Organizational Context

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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 Chuan-Fen Liu PhD5,6
• Edmund Chaney PhD5,6
• Lisa Rubenstein MD1,7,8
• 1VA Greater Los Angeles HSRD 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

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Background

• Its not your fathers Army any more
• Its not your fathers VA any more either
• VAs 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

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Quality Enhancement Research Initiative (QUERI)

• 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

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Depression Collaborative 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

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Substantial Evidence Base DemonstratesEffectivene
ss 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

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Models Increase Efficiency

• 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

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Research Objective

• 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

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Factors Associated with Adoption and Diffusion of
Collaborative Care as an Organizational Innovation
INDIVIDUAL (LEADER) CHARACTERISTICS
ORGANIZATIONAL INNOVATION
INTERNAL CHARACTERISTICS OF ORGANIZATIONAL STRUCTU
RE
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.
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Study Design Sample

• 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 1st-generation primary care practices
• Across 3 VA networks spanning 5 states

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Data Sources Measures

• 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
  semi-structured stakeholder interviews
• Senior/mid-level health care managers, PC/MH
  providers, depression care managers

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Principal Findings

• 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

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PC Provider Penetration
PCPs Started 1st 6 Months
Network 2
Network 3
Network 1
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PC Provider Penetration
PCPs Started 1st 6 Months
Referrals/PCP FTEs
Network 2
Network 3
Network 1
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Organizational Context Penetration
Referrals/PCP FTE
MED
Levels of early PCP penetration
MED
MED
HIGH
HIGH
HIGH
LOW
Months
16 20 18 2
6 9 21
Small Small Rural Small
Small Semi- Rural city city
city city
rural
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Organizational Context Penetration
High Penetration Low Penetration
Low practice authority Variable resources QI activity variable PC education low No PC-MH case confs Med-to-high authority Variable resources QI activity variable PC education med-hi No PC-MH case confs
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Organizational Context Penetration

• 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 (gt14,000 patients)
• Initiating early collaborative care referral did
  not predict future referral behavior
• Highest referral rates typically among practices
  with lowest perceived MH staffing

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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 ? time for PDSA
• Time to adopt/adapt ? as opposed to high burn

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