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Implementation Science at UW

Keywords: Quality Improvement

✪ The role of psychological safety and learning behavior in the development of effective quality improvement teams in Ghana: an observational study

Authors:

Jordan A. Albritton, Bruce Fried, Kavita Singh, Bryan J. Weiner, Bryce Reeve, & Jeffrey R. Edwards

University of Washington affiliated authors are displayed in bold.

Published: June 2019

Read the full text in the open access journal BMC Health Services Research

Abstract:

Background

As lower-income countries look to develop a mature healthcare workforce and to improve quality and reduce costs, they are increasingly turning to quality improvement (QI), a widely-used strategy in higher-income countries. Although QI is an effective strategy for promoting evidence-based practices, QI interventions often fail to deliver desired results. This failure may reflect a problem with implementation. As the key implementing unit of QI, teams are critical for the success or failure of QI efforts. Thus, we used the model of work-team learning to identify factors related to the effectiveness of newly-formed hospital-based QI teams in Ghana.

Methods

This was a cross-sectional, observational study. We used structural equation modeling to estimate relationships between coaching-oriented team leadership, perceived support for teamwork, team psychological safety, team learning behavior, and QI implementation. We used an observer-rated measure of QI implementation, our outcome of interest. Team-level factors were measured using aggregated survey data from 490 QI team members, resulting in a sample size of 122 teams. We assessed model fit and tested significance of standardized parameters, including direct and indirect effects.

Results

Learning behavior mediated a positive relationship between psychological safety and QI implementation (β = 0.171, p = 0.001). Psychological safety mediated a positive relationship between team leadership and learning behavior (β = 0.384, p = 0.068). Perceived support for teamwork did not have a significant effect on psychological safety or learning behavior.

Conclusion

Psychological safety and learning behavior are key for the success of newly formed QI teams working in lower-income countries. Organizational leaders and implementation facilitators should consider these leverage points as they work to establish an environment where QI and other team-based activities are supported and encouraged.

**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**

✪ Data-driven quality improvement in low-and middle-income country health systems: lessons from seven years of implementation experience across Mozambique, Rwanda, and Zambia

Authors:

Bradley H. Wagenaar, Lisa R. Hirschhorn, Catherine Henley, Artur Gremu, Ntazana Sindano, Roma Chilengi and the AHI PHIT Partnership Collaborative

University of Washington affiliated authors are displayed in bold.

✪ Open Access

Published: December 2017

Read the full text in the open access journal BMC Health Services Research

Abstract:

Background

Well-functioning health systems need to utilize data at all levels, from the provider, to local and national-level decision makers, in order to make evidence-based and needed adjustments to improve the quality of care provided. Over the last 7 years, the Doris Duke Charitable Foundation’s African Health Initiative funded health systems strengthening projects at the facility, district, and/or provincial level to improve population health. Increasing data-driven decision making was a common strategy in Mozambique, Rwanda and Zambia. This paper describes the similar and divergent approaches to increase data-driven quality of care improvements (QI) and implementation challenge and opportunities encountered in these three countries.

Methods

Eight semi-structured in-depth interviews (IDIs) were administered to program staff working in each country. IDIs for this paper included principal investigators of each project, key program implementers (medically-trained support staff, data managers and statisticians, and country directors), as well as Ministry of Health counterparts. IDI data were collected through field notes; interviews were not audio recorded. Data were analyzed using thematic analysis but no systematic coding was conducted. IDIs were supplemented through donor report abstractions, a structured questionnaire, one-on-one phone calls, and email exchanges with country program leaders to clarify and expand on key themes emerging from IDIs.

Results

Project successes ranged from over 450 collaborative action-plans developed, implemented, and evaluated in Mozambique, to an increase from <10% to >80% of basic clinical protocols followed in intervention facilities in rural Zambia, and a shift from a lack of awareness of health data among health system staff to collaborative ownership of data and using data to drive change in Rwanda.

Conclusion

Based on common successes across the country experiences, we recommend future data-driven QI interventions begin with data quality assessments to promote that rapid health system improvement is possible, ensure confidence in available data, serve as the first step in data-driven targeted improvements, and improve staff data analysis and visualization skills. Explicit Ministry of Health collaborative engagement can ensure performance review is collaborative and internally-driven rather than viewed as an external “audit.”

**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**

✪ Improving data quality across 3 sub-Saharan African countries using the Consolidated Framework for Implementation Research (CFIR): results from the African Health Initiative

Authors:

Sarah Gimbel, Moses Mwanza, Marie Paul Nisingizwe, Cathy Michel, Lisa Hirschhorn and the AHI PHIT Partnership Collaborative

University of Washington affiliated authors are displayed in bold.

✪ Open Access

Published: December 2017

Read the full text in the open access journal BMC Health Services Research

Abstract:

Background

High-quality data are critical to inform, monitor and manage health programs. Over the seven-year African Health Initiative of the Doris Duke Charitable Foundation, three of the five Population Health Implementation and Training (PHIT) partnership projects in Mozambique, Rwanda, and Zambia introduced strategies to improve the quality and evaluation of routinely-collected data at the primary health care level, and stimulate its use in evidence-based decision-making. Using the Consolidated Framework for Implementation Research (CFIR) as a guide, this paper: 1) describes and categorizes data quality assessment and improvement activities of the projects, and 2) identifies core intervention components and implementation strategy adaptations introduced to improve data quality in each setting.

Methods

The CFIR was adapted through a qualitative theme reduction process involving discussions with key informants from each project, who identified two domains and ten constructs most relevant to the study aim of describing and comparing each country’s data quality assessment approach and implementation process. Data were collected on each project’s data quality improvement strategies, activities implemented, and results via a semi-structured questionnaire with closed and open-ended items administered to health management information systems leads in each country, with complementary data abstraction from project reports.

Results

Across the three projects, intervention components that aligned with user priorities and government systems were perceived to be relatively advantageous, and more readily adapted and adopted. Activities that both assessed and improved data quality (including data quality assessments, mentorship and supportive supervision, establishment and/or strengthening of electronic medical record systems), received higher ranking scores from respondents.

Conclusion

Our findings suggest that, at a minimum, successful data quality improvement efforts should include routine audits linked to ongoing, on-the-job mentoring at the point of service. This pairing of interventions engages health workers in data collection, cleaning, and analysis of real-world data, and thus provides important skills building with on-site mentoring. The effect of these core components is strengthened by performance review meetings that unify multiple health system levels (provincial, district, facility, and community) to assess data quality, highlight areas of weakness, and plan improvements.

**This abstract is posted with permission under the Creative Commons Attribution 4.0 International License**