||1R01CA255382-01 Interpret this number
||Columbia University Health Sciences
||DE-Implementation of Mammography Overuse in Older Racially and Ethnically Diverse Women
De-implementation is recognized as a critical but understudied area within implementation science (IS).
Research is needed to determine the optimal methods and approaches for identifying, selecting, and tailoring
de-implementation strategies. De-implementation of routine cancer screening in older adults, such as
mammography screening for breast cancer, offers excellent opportunities for both advancing the science of de-
implementation and improving care delivery and health outcomes in older adults. While national guidelines do
not support routine mammography in older women and recommend consideration of morbidities, life
expectancy and patients’ informed preferences, ~56% of women ≥75 years report receiving mammography,
including 50% of women with life expectancy <10 years. Our preliminary research identified multi-level barriers
and facilitators to de-implementation of mammography overuse among older women at the organizational (e.g.
system alerts, patient reminder letters), provider/clinic (e.g., knowledge, clinic norms), and patient (e.g. habit,
knowledge) levels. Informed by the Knowledge-to-Action Model, we propose a study for de-implementation of
mammography overuse in older women (i.e., reducing the frequency or cessation of mammography) in older
women across a large healthcare system serving a racially/ethnically diverse population in New York City. In
Aim 1, we will identify a range of de-implementation strategies at the patient, provider, and organizational
levels for reducing mammography overuse in women ages ≥75 years. We will use a crowdsourcing method,
successfully applied in an emerging participatory IS approach (innovation tournament) to generate rapid data
collection from diverse stakeholders (80-100 patients/family members, 80-100 providers/administrators from
the community and multiple healthcare systems) on factors that influence de-implementation. Combining this
data with our rich qualitative preliminary data, and principles from Dual Process Theory, we will develop distinct
de-implementation strategies for refinement in Aim 2. In Aim 2, we will prioritize and tailor de-implementation
strategies at patient, provider/clinic, and organizational levels. We will recruit 12-15 experts to prioritize
strategies based on feasibility and acceptability, and propose key attributes (e.g., duration, frequency, content)
for each strategy, and employ discrete choice experiment to elicit patient (n=75-100) and provider (n=75-100)
preferences for modifiable attributes of each prioritized strategy. In Aim 3, we will evaluate the feasibility,
acceptability, and use of the tailored de-implementation strategies in a pilot cluster randomized trial (8 clinics).
Using a sequential mixed-methods design, we will assess use of strategies, de-adoption outcomes (e.g.
reduction of mammography overuse), and theoretical mechanisms of strategies at the patient, provider, and
organizational levels. Data will establish feasibility and provide preliminary data for effectiveness of strategies
to be tested in future Hybrid 2 trial, and lay the groundwork for advancing de-implementation frameworks and
methodological approaches for selecting de-implementation strategies to reduce the use of low-value care.
Revisiting concepts of evidence in implementation science.
, Shelton R.C.
, Geng E.H.
, Glasgow R.E.
Implementation science : IS, 2022-04-12; 17(1), p. 26.