Roadmap to an effective quality improvement and patient safety program implementation in a rural hospital setting

Service delivery interventions to improve maternal and newborn health in low- and middle-income countries: scoping review of quality improvement, implementation research and health system strengthening approaches

INTRODUCTION

This review explores the characteristics of service delivery-related interventions to improve maternal and newborn health (MNH) in low-and middle-income countries (LMICs) over the last two decades, comparing three common framings of these interventions, namely, quality improvement (QI), implementation science/research (IS/IR), and health system strengthening (HSS).

METHODS

The review followed the staged scoping review methodology proposed by Levac et al. (2010). We developed and piloted a systematic search strategy, limited to English language peer-reviewed articles published on LMICs between 2000 and March 2022. Analysis was conducted in two-quantitative and qualitative-phases. In the quantitative phase, we counted the year of publication, country(-ies) of origin, and the presence of the terms 'quality improvement', 'health system strengthening' or 'implementation science'/ 'implementation research' in titles, abstracts and key words. From this analysis, a subset of papers referred to as 'archetypes' (terms appearing in two or more of titles, abstract and key words) was analysed qualitatively, to draw out key concepts/theories and underlying mechanisms of change associated with each approach.

RESULTS

The searches from different databases resulted in a total of 3,323 hits. After removal of duplicates and screening, a total of 231 relevant articles remained for data extraction. These were distributed across the globe; more than half (n = 134) were published since 2017. Fifty-five (55) articles representing archetypes of the approach (30 QI, 16 IS/IR, 9 HSS) were analysed qualitatively. As anticipated, we identified distinct patterns in each approach. QI archetypes tended towards defined process interventions (most typically, plan-do-study-act cycles); IS/IR archetypes reported a wide variety of interventions, but had in common evaluation methodologies and explanatory theories; and HSS archetypes adopted systemic perspectives. Despite their distinctiveness, there was also overlap and fluidity between approaches, with papers often referencing more than one approach. Recognising the complexity of improving MNH services, there was an increased orientation towards participatory, context-specific designs in all three approaches.

CONCLUSIONS

Programmes to improve MNH outcomes will benefit from a better appreciation of the distinctiveness and relatedness of different approaches to service delivery strengthening, how these have evolved and how they can be combined.

Harnessing the health systems strengthening potential of quality improvement using realist evaluation: an example from southern Tanzania

Quality improvement (QI) is a problem-solving approach in which stakeholders identify context-specific problems and create and implement strategies to address these. It is an approach that is increasingly used to support health system strengthening, which is widely promoted in Sub-Saharan Africa. However, few QI initiatives are sustained and implementation is poorly understood. Here, we propose realist evaluation to fill this gap, sharing an example from southern Tanzania. We use realist evaluation to generate insights around the mechanisms driving QI implementation. These insights can be harnessed to maximize capacity strengthening in QI and to support its operationalization, thus contributing to health systems strengthening. Realist evaluation begins by establishing an initial programme theory, which is presented here. We generated this through an elicitation approach, in which multiple sources (theoretical literature, a document review and previous project reports) were collated and analysed retroductively to generate hypotheses about how the QI intervention is expected to produce specific outcomes linked to implementation. These were organized by health systems building blocks to show how each block may be strengthened through QI processes. Our initial programme theory draws from empowerment theory and emphasizes the self-reinforcing nature of QI: the more it is implemented, the more improvements result, further empowering people to use it. We identified that opportunities that support skill- and confidence-strengthening are essential to optimizing QI, and thus, to maximizing health systems strengthening through QI. Realist evaluation can be used to generate rich implementation data for QI, showcasing how it can be supported in ‘real-world’ conditions for health systems strengthening.

Cost-effectiveness of a mentorship and quality improvement intervention to enhance the quality of antenatal care at rural health centers in Rwanda

OBJECTIVE

To estimate cost-effectiveness of Mentorship, Enhanced Supervision for Healthcare and Quality Improvement (MESH-QI) intervention to strengthen the quality of antenatal care at rural health centers in rural Rwanda.

DESIGN

Cost-effectiveness analysis of the MESH-QI intervention using the provider perspective.

SETTING

Kirehe and Rwinkwavu District Hospital catchment areas, Rwanda.

INTERVENTION

MESH-QI.

MAIN OUTCOME MEASURES

Incremental cost per antenatal care visit with complete danger sign and vital sign assessments.

RESULTS

The total annual costs of standard antenatal care supervision was 10 777.21 USD at the baseline, whereas the total costs of MESH-QI intervention was 19 656.53 USD. Human resources (salary and benefits) and transport drove the majority of program expenses, (44.8% and 40%, respectively). Other costs included training of mentors (12.9%), data management (6.5%) and equipment (6.5%). The incremental cost per antenatal care visit attributable to MESH-QI with all assessment items completed was 0.70 USD for danger signs and 1.10 USD for vital signs.

CONCLUSIONS

MESH-QI could be an affordable and effective intervention to improve the quality of antenatal care at health centers in low-resource settings. Cost savings would increase if MESH-QI mentors were integrated into the existing healthcare systems and deployed to sites with higher volume of antenatal care visits.

A retrospective study of neonatal case management and outcomes in rural Rwanda post implementation of a national neonatal care package for sick and small infants

Background

Despite worldwide efforts to reduce neonatal mortality, 44% of under-five deaths occur in the first 28 days of life. The primary causes of neonatal death are preventable or treatable. This study describes the presentation, management and outcomes of hospitalized newborns admitted to the neonatal units of two rural district hospitals in Rwanda after the 2012 launch of a national neonatal protocol and standards.

Methods

We retrospectively reviewed routinely collected data for all neonates (0 to 28 days) admitted to the neonatal units at Rwinkwavu and Kirehe District Hospitals from January 1, 2013 to December 31, 2014. Data on demographic and clinical characteristics, clinical management, and outcomes were analyzed using median and interquartile ranges for continuous data and frequencies and proportions for categorical data. Clinical management and outcome variables were stratified by birth weight and differences between low birth weight (LBW) and normal birth weight (NBW) neonates were assessed using Fisher’s exact or Wilcoxon rank-sum tests at the α = 0.05 significance level.

Results

A total of 1723 neonates were hospitalized over the two-year study period; 88.7% were admitted within the first 48 h of life, 58.4% were male, 53.8% had normal birth weight and 36.4% were born premature. Prematurity (27.8%), neonatal infection (23.6%) and asphyxia (20.2%) were the top three primary diagnoses. Per national protocol, vital signs were assessed every 3 h within the first 48 h for 82.6% of neonates (n = 965/1168) and 93.4% (n = 312/334) of neonates with infection received antibiotics. The overall mortality rate was 13.3% (n = 185/1386) and preterm/LBW infants had similar mortality rate to NBW infants (14.7 and 12.2% respectively, p = 0.131). The average length of stay in the neonatal unit was 5 days.

Conclusions

Our results suggest that it is possible to provide specialized neonatal care for both LBW and NBW high-risk neonates in resource-limited settings. Despite implementation challenges, with the introduction of the neonatal care package and defined clinical standards these most vulnerable patients showed survival rates comparable to or higher than neighboring countries.

A quality improvement project using statistical process control methods for type 2 diabetes control in a resource-limited setting

Quality issue

Quality improvement (QI) is a key strategy for improving diabetes care in low- and middle-income countries (LMICs). This study reports on a diabetes QI project in rural Guatemala whose primary aim was to improve glycemic control of a panel of adult diabetes patients.

Initial assessment

Formative research suggested multiple areas for programmatic improvement in ambulatory diabetes care.

Choice of solution

This project utilized the Model for Improvement and Agile Global Health, our organization's complementary healthcare implementation framework.

Implementation

A bundle of improvement activities were implemented at the home, clinic and institutional level.

Evaluation

Control charts of mean hemoglobin A1C (HbA1C) and proportion of patients meeting target HbA1C showed improvement as special cause variation was identified 3 months after the intervention began. Control charts for secondary process measures offered insights into the value of different components of the intervention. Intensity of home-based diabetes education emerged as an important driver of panel glycemic control.

Lessons learned

Diabetes QI work is feasible in resource-limited settings in LMICs and can improve glycemic control. Statistical process control charts are a promising methodology for use with panels or registries of diabetes patients.

Developing Process Maps as a Tool for a Surgical Infection Prevention Quality Improvement Initiative in Resource-Constrained Settings

BACKGROUND

Surgical infections cause substantial morbidity and mortality in low-and middle-income countries (LMICs). To improve adherence to critical perioperative infection prevention standards, we developed Clean Cut, a checklist-based quality improvement program to improve compliance with best practices. We hypothesized that process mapping infection prevention activities can help clinicians identify strategies for improving surgical safety.

STUDY DESIGN

We introduced Clean Cut at a tertiary hospital in Ethiopia. Infection prevention standards included skin antisepsis, ensuring a sterile field, instrument decontamination/sterilization, prophylactic antibiotic administration, routine swab/gauze counting, and use of a surgical safety checklist. Processes were mapped by a visiting surgical fellow and local operating theater staff to facilitate the development of contextually relevant solutions; processes were reassessed for improvements.

RESULTS

Process mapping helped identify barriers to using alcohol-based hand solution due to skin irritation, inconsistent administration of prophylactic antibiotics due to variable delivery outside of the operating theater, inefficiencies in assuring sterility of surgical instruments through lack of confirmatory measures, and occurrences of retained surgical items through inappropriate guidelines, staffing, and training in proper routine gauze counting. Compliance with most processes improved significantly following organizational changes to align tasks with specific process goals.

CONCLUSIONS

Enumerating the steps involved in surgical infection prevention using a process mapping technique helped identify opportunities for improving adherence and plotting contextually relevant solutions, resulting in superior compliance with antiseptic standards. Simplifying these process maps into an adaptable tool could be a powerful strategy for improving safe surgery delivery in LMICs.

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

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."

Mentorship and coaching to support strengthening healthcare systems: lessons learned across the five Population Health Implementation and Training partnership projects in sub-Saharan Africa

Background

Despite global efforts to increase health workforce capacity through training and guidelines, challenges remain in bridging the gap between knowledge and quality clinical practice and addressing health system deficiencies preventing health workers from providing high quality care. In many developing countries, supervision activities focus on data collection, auditing and report completion rather than catalyzing learning and supporting system quality improvement. To address this gap, mentorship and coaching interventions were implemented in projects in five African countries (Ghana, Mozambique, Rwanda, Tanzania, and Zambia) as components of health systems strengthening (HSS) strategies funded through the Doris Duke Charitable Foundation’s African Health Initiative. We report on lessons learned from a cross-country evaluation.

Methods

The evaluation was designed based on a conceptual model derived from the project-specific interventions. Semi-structured interviews were administered to key informants to capture data in six categories: 1) mentorship and coaching goals, 2) selection and training of mentors and coaches, 3) integration with the existing systems, 4) monitoring and evaluation, 5) reported outcomes, and 6) challenges and successes. A review of project-published articles and technical reports from the individual projects supplemented interview information.

Results

Although there was heterogeneity in the approaches to mentorship and coaching and targeted areas of the country projects, all led to improvements in core health system areas, including quality of clinical care, data-driven decision making, leadership and accountability, and staff satisfaction. Adaptation of approaches to reflect local context encouraged their adoption and improved their effectiveness and sustainability.

Conclusion

We found that incorporating mentorship and coaching activities into HSS strategies was associated with improvements in quality of care and health systems, and mentorship and coaching represents an important component of HSS activities designed to improve not just coverage, but even further effective coverage, in achieving Universal Health Care.

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

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.