Kristin M Kostick1, Meredith Trejo2, Arvind Bhimaraj3, Andrew Civitello4, Jonathan Grinstein5, Douglas Horstmanshof6, Ulrich P Jorde7, Matthias Loebe8, Mandeep R Mehra9, Nasir Z Sulemanjee10, Vinay Thohan11, Barry H Trachtenberg3, Nir Uriel12, Robert J Volk13, Jerry D Estep14, J S Blumenthal-Barby2. 1. Center for Medical Ethics and Health Policy, Baylor College of Medicine, One Baylor Plaza MC: 420, Houston, TX, 77030, USA. kristin.kostick@bcm.edu. 2. Center for Medical Ethics and Health Policy, Baylor College of Medicine, One Baylor Plaza MC: 420, Houston, TX, 77030, USA. 3. Division of Heart Failure, Houston Methodist Hospital, Smith Tower, 6550 Fannin St., Ste 1901, Houston, TX, 77030, USA. 4. Baylor St. Luke's Medical Center, Texas Heart Institute, 7200 Cambridge Street, Ste 6C, Houston, TX, 77030, USA. 5. Duchossois Center for Advanced Medicine - Hyde Park, University of Chicago Medicine, 5758 S. Maryland Ave., Chicago, IL, 60637, USA. 6. INTREGIS Advanced Cardiac Care, 3400 N.W. Expressway, Bldg C. Suite 200, Oklahoma City, OK, 73112, USA. 7. Division of Cardiology, Montefiore Medical Center, Bronx, NY, 10467, USA. 8. Miami Transplant Institute, University of Miami Health System, Miami, FL, 33136, USA. 9. Cardiovascular Medicine, Brigham and Women's Hospital, 75 Francis St., Boston, MA, 02115, USA. 10. Aurora St. Luke's Medical Center, 2900 W Oklahoma Ave, Milwaukee, WI, 53215, USA. 11. Asheville Cardiology Associates, 5 Vanderbilt Park Dr., Asheville, NC, 28803, USA. 12. Columbia Presbyterian Medical Center, Columbia University Irving Medical Center, 622 West 168th St., Room 129, New York, NY, 10032, USA. 13. Department of Health Services Research, Division of Cancer Prevention and Population Services, University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1465, Houston, TX, USA. 14. Miller Family Heart and Vascular Institute, Cleveland Clinic, 9500 Euclid Ave., Cleveland, OH, 44195, USA.
Abstract
BACKGROUND: A central goal among researchers and policy makers seeking to implement clinical interventions is to identify key facilitators and barriers that contribute to implementation success. Despite calls from a number of scholars, empirical insights into the complex structural and cultural predictors of why decision aids (DAs) become routinely embedded in health care settings remains limited and highly variable across implementation contexts. METHODS: We examined associations between "reach", a widely used indicator (from the RE-AIM model) of implementation success, and multi-level site characteristics of nine LVAD clinics engaged over 18 months in implementation and dissemination of a decision aid for left ventricular assist device (LVAD) treatment. Based on data collected from nurse coordinators, we explored factors at the level of the organization (e.g. patient volume), patient population (e.g. health literacy; average sickness level), clinician characteristics (e.g. attitudes towards decision aid; readiness for change) and process (how the aid was administered). We generated descriptive statistics for each site and calculated zero-order correlations (Pearson's r) between all multi-level site variables including cumulative reach at 12 months and 18 months for all sites. We used principal components analysis (PCA) to examine any latent factors governing relationships between and among all site characteristics, including reach. RESULTS: We observed strongest inclines in reach of our decision aid across the first year, with uptake fluctuating over the second year. Average reach across sites was 63% (s.d. = 19.56) at 12 months and 66% (s.d. = 19.39) at 18 months. Our PCA revealed that site characteristics positively associated with reach on two distinct dimensions, including a first dimension reflecting greater organizational infrastructure and standardization (characteristic of larger, more established clinics) and a second dimension reflecting positive attitudinal orientations, specifically, openness and capacity to give and receive decision support among coordinators and patients. CONCLUSIONS: Successful implementation plans should incorporate specific efforts to promote supportive and mutually informative interactions between clinical staff members and to institute systematic and standardized protocols to enhance the availability, convenience and salience of intervention tool in routine practice. Further research is needed to understand whether "core predictors" of success vary across different intervention types.
BACKGROUND: A central goal among researchers and policy makers seeking to implement clinical interventions is to identify key facilitators and barriers that contribute to implementation success. Despite calls from a number of scholars, empirical insights into the complex structural and cultural predictors of why decision aids (DAs) become routinely embedded in health care settings remains limited and highly variable across implementation contexts. METHODS: We examined associations between "reach", a widely used indicator (from the RE-AIM model) of implementation success, and multi-level site characteristics of nine LVAD clinics engaged over 18 months in implementation and dissemination of a decision aid for left ventricular assist device (LVAD) treatment. Based on data collected from nurse coordinators, we explored factors at the level of the organization (e.g. patient volume), patient population (e.g. health literacy; average sickness level), clinician characteristics (e.g. attitudes towards decision aid; readiness for change) and process (how the aid was administered). We generated descriptive statistics for each site and calculated zero-order correlations (Pearson's r) between all multi-level site variables including cumulative reach at 12 months and 18 months for all sites. We used principal components analysis (PCA) to examine any latent factors governing relationships between and among all site characteristics, including reach. RESULTS: We observed strongest inclines in reach of our decision aid across the first year, with uptake fluctuating over the second year. Average reach across sites was 63% (s.d. = 19.56) at 12 months and 66% (s.d. = 19.39) at 18 months. Our PCA revealed that site characteristics positively associated with reach on two distinct dimensions, including a first dimension reflecting greater organizational infrastructure and standardization (characteristic of larger, more established clinics) and a second dimension reflecting positive attitudinal orientations, specifically, openness and capacity to give and receive decision support among coordinators and patients. CONCLUSIONS: Successful implementation plans should incorporate specific efforts to promote supportive and mutually informative interactions between clinical staff members and to institute systematic and standardized protocols to enhance the availability, convenience and salience of intervention tool in routine practice. Further research is needed to understand whether "core predictors" of success vary across different intervention types.
Entities:
Keywords:
Decision support intervention; Facilitators and barriers; Implementation success; Principal components analysis
Authors: Kristin M Kostick; Charles G Minard; L A Wilhelms; Estevan Delgado; Mackenzie Abraham; Courtenay R Bruce; Jerry D Estep; Matthias Loebe; Robert J Volk; J S Blumenthal-Barby Journal: J Heart Lung Transplant Date: 2016-01-21 Impact factor: 10.247
Authors: Kristin M Kostick; Courtenay R Bruce; Charles G Minard; Robert J Volk; Andrew Civitello; Selim R Krim; Douglas Horstmanshof; Vinay Thohan; Matthias Loebe; Mazen Hanna; Brian A Bruckner; J S Blumenthal Barby; Jerry D Estep Journal: J Card Fail Date: 2018-09-07 Impact factor: 5.712
Authors: Jennifer S Blumenthal-Barby; Kristin M Kostick; Estevan D Delgado; Robert J Volk; Holland M Kaplan; L A Wilhelms; Sheryl A McCurdy; Jerry D Estep; Matthias Loebe; Courtenay R Bruce Journal: J Heart Lung Transplant Date: 2015-03-31 Impact factor: 10.247
Authors: Thomas J Waltz; Byron J Powell; Monica M Matthieu; Laura J Damschroder; Matthew J Chinman; Jeffrey L Smith; Enola K Proctor; JoAnn E Kirchner Journal: Implement Sci Date: 2015-08-07 Impact factor: 7.327