Organizational Context & Penetration of QI
Interventions: Case Studies from Implementing
Depression Collaborative Care
Elizabeth Yano PhD 1, 2 ; JoAnn Kirchner MD 3, 4 ;
Jacqueline Fickel PhD 1 ; Louise Parker PhD 3 ;
Mona Ritchie MSW 3 ; Chuan-Fen Liu PhD 5,6 ;
Edmund Chaney PhD 5,6 ;
Lisa Rubenstein MD 1,7,8
1 VA Greater Los Angeles HSR&D Center of Excellence; 2 UCLA School of Public Health; 3 Center for
Mental Health Outcomes Research, Little Rock AR; 4 University of Arkansas Medical Sciences;
5 Northwest Center for Outcomes Research, Seattle WA; 6 University of Washington, Seattle;
7 UCLA School of Medicine; 8 RAND Health

“It’s not your father’s Army any more…”
– It’s not your father’s VA any more either
VA’s quality transformation
(1990s to current)
– Reorganization towards primary care
– Adoption of electronic medical records
– Incentivized performance audit-and-feedback
– Capitated budgets/resource allocation
Parallel with substantial HSR investment

National disease targets  QUERI Centers
Research-clinical partnerships designed to implement research into practice
Mental Health QUERI
– Depression particularly common and disabling
– Implementation of depression collaborative care as national strategic priority for primary care

Forges shared care between PC and MH
PC provider education
Informatics-based decision support
Leadership support
Depression care manager
– Telephone assessment of + screens
– Telephone management and follow-up
– Based in PC but supervised by MH specialist

Substantial Evidence Base Demonstrates
Effectiveness of Collaborative Care
Feasible, cost-effective care models show
– Improved quality of life for up to five years
– Reduced job loss
– Improved financial status
– Higher satisfaction and participation in care
– Reduced disparities in care and outcomes
– Improved chronic disease status (HbA1C)
More than 10 randomized controlled trials

Reduce primary care visits
Maintain current rate of MHS visits
Use MHS resources more effectively
Cost-saving (due to reduced medical care costs) after first year
– One randomized trial, included VA

Routine-care implementation of depression collaborative care in VA primary care practices
– Little known about factors underlying intervention penetration
– Objective: To evaluate influences of organizational characteristics on degree of penetration during implementation

Factors Associated with Adoption and Diffusion of
Collaborative Care as an Organizational Innovation
INDIVIDUAL (LEADER)
CHARACTERISTICS
INTERNAL
CHARACTERISTICS OF
ORGANIZATIONAL
STRUCTURE
Centralization (-)
Complexity (+)
Formalization (-)
Interconnectedness (+)
Organizational slack (+)
Size (+)
ORGANIZATIONAL
INNOVATION
Collaborative Care for
Depression in VA
EXTERNAL
CHARACTERISTICS OF
THE ORGANIZATION
System openness
Source: Adapted from Rogers EM. Diffusion of innovations. New York: The Free Press, 1995.

Part of larger group RCT of collab care
Implementation thru evidence-based QI
– Expert-panel consensus development among
PC and MH leaders
Implementation priorities
Care model specifications
Seven 1 st -generation primary care practices
– Across 3 VA networks spanning 5 states

VA administrative data (“Austin”) (caseload)
Organizational site surveys
– Measures of internal organizational structure (e.g., centralization, complexity)
– Measures of external organizational context (e.g., urban/rural location)
Intervention penetration reports
– % PC providers referring patients, # consults/FTE
Validated by qualitative data from semistructured stakeholder interviews
– Senior/mid-level health care managers, PC/MH providers, depression care managers

Practices ranged from 4,600-14,000 patients among 4-11 PCPs
Depression diagnosis ranged from 1-10% of population of PC patients
Reported level of implementation high (7-9 out of
9-point scale)
Sense of PC-MH collaboration variable
– Difficulty deciding if PC or MH responsible
Penetration highly variable
Limited regional consistency
– One VISN high penetration but different approaches

% PCPs Started 1 st 6 Months
60
50
40
30
20
10
0
100
90
80
70
A1 A2 B1
Network #1
B2
Network #2
B3 C1 C2
Network #3

50
40
30
20
10
0
% PCPs Started 1 st 6 Months
100
90
80
% PCPs Started
Consults/FTE
70
60
A1 A2
Network #1
B1 B2
Network #2
B3
Referrals/PCP FTEs
30
C1 C2
Network #3
25
20
15
10
5
0

Referrals/PCP FTE
30
25
20
15
MED
10
MED
MED
Levels of early PCP penetration
5
HIGH HIGH
HIGH
LOW
0
A1 A2 B2 C1 C2 B3 B1
# Months: 16 20 18 2 6 9 21
Small Small Rural Small Small SemiRural city city city city rural

High Penetration
Low practice authority
Variable resources
QI activity variable
PC education ~low
No PC-MH case confs
Low Penetration
Med-to-high authority
Variable resources
QI activity variable
PC education med-hi
No PC-MH case confs

Speed or extent of penetration not influenced by:
– PC and MH provider relationships
– Area characteristics (eg, urban/rural location)
– Practice size
Except for largest practice (>14,000 patients)
Initiating early collaborative care referral did not predict future referral behavior
Highest referral rates typically among practices with lowest perceived MH staffing

VA an exceptional laboratory in which to translate research into practice
– Common electronic medical records
– Identifiable management structures
– Common policies and procedures
Effective penetration may have less to do with these enablers than local clinic characteristics, needs and approach
– Moderate penetration  time for PDSA
– Time to adopt/adapt  as opposed to “high burn”