Monitoring, Evaluation, and Quality Improvement in Global Health

2021 ◽  
pp. 245-268
Author(s):  
Joia S. Mukherjee
Keyword(s):  
Quality Improvement ◽  
Global Health ◽  
Health Information ◽  
Health Surveys ◽  
Program Planning ◽  
Health Data ◽  
Quality Data ◽  
Electronic Systems ◽  
Health Delivery ◽  
Use Of Data

Quality data are necessary to make good decisions in health delivery, for both individuals and populations. Data can be used to improve care and achieve equity. However, the collection of health data has been weak in most impoverished countries, where health data are compiled in stacks of poorly organized paper records. Efforts to streamline and improve health information discussed in this chapter include patient-held booklets, demographic health surveys, and the use of common indicators. This chapter also focuses on the evolution of medical records, including electronic systems. The use of data for monitoring, evaluation, and quality improvement is explained. Finally, this chapter reviews the use of frameworks—such as logic models and log frames—for program planning, evaluation, and improvement.

Monitoring, Evaluation, Disease Surveillance, and Quality Improvement

2017 ◽  
Author(s):  
Joia S. Mukherjee
Keyword(s):  
Quality Improvement ◽  
Medical Records ◽  
Disease Surveillance ◽  
Program Planning ◽  
Health Data ◽  
Quality Data ◽  
Electronic Systems ◽  
Health Delivery ◽  
Use Of Data ◽  
Improve Health

Quality data are necessary to make good decisions in health delivery for both individuals and populations. Data can be used to improve care and achieve equity. However, systems for health data management were historically weak in most impoverished countries. Health data are not uncommonly compiled in stacks of poorly organized paper records. Efforts to streamline and improve health information discussed in this chapter include patient-held booklets, demographic health surveys, and the use of common indicators. This chapter also focuses on the evolution of medical records, including electronic systems. The use of data for monitoring, evaluation, and quality improvement is explained. Finally, this chapter reviews the use of frameworks—such as logic models and log frames—for program planning, evaluation, and improvement.

Health Data Security Considerations in Global Health Partnerships (Preprint)

2021 ◽  
Author(s):  
Juan Espinoza ◽  
Abu Sikder ◽  
James Dickhoner ◽  
Thomas Lee
Keyword(s):  
Information Technology ◽  
Global Health ◽  
Conceptual Framework ◽  
Health Information Technology ◽  
Health Information ◽  
Data Security ◽  
Quantitative Model ◽  
Health Data ◽  
Framework Analysis ◽  
Middle Income

BACKGROUND Healthcare databases contain a wealth of information that can be used to develop programs and mature healthcare systems. Of concern, the sensitive nature of health data (e.g. ethnicity, reproductive health, sexually transmitted infections, lifestyle information, etc.) can have significant impact on individuals if misused, particularly among vulnerable and marginalized populations. As academic institutions, NGOs, and international agencies begin to collaborate with low and middle-income countries (LMICs) to develop and deploy health information technology (HIT), it is important to understand the technical and practical security implications of these initiatives. OBJECTIVE Our aim was to develop a conceptual framework for risk stratifying global health data partnerships and HIT projects. In addition to identifying key conceptual domains, we mapped each domain to a variety of publicly available indices that could be used to inform a quantitative model. METHODS We conducted a non-systematic review of the literature to identify relevant publications, position statements, white papers, and reports. The research team reviewed all sources and used the Framework Method and Conceptual Framework Analysis to name and categorize key concepts, integrate them into domains, and synthesize them into an overarching conceptual framework. Once key domains were identified, public international data sources were searched for relevant structured indices to generate a quantitative counterpart. RESULTS We identified five key domains to inform our conceptual framework: 1) State of Health Information Technology, 2) Economics of Healthcare, 3) Demographics and Equity, 4) Societal Freedom and Safety, and 5) Partnership and Trust. Each of these domains was mapped to a number of structured indices. CONCLUSIONS There is a complex relationship between the legal, economic, and social domains of healthcare, which impacts the state of HIT in LMICs and associated data security risks. The strength of partnership and trust between collaborating organizations is an important moderating factor. Additional work is needed to formalize the assessment of partnerships and trust, and to develop a quantitative model of the conceptual framework that can help support organization decision-making.

scholarly journals Mapping European research networks providing health data: results from the InfAct Joint Action on health information

2022 ◽  
Vol 80 (1) ◽  
Author(s):  
Brigid Unim ◽  
Elsi Haverinen ◽  
Eugenio Mattei ◽  
Flavia Carle ◽  
Andrea Faragalli ◽  
...  
Keyword(s):  
Data Collection ◽  
Quality Assessment ◽  
Health Information ◽  
Information Exchange ◽  
Ad Hoc ◽  
Health Information System ◽  
Data Access ◽  
Health Data ◽  
Quality Data ◽  
Research Networks

Abstract Background Research networks offer multidisciplinary expertise and promote information exchange between researchers across Europe. They are essential for the European Union’s (EU) health information system as providers of health information and data. The aim of this mapping exercise was to identify and analyze EU research networks in terms of health data collection methods, quality assessment, availability and accessibility procedures. Methods A web-based search was performed to identify EU research networks that are not part of international organizations (e.g., WHO-Europe, OECD) and are involved in collection of data for health monitoring or health system performance assessment. General characteristics of the research networks (e.g., data sources, representativeness), quality assessment procedures, availability and accessibility of health data were collected through an ad hoc extraction form. Results Fifty-seven research networks, representative at national, international or regional level, were identified. In these networks, data are mainly collected through administrative sources, health surveys and cohort studies. Over 70% of networks provide information on quality assessment of their data collection procedures. Most networks share macrodata through articles and reports, while microdata are available from ten networks. A request for data access is required by 14 networks, of which three apply a financial charge. Few networks share data with other research networks (8/49) or specify the metadata-reporting standards used for data description (9/49). Conclusions Improving health information and availability of high quality data is a priority in Europe. Research networks could play a major role in tackling health data and information inequalities by enhancing quality, availability, and accessibility of health data and data sharing across European networks.

scholarly journals Understanding challenges of using routinely collected health data to address clinical care gaps: a case study in Alberta, Canada

2022 ◽  
Vol 11 (1) ◽  
pp. e001491
Author(s):  
Taylor McGuckin ◽  
Katelynn Crick ◽  
Tyler W Myroniuk ◽  
Brock Setchell ◽  
Roseanne O Yeung ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Clinical Care ◽  
Healthcare Delivery ◽  
Health Data ◽  
Healthcare Research ◽  
Data Availability ◽  
Quality Data ◽  
Sources Of Information ◽  
Learning Program ◽  
Routinely Collected Health Data

High-quality data are fundamental to healthcare research, future applications of artificial intelligence and advancing healthcare delivery and outcomes through a learning health system. Although routinely collected administrative health and electronic medical record data are rich sources of information, they have significant limitations. Through four example projects from the Physician Learning Program in Edmonton, Alberta, Canada, we illustrate barriers to using routinely collected health data to conduct research and engage in clinical quality improvement. These include challenges with data availability for variables of clinical interest, data completeness within a clinical visit, missing and duplicate visits, and variability of data capture systems. We make four recommendations that highlight the need for increased clinical engagement to improve the collection and coding of routinely collected data. Advancing the quality and usability of health systems data will support the continuous quality improvement needed to achieve the quintuple aim.

scholarly journals Data accuracy and completeness of monthly midwifery returns indicators of Ejisu Juaben Health Directorate of Ghana

2018 ◽  
Vol 8 (1) ◽  
pp. 106
Author(s):  
Richard Okyere Boadu ◽  
Peter Agyei-Baffour ◽  
Anthony Kwaku Edusei
Keyword(s):  
Quality Improvement ◽  
Data Quality ◽  
Health Information ◽  
Low Income ◽  
Management Practices ◽  
Health Benefit ◽  
Information Management System ◽  
Data Accuracy ◽  
Low Income Countries ◽  
Quality Data

<span lang="EN-US">The broad range of activities contained in the provision of Primary Health Care (PHC) places a burden on providers to make optimal use of limited resources to achieve maximal health benefit to the population served. All too often, ad hoc decisions and personal preferences guide PHC resource allocations, making accountability for results impossible. Problems constraining Routine Health Information System (RHIS) performance in low-income countries include: poor data quality; limited use of available information; weaknesses in how data are analyzed and poor RHIS management practices. This study sought to investigate these constraints.</span><span> A non-experimental before and after study involving bassline assessment of data accuracy and completeness, application of innovative strategies such as mentoring and coaching of Health Information Officers in data quality improvement process. Coincidentally, the intervention </span><span lang="EN-US">improved both data accuracy and completeness performance significantly among the participating facilities. The outstanding performance may be attributed to management’s new orientation and growing interest towards quality data. Engaging frontline staff in data quality improvement work and provision of regular feedback leads to improvement in data accuracy and completeness. This has implications for decision-making and resource allocation, especially in low-income countries, where the routine health information management system relies heavily on paper work</span><span lang="EN-US">.</span>

scholarly journals 4.C. Round table: Joining forces: frameworks for international and multi-sectoral collaborations in health information

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Health Information ◽  
Population Health ◽  
Round Table ◽  
Secondary Use ◽  
Use Of Data ◽  
National Initiatives ◽  
Information Research

Abstract Countries have a wide range of lifestyles, environmental exposures and different health(care) systems providing a large natural experiment to be investigated. Through pan-European comparative studies, underlying determinants of population health can be explored and provide rich new insights into the dynamics of population health and care such as the safety, quality, effectiveness and costs of interventions. Additionally, in the big data era, secondary use of data has become one of the major cornerstones of digital transformation for health systems improvement. Several countries are reviewing governance models and regulatory framework for data reuse. Precision medicine and public health intelligence share the same population-based approach, as such, aligning secondary use of data initiatives will increase cost-efficiency of the data conversion value chain by ensuring that different stakeholders needs are accounted for since the beginning. At EU level, the European Commission has been raising awareness of the need to create adequate data ecosystems for innovative use of big data for health, specially ensuring responsible development and deployment of data science and artificial intelligence technologies in the medical and public health sectors. To this end, the Joint Action on Health Information (InfAct) is setting up the Distributed Infrastructure on Population Health (DIPoH). DIPoH provides a framework for international and multi-sectoral collaborations in health information. More specifically, DIPoH facilitates the sharing of research methods, data and results through participation of countries and already existing research networks. DIPoH's efforts include harmonization and interoperability, strengthening of the research capacity in MSs and providing European and worldwide perspectives to national data. In order to be embedded in the health information landscape, DIPoH aims to interact with existing (inter)national initiatives to identify common interfaces, to avoid duplication of the work and establish a sustainable long-term health information research infrastructure. In this workshop, InfAct lays down DIPoH's core elements in coherence with national and European initiatives and actors i.e. To-Reach, eHAction, the French Health Data Hub and ECHO. Pitch presentations on DIPoH and its national nodes will set the scene. In the format of a round table, possible collaborations with existing initiatives at (inter)national level will be debated with the audience. Synergies will be sought, reflections on community needs will be made and expectations on services will be discussed. The workshop will increase the knowledge of delegates around the latest health information infrastructure and initiatives that strive for better public health and health systems in countries. The workshop also serves as a capacity building activity to promote cooperation between initiatives and actors in the field. Key messages DIPoH an infrastructure aiming to interact with existing (inter)national initiatives to identify common interfaces, avoid duplication and enable a long-term health information research infrastructure. National nodes can improve coordination, communication and cooperation between health information stakeholders in a country, potentially reducing overlap and duplication of research and field-work.

scholarly journals Imagining the postantibiotic future: the visual culture of a global health threat

2020 ◽  
pp. medhum-2020-011884
Author(s):  
Rachel Irwin
Keyword(s):  
Global Health ◽  
Visual Culture ◽  
Bacterial Infections ◽  
Health Data ◽  
Data Sources ◽  
Story Telling ◽  
Specific Data ◽  
Public Health Data ◽  
Collective Imagination ◽  
Global Data

This article is concerned with the visual culture of global health data using antimicrobial resistance (AMR) as an example. I explore how public health data and knowledge are repackaged into visualisations and presented in four contemporary genres: the animation, the TED Talk, the documentary and the satire programme. I focus on how different actors describe a world in which there are no or few antibiotics that are effective against bacterial infections. I examine the form, content and style of the visual cultural of AMR, examining how these genres tell a story of impending apocalypse while also trying to advert it. This is a form of story-telling based around the if/then structure: we are told that if we do not take certain actions today, then we will face a postantibiotic future with certain, often catastrophic, consequences. Within this if/then structure, there are various aims and objectives: the goal may be preventing further spread of AMR, building awareness or pushing for certain policy or funding decisions. These stories also serve to place or deflect blame, on animals, occupations, patients, industries and others and to highlight risks and consequences. These examples share similarities in the forms of story-telling and narrative, and in the use of specific data sources and other images. By using several Swedish examples, I demonstrate how global data are reinterpreted for a national audience. Overall, I argue that while the convergence of a dominant narrative indicates scientific consensus, this consensus also stifles our collective imagination in finding new solutions to the problem. Finally, I also use the example of AMR to discuss the need for a broader social science and humanities engagement with the visual culture of global health data.

An Introduction to Global Health Delivery

2021 ◽  
Author(s):  
Joia Mukherjee ◽  
Paul Farmer
Keyword(s):  
Health Care ◽  
Global Health ◽  
Access To Health Care ◽  
Right To Health ◽  
Treatment Access ◽  
The Road ◽  
Health Delivery ◽  
Positive Side ◽  
Access To Health ◽  
The Right

What has called so many young people to the field of global health is the passion to be a force for change, to work on the positive side of globalization, and to be part of a movement for human rights. This passion stems from the knowledge that the world is not OK. Impoverished people are suffering and dying from treatable diseases, while the wealthy live well into their 80s and 90s. These disparities exist between and within countries. COVID-19 has further demonstrated the need for global equity and our mutual interdependence. Yet the road to health equity is long. People living in countries and communities marred by slavery, colonialism, resource extraction, and neoliberal market policies have markedly less access to health care than the wealthy. Developing equitable health systems requires understanding the history and political economy of communities and countries and working to adequately resource health delivery. Equitable health care also requires strong advocacy for the right to health. In fact, the current era in global health was sparked by advocacy—the activist movement for AIDS treatment access, for the universality of the right to health and to a share of scientific advancement. The same advocacy is needed now as vaccines and treatments are developed for COVID-19. This book centers global health in principles of equity and social justice and positions global health as a field to fulfill the universal right to health.

scholarly journals Project Tycho 2.0: a repository to improve the integration and reuse of data for global population health

2018 ◽  
Vol 25 (12) ◽  
pp. 1608-1617 ◽  
Author(s):  
Willem G van Panhuis ◽  
Anne Cross ◽  
Donald S Burke
Keyword(s):  
United States ◽  
Global Health ◽  
The United States ◽  
Health Data ◽  
Digital Object Identifier ◽  
Software Applications ◽  
Open Access Database ◽  
Fair Principles ◽  
Version 2.0 ◽  
Conference Papers

Abstract Objective In 2013, we released Project Tycho, an open-access database comprising 3.6 million counts of infectious disease cases and deaths reported for over a century by public health surveillance in the United States. Our objective is to describe how Project Tycho version 1 (v1) data has been used to create new knowledge and technology and to present improvements made in the newly released version 2.0 (v2). Materials and Methods We analyzed our user database and conducted online searches to analyze the use of Project Tycho v1 data. For v2, we added new US data and dengue data for other countries, and grouped data into 360 datasets, each with a digital object identifier and rich metadata. In addition, we used standard vocabularies to encode data where possible, improving compliance with FAIR (findable, accessible, interoperable, reusable) guiding principles for data management. Results Since release, 3174 people have registered to use Project Tycho data, leading to 18 new peer-reviewed papers and 27 other creative works, such as conference papers, student theses, and software applications. Project Tycho v2 comprises 5.7 million counts of infectious diseases in the United States and of dengue-related conditions in 98 additional countries. Discussion Project Tycho v2 contributes to improving FAIR compliance of global health data, but more work is needed to develop community-accepted standard representations for global health data. Conclusion FAIR principles are a valuable guide for improving the integration and reuse of data in global health to improve disease control and save lives.

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