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1 Co Community Pathways to Health Innovations in Data, Technology and Financing Presented by Dora Barilla, DrPH, Group Vice President, Community Health Investment

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Page 1: Co Community Pathways to Health - CHA

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Co

Community Pathways to Health Innovations in Data, Technology and Financing

Presented byDora Barilla, DrPH, Group Vice President, Community Health Investment

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Providence St. Joseph Health – Our Footprint

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Population Health Connections within the CommunityRelevance of Social Determinants of Health

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An initiative facilitated by: Key partners: AHA/HRET, IHI, NRHI, PHI, Stakeholder Health

Funded by: Robert Wood Johnson Foundation

Pathways to Population Health

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Distribution of Health Care Expenditures

Source: AHRQ https://meps.ahrq.gov/data_files/publications/st497/stat497.pdf

Top 1%

Top 5%

Top 10%

Bottom 50%2.8%

66.2%

50.4%

22.8%

Percentage of Population

Percentage of Health care Costs

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There is an opportunity to align community benefit distribution to areas of greatest need

As a nation, we spend a lot on access to healthcare But it’s only part of the nation’s health challenge!

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Most Frequently Prioritized Community Needs

Mental health

Substance abuse Nutrition/Food Insecurity

Access to affordablehealth care

Chronic Conditions/Obesity

Housing

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Social DeterminantsHealth Status IndicatorsService Utilization Primary Care Network DesignCommunity AssetsPeople Potential

Community Pathways to Health – Future CHNA Framework with Multi Source/Integrated Data

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CHNA Data Collection & Analysis

Set Strategic Priorities

Connect Data to Overall Strategic Plan

Identify Strategic Priorities and Partners

Measure & Evaluate

Identify Best Practices and Interventions

Identify Key Metrics

Disseminate Results

Galvanize Community Toward Collective Action

CONNECTING COMMUNITY HEALTHTO STRATEGIC PLANNING

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We know Social Determinants of Health (SDH) are an essential element in risk stratification

According to the CDC, 70% of a person’s health comes from socio-economic environment (e.g. family structure, location etc.) Social and economic factors drive ~ 40% of

consumer health and behavioral elements account for another 30%.

Key elements that contribute to the sustained health of a patient often are not captured in EHR Need to look for external data

Source: http://barhii.org/framework/

Upstream Downstream

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CPH – A Multi-Source Integrated Data PlatformEPIC EHR data at the patient level

Clinical information (e.g. diagnosis of dementia) Social history (e.g. history of smoking) Demographics (e.g. age, gender, etc.) Resource utilization (including charges from Insights) Addresses for patients, clinics, and hospitals for geo-

coding

Social Determinants of HealthIncome and poverty

Population receiving SNAP benefits Population with housing assistance Population in poverty Median household income

Community and housing Overcrowding Units in substandard conditions Median home value

Community Demographics Population with disabilities Linguistically isolated population

Transportation Households without car Population using public transportation

Food Access

Air Quality

Transform raw data to actionable information

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Community Pathways to Health Goals A platform used to identify areas of need where patients are most vulnerable, allowing

us to provide proactive outreach and service before neighbors become patients. Our goal is to meet people’s health needs where they need it most, in their communities.

To build or strengthen community partnerships to address social determinants of health

By leveraging the predictive functionality and extensive curated datasets within this platform, we can help patients to avoid the ED, readmission, and extended length of stays.

For Providence St. Joseph Health, this means we can better more promptly meet care management needs, prioritize our resource allocation, and ensure high quality, timely care.

Collaboration between Healthcare Intelligence, Community Health Investment, and Population Health

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Social determinants from CHNA, combined with an individual patient’s medical history, create a more

comprehensive predictive model

EPIC EHR data at the patient level Clinical information (e.g. diagnosis of dementia) Social history (e.g. history of smoking) Demographics (e.g. age, gender, etc.) Resource utilization (including charges from Insights) Addresses for patients, clinics, and hospitals for geo-coding

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Example Model 1: Usage Of Services (Adult Medicaid) Less likely to use ER, Inpatient, or

Observation Older patients Females English speakers Married/Significant Other Medicaid HMO or Dual Eligible With PCP assigned Living in economically and socially stable area

(Highest quantile GEO score=117) vs 80 Having Work and cell phone documented in EPIC Had 1+ office visits in the last year

Increased risk of using ER, Inpatient, or Observation Housing instability With history of drug, alcohol, tobacco With dementia High BP Multiple mental health dx on problem list Increase in number of med orders, lab orders, and images

0.340.950.780.89

0.540.67

0.740.82

0.200.78

4.721.38

1.061.551.651.72

1.56

1.031.06

1.230.94

0.00 1.00 2.00 3.00 4.00 5.00

Age >=65 vs Young adults 18-25Gender: F vs M

Language: English vs SpanishMarital Status: Married/Sig Other vs…

PrimPayorType: Dual Eligible vs…PrimPayorType: Medicaid HMO vs…

CellPhone: Yes vs NoWorkPhone: Yes vs No

PatAssigned2PCP: Yes vs NoGeo Quality Quantiles Last vs First

HomelessLast2Yrs: Yes vs NoHXTobaccoUser: Yes vs NoHXAlcoholUser: Yes vs NoHXIllDrugUser: Yes vs No

Dementia: Yes vs NoHighBP >=140/90 vs <140/90

DXMentalHealth: 4+ vs 0

MedOrdersLast1YrLabsResultedLast1Yr

ImagesLast1YrOfficeVisitsLast1Yr

Patients with dementia are 65% more likely to be seen treated in a hospital ER, inpatient or observation in a hospital as compared to patients without..

Female patients are 5% less likely to be seen in ER, inpatient or observation as compared to

male patients

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Example Model 2: Charges ($) Among Users

0.971.22

0.721.03

0.951.04

0.940.95

1.151.10

1.091.161.20

1.04

1.191.10

1.551.16

1.65

0.00 0.50 1.00 1.50 2.00

Months on MedicaidAge >=65 vs 18-25

PatAssigned to PCP Dual Eligible vs Medicaid Traditional

Medicaid HMO vs. Medicaid…BMI Category: Obese vs. Normal

Language: English vs. SpanishMaritalStatus: Married/Sig Other vs.…

HomelessLast2YrsHXIllDrugUser

DXChronicMildLiverDXChronicMyocardialI

DXChronicCVDDXChronicCancer

AlcoholRelatedBipolar

IntentionalSelfHaOtherMentalHealth

Schizophrenia On average with lower charges Patients with longer coverage Assigned to PCP HMO-covered English speakers Married/Significant other With work phone

On average with higher charges With dx of schizophrenia Various mental health problems With suicidal ideations With Dx of bipolar With Dx of alcohol related mental health problems Dx of CVD, MI, Chronic Liver Being homeless With history of alcohol and drug abuseOn average, charges among patients

with CVD are 20% higher than among patients without CVD

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We also include community factors affecting patient’s health such as population density, average age, percentage of renters/home owners, as well as varied social determinants such as primary language, employment status, and members in household

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Maps and overlays make data visible, actionable

Medicaid enrollees, by zip of residence with 1+ avoidable ED visit in 2016

PSJH care facilities Walgreen’s Locations Starbucks locations

(proxy for foot traffic) Other overlays in

development, including community resources, FQHCs, and more

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CPH Patient SnapAvoidable ED

The Patient Snap helps us understand the care patterns and factors determining high risk for a given patient.

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CPH Patient SnapOverall Use of Hospital (IP, ED, Obs)

Young woman, managing chronic disability (41-64, Medicare & Medicaid)

Dealing with significant underlying medical conditions

Additional impact of underlying behavioral health issues (bipolar disorder)

Additional socialfactors

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Population Profile Data

Insights:• Understand and

Know our Populations

• What do we know about this segment

• Different strategies• Different Touch

Points

• Strategically identify community benefit investments

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“Know Me”

WHAT WE DO

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”Care for Me”Patient Level Profiles Whole Person Care

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Proposed Immediate Next StepsCustomer Refinement• Community Health Investment• Operational and Clinical leaders, Health Plans• Strategic analytics

Growth•Evaluate for community partners

Platform•Scalability •Expand sources

Maintenance• Monthly Refresh

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DiscussionThank you

Dora BarillaDora. [email protected]