Implementation support for the data-driven components of care in a multisite network of coordinated specialty care programs

Beyond training providers prior to the roll-out of coordinated specialty care (CSC) for first-episode psychosis in clinical settings, implementation support for data-informed care remains an area that has received very little attention. The current paper describes the development and refinement of implementation support for the data-driven components of care in the New Journeys network, Washington State's CSC model for psychosis, comprised of 14 CSC programs. Using the Evidence-Based System for Innovation Support Logic Model, this paper outlines the individual components for implementation support, tools, training, technical assistance, and quality improvement/evaluation that have been developed for the New Journeys network. We present examples of modifications that have occurred over nine years to address the needs of clients, providers, and state-level network administrators to facilitate the data-driven components of care. We conclude with recommendations based on lessons learned in Washington State aimed at improving implementation of data-driven care in CSC models throughout the USA.

The Applied Data Analytics in Medicine Program: Lessons Learned From Four Years' Experience With Personalizing Health Care in an Academic Teaching Hospital

The University Medical Center (UMC) Utrecht piloted a hospital-wide innovation data analytics program over the past 4 years. The goal was, based on available data and innovative data analytics methodologies, to answer clinical questions to improve patient care. In this viewpoint, we aimed to support and inspire others pursuing similar efforts by sharing the three principles of the program: the data analytics value chain (data, insight, action, value), the innovation funnel (structured innovation approach with phases and gates), and the multidisciplinary team (patients, clinicians, and data scientists). We also discussed our most important lessons learned: the importance of a clinical question, collaboration challenges between health care professionals and different types of data scientists, the win-win result of our collaboration with external partners, the prerequisite of available meaningful data, the (legal) complexity of implementation, organizational power, and the embedding of collaborative efforts in the health care system as a whole.