An Introduction to Social Networks
Methods in Health Services Research
AcademyHealth Annual Meetings, 2007
Doug Wholey
Health Policy & Management, School of Public Health, University of Minnesota
David Krackhardt
The H. John Heinz III School of Public Policy and Management, Carnegie Mellon
University
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Social Networks Methods in Health Services Research
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Presentation Organization


Introduction to social networks
methods
Three examples of social networks
research in health services research
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Presenters

Doug Wholey


Professor, Division of Health Policy &
Management, School of Public Health,
University of Minnesota
David Krackhardt


Professor of Organizations at the Heinz School
of Public Policy and Management and the
Tepper School of Business, Carnegie Mellon
University
Center for Computational Analysis of Social and
Organizational Systems (CASOS)

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2007 Summer Institute, June 25 - July 1, 2007
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Handouts
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Social Networks Resources
Life in the Pentagon
Presentation
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Introduction to Social Networks
Methods
David Krackhardt
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Examples of Social Network Studies in
Health Services Research
Doug Wholey
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Examples

Evaluating the Minnesota Cancer Alliance
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Evaluating an intervention to coordinate
agencies providing services to people in
prostitution in North Minneapolis
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Collaborator: Michael Pfeffer, MPH candidate
Collaborator: Kate Downing, MPH candidate
Teams and sub-teams in medical practice
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Collaborators: David Knoke, Bill Riley, and Amy
Wilson
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Issues in Studying Networks:
Identifying Network Boundaries

Realist

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
Network exists as a social object, there is a defined
boundary and membership, there is shared knowledge
Minnesota Cancer Alliance, medical groups, associations,
rural health networks, assertive community treatment
teams
Nominalist
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Network membership is defined by research interests
Agencies serving people in prostitution agencies
Public health systems – organizations providing public
health related services in a geographic area
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Issues in Studying Networks: Which
Relationships?

Types of relations

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Collaboration
Levels of collaboration
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Who do you share information with about
program development?
Who do you work with to develop
programs?
Who are you collaborating with to obtain
funding for programs?
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Issues in Studying Networks:
Relational, Attribute, & Event Data

Relational data



Attribute data

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
N (actor) x N (actor) matrix
Who interacts with who?
N (actor) x A (attribute)
Can use to construct a relational matrix showing similarity
between two actors (same gender, difference in age)
Event/Affiliation data
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N (actor) x A (event) matrix
Can use to construct a relational matrix to who who
attends the same meetings
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Issues in Studying Networks: Event
Data to Relational Data
George
Sam
Jane
Attended Meeting
1
2
3
4
0
1
1
1
1
0
1
1
1
0
0
0
AxA’ (Common Attendance) =
Use UCINET’s /Data/Affiliations tool
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= A (Event Matrix)
George
Sam
G
2
2
S
2
3
J
0
1
Jane
0
1
1
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The Minnesota Cancer Alliance

A coalition of health care organizations
founded in 2005 with the goals of
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Reducing disparities in cancer screening and
treatment
Improving access to information about locallyavailable services for cancer patients and their
families
Increasing colorectal cancer screening
Increasing the tobacco excise tax and
expanding clean indoor air
http://www.cancerplanmn.org/The_Minnesota_C
ancer_Alliance.html
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Comprehensive Cancer Control
Collaborative

For cancer prevention and treatment in a
community,
Develop a shared comprehensive vision

Eliminate duplications, integrate efforts, reduce
organizational and programmatic fragmentation

Plan and implement an evidence based program

Identify gaps and prioritize action

Reallocate resources

Advocate with one voice

“Increased coordination of partner activities and enhanced
collaboration” (6).
Leslie S. Given, Bruce Black, Garry Lowry, Philip Huang, &
Jon F. Kerner, 2005, Collaborating to conquer cancer: A
comprehensive approach to cancer control, Cancer Causes
and Control 16(Suppl): 3-14.
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Alliance Evaluation and Committees

Evaluation
Who is active where?



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People
Organizations
How does participation
evolve?
Who is central in each
committee?
Who is central across
committees?
What is the structure of
interaction
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Structure = repeated
patterns of interaction

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Committees
Community Health Worker
Cervical Cancer Screening
Colorectal Cancer
Screening
Access to Information
Health Disparities
Steering Committee
Evaluation Committee
Communications
Committee
Finance Committee
Membership Committee
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Methods

Data Source: Meeting Archives

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Meeting Attendance
Individual - Organizational
Methods:


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Used UCINET to translate affiliation data
(copmmon meetings attended) to relational data
Number of meetings attended by both members
of a dyad is strength of attachment to committee
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Health Disparities Committee
(Three or More Meetings)
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Health Disparities Committee
(Four or More Meetings)
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Health Disparities Committee
(Four or More Meetings)
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Colorectal Cancer Committee
(1 or More Meetings)
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Colorectal Cancer Committee
(6 or More Meetings)
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Colorectal Cancer Committee
(14 or More Meetings)
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Further Analyses: Which actors are
central across committees?
…
Disparities
Access to Information
Cervical
Colorectal
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What Can We Learn from Meeting
Attendance

Which members within the committee?



Are there core and peripheral members?
How stable are relationships?
Which actors coordinate/bridge across
committees?
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Next Steps


Initial analyses took advantage of
unobtrusive measures: Meeting attendance
Why are some members more involved than
others in a committee?

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Research to develop a better understanding
of committees and involvement


What is the content of the relationship?
Watching? Collaborating?
Field experience by Michael Pfeffer
Develop a survey to understand
involvement and describe networks better
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Improving Service Delivery for People
in Prostitution

Research and Intervention led by
Folwell Center for Urban Initiatives
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North Minneapolis, MN
Lauren Martin, Ph.D.
Kate Downing, MPH Candidate
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The Problem – North Minneapolis and
Prostitution

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
Poverty
Population of Color
Mental Health and Substance Abuse
A doubly stigmatized, vulnerable
population
Project Focus Area, North Minneapolis
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The Problem: Service Delivery

Access to Services

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Politics of Delivery
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Few prostitution specific services
Ideological Barriers
Funding Scarcity
A difficult context to organize
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The Evaluation


Evaluate an intervention to coordinate
agencies
Network Analysis


Understanding the mechanism
Pre-Post Evaluation
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Funding search in progress for intervention
and evaluation
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The Evaluation

What does Collaboration really mean?


What does it mean to ‘work with?’
Asking probing questions:
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Do you go to the same meetings?
Do you collaborate on programs?
Do you go after funding together?
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The Intervention:
Northside Women’s Space

A point of connection:

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
For service providers to work together
For women involved in prostitution to find safety,
community and services
The space will offer:

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Resource guide
Non-judgmental referral services
Workshops
Safe sex supplies, snacks, etc.
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The Intervention:
Northside Women’s Space

One central actor managing the space


Folwell Center for Urban Initiatives
Kate Downing
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Support development and research
Other agencies will staff the space
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Adhering to soon available best practices
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Challenges


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
The questions we can’t ask…
Maintaining neutrality and building
bridges
Developing trust and keeping it
Unknown, invisible population size

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A barrier to political will, funding
Identifying agencies to survey
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Expected Outcome:
Northside Women’s Space


More network ties
Central node for
the Northside

Success means:


Women’s Space
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Care integration
Improved quality of
care and service
Improved
outcomes
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


Greater visibility of the
problem
Improvement in
service delivery
Continuing needs
assessment
Advocacy for change
through collaboration
Shift from crisis
oriented care to primary
prevention
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Care Teams in Medical Practice
The Context & Argument




Health care is becoming more
differentiated

New technologies and
technicians

Professional jurisdictions
shifts
Care is shifting towards chronic
rather than acute conditions
Chronic conditions require
integration of a large variety of
services: Social, mental, and
medical services
Lack of care integration results in
inadequate care for individuals
with chronic conditions
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The Mechanism


Care teams

Interdisciplinary teams
serving a population

Task work (service provision)
coordinated with team work
(systems, coordinating
service delivery)
Hope

Integrated care

Greater productivity through
reduced waste (lean
management, QI)

Better patient outcomes

Better team member
outcomes
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Care Team Examples

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Chronic Care Teams
http://www.improvingchroniccare.org /
Assertive Community Treatment (ACT)
Teams
(http://www.actassociation.org/actModel/ )
Prepared Practice Teams
(http://www.ihi.org/IHI/Topics/ChronicConditi
ons/AllConditions/ImprovementStories/Purs
uingPerfectionReportfromHealthPartnerson
PreparedPracticeTeams.htm)
Medical Homes and Patient Homes
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Teams

“A team can be defined as
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(a) two or more individuals who
(b) socially interact (face-to-face or, increasingly, virtually);
(c) possess one or more common goals;
(d) are brought together to perform organizationally relevant
tasks;
(e) exhibit interdependencies with respect to workflow, goals,
and outcomes;
(f) have different roles and responsibilities; and
(g) are together embedded in an encompassing organizational
system, with boundaries and linkages to the broader system
context and task environment.”
Steve W. J. Kozlowski and Daniel R. Ilgen. 2006. "Enhancing
the Effectiveness of Work Groups and Teams." Psychological
Science in the Public Interest 7:77-124 (79).
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Issues in Identifying Care Teams

Boundary specification – who is a team
member?


Teams and sub-teams

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
Floaters? Visiting consultants? Anyone who has
anything do do with care delivery?
Assertive Community Treatment & Individual
Treatment Teams
Football teams
Formal and informal teams
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Emergent teams
Inertial teams
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Identifying Teams and Sub-teams
Realist
(Social Fact)
Nominalist
(Researcher
Defined)
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Structural
Individual
ACT teams
Team
membership
perceptions
Top
management
teams, Clinics
Groups of
interdependent
actors
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Using Interdependence to Identify
Teams

Organizational theory and complexity theory: “Organizing at the edge
of chaos”



Tim Carroll and Richard M. Burton. 2001. "Organizations and
Complexity: Searching for the Edge of Chaos." Computational &
Mathematical Organization Theory 6:319-337.
David Krackhardt, 1994, "Constraints on the Interactive Organization as
an Ideal Type." In Charles Heckscher & Anne Donnellan (eds.), The
Post-Bureaucratic Organization. Beverly Hills, CA: Sage, p. 211-222.
Who do you work with to provide patient care?

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Work with means that you and the other person are interdependent
during work – your actions affect each other directly. It does not refer to
the situation where you and the other person contribute separately to
clinic performance while you work in the same location. Please answer
these questions for all others with whom you work (e.g., physicians,
rooming nurses, staff, laboratory technicians, and receptionists).
Patient care means all direct patient care and patient care related
activities (e.g., ordering tests, calling in prescriptions, talking with patients
on phone).
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Identifying Teams Using Structural
Equivalence

(g) are together embedded in an encompassing organizational system, with
boundaries and linkages to the broader system context and task environment.


(a) two or more individuals who

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Occupational heterogeneity
(b) socially interact (face-to-face or, increasingly, virtually)

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Dense interdependence
(f) have different roles and responsibilities;


At least two actors
(e) exhibit interdependencies with respect to workflow, goals, and outcomes;


Groups based on structurally equivalence: “Two nodes are said to be exactly
structurally equivalent if they have the same relationships to all other nodes.”
Informal consulting
Inferred

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(c) possess one or more common goals;
(d) are brought together to perform organizationally relevant tasks;
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A Work With Sociomatrix:
Pediatrics Clinic
MD08
MD21
MD25
MD54
MD64
MD82
N00
N08
N21
N25
N54
N71
N79
N82
MD08
0
0
0
0
0
0
3
1
2
2
3
0
3
2
MD21
0
0
2
0
0
2
3
2
2
3
3
0
3
2
MD25
0
2
0
0
0
3
3
1
2
3
3
0
3
2
MD54
0
0
0
0
0
0
1
3
0
0
0
0
0
1
MD64
0
0
0
0
0
0
3
2
0
2
2
0
2
2
MD82
0
2
3
0
0
0
2
2
1
2
2
0
2
2
N00
3
3
3
1
3
2
0
1
1
1
3
2
2
2
N08
1
2
1
3
2
2
1
0
2
2
2
2
2
1
N21
2
2
2
0
0
1
1
2
0
1
2
1
1
1
N25
2
3
3
0
2
2
1
2
1
0
2
1
2
1
N54
3
3
3
0
2
2
3
2
2
2
0
3
2
2
N71
0
0
0
0
0
0
2
2
1
1
3
0
3
2
N79
3
3
3
0
2
2
2
2
1
2
2
3
0
2
N82
2
2
2
1
2
2
2
1
1
1
2
2
2
0
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Graphing the Full Sociomatrix:
Pediatrics Clinic
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Structurally Equivalent Actors:
Pediatrics Clinic
Occupational
Grouping
2N
6 MDs, 1 N
4N
1N
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Graphing the Full Sociomatrix:
Family Practice Clinic
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Structurally Equivalent Actors:
Family Practice Clinic
2 MD, 1 ST
Teams
2 MD, 2 N
2 MD, 5 N
4 ST
2 MD, 1 N,
2 ST, 1 T
N, 3 ST, T
2 ST
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Predictive Validity for Interdependence
Method for Identifying Teams

Informal consulting



Teams consistent with clinic staffing rules



is more likely to occur within teams than
between teams
Is greater in more cross-functional and
interdependent teams
Pediatrics – pool staffing
Family practice – practice teams
Consistent with organizational and
complexity theory
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Organizing at the edge of chaos
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Usefulness of Methodology

Where are sub-teams in




Chronic care teams?
Primary care practices?
Assertive community treatment teams?
How are sub-teams connected?


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Is it too many? Process loss and
perturbations?
Is it too few? So loosely coupled system
is not integrated.
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Social Networks Research Challenges
(And Benefits)



Observing relations rather than actors
Defining network boundaries
Defining relations to measure



Friendship? Respect? Work-with?
Helping?
Level of detail
Obtaining high response rates
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Managerial Benefits of a Social
Network Approach

Managing relations is a key function of
management


Relations can be managed with relatively
simple interventions (staffing)
Measuring networks supports

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Accurate understanding of network
structure, which supports effective
management
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Research Benefits of a Network
Approach


Evaluate interventions designed to
integrate care
Understand the black box between
formal and informal structure



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Paths (or lack of paths) for information
flow
Disconnects
Too many connections
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