Data-driven approach to implementation mapping for the selection of implementation strategies: a case example for risk-aligned bladder cancer surveillance

Abstract

Background Implementation Mapping is an organized method to select implementation strategies. However, there are 73 Expert Recommendations for Implementing Change (ERIC) strategies. Thus, it is difficult for implementation scientists to map all potential strategies to the determinants of their chosen implementation science framework. Prior work using Implementation Mapping employed advisory panels to select implementation strategies. This article presents a data-driven approach to implementation mapping, in which we systematically evaluated all 73 ERIC strategies using the Tailored Implementation for Chronic Diseases (TICD) framework. We illustrate our approach using implementation of risk-aligned bladder cancer surveillance as a case example. Methods We developed objectives based on previously collected qualitative data organized by TICD determinants, i.e., what needs to be changed to achieve more risk-aligned surveillance. Next, we evaluated all 73 ERIC strategies, excluding those that were not applicable to our clinical setting. The remaining strategies were mapped to the objectives using data visualization techniques to make sense of the large matrices. Finally, we selected strategies with high impact, based on (1) broad scope, defined as a strategy addressing more than the median number of objectives, (2) requiring low or moderate time commitment from clinical teams, and (3) evidence of effectiveness from the literature. Results We identified 63 unique objectives. Of the 73 ERIC strategies, 45 were excluded because they were not applicable to our clinical setting (e.g., not feasible within the confines of the setting, not appropriate for the context). Thus, 28 ERIC strategies were mapped to the 63 objectives. Strategies addressed 0 to 26 objectives (median 10.5). Of the 28 ERIC strategies, 10 required low and 8 moderate time commitments from clinical teams. We selected 9 strategies based on high impact, each with a clearly documented rationale for selection. Conclusions We enhanced Implementation Mapping via a data-driven approach to the selection of implementation strategies. Our approach provides a practical method for other implementation scientists to use when selecting implementation strategies and has the advantage of favoring data-driven strategy selection over expert opinion.

Use of a multi-phased approach to identify and address facilitators and barriers to the implementation of a population-wide genomic screening program

“…Deciding which strategies to prioritize and which workgroups to work with is a critical next step. We plan to take a practical approach and focus on implementation strategies that are most endorsed by the study team and leadership [ 28 , 29 ]. Endorsement is currently completed informally through group discussion and consensus building, prioritizing implementation strategies that are both feasible and appropriate for the project.…”

Digital Health Implementation Strategies Coproduced With Adults With Acquired Brain Injury, Their Close Others, and Clinicians: Mixed Methods Study With Collaborative Autoethnography and Network Analysis

“…A complexity-informed approach identified that user and persona testing (strategy 4.1) may be the single most impactful leverage point to support digital health implementation for people with ABI, reflecting findings from a recent systematic review highlighting the potential to address the complexities for “adopters” [ 41 ]. The fruitful use of VNA in this study directly addresses an identified need in implementation science for data-driven methods to prioritize and select implementation strategies from long lists of possibilities [ 82 ], especially given potential time and resource constraints. In addition, a focus on strategy 4.1 to target diverse considerations in the “condition” domain offers specific guidance on how to address a previously identified gap in implementation knowledge for people with ABI who have comorbidities and additional challenges [ 41 ], specifically, how to practically address the complexity of ABI, rather than reduce it through participant exclusion criteria.…”

Examining Longitudinal Markers of Bladder Cancer Recurrence Through a Semi-Autonomous Machine Learning System for Quantifying Specimen Atypia from Urine Cytology

“…As bladder cancer recurrence is a significant concern for patients and healthcare providers, various methods have been developed to predict and monitor the likelihood of recurrence. While computer-aided assessment of the primary tumor has been shown to be predictive of likelihood for recurrence 41,42 , this examination presents only a snapshot in time, which could be augmented by repeated urine cytology exams [43][44][45] . However, there is currently little to no research on how repeat urine cytology exams can be leveraged to derive longitudinal markers of recurrence [46][47][48][49] .…”