to subgroups

Segmentation was completed for purposes of the study. The authors suggested the following resources to provide patients in each segment: • Class 1: Community based outreach services, or services embedded in an ED setting. Services should include multidisciplinary staff with a strong focus on housing, social support, and SUD services • Class 2: Optimized medical management with alternative primary-care models (e.g., ambulatory intensive care unit) • Class 3: Care coordination, patient navigation, or community health worker services embedded within the primary care setting • Class 4: Services either strongly aligned with or embedded within a formal MH treatment agency that also has co-occurring addiction expertise • Class 5: Screening in primary care and a strong linkage to MH and addiction services

DEVELOPING A REAL-TIME PREDICTIVE MODEL FOR

IDENTIFYING HIGH-NEEDS PATIENTS

Author, Program Brower et al.

Target population

Ambulatory adult patients at Atrius Health (independent physicians’ group) who are continuously enrolled in global risk Medicare, Medicaid, and commercial contracts

Targeted outcomes

Create a tool that determines patients’ risk levels and makes them easily visible to providers so that they can deliver needed care

Segmentation Process

Atrius developed a predictive modelling tool, the Clinical Risk Prediction Initiative. The tool takes the following steps to segment the high-risk population: 1.Pulls EHR and claims data, in addition to patient hospital admission feeds 2.Uses 138 variables across medical and pharmacy utilization, diagnoses, and sociodemographic factors to predict a patient’s risk of hospitalization within six months (low risk: < 20 percent; moderate risk: 20–50 percent; and high risk: > 50 percent) 3.Segments high-risk patients into four subgroups

Subgroups 1.Advanced illness 2.High risk 3.Complex rising risk 4.Risk prevention

Data sources Quantitative = Data from Atrius Health’s enterprise data warehouse, including clinical data from the Epic EHR; admissions, discharges, and transfers feeds; and payer claims

Resources provided to subgroups

Author did not indicate what resources Atrius Health provides to high-risk patients.

46

47

48

h t t ps : / /www. m a ss .g ov / f i l e s /do cume nts /20 16 /1 0 /pp /1 61 0-uma ss -mod e l i n g -sd h-summary - re por t .p d f h t t ps : / /www. h e a l t hma n ag ement . com/wp -co n te n t /u p lo a ds /SHVS_ Soc ia lDe te rm in an ts_ HMA_ J u l y2 0 17 .p d f h t t ps : / / eg ems .ac ad emyh ea l t h .o rg /a r t i c l e /10 .1 3 06 3 /23 27 -92 14 .1 28 2 / h t t ps : / /www. a j p mon l i n e .o rg /a r t i c l e /S0 74 9-3 79 7(1 8)32 43 3-4 /p d f h t t ps : / / ac ad emic .o up .c om/ j am ia /a r t i c l e -a bs t rac t /2 5 /1 /4 7 /46 45 25 5? red i rec te dF rom=fu l l t ex t h t t ps : / /www. n c b i . n lm .n ih .g ov /pu bme d/29 86 66 43 h t t ps : / /www. c o mmon we a l t h fu nd .o rg /pu b l i c a t i o n s / i s su e-b r i e f s /20 17 /n ov /a dd ress ing -so c ia l - d e te rmin a n ts -he a l t h - th ro ug h-med i c a id -ma nage d h t t ps : / /www. wh o . i n t / sdh co n fe re nc e / re so urce s /Con ce p tua l f ra mewo rk fo rac t i o no nSDH_ e ng .p d f h t t ps : / / an n a l s .o rg /a im / fu l l a r t i c l e /2 67 85 05 /a dd ress ing -so c ia l - d e te rmin a n ts - impro ve -p a t i e n t -c a re -p ro mote -he a l t h -eq u i t y -amer i c a n h t t ps : / /www. c o mmon we a l t h fu nd .o rg /pu b l i c a t i o n s / i s su e-b r i e f s /20 19 / j an /h ow- a co s -us e -seg menta t i o n -h i g h -n ee d-h ig h -co s t h t t ps : / /www. a h a j ou rna l s .o rg /d o i / f u l l / 1 0 .11 6 1 /CIR .00 0 00 00 00 00 00 2 28 h t t ps : / /www. s e ma nt i c sch o la r .o rg /pa pe r /C l i n i c i a n -c on s id e ra t i o n s -wh e n-se le c t i ng -h i g h - r i sk -Ha ime-

Ho n g /5 e4 d1 a 91 7 f84 7b 48 0 f fb d 63 24 34 48 53 7 b1 b8 0b d6 h t t ps : / /www. c h c f .o rg /p u b l i c a t i o n / f i nd i ng -a -match -h o w-s uc ce ss fu l - c omp le x -ca re -p rog rams- i d e n t i f y -p a t i e n t s / h t t ps : / /www. n c b i . n lm .n ih .g ov /pu bme d/27 03 17 38 h t t ps : / / na m.e du /wp - con ten t / up lo ad s /2 0 17 /0 6 /E f f ec t i ve -Care - fo r -H ig h -Ne e d-Pa t i e n t s -Exe cu t i ve -Summary .pd f h t t ps : / /www. n c b i . n lm .n ih .g ov /pu bme d/27 63 27 68 \ h t t ps : / /www. a c c o un ta b le c a re l c .o rg / s i t e s /d e fau l t / f i l e s /ACL C_CSB_ At r i u sHe a l t h _F in a l_0 .pd f h t t ps : / /www. n c b i . n lm .n ih .g ov /pmc /a r t i c l e s /PMC5 7 46 71 2 / h t t ps : / /www. n c b i . n lm .n ih .g ov /pu bme d/31 00 04 81 h t t ps : / /www. s c ie n ce d i re c t .c om/s c ie nc e /a r t i c l e /p i i /S0 74 93 79 7 18 32 43 34 ?v ia %3 D ih ub h t t ps : / /www. c o mmon we a l t h fu nd .o rg /pu b l i c a t i o n s / i s su e-b r i e f s /20 19 / f eb / t a rg e t i ng -h i g h -ne ed-b e ne f i c i a r i e s -med i c a re -a dva n tag e h t t ps : / /www. c o mmon we a l t h fu nd .o rg /pu b l i c a t i o n s / j ou rna l -a r t i c l e /2 0 16 /mar /a d d ing -mea sure -pa t i e n t - se l f -man ag eme nt -c ap ab i l i t y - r i sk h t t ps : / / pd f s . s emant i c sc ho la r . o rg /a 60 c /b3 40 d5 95 41 f d2 2 fd4 61 25 ce 8b 55 8e 5 c4e a 19 .p d f h t t ps : / /www. n c b i . n lm .n ih .g ov /pmc /a r t i c l e s /PMC4 5 05 90 5 / h t t p : / / an es th es io l o g y .pu bs .a sah q .o rg /a r t i c l e .a sp x?a r t i c l e i d=1 91 81 52 h t t p : / / g ran tome .com/g ran t /N IH /R0 1-MD0 1 16 0 7-01 h t t ps : / /www. n c b i . n lm .n ih .g ov /pu bme d/28 47 64 61 h t t ps : / /www. n c b i . n lm .n ih .g ov /pu bme d/26 88 13 87 h t t ps : / /www. n c b i . n lm .n ih .g ov /pu bme d/24 72 18 38 49

REFERENCES