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

Background “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

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

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

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

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

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

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

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

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

Principal Findings Practices ranged from 4,600-14,000 patients among 4-11 PCPs 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

PC Provider Penetration % PCPs Started 1 st 6 Months Network #1 Network #2Network #3

PC Provider Penetration % PCPs Started 1 st 6 Months Network #1 Network #2Network #3 Referrals/PCP FTEs

Organizational Context & Penetration Referrals/PCP FTE MED HIGH LOW # Months: Small Small Rural Small Small Semi- Rural city city city city rural Levels of early PCP penetration

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

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 (>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  time for PDSA –Time to adopt/adapt  as opposed to “high burn”