The importance of evaluating performance to understand changes

Recently, the journal published two papers that described findings on the evaluation of performance in different clinical conditions and settings but with the same goal: to give explanation to findings observed after the placement systemic interventions or established programs in real world environments. The first one from Aron et al. explores the issue of the results of fighting overtreatment in diabetic patients, resulting in undertreatment as an unintended consequence and haven’t used balancing measures to detect so during the implementation of the strategy. The second one from Hillen et al. describes the frequency of appropriate usage of medication in a vulnerable population such as the ones in residential care, which shows huge gaps in adequate use. The clinical situations described in the two studies are not the main point to comment on. The case I like to highlight is the importance of evaluating performance to detect opportunities for improvement. Both studies take profit from the retrospective review of administrative databases usually available in mature health systems as frequently observed in developed countries. This is huge asset and is a cornerstone of health services research, what constitutes the base for further research to confirm or reject hypothesis to and develop solutions in reaction to problems.Fil: Garcia Elorrio, Ezequiel. Instituto de Efectividad Clínica y Políticas de Salud; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Research versus practice in quality improvement? Understanding how we can bridge the gap

The gap between implementers and researchers of quality improvement (QI) has hampered the degree and speed of change needed to reduce avoidable suffering and harm in health care. Underlying causes of this gap include differences in goals and incentives, preferred methodologies, level and types of evidence prioritized and targeted audiences. The Salzburg Global Seminar on 'Better Health Care: How do we learn about improvement?' brought together researchers, policy makers, funders, implementers, evaluators from low-, middle- and high-income countries to explore how to increase the impact of QI. In this paper, we describe some of the reasons for this gap and offer suggestions to better bridge the chasm between researchers and implementers. Effectively bridging this gap can increase the generalizability of QI interventions, accelerate the spread of effective approaches while also strengthening the local work of implementers. Increasing the effectiveness of research and work in the field will support the knowledge translation needed to achieve quality Universal Health Coverage and the Sustainable Development Goals.Fil: Hirschhorn, Lisa R.. Northwestern University; Estados UnidosFil: Ramaswamy, Rohit. University of North Carolina; Estados UnidosFil: Devnani, Mahesh. Post Graduate Institute of Medical Education & Research; IndiaFil: Wandersman, Abraham. University Of South Carolina; Estados UnidosFil: Simpson, Lisa A.. Academy Health; Estados UnidosFil: Garcia Elorrio, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Efectividad Clínica y Sanitaria; Argentin

Unpacking the black box of improvement

During the Salzburg Global Seminar Session 565-Better Health Care: How do we learn about improvement, participants discussed the need to unpack the black box of improvement. The black box refers to the fact that when quality improvement interventions are described or evaluated, there is a tendency to assume a simple, linear path between the intervention and the outcomes it yields. It is also assumed that it is enough to evaluate the results without understanding the process of by which the improvement took place. However, quality improvement interventions are complex, nonlinear and evolve in response to local settings. To accurately assess the effectiveness of quality improvement and disseminate the learning, there must be a greater understanding of the complexity of quality improvement work. To remain consistent with the language used in Salzburg, we refer to this as unpacking the black box of improvement. To illustrate the complexity of improvement, this article introduces four quality improvement case studies. In unpacking the black box, we present and demonstrate how Cynefin framework from complexity theory can be used to categorize and evaluate quality improvement interventions. Many quality improvement projects are implemented in complex contexts, necessitating an approach defined as probesense- respond. In this approach, teams experiment, learn and adapt their changes to their local setting. Quality improvement professionals intuitively use the probe-sense-respond approach in their work but document and evaluate their projects using language for simple or complicated' contexts, rather than the complex contexts in which they work. As a result, evaluations tend to ask 'How can we attribute outcomes to the intervention, rather than 'What were the adaptations that took place. By unpacking the black box of improvement, improvers can more accurately document and describe their interventions, allowing evaluators to ask the right questions and more adequately evaluate quality improvement interventions.Fil: Ramaswamy, Rohit. University of North Carolina; Estados UnidosFil: Reed, Julie. Nihr Clarch Northwest London; Estados UnidosFil: Livesley, Nigel. Institute for Healthcare Improvement; Estados UnidosFil: Boguslavsky, Victor. University Research Co; Estados UnidosFil: Garcia Elorrio, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Sax, Sylvia. University of Heidelberg; AlemaniaFil: Houleymata, Diarra. Applying Science to Strengthen and Improve Systems Project,; MalíFil: Kimble, Leighann. University Research Co; Estados UnidosFil: Parry, Gareth. Institute of Healthcare Improvement; Estados Unido

Transformational improvement in quality care and health systems: The next decade

Background: Healthcare is amongst the most complex of human systems. Coordinating activities and integrating newer with older ways of treating patients while delivering high-quality, safe care, is challenging. Three landmark reports in 2018 led by (1) the Lancet Global Health Commission, (2) a coalition of the World Health Organization, the Organisation for Economic Co-operation and Development and the World Bank, and (3) the National Academies of Sciences, Engineering and Medicine of the United States propose that health systems need to tackle care quality, create less harm and provide universal health coverage in all nations, but especially low- and middle-income countries. The objective of this study is to review these reports with the aim of advancing the discussion beyond a conceptual diagnosis of quality gaps into identification of practical opportunities for transforming health systems by 2030. Main body: We analysed the reports via text-mining techniques and content analyses to derive their key themes and concepts. Initiatives to make progress include better measurement, using the capacities of information and communications technologies, taking a systems view of change, supporting systems to be constantly improving, creating learning health systems and undergirding progress with effective research and evaluation. Our analysis suggests that the world needs to move from 2018, the year of reports, to the 2020s, the decade of action. We propose three initiatives to support this move: first, developing a blueprint for change, modifiable to each country’s circumstances, to give effect to the reports’ recommendations; second, to make tangible steps to reduce inequities within and across health systems, including redistributing resources to areas of greatest need; and third, learning from what goes right to complement current efforts focused on reducing things going wrong. We provide examples of targeted funding which would have major benefits, reduce inequalities, promote universality and be better at learning from successes as well as failures. Conclusion: The reports contain many recommendations, but lack an integrated, implementable, 10-year action plan for the next decade to give effect to their aims to improve care to the most vulnerable, save lives by providing high-quality healthcare and shift to measuring and ensuring better systems- and patient-level outcomes. This article signals what needs to be done to achieve these aims.Fil: Braithwaite, Jeffrey. Macquarie University; AustraliaFil: Vincent, Charles. University of Oxford; Reino UnidoFil: Garcia Elorrio, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Imanaka, Yuichi. Kyoto University; JapónFil: Nicklin, Wendy. Ceo International Society For Quality In Health Care; IrlandaFil: Sodzi Tettey, Sodzi. Institute For Healthcare Improvement; Estados UnidosFil: Bates, David W.. Harvard Medical School; Estados Unido

"Adiós Bacteriemias": a multi-country quality improvement collaborative project to reduce the incidence of CLABSI in Latin American ICUs

Quality Problem: The incidence of central line-associated bloodstream infections (CLABSI) in Latin America has been estimated at 4.9 episodes per 1000 central line (CL) days, compared to a pooled incidence of 0.9 in the United States. CLABSI usually result from not adhering to standardized health procedures and can be prevented using evidence-based practices. Initial Assessment: The first phase of the ?Adiós Bacteriemias? Collaborative was implemented in 39 intensive care units (ICUs) from Latin America from September 2012 to September 2013 with a 56% overall reduction in the incidence of CLABSI. Choice of Solution: Bundles of care for the processes of insertion and maintenance of CLs have proven to be effective in the reduction of CLABSI across different settings. Implementation: Building on the results of the first phase, we implemented a second phase of the ?Adiós Bacteriemias? Collaborative between June 2014-July 2015. We adapted the Breakthrough Series (BTS) Collaborative model to guide the adoption of bundles of care for CLABSI prevention through virtual learning sessions and continuous feedback. Evaluation: Eighty-three ICUs from five Latin American countries actively reported process and outcome measures. The overall reduction in the CLABSI incidence rate was 22% (incidence rate 0.78; 95% CI 0.65, 0.95), from 2.58 episodes per 1000 CL days at baseline to 2.02 episodes per 1000 CL days (P < 0.01) during the intervention period. Lessons Learned: Adiós Bacteriemias was effective in reducing the incidence of CLABSI and improving the adherence to good practices for CL insertion and maintenance processes in participating ICUs in Latin America.Fil: Arrieta, Jafet. Harvard University. Harvard School of Public Health; Estados UnidosFil: Orrego, Carola. Fundacion Avedis Donabedian; EspañaFil: Macchiavello, Dolores. Instituto Alexander Fleming; ArgentinaFil: Mora, Nuria. Fundacion Avedis Donabedian; EspañaFil: Delgado, Pedro. Harvard University. Harvard School of Public Health; Estados UnidosFil: Giuffré, Carolina. Hospital Británico de Buenos Aires; ArgentinaFil: Garcia Elorrio, Ezequiel. Hospital Alemán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rodriguez, Viviana. Hospital Alemán; Argentin

Institutionalizing quality within national health systems: Key ingredients for success

Quality improvement initiatives can be fragmented and short-term, leading to missed opportunities to improve quality in a systemic and sustainable manner. An overarching national policy or strategy on quality, informed by frontline implementation, can provide direction for quality initiatives across all levels of the health system. This can strengthen service delivery along with strong leadership, resources, and infrastructure as essential building blocks for the health system. This article draws on the proceedings of an ISQua conference exploring factors for institutionalizing quality of care within national systems. Active learning, inclusive of peer-to-peer learning and exchange, mentoring and coaching, emerged as a critical success factor to creating a culture of quality. When coupled by reinforcing elements like strong partnerships and coordination across multiple levels, engagement at all health system levels and strong political commitment, this culture can be cascaded to all levels requiring policy, leadership, and the capabilities for delivering quality healthcare.Fil: Kandasami, Stephanie. No especifíca;Fil: Babar Syed, Shamsuzzoha. No especifíca;Fil: Edward, Anbrasi. No especifíca;Fil: Sodzi Tettey, Sodzi. Institute for Healthcare Improvement; Estados UnidosFil: Garcia Elorrio, Ezequiel. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mensah Abrampah, Nana. No especifíca;Fil: Hansen, Peter M.. No especifíca

Quality improvement and emerging global health priorities

Quality improvement approaches can strengthen action on a range of global health priorities. Quality improvement efforts are uniquely placed to reorient care delivery systems towards integrated people-centred health services and strengthen health systems to achieve Universal Health Coverage (UHC). This article makes the case for addressing shortfalls of previous agendas by articulating the critical role of quality improvement in the Sustainable Development Goal era. Quality improvement can stimulate convergence between health security and health systems; address global health security priorities through participatory quality improvement approaches; and improve health outcomes at all levels of the health system. Entry points for action include the linkage with antimicrobial resistance and the contentious issue of the health of migrants. The work required includes focussed attention on the continuum of national quality policy formulation, implementation and learning; alongside strengthening the measurement-improvement linkage. Quality improvement plays a key role in strengthening health systems to achieve UHC.Fil: Abrampah, Nana Mensah. Organizacion Mundial de la Salud; ArgentinaFil: Syed, Shamsuzzoha Babar. Organizacion Mundial de la Salud; ArgentinaFil: Hirschhorn, Lisa R.. Northwestern University; Estados UnidosFil: Nambiar, Bejoy. Malawi University of Science and Technology; Malaui. Colegio Universitario de Londres; Reino UnidoFil: Iqbal, Usman. Taipei Medical University.; República de ChinaFil: Garcia Elorrio, Ezequiel. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Chattu, Vijay Kumar. University of the West Indies; Trinidad y TobagoFil: Devnani, Mahesh. Post Graduate Institute of Medical Education and Research; IndiaFil: Kelley, Edward. Organizacion Mundial de la Salud; Argentin

Contributing factors for acute stress in healthcare workers caring for COVID-19 patients in Argentina, Chile, Colombia, and Ecuador

This study analyzed the frequency and intensity of acute stress among health professionals caring for COVID-19 patients in four Latin American Spanish-speaking countries during the outbreak. A cross-sectional study involved a non-probability sample of healthcare professionals in four Latin American countries. Participants from each country were invited using a platform and mobile application designed for this study. Hospital and primary care workers from different services caring for COVID-19 patients were included. The EASE Scale (SARS-CoV-2 Emotional Overload Scale, in Spanish named Escala Auto-aplicada de Sobrecarga Emocional) was a previously validated measure of acute stress. EASE scores were described overall by age, sex, work area, and experience of being ill with COVID-19. Using the Mann–Whitney U test, the EASE scores were compared according to the most critical moments of the pandemic. Univariate and multivariate analysis was performed to investigate associations between these factors and the outcome ‘acute stress’. Finally, the Kruskal–Wallis was used to compare EASE scores and the experience of being ill. A total of 1372 professionals responded to all the items in the EASE scale: 375 (27.3%) Argentines, 365 (26.6%) Colombians, 345 (25.1%) Chileans, 209 (15.2%) Ecuadorians, and 78 (5.7%) from other countries. 27% of providers suffered middle-higher acute stress due to the outbreak. Worse results were observed in moments of peak incidence of cases (14.3 ± 5.3 vs. 6.9 ± 1.7, p < 0.05). Higher scores were found in professionals in COVID-19 critical care (13 ± 1.2) than those in non-COVID-19 areas (10.7 ± 1.9) (p = 0.03). Distress was higher among professionals who were COVID-19 patients (11.7 ± 1) or had doubts about their potential infection (12 ± 1.2) compared to those not infected (9.5 ± 0.7) (p = 0.001). Around one-third of the professionals experienced acute stress, increasing in intensity as the incidence of COVID-19 increased and as they became infected or in doubt whether they were infected. EASE scale could be a valuable asset for monitoring acute stress levels among health professionals in Latin America.Fil: Martin Delgado, Jimmy. Universidad Catolica de Santiago de Guayaquil; EcuadorFil: Poblete, Rodrigo. Universidad de Santiago de Chile; ChileFil: Serpa, Piedad. Universidad Industrial Santander; ColombiaFil: Mula, Aurora. Hospital Universitario de Sant Joan D´alacant; EspañaFil: Carrillo, Irene. Universidad de Miguel Hernández; EspañaFil: Fernández, Cesar. Universidad de Miguel Hernández; EspañaFil: Vicente Ripoll, María Asunción. Universidad de Miguel Hernández; EspañaFil: Loudet, Cecilia. General José de San Martín de la Plata General Hospital; ArgentinaFil: Jorro, Facundo. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños Pedro Elizalde (ex Casa Cuna); ArgentinaFil: Garcia Elorrio, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Epidemiología y Salud Pública. Instituto de Efectividad Clínica y Sanitaria. Centro de Investigaciones en Epidemiología y Salud Pública; ArgentinaFil: Guilabert, Mercedes. Universidad de Miguel Hernández; EspañaFil: Mira, José Joaquín. Universidad de Miguel Hernández; Españ

Reducing medication errors for adults in hospital settings

Background: Medication errors are preventable events that may cause or lead to inappropriate medication use or patient harm while the medication is in the control of the healthcare professional or patient. Medication errors in hospitalised adults may cause harm, additional costs, and even death. Objectives: To determine the effectiveness of interventions to reduce medication errors in adults in hospital settings. Search methods: We searched CENTRAL, MEDLINE, Embase, five other databases and two trials registers on 16 January 2020. Selection criteria: We included randomised controlled trials (RCTs) and interrupted time series (ITS) studies investigating interventions aimed at reducing medication errors in hospitalised adults, compared with usual care or other interventions. Outcome measures included adverse drug events (ADEs), potential ADEs, preventable ADEs, medication errors, mortality, morbidity, length of stay, quality of life and identified/solved discrepancies. We included any hospital setting, such as inpatient care units, outpatient care settings, and accident and emergency departments. Data collection and analysis: We followed the standard methodological procedures expected by Cochrane and the Effective Practice and Organisation of Care (EPOC) Group. Where necessary, we extracted and reanalysed ITS study data using piecewise linear regression, corrected for autocorrelation and seasonality, where possible. Main results: We included 65 studies: 51 RCTs and 14 ITS studies, involving 110,875 participants. About half of trials gave rise to 'some concerns' for risk of bias during the randomisation process and one-third lacked blinding of outcome assessment. Most ITS studies presented low risk of bias. Most studies came from high-income countries or high-resource settings. Medication reconciliation –the process of comparing a patient's medication orders to the medications that the patient has been taking– was the most common type of intervention studied. Electronic prescribing systems, barcoding for correct administering of medications, organisational changes, feedback on medication errors, education of professionals and improved medication dispensing systems were other interventions studied. Medication reconciliation. Low-certainty evidence suggests that medication reconciliation (MR) versus no-MR may reduce medication errors (odds ratio [OR] 0.55, 95% confidence interval (CI) 0.17 to 1.74; 3 studies; n=379). Compared to no-MR, MR probably reduces ADEs (OR 0.38, 95%CI 0.18 to 0.80; 3 studies, n=1336; moderate-certainty evidence), but has little to no effect on length of stay (mean difference (MD) -0.30 days, 95%CI -1.93 to 1.33 days; 3 studies, n=527) and quality of life (MD -1.51, 95%CI -10.04 to 7.02; 1 study, n=131). Low-certainty evidence suggests that, compared to MR by other professionals, MR by pharmacists may reduce medication errors (OR 0.21, 95%CI 0.09 to 0.48; 8 studies, n=2648) and may increase ADEs (OR 1.34, 95%CI 0.73 to 2.44; 3 studies, n=2873). Compared to MR by other professionals, MR by pharmacists may have little to no effect on length of stay (MD -0.25, 95%CI -1.05 to 0.56; 6 studies, 3983). Moderate-certainty evidence shows that this intervention probably has little to no effect on mortality during hospitalisation (risk ratio (RR) 0.99, 95%CI 0.57 to 1.7; 2 studies, n=1000), and on readmissions at one month (RR 0.93, 95%CI 0.76 to 1.14; 2 studies, n=997); and low-certainty evidence suggests that the intervention may have little to no effect on quality of life (MD 0.00, 95%CI -14.09 to 14.09; 1 study, n=724). Low-certainty evidence suggests that database-assisted MR conducted by pharmacists, versus unassisted MR conducted by pharmacists, may reduce potential ADEs (OR 0.26, 95%CI 0.10 to 0.64; 2 studies, n=3326), and may have no effect on length of stay (MD 1.00, 95%CI -0.17 to 2.17; 1 study, n=311). Low-certainty evidence suggests that MR performed by trained pharmacist technicians, versus pharmacists, may have little to no difference on length of stay (MD -0.30, 95%CI -2.12 to 1.52; 1 study, n=183). However, the CI is compatible with important beneficial and detrimental effects. Low-certainty evidence suggests that MR before admission may increase the identification of discrepancies compared with MR after admission (MD 1.27, 95%CI 0.46 to 2.08; 1 study, n=307). However, the CI is compatible with important beneficial and detrimental effects. Moderate-certainty evidence shows that multimodal interventions probably increase discrepancy resolutions compared to usual care (RR 2.14, 95%CI 1.81 to 2.53; 1 study, n=487). Computerised physician order entry (CPOE)/clinical decision support systems (CDSS). Moderate-certainty evidence shows that CPOE/CDSS probably reduce medication errors compared to paper-based systems (OR 0.74, 95%CI 0.31 to 1.79; 2 studies, n=88). Moderate-certainty evidence shows that, compared with standard CPOE/CDSS, improved CPOE/CDSS probably reduce medication errors (OR 0.85, 95%CI 0.74 to 0.97; 2 studies, n=630). Low-certainty evidence suggests that prioritised alerts provided by CPOE/CDSS may prevent ADEs compared to non-prioritised (inconsequential) alerts (MD 1.98, 95%CI 1.65 to 2.31; 1 study; participant numbers unavailable). Barcode identification of participants/medications. Low-certainty evidence suggests that barcoding may reduce medication errors (OR 0.69, 95%CI 0.59 to 0.79; 2 studies, n=50,545). Reduced working hours. Low-certainty evidence suggests that reduced working hours may reduce serious medication errors (RR 0.83, 95%CI 0.63 to 1.09; 1 study, n=634). However, the CI is compatible with important beneficial and detrimental effects. Feedback on prescribing errors. Low-certainty evidence suggests that feedback on prescribing errors may reduce medication errors (OR 0.47, 95%CI 0.33 to 0.67; 4 studies, n=384). Dispensing system. Low-certainty evidence suggests that dispensing systems in surgical wards may reduce medication errors (OR 0.61, 95%CI 0.47 to 0.79; 2 studies, n=1775). Authors' conclusions: Low- to moderate-certainty evidence suggests that, compared to usual care, medication reconciliation, CPOE/CDSS, barcoding, feedback and dispensing systems in surgical wards may reduce medication errors and ADEs. However, the results are imprecise for some outcomes related to medication reconciliation and CPOE/CDSS. The evidence for other interventions is very uncertain. Powered and methodologically sound studies are needed to address the identified evidence gaps. Innovative, synergistic strategies –including those that involve patients– should also be evaluated.Fil: Ciapponi, Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Epidemiología y Salud Pública. Instituto de Efectividad Clínica y Sanitaria. Centro de Investigaciones en Epidemiología y Salud Pública; ArgentinaFil: Fernandez Nievas, Simon E. No especifíca;Fil: Seijo, Mariana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Inmunología, Genética y Metabolismo. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Inmunología, Genética y Metabolismo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Epidemiología y Salud Pública. Instituto de Efectividad Clínica y Sanitaria. Centro de Investigaciones en Epidemiología y Salud Pública; ArgentinaFil: Rodriguez, Maria Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Epidemiología y Salud Pública. Instituto de Efectividad Clínica y Sanitaria. Centro de Investigaciones en Epidemiología y Salud Pública; ArgentinaFil: Vietto, Valeria. Instituto Universidad Escuela de Medicina del Hospital Italiano; ArgentinaFil: García Perdomo, Herney A.. Universidad del Valle; ColombiaFil: Virgilio, Sacha. No especifíca;Fil: Fajreldines, Ana V.. Universidad Austral; ArgentinaFil: Tost, Josep. No especifíca;Fil: Rose, Christopher J.. No especifíca;Fil: Garcia Elorrio, Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones en Epidemiología y Salud Pública. Instituto de Efectividad Clínica y Sanitaria. Centro de Investigaciones en Epidemiología y Salud Pública; Argentin

PHC Progression Model: A novel mixed-methods tool for measuring primary health care system capacity

High-performing primary health care (PHC) is essential for achieving universal health coverage. However, in many countries, PHC is weak and unable to deliver on its potential. Improvement is often limited by a lack of actionable data to inform policies and set priorities. To address this gap, the Primary Health Care Performance Initiative (PHCPI) was formed to strengthen measurement of PHC in low-income and middle-income countries in order to accelerate improvement. PHCPI´s Vital Signs Profile was designed to provide a comprehensive snapshot of the performance of a country´s PHC system, yet quantitative information about PHC systems´ capacity to deliver high-quality, effective care was limited by the scarcity of existing data sources and metrics. To systematically measure the capacity of PHC systems, PHCPI developed the PHC Progression Model, a rubric-based mixed-methods assessment tool. The PHC Progression Model is completed through a participatory process by in-country teams and subsequently reviewed by PHCPI to validate results and ensure consistency across countries. In 2018, PHCPI partnered with five countries to pilot the tool and found that it was feasible to implement with fidelity, produced valid results, and was highly acceptable and useful to stakeholders. Pilot results showed that both the participatory assessment process and resulting findings yielded novel and actionable insights into PHC strengths and weaknesses. Based on these positive early results, PHCPI will support expansion of the PHC Progression Model to additional countries to systematically and comprehensively measure PHC system capacity in order to identify and prioritise targeted improvement efforts.Fil: Ratcliffe, Hannah L.. Brigham And Women's Hospital; Estados Unidos. Harvard T.H. Chan School of Public Health; Estados UnidosFil: Schwarz, Dan. Harvard T.H. Chan School of Public Health; Estados Unidos. Brigham And Women's Hospital; Estados UnidosFil: Hirschhorn, Lisa R.. Northwestern University; Estados UnidosFil: Cejas, Cintia. Ministerio de Desarrollo Social; Argentina. Ministerio de Salud de la Nación; ArgentinaFil: DIallo, Abdoulaye. Ministry Of Health And Social Action; SenegalFil: Garcia Elorrio, Ezequiel. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Fifield, Jocelyn. Brigham And Women's Hospital; Estados Unidos. Harvard T.H. Chan School of Public Health; Estados UnidosFil: Gashumba, DIane. Ministry of Health; RuandaFil: Hartshorn, Lucy. Harvard T.H. Chan School of Public Health; Estados Unidos. Brigham And Women's Hospital; Estados UnidosFil: Leydon, Nicholas. Bill And Melinda Gates Foundation; Estados UnidosFil: Mohamed, Mohamed. Ministry Of Health And Social Welfare Dar Es Salaam; TanzaniaFil: Nakamura, Yoriko. Results For Development; Estados UnidosFil: Ndiaye, Youssoupha. Ministry Of Health And Social Action; SenegalFil: Novignon, Jacob. Kwame Nkrumah University Of Science And Technology; GhanaFil: Ofosu, Anthony. Ghana Health Service; GhanaFil: Roder Dewan, Sanam. Organización de las Naciones Unidas. Unicef. Fondo de las Naciones Unidas para la Infancia; ArgentinaFil: Rwiyereka, Angelique. Global Health Issues and Solutions; Estados UnidosFil: Secci, Federica. The World Bank Group; Estados UnidosFil: Veillard, Jeremy H.. The World Bank Group; Estados UnidosFil: Bitton, Asaf. Harvard T.H. Chan School of Public Health; Estados Unidos. Brigham And Women's Hospital; Estados Unido