Implementation methodology from a social systems informatics and engineering perspective applied to a parenting training program.

OBJECTIVE: For implementation of an evidence-based program to be effective, efficient, and equitable across diverse populations, we propose that researchers adopt a systems approach that is often absent in efficacy studies. To this end, we describe how a computer-based monitoring system can support the delivery of the New Beginnings Program (NBP), a parent-focused evidence-based prevention program for divorcing parents. METHOD: We present NBP from a novel systems approach that incorporates social system informatics and engineering, both necessary when utilizing feedback loops, ubiquitous in implementation research and practice. Examples of two methodological challenges are presented: how to monitor implementation, and how to provide feedback by evaluating system-level changes due to implementation. RESULTS: We introduce and relate systems concepts to these two methodologic issues that are at the center of implementation methods. We explore how these system-level feedback loops address effectiveness, efficiency, and equity principles. These key principles are provided for designing an automated, low-burden, low-intrusive measurement system to aid fidelity monitoring and feedback that can be used in practice. DISCUSSION: As the COVID-19 pandemic now demands fewer face-to-face delivery systems, their replacement with more virtual systems for parent training interventions requires constructing new implementation measurement systems based on social system informatics approaches. These approaches include the automatic monitoring of quality and fidelity in parent training interventions. Finally, we present parallels of producing generalizable and local knowledge bridging systems science and engineering method. This comparison improves our understanding of system-level changes, facilitates a program's implementation, and produces knowledge for the field. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Improving management of hospitalised patients with COVID-19: algorithms and tools for implementation and measurement.

BACKGROUND: The COVID-19 pandemic represents an unprecedented challenge to healthcare systems and nations across the world. Particularly challenging are the lack of agreed-upon management guidelines and variations in practice. Our hospital is a large, secondary-care government hospital in Kuwait, which has increased its capacity by approximately 28% to manage the care of patients with COVID-19. The surge in capacity has necessitated the redeployment of staff who are not well-trained to manage such conditions. There was a great need to develop a tool to help redeployed staff in decision-making for patients with COVID-19, a tool which could also be used for training. METHODS: Based on the best available clinical knowledge and best practices, an eight member multidisciplinary group of clinical and quality experts undertook the development of a clinical algorithm-based toolkit to guide training and practice for the management of patients with COVID-19. The team followed Horabin and Lewis' seven-step approach in developing the algorithms and a five-step method in writing them. Moreover, we applied Rosenfeld et al's five points to each algorithm. RESULTS: A set of seven clinical algorithms and one illustrative layout diagram were developed. The algorithms were augmented with documentation forms, data-collection online forms and spreadsheets and an indicators' reference sheet to guide implementation and performance measurement. The final version underwent several revisions and amendments prior to approval. CONCLUSIONS: A large volume of published literature on the topic of COVID-19 pandemic was translated into a user-friendly, algorithm-based toolkit for the management of patients with COVID-19. This toolkit can be used for training and decision-making to improve the quality of care provided to patients with COVID-19.

A Health System Response to COVID-19 in Long-Term Care and Post-Acute Care: A Three-Phase Approach.

BACKGROUND: The Seattle, WA, area was ground zero for coronavirus disease 2019 (COVID-19). Its initial emergence in a skilled nursing facility (SNF) not only highlighted the vulnerability of its patients and residents, but also the limited clinical support that led to national headlines. Furthermore, the coronavirus pandemic heightened the need for improved collaboration among healthcare organizations and local and state public health. METHODS: The University of Washington Medicine's (UWM's) Post-Acute Care (PAC) Network developed and implemented a three-phase approach within its pre-existing network of SNFs to help slow the spread of the disease, support local area SNFs from becoming overwhelmed when inundated with COVID-19 cases or persons under investigation, and help decrease the burden on area hospitals, clinics, and emergency medical services. RESULTS: Support of local area SNFs consisted of the following phases that were implemented at various times as COVID-19 impacted each facility at different times. Initial Phase: This phase was designed to (1) optimize communication, (2) review infection control practices, and (3) create a centralized process to track and test the target population. Delayed Phase: The goals of the Delayed Phase were to slow the spread of the disease once it is present in the SNF by providing consistent education and reinforcing infection prevention and control practices to all staff. Surge Phase: This phase aimed to prepare facilities in response to an outbreak by deploying a "Drop Team" within 24 hours to the facility to expeditiously test patients and exposed employees, triage symptomatic patients, and coordinate care and supplies with local public health authorities. CONCLUSIONS: The COVID-19 Three-Phase Response Plan provides a standardized model of care that may be implemented by other health systems and SNFs to help prepare and respond to COVID-19. J Am Geriatr Soc 68:1155-1161, 2020.