Background
Organizational Context & Penetration of QI
Interventions: Case Studies from Implementing
Depression Collaborative Care
Elizabeth Yano PhD1, 2; JoAnn Kirchner MD3, 4;
Jacqueline Fickel PhD1; Louise Parker PhD3;
Mona Ritchie MSW3; ChuanChuan-Fen Liu PhD5,6;
Edmund Chaney PhD5,6;
Lisa Rubenstein MD1,7,8
1VA
Greater Los Angeles HSR&D Center of Excellence; 2UCLA School of Public Health; 3Center for
Mental Health Outcomes Research, Little Rock AR; 4University of Arkansas Medical Sciences;
5Northwest Center for Outcomes Research, Seattle WA; 6University of Washington, Seattle;
7UCLA School of Medicine; 8RAND Health
Quality Enhancement Research
Initiative (QUERI)
National disease targetsÆ
targetsÆQUERI Centers
ResearchResearch-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
Substantial Evidence Base Demonstrates
Effectiveness of Collaborative Care
Feasible, costcost-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)
“It’
It’s not your father’
father’s Army any more…”
more…”
– It’
It’s not your father’
father’s VA any more either
VA’
VA’s quality transformation (1990s to current)
– Reorganization towards primary care
– Adoption of electronic medical records
– Incentivized performance auditaudit-andand-feedback
– Capitated budgets/resource allocation
Parallel with substantial HSR investment
Depression Collaborative Care
Forges shared care between PC and MH
PC provider education
InformaticsInformatics-based decision support
Leadership support
Depression care manager
– Telephone assessment of + screens
– Telephone management and followfollow-up
– Based in PC but supervised by MH specialist
Models Increase Efficiency…
Efficiency…
Reduce primary care visits
Maintain current rate of MHS visits
Use MHS resources more effectively
CostCost-saving (due to reduced medical care
costs) after first year
– One randomized trial, included VA
More than 10 randomized controlled trials
1
Research Objective
RoutineRoutine-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
ORGANIZATIONAL
INNOVATION
Centralization (-)
Complexity (+)
Formalization (-)
Collaborative Care for
Depression in VA
Interconnectedness (+)
Organizational slack (+)
Size (+)
EXTERNAL
CHARACTERISTICS OF
THE ORGANIZATION
System openness
Source: Adapted from Rogers EM. Diffusion of innovations. New York: The Free Press, 1995.
Study Design & Sample
Part of larger group RCT of collab care
Implementation thru evidenceevidence-based QI
– ExpertExpert-panel consensus development among
PC and MH leaders
Implementation priorities
Care model specifications
Seven 1st-generation primary care
practices
– Across 3 VA networks spanning 5 states
Data Sources & Measures
VA administrative data (“
(“Austin”
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 semisemistructured stakeholder interviews
– Senior/midSenior/mid-level health care managers, PC/MH
providers, depression care managers
PC Provider Penetration
Principal Findings
Practices ranged from 4,6004,600-14,000 patients
among 44-11 PCPs
Depression diagnosis ranged from 11-10% of
population of PC patients
Reported level of implementation high (7(7-9 out of
9-point scale)
Sense of PCPC-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 1st 6 Months
100
90
80
70
60
50
40
30
20
10
0
A1
A2
Network #1
B1
B2
Network #2
B3
C1
C2
Network #3
2
Organizational Context & Penetration
PC Provider Penetration
% PCPs Started 1st 6 Months
Referrals/PCP FTE
30
Referrals/PCP FTEs
100
30
% PCPs Started
Consults/FTE
90
25
80
70
20
60
50
15
40
10
30
20
5
10
0
25
20
15
MED
A2
Network #1
B1
B2
B3
Network #2
C1
C2
Network #3
Levels of early PCP penetration
MED
5
HIGH
HIGH
HIGH
0
0
A1
MED
10
# Months:
A1
A2
B2
C1
C2
B3
16
20
18
2
6
9
Rural
Small
city
Small
city
Small
city
Small
city
Semirural
LOW
B1
21
Rural
Organizational Context & Penetration
Organizational Context & Penetration
High Penetration
Low practice authority
Variable resources
QI activity variable
PC education ~low
No PCPC-MH case confs
Speed or extent of penetration not influenced by:
Low Penetration
MedMed-toto-high authority
Variable resources
QI activity variable
PC education medmed-hi
No PCPC-MH case confs
– PC and MH provider relationships
– Area characteristics (eg
(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
Implications
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 Æ time for PDSA
– Time to adopt/adapt Æ as opposed to “high burn”
burn”
3