Optimize Audit and feedbaCk To Implement eVidence-based prAcTices in Primary Health carE in Nepal, Mozambique, Tanzania and China: a Factorial Trial (ACTIVATE Trial)
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 344
- 主要终点
- The proportion of completed guideline-recommended quality checklist items for consultation of hypertension cases and Type II diabetes cases of the primary healthcare (PHC) providers among all of the items
研究概览
简要总结
The investigators will use two phases of Multiphase Optimization Strategy (MOST) - preparation and optimization phases. In the preparation phase, Audit and Feedback (AnF) intervention will be prepared. First, the investigators will use scoping review to develop conceptual frameworks for AnF components. The outcome indicators and resource constraints for intervention will be identified based on the literature reviews. Second, an expert consultation meeting will be conducted to develop a set of candidate components for the AnF intervention. Around 10 relevant scholars and primary healthcare workers will be invited to rank the components that researchers conclude from the literature. The top 7 ranked components will be assessed by Best-Worst Scaling (BWS) questionnaires to finally identify 3 key components for AnF intervention.
In the optimization phase, the investigators will identify AnF intervention that will lead to the best desired results within key resource constraints in terms of effectiveness , efficiency, economy and scalability. First, the investigators will realistically and comprehensively assess the quality of care provided by primary healthcare facilities of the four Low and Middle Income countries (LMICs) using USP. Second, a 2×2×2 factorial design (RCT) will be conducted to determine how the results of quality of care can be fed back to primary healthcare workers in the four LMICs in order to optimize the impact of improving healthcare quality. To achieve this goal, the factorial trial will involve the 3 identified key AnF components at 2 levels each, for a total of 8 intervention groups (i.e. 8 different ways to conduct audit and feedback). By randomly assigning healthcare facilities to one of these 8 different ways to conduct audit and feedback, the investigators can obtain the change in the quality of care after implementing audit and feedback interventions in these facilities. Then, through statistical analysis, the investigators can estimate main and interaction effects for AnF components on improving the quality of primary health care. After that, the optimal combination of AnF components will be determined by trade off of the effects of AnF components and resource constraints in local primary healthcare implementation settings. Study details are as follows.
详细描述
Researchers and experts will have a consultation meeting to generate the top 7 AnF intervention components, and a BWS survey will be employed to rank these components according to their importance and further select 3 potentially most effective and feasible components for effectiveness validation through the factorial trial in the next step. The BWS survey is a screening experiment, based on random utility theory, in which a trade-off mechanism is triggered by participants choosing the best and worst of a set of components or options, thereby quantifying the relative importance of each component and distinguishing the most salient among a set of important components. The BWS questionnaire will be developed and tested using a mixed-method approach based on the previous research results to obtain healthcare workers' prioritized acceptance of the different AnF components (relative importance) when deciding to improve the quality of care (completion rates of guideline entries), in order to further identify potentially the most important few components out of the range of components.
The Balanced Incomplete Block Design (BIBD) is an experimental design used in BWS for improving results by organizing items into blocks and balancing the number of presentations of items across participants, which allows researchers to efficiently compare a set of items with each other. BIBD ensures equal number of times of occurrence for items in blocks and pairs items equally, reducing bias and increasing statistical precision of ratings. BIBD is especially valuable with a larger number of ranked items. The investigators will use BIBD in BWS in a typical way, by dividing items randomly into subsets (i.e. blocks) and assigning a questionnaire with all blocks to each participant, ensuring robust preference rankings. The investigators will use the %MktBSize macro in SAS 9.4 software to realize BIBD for questionnaire development. In our study, for the 7 AnF intervention components (treatments), the investigators will have 7 different blocks, each containing 3 AnF components (treatments). the investigators will invite participants to reflect on which AnF component of these 7 different blocks is most effective and which AnF component is least effective. The investigators will be giving 1 point when a component is chosen as most effective, and -1 when a component is chosen as least effective. Then, based on the standardized score of each component, the investigators will be finalizing the 3 most effective components from the BWS survey.
In the optimization phase, a 2×2×2 factorial design (RCT) will be conducted, with three two-level components making up a total of 8 groups of AnF intervention. After obtaining consents from primary healthcare facilities and workers, all facilities will be randomly assigned to these 8 intervention groups. Then the investigators measure changes in healthcare quality from various audits and feedback in these facilities, and use statistical analysis to estimate main and interaction effects for AnF components on improving primary healthcare quality. The optimal AnF combination will be determined by considering effects and resource constraints in local implementation settings.
The investigators assume that the following 3 AnF components with 2 levels each are selected from the BWS survey.
- Source of Feedback:
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Factorial
- 主要目的
- Health Services Research
- 盲法
- Single (Outcomes Assessor)
盲法说明
The data analysis will be done by a team and those team members will be masked on the intervention provided.
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •In Mozambique, the inclusion criteria are nurses, technicians of general medicine and doctors in public primary healthcare facilities.
- •In Zanzibar, Tanzania, the inclusion criteria are clinical officers, nurses and doctors in public primary healthcare facilities.
- •In Nepal, the inclusion criteria are doctors, health assistants, and senior auxiliary health workers in public primary healthcare facilities.
- •In China, the inclusion criteria are doctors practicing in public primary healthcare facilities.
排除标准
- •The exclusion criteria are endocrinologists or diabetes specialists, interns, or students working during the time of visit.
结局指标
主要结局
The proportion of completed guideline-recommended quality checklist items for consultation of hypertension cases and Type II diabetes cases of the primary healthcare (PHC) providers among all of the items
时间窗: An average of 1 month and 3 months
The primary outcome is a continuous score ranging from 0 to 100%. It will be assessed by Unannounced Standardized Patients (USPs).
次要结局
- The proportion of completed guideline-recommended quality checklist items for physical and laboratory exams of hypertension cases and Type II diabetes cases of the PHC providers among all of the items(An average of 1 month and 3 months)
- Correctness of diagnosis of hypertension cases and type II diabetes cases by PHC providers(An average of 1 month and 3 months)
- Correctness of treatment of hypertension cases and type II diabetes cases by PHC providers(An average of 1 month and 3 months)
- Timeliness of hypertension and type II diabetes services in primary healthcare settings.(An average of 1 month and 3 months)
- Patient-centered quality of healthcare in primary healthcare settings.(An average of 1 month and 3 months)
- Implementation outcome: Adoption of Audit and Feedback (AnF) intervention by study participants(An average of 3 months)
- Implementation outcome: Costs to researchers of developing and implementing Audit and Feedback (AnF) intervention(An average of 3 months)
- Implementation outcome: Participants score of acceptability of AnF intervention(An average of 3 months)
研究者
Dr. Huanyuan Luo
Post Doctoral Researcher
Southern Medical University, China