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Understanding the Role of External Facilitation to Drive Quality Improvement for Stroke Care in Hospitals.

ABSTRACT: The use of external facilitation within the context of multicomponent quality improvement interventions (mQI) is growing. We aimed to evaluate the influence of external facilitation for improving the quality of acute stroke care. Clinicians from hospitals participating in mQI (Queensland, Australia) as part of the Stroke123 study were supported by external facilitators in a single, on-site workshop to review hospital performance against eight clinical processes of care (PoCs) collected in the Australian Stroke Clinical Registry (AuSCR) and develop an action plan. Remote support (i.e., telephone/email) after the workshop was provided. As part of a process evaluation for Stroke123, we recorded the number and mode of contacts between clinicians and facilitators; type of support provided; and frequency of self-directed, hospital-level stroke registry data reviews. Analysis: We measured the association between amount/type of external facilitation, (i) development of action plans, and (ii) adherence to PoCs before and after the intervention using AuSCR data from 2010 to 2015. In total, 14/19 hospitals developed an action plan. There was no significant difference in amount or type of external facilitator support provided between hospitals that did, and did not, develop an action plan. There was no relationship between the amount of external facilitation and change in adherence to PoCs. Most (95%) hospitals accessed stroke registry performance data. In the Stroke123 study, the amount or type of external facilitation did not influence action plan development, and the amount of support did not influence the changes achieved in adherence to PoCs. Remote support may not add value for mQI.

SUBMITTER: Thayabaranathan T

PROVIDER: S-EPMC8471416 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRelatively little guidance exists on how to use virtual implementation facilitation to successfully implement evidence-based practices and innovations into clinical programs. Yet virtual methods are increasingly common. They have potentially wider reach, emergent public health situations necessitate their use, and restrictions on resources can make them more attractive. We therefore outline a set of principles for virtual external implementation facilitation and a series of recommendations based on extensive experience successfully using virtual external implementation facilitation in a national program.Model and recommendationsSuccess in virtual external implementation facilitation may be achieved by facilitators applying three overarching principles: pilot everything, incorporate a model, and prioritize metacognition. Five practical principles also help: plan in advance, communicate in real time, build relationships, engage participants, and construct a virtual room for participants. We present eight concrete suggestions for enacting the practical principles: (1) assign key facilitation roles to facilitation team members to ensure the program runs smoothly; (2) create small cohorts of participants so they can have meaningful interactions; (3) provide clarity and structure for all participant interactions; (4) structure program content to ensure key points are described, reinforced, and practiced; (5) use visuals to supplement audio content; (6) build activities into the agenda that enable participants to immediately apply knowledge at their own sites, separate from the virtual experience; (7) create backup plans whenever possible; and (8) engage all participants in the program. These principles represent a novel conceptualization of virtual external implementation facilitation, giving structure to a process that has been, to date, inadequately described. The associated actions are demonstrably useful in supporting the principles and offer teams interested in virtual external implementation facilitation concrete methods by which to ensure success. Our examples stem from experiences in healthcare. But the principles can, in theory, be applied to virtual external implementation facilitation regardless of setting, as they and the associated actions are not setting specific.

Project description:Treatment of psychotic disorders consists primarily of second generation antipsychotics, which are associated with metabolic side effects such as overweight/obesity, diabetes, and dyslipidemia. Evidence-based clinical practice guidelines recommend timely assessment and management of these conditions; however, research studies show deficits and delays in metabolic monitoring and management for these patients. This protocol article describes the project 'Monitoring and Management for Metabolic Side Effects of Antipsychotics,' which is testing an approach to implement recommendations for these practices.This project employs a cluster randomized clinical trial design to test effectiveness of an evidence-based quality improvement plus facilitation intervention. Eligible study sites were VA Medical Centers with ?300 patients started on a new antipsychotic prescription in a six-month period. A total of 12 sites, matched in pairs based on scores on an organizational practice survey, were then randomized within pairs to intervention or control conditions.Study participants include VA employees involved in metabolic monitoring and management of patients treated with antipsychotics at participating sites. The intervention involves researchers partnering with clinical stakeholders to facilitate tailoring of local implementation strategies to address barriers to metabolic side-effect monitoring and management. The intervention includes a Design Phase (initial site visit and subsequent development of a local implementation plan); Implementation Phase (guided by an experienced external facilitator); and a Sustainability Phase. Evaluation includes developmental, implementation-focused, progress-focused and interpretative formative evaluation components, as well as summative evaluation. Evaluation methods include surveys, qualitative data collection from provider participants, and quantitative data analysis of data for all patients prescribed a new antipsychotic medication at a study site who are due for monitoring or management of metabolic side effects during the study phases. Changes in rates of recommended monitoring and management actions at intervention and control sites will be compared using time series analyses.Improving monitoring for metabolic side effects of antipsychotics, as well as promoting timely evidence-based management when these effects emerge, will lead to improved patient safety and long-term outcomes. This article discusses key strengths and challenges of the study.NCT01875861.

Project description:Background Insight into outcome variation between hospitals could help to improve quality of care. We aimed to assess the validity of early outcomes as quality indicators for acute ischemic stroke care for patients treated with endovascular therapy (EVT). Methods and Results We used data from the MR CLEAN (Multicenter Randomized Controlled Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands) Registry, a large multicenter prospective cohort study including 3279 patients with acute ischemic stroke undergoing EVT. Random effect linear and proportional odds regression were used to analyze the effect of case mix on between-hospital differences in 2 early outcomes: the National Institutes of Health Stroke Scale (NIHSS) score at 24 to 48 hours and the expanded thrombolysis in cerebral infarction score. Between-hospital variation in outcomes was assessed using the variance of random hospital effects (tau2). In addition, we estimated the correlation between hospitals' EVT-patient volume and (case-mix-adjusted) outcomes. Both early outcomes and case-mix characteristics varied significantly across hospitals. Between-hospital variation in the expanded thrombolysis in cerebral infarction score was not influenced by case-mix adjustment (tau 2=0.17 in both models). In contrast, for the NIHSS score at 24 to 48 hours, case-mix adjustment led to a decrease in variation between hospitals (tau 2 decreases from 0.19 to 0.17). Hospitals' EVT-patient volume was strongly correlated with higher expanded thrombolysis in cerebral infarction scores (r=0.48) and weakly with lower NIHSS score at 24 to 48 hours (r=0.15). Conclusions Between-hospital variation in NIHSS score at 24 to 48 hours is significantly influenced by case-mix but not by patient volume. In contrast, between-hospital variation in expanded thrombolysis in cerebral infarction score is strongly influenced by EVT-patient volume but not by case-mix. Both outcomes may be suitable for comparing hospitals on quality of care, provided that adequate adjustment for case-mix is applied for NIHSS score.

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Understanding the Role of External Facilitation to Drive Quality Improvement for Stroke Care in Hospitals.

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