↓ Skip to main content

The facilitators and barriers associated with implementation of a patient-centered medical home in VHA

Overview of attention for article published in Implementation Science, February 2016
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)

Mentioned by

twitter
7 X users

Citations

dimensions_citation
27 Dimensions

Readers on

mendeley
90 Mendeley
Title
The facilitators and barriers associated with implementation of a patient-centered medical home in VHA
Published in
Implementation Science, February 2016
DOI 10.1186/s13012-016-0386-6
Pubmed ID
Authors

Christian D. Helfrich, Philip W. Sylling, Randall C. Gale, David C. Mohr, Susan E. Stockdale, Sandra Joos, Elizabeth J. Brown, David Grembowski, Steven M. Asch, Stephan D. Fihn, Karin M. Nelson, Lisa S. Meredith

Abstract

The patient-centered medical home (PCMH) is a team-based, comprehensive model of primary care. When effectively implemented, PCMH is associated with higher patient satisfaction, lower staff burnout, and lower hospitalization for ambulatory care-sensitive conditions. However, less is known about what factors contribute to (or hinder) PCMH implementation. We explored the associations of specific facilitators and barriers reported by primary care employees with a previously validated, clinic-level measure of PCMH implementation, the Patient Aligned Care Team Implementation Progress Index (Pi(2)). We used a 2012 survey of primary care employees in the Veterans Health Administration to perform cross-sectional, respondent-level multinomial regressions. The dependent variable was the Pi(2) categorized as high implementation (top decile, 54 clinics, 235 respondents), medium implementation (middle eight deciles, 547 clinics, 4537 respondents), and low implementation (lowest decile, 42 clinics, 297 respondents) among primary care clinics. The independent variables were ordinal survey items rating 19 barriers to patient-centered care and 10 facilitators of PCMH implementation. For facilitators, we explored clinic Pi(2) score decile both as a function of respondent-reported availability of facilitators and of rating of facilitator helpfulness. The availability of five facilitators was associated with higher odds of a respondent's clinic's Pi(2) scores being in the highest versus lowest decile: teamlet huddles (OR = 3.91), measurement tools (OR = 3.47), regular team meetings (OR = 2.88), information systems (OR = 2.42), and disease registries (OR = 2.01). The helpfulness of four facilitators was associated with higher odds of a respondent's clinic's Pi(2) scores being in the highest versus lowest decile. Six barriers were associated with significantly higher odds of a respondent's clinic's Pi(2) scores being in the lowest versus highest decile, with the strongest associations for the difficulty recruiting and retaining providers (OR = 2.37) and non-provider clinicians (OR = 2.17). Results for medium versus low Pi(2) score clinics were similar, with fewer, smaller significant associations, all in the expected direction. A number of specific barriers and facilitators were associated with PCMH implementation, notably recruitment and retention of clinicians, team huddles, and local education. These findings can guide future research, and may help healthcare policy makers and leaders decide where to focus attention and limited resources.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
Unknown 89 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 18%
Researcher 13 14%
Student > Ph. D. Student 11 12%
Student > Doctoral Student 9 10%
Professor 4 4%
Other 15 17%
Unknown 22 24%
Readers by discipline Count As %
Medicine and Dentistry 16 18%
Nursing and Health Professions 14 16%
Social Sciences 9 10%
Psychology 6 7%
Engineering 4 4%
Other 16 18%
Unknown 25 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 May 2017.
All research outputs
#6,298,613
of 22,994,508 outputs
Outputs from Implementation Science
#1,086
of 1,723 outputs
Outputs of similar age
#87,471
of 299,302 outputs
Outputs of similar age from Implementation Science
#31
of 37 outputs
Altmetric has tracked 22,994,508 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,723 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 299,302 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.