Data-driven quality improvement program to prevent hospitalisation and improve care of people living with coronary heart disease: Protocol for a process evaluation

作者：Nashid Hafiz, Karice Hyun, Qiang Tu, Andrew Knight, Charlotte Hespe, Clara K Chow, Tom Briffa, Robyn Gallagher, Christopher M. Reid, David L. Hare, Nicholas Zwar, Mark Woodward, Stephen Jan, Emily Atkins, Tracey‐Lea Laba, Elizabeth Halcomb, Tracey Johnson, Timothy Usherwood, Julie Redfern · 发表于：Contemporary Clinical Trials · 年份：2022 · DOI：10.1016/j.cct.2022.106794 · 被引用次数：5 · 研究领域：Health Policy Implementation Science、Primary Care and Health Outcomes、Acute Myocardial Infarction Research

BACKGROUND: Practice-level quality improvement initiatives using rapidly advancing technology offers a multidimensional approach to reduce cardiovascular disease burden. For the "QUality improvement in primary care to prevent hospitalisations and improve Effectiveness and efficiency of care for people Living with heart disease" (QUEL) cluster randomised controlled trial, a 12-month quality improvement intervention was designed for primary care practices to use data and implement progressive changes using "Plan, Do, Study, Act" cycles within their practices with training in a series of interactive workshops. This protocol aims to describe the systematic methods to conduct a process evaluation of the data-driven intervention within the QUEL study. METHODS: A mixed-method approach will be used to conduct the evaluation. Quantitative data collected throughout the intervention period, via surveys and intervention materials, will be used to (1) identify the key elements of the intervention and how, for whom and in what context it was effective; (2) determine if the intervention is delivered as intended; and (3) describe practice engagement, commitment and capacity associated with various intervention components. Qualitative data, collected via semi-structured interviews and open-ended questions, will be used to gather in-depth understanding of the (1) satisfaction, utility, barriers and enablers; (2) acceptability, uptake and feasibility, and (3) effect of the COVID-19 pandemic on ...