BUILDing Multi-Sector

Collaborations to Advance

Community Health

• Peter Eckart, Co-Director, Data Across Sectors for Health (DASH); Director for Health Information and Technology, Illinois Public Health Institute

• Alison Rein, Director, Community Health Peer Learning Program (CHP); Senior Director, Evidence Generation and Translation, AcademyHealth

• Gretchen Benson, Manager, Healthcare Systems Integration, Minneapolis Heart Institute Foundation

• Rebecca Lindberg, Director, Population Health, Minneapolis Heart Institute Foundation

• Stephanie Fenniri, Senior Community Partnerships Manager, Parkland Center for Clinical Innovation

All In: Data for Community Health

1. Support a data movement that empowers communities to address social determinants of health

2. Build an evidence base for the field of multi-sector data use to improve health

3. Stimulate and support peer learning and collaboration

DASH and CHP are All In!

Community Health Peer Learning Program (CHP)

NPO: AcademyHealth, Washington DC; with National Partnership for Women & Families and NORC as partners

Funded by the Office of the National Coordinator for Health IT

15 communities: 10 Participant and 5 Subject Matter Expert

Data Across Sectors for Health (DASH)

NPO: Illinois Public Health Institute in partnership with the Michigan Public Health Institute

Funded by the Robert Wood Johnson Foundation

10 communities

DASH and CHP Theory of Change

Shared data and

information

Multi-sector

Collaboration

Outcome:

Capacity Building to Drive

Community Health

Improvement

The Metcalfe Network Effect

All In is a Learning Collaboration

Total Network of 25 Projects 10 projects – DASH Cohort

15 projects – CHP Cohort

Geographic Scale

0 1 2 3 4 5 6 7 8

Metropolitan Area

Neighborhood

Tribal Area

State

Multiple Neighborhoods

Multi-County Region

City or Town

County

CHP DASH

Sectors Represented

0 5 10 15 20 25

Tribal

Community Development

Transportation

Economic Development

Private

Environment

Criminal Justice

Government

Academia

Education

Housing

Nonprofit/Community-based Organizations

Public Health

Behavioral Health & Social Services

Health Care

CHP DASH

Data Types / Sources

0 2 4 6 8 10 12 14 16

Community Health Needs Assessment

Service Utilization

Other

Social Service

Survey Data

Public Health

Geographic, Temporal Data

Health Information Exchange

Insurance Claims

Private Records

Public Records

Electronic Health Records

CHP DASH

Collaboration: A National Perspective

Organizational missions both drive and inhibit collaboration

Collaboration slows down the work, at least initially

In-person encounters are critical to relationship building

Meaningful peer-to-peer collaboration must be staffed

Opportunities for learning exist at ALL levels:

• NPO-to-NPO

• Grantee-to-grantee

Distributed leadership requires clear delineation and documentation of roles, responsibilities and accountability

Honesty, respect, and compassion are key ingredients

It helps (a lot) to like your collaborators

Indicators of Progress

Enabling Factors

• Community collaboration

• Resources

• Data & technology infrastructure

System Features

• Structure and process• Governance

• Workflow

• Training

• Technical factors• User-

orientation

• Timeliness

• Interoperability

Successful Use Cases

• Number and variety of use cases

• Participating sectors

• Usefulness

• Acceptability

• Sustainability

@MHIF_Heart

@HeartofNewUlm

HONU is a 10-year demonstration project designed to apply and widely disseminate established, evidence-informed health improvement practices, based on the community’s own level of risk and customized to their preferences.

Project ObjectivesLong-term: Reduce the number of heart attacks over 10 years among residents age 40-79 years

Moderate-term: Improve the proportion of residents with controlled modifiable heart disease risk factors over 5 years.

1. Elevated blood lipids (i.e., total/LDL/HDL cholesterol, triglycerides)

2. High blood pressure

3. Uncontrolled glucose (i.e., type 2 diabetes, pre-diabetes)

4. Obesity

5. Tobacco use

6. Physical inactivity

7. Low fruit/vegetable consumption

8. Uncontrolled stress

9. Medication (i.e., antithrombotics, antidyslipidemia, antihypertension) underutilization/non-adherence

Challenge #1

Most health related behaviors

are not systematically tracked

in the electronic health record

Collecting & Utilizing Data

Data Integration Plan

Synthesize & share with target

audiences

Community Needs

Resident surveys

Focus groups

Parent surveys

Classroom tallies

Environmental assessments

Built environment

Nutrition environment

Policy assessment

Electronic health records

90% of residents have data in the record

(80% of target population)

Screening data

Behavioral

Health-related data

Desired Impact

Create interventions to improve population health

New Ulm, MN - 2009 Community Diagnosis

41% Obese

35% Overweight

38% Metabolic

syndrome

17% Consumed 5 fruits

and vegetables a day

Heart of New Ulm Project Approach

Healthcare

CommunityWorksite

Effective Interventions

Smoking policies, complete streets

Program partnerships, farmer’s markets/ CSAs,

community-wide health challenges, social marketing campaigns

NUMC interventions, 100 largest employer worksite wellness programs,

restaurants, grocery store, convenience store interventions, Safe

Routes to School

Health education components to worksite, clinical and community

programs

HBC phone coaching, NUMC provider initiatives, lipid clinic

NUMC’s RoleLeveraging data to engage key

stakeholders & create community

ownership

Challenge #2

Communications Strategy

Spread Educational Lifestyle Messages Everywhere

Insert visual data

example?

Comparison of HONU Changes to NHANESNHANES

2009-10

NHANES

2011-12

NHANES

Change

HONU

2008-09

HONU

2012-13

HONU

Change

BP at goal

(<140/90 mmHg)83.1% 82.5% -0.6 79.3% 86.0% +6.7

BP medication 35.2% 36.8% +1.6 38.3% 47.6% +9.3

LDL at goal

(< 130 mg/dL)64.3% 63.7% -0.6 68.0% 72.0% +4.0

Cholesterol at goal

(<200 mg/dL)47.5% 46.9% -0.6 58.3% 65.1% +6.8

Not Obese

(BMI <30)62.5% 62.3% -0.2 55.9% 55.2% -0.7

NHANES data selected for participants age 40-79, white non-Hispanic to provide a comparison group similar

to New Ulm resident demographics, sample weights applied for analysis

Behavior Changes Among Screening Participants age 40-79

Measure 2009

(n = 3123)

2011

(n = 1976)

2014/15

(n = 1008)

Smoking 7.9 7.4 5.5

Physical Activity

(at least 150

minutes / week)

63.9 96.0 96.2

Fruit and

Vegetables (5 or

more servings per

day)

16.3 26.6 30.2

Screening analysis is age and gender adjusted to account for

differential age and gender distributions in each screening time

period.

Current challenge

‣How do we continue to track behaviors over time to continue to inform progress and decisions going forward?

How do we continue to

track behaviors over

time?

Current Challenge

Contact information

Rebecca Lindberg, MPH, RD

Director, Population Health

Minneapolis Heart Institute FoundationP: 612.863.4087

rlindberg@mhif.org Twitter: @relindberg

Gretchen Benson, RD, CDE

Manager, Healthcare Systems Integration

Minneapolis Heart Institute Foundation P: 612.863.4222

gbenson@mhif.org Twitter: @gbenson300

Food for Health: Coordinating Care Across Sectors

to Improve Health Among Vulnerable Populations

PCCIPIECES.ORG

DIABETES HYPERTENSION

COMMUNITY HEALTH ISSUES

Pieces

Plexus™

Buildinghealthiercommunities.

CHALLENGESFor DASH, Workflows Inform Technology

A framework for challenges

Technical & Operational

Relationship Management

Communication & Governance

Trust & Control

Making the Value Case

Interoperability

Data Quality & Usefulness

Familiarity with Data

Resources

Our Role: To listen, identify, characterize, and then (try) to help resolve

As two coordinating nodes on the All In network, DASH and CHP are continuously:

Monitoring and reflecting back what we hear as being major challenges and areas of mutual concern

Cultivating opportunities for peer-learning and collaboration

This is often an organic process, but sometimes we explicitly ask

With a collective cohort of 25/43, we have started to solicit feedback regarding key challenges and (early) lessons learned

Learning from 43 projects: technical challenges

Partners are ready, but vendors are not; vendor solutions are often clunky with poor user interface

Patient/client matching is hard and under resourced

Building technical interfaces for multiple EHR systems is time and resource intensive, and not scalable

Few standards exist for capture, sharing and integration of social determinants data elements

Learning from 43 projects: governance challenges

Policies on data sharing differ by sector, and within government

Establishing trust relationships within healthcare is (very) hard; tougher still with increased number and nature of partners

HIPAA provides useful frame for data use within healthcare absent consent, but this does not (necessarily) extend to other sectors / other use cases.

Learning from 43 projects: communicating value

Story telling; use case based narrative

Tailor scenarios to specific audiences

Each service offering and use case has a different value proposition; consider what value the data sharer receives

Vertical alignment of missions can demonstrate potential to accomplish everyone's goals together

Show people the PRODUCT. Show them a beautifully designed data display that enables them to answer critical questions, and they will understand the value

Learning from 43 projects: advice

Build on existing trusting relationships

Technology is the least of your concerns - you'll acquire that through a great relationship

This is new to a lot of people; you're not preaching to the converted, so don't underestimate the number of times you have to say the same thing - five different ways!

Include an influential non-government neutral visionary

Get community buy-in and agreement on key evaluation measures

Relationships are the key to being able to move (integration) forward

Discussion Questions

Moderators’ prerogative to begin …

Four tiers to build the All In network

Go All In!

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