Facilitating future implementation and translation to clinical practice: The Implementation Planning Assessment Tool for clinical trials.
Christine P KowalskiLinda M KawentelTassos C KyriakidesLori DavisNicholas W BowersoxAmy M KilbourneGrant D HuangAndrea L NevedalPublished in: Journal of clinical and translational science (2022)
Implementation assessment plans are crucial for clinical trials to achieve their full potential. Without a proactive plan to implement trial results, it can take decades for one-fifth of effective interventions to be adopted into routine care settings. The Veterans Health Administration Office of Research and Development is undergoing a systematic transformation to embed implementation planning in research protocols through the Cooperative Studies Program, its flagship clinical research program. This manuscript has two objectives: 1) to introduce an Implementation Planning Assessment (IPA) Tool that any clinical trialist may use to facilitate post-trial implementation of interventions found to be effective and 2) to provide a case study demonstrating the IPA Tool's use. The IPA Tool encourages study designers to initially consider rigorous data collection to maximize acceptability of the intervention by end-users. It also helps identify and prepare potential interested parties at local and national leadership levels to ensure, upon trial completion, interventions can be integrated into programs, technologies, and policies in a sustainable way. The IPA Tool can alleviate some of the overwhelming nature of implementation science by providing a practical guide based on implementation science principles for researchers desiring to scale up and spread effective, clinical trial-tested interventions to benefit patients.
Keyphrases
- quality improvement
- clinical trial
- healthcare
- primary care
- public health
- phase ii
- phase iii
- study protocol
- physical activity
- clinical practice
- end stage renal disease
- chronic kidney disease
- mental health
- newly diagnosed
- palliative care
- open label
- electronic health record
- prognostic factors
- peritoneal dialysis
- deep learning
- double blind
- patient reported outcomes
- patient reported