scholarly journals Evaluating Team Characteristics for Health Engagements in Three Countries in Central America: 2012-2017

2021 ◽  
Author(s):  
Casey Perez ◽  
Diana Aguirre ◽  
Brian Neese ◽  
Joshua Vess ◽  
Edwin K Burkett
Keyword(s):  
Information System ◽  
Global Health ◽  
Health Outcomes ◽  
Team Member ◽  
Humanitarian Assistance ◽  
Direct Care ◽  
Team Size ◽  
Security Cooperation ◽  
Operational Readiness ◽  
Team Characteristics

ABSTRACT Background The U.S. DoD is a multidimensional agency of the government that employs health engagement activities within partner nations for medical operations, humanitarian assistance, threat reduction, and improved health outcomes toward sustainable global health and security. The composition and size of a health engagement team is critical for effective implementation; however, an ideal team makeup to achieve optimal operational readiness, health outcomes, and security cooperation objectives has not been established. This study was conducted to retrospectively describe and analyze medical mission activities in relation to ideal team characteristics in El-Salvador, Guatemala, and Honduras between 2012 and 2017. Methods A retrospective analysis was conducted on data from unclassified versions of the Global-Theater Security Cooperation Management Information System), Overseas Humanitarian Assistance Shared Information System databases, and mission files provided by U. S. Southern Command and its component commands. Data included 565 mission activities carried out by U.S. Military health teams in the selected host nations between 2012 and 2017. The mission activities were stratified and coded into nine distinct analyzable categories with subelements including but not limited to year, country, mission type, mission duration, team size, team language capability, team joint representation, and team member skillset. The analysis identifies mission objectives in the three subcategories of operational readiness, security cooperation, and health outcomes although the analysis did not include measurement of those objectives. Global Health Engagement mission types were broken down into five categories: direct care, health project, education & training (E&T), engineering, veterinary, or a combination. Data were analyzed using Excel. Results A total of 414 health engagement activities were found in the data analyzed during 2012 and 2017 accounting for duplication among the sources. Team size was documented in 23.4% (n = 97); team skillset makeup in 17.1% (n = 71); 2.7% (n = 11) showed that at least one team member had language capability for the country visited; and 3.6% (n = 15) documented that professional interpretation was available. The types of health engagement activities were broken down as follows: 64.3% were direct care, 12.2% were health projects, 10.9% were engineering, 9.1% were E&T, and 1.3% were veterinary. Overall, only 20.8% (n = 86) of the missions had a clear mission objective from the three categories of security cooperation, operational readiness, and health outcomes objectives. Individually, each category of objective was noted with the following: 74 with security cooperation (17.9%), 82 with operational readiness (19.8%), and 71 with health outcome objectives (17.1%). Conclusion Findings from this study reveal a broad spectrum of health and medical missions conducted in El Salvador, Guatemala, and Honduras between 2012 and 2017 by DoD. Critical elements indicative of overall team capability for successful engagement such as team size, team member skillset, global health expertise, and appropriate language capability were rarely documented. Team characteristics could not be well-correlated with the Global Health Engagement type or desired mission outcomes. In the future, deliberate crafting and preparation of health engagement teams aimed at attaining desired security cooperation impact, operational readiness development, and positive health outcomes is essential for more effective Global Health Engagement.

scholarly journals The Impacts of Unrecognized Language and Cultural Barriers During an Educational and Training Activity

2021 ◽  
Author(s):  
Michael D Owens ◽  
Franck A Nzumba
Keyword(s):  
Global Health ◽  
Health Outcomes ◽  
Important Variable ◽  
Poor Health ◽  
Cultural Barriers ◽  
Training Activity ◽  
And Training

ABSTRACT Language and cultural barriers are associated with poor health outcomes. Communication is arguably the most important variable associated with a successful educational and training Global Health Engagement (GHE) and often unrecognized even when attempts are made to address this barrier. Madagascar’s GHE activity improved after the addition of local Malagasy translation to fully translated official French instruction.

scholarly journals The power-trust cycle in global health: Trust as belonging in relations of dependency

2021 ◽  
pp. 1-21
Author(s):  
Emma-Louise Anderson ◽  
Laura Considine ◽  
Amy S. Patterson
Keyword(s):  
Global Health ◽  
Health Outcomes ◽  
Positive Health ◽  
Global Health Initiatives ◽  
Health Initiatives ◽  
Power Trust ◽  
Trust And Power ◽  
Positive Health Outcomes ◽  
Exercise Of Discretion ◽  
The Relationship

Abstract Trust between actors is vital to delivering positive health outcomes, while relationships of power determine health agendas, whose voices are heard and who benefits from global health initiatives. However, the relationship between trust and power has been neglected in the literatures on both international politics and global health. We examine this relationship through a study of relations between faith based organisations (FBO) and donors in Malawi and Zambia, drawing on 66 key informant interviews with actors central to delivering health care. From these two cases we develop an understanding of ‘trust as belonging’, which we define as the exercise of discretion accompanied by the expression of shared identities. Trust as belonging interacts with power in what we term the ‘power-trust cycle’, in which various forms of power undergird trust, and trust augments these forms of power. The power-trust cycle has a critical bearing on global health outcomes, affecting the space within which both local and international actors jockey to influence the ideologies that underpin global health, and the distribution of crucial resources. We illustrate how the power-trust cycle can work in both positive and negative ways to affect possible cooperation, with significant implications for collective responses to global health challenges.

scholarly journals A framework for identifying and learning from countries that demonstrated exemplary performance in improving health outcomes and systems

2020 ◽  
Vol 5 (12) ◽  
pp. e002938
Author(s):  
Austin Carter ◽  
Nadia Akseer ◽  
Kevin Ho ◽  
Oliver Rothschild ◽  
Niranjan Bose ◽  
...  
Keyword(s):  
Mixed Methods ◽  
Global Health ◽  
Health Outcomes ◽  
Mixed Methods Research ◽  
Multidisciplinary Teams ◽  
Ongoing Research ◽  
Qualitative And Quantitative ◽  
Success Stories ◽  
Quantitative Analyses ◽  
Comprehensive Framework

This paper introduces a framework for conducting and disseminating mixed methods research on positive outlier countries that successfully improved their health outcomes and systems. We provide guidance on identifying exemplar countries, assembling multidisciplinary teams, collecting and synthesising pre-existing evidence, undertaking qualitative and quantitative analyses, and preparing dissemination products for various target audiences. Through a range of ongoing research studies, we illustrate application of each step of the framework while highlighting key considerations and lessons learnt. We hope uptake of this comprehensive framework by diverse stakeholders will increase the availability and utilisation of rigorous and comparable insights from global health success stories.

scholarly journals Optimizing the Self-Organizing Team Size Using a Genetic Algorithm in Agile Practices

2018 ◽  
Vol 29 (1) ◽  
pp. 1151-1165
Author(s):  
Wael Almadhoun ◽  
Mohammad Hamdan
Keyword(s):  
Genetic Algorithm ◽  
Software Development ◽  
Objective Function ◽  
The Self ◽  
Performance Characteristics ◽  
Team Size ◽  
Agile Practices ◽  
Team Characteristics ◽  
Agile Software ◽  
Self Organizing

Abstract In agile software processes, the issue of team size is an important one. In this work we look at how to find the optimal, or near optimal, self-organizing team size using a genetic algorithm (GA) which considers team communication efforts. Communication, authority, roles, and learning are the team’s performance characteristics. The GA has been developed according to performance characteristics. A survey was used to evaluate the communication weight factors, which were qualitatively assessed and used in the algorithm’s objective function. The GA experiments were performed in different stages: each stage results were tested and compared with the previous results. The results show that self-organizing teams of sizes ranged from five to nine members scored more. The model can be improved by adding other team characteristics, i.e. software development efforts and costs.

Community Involvement in Health Systems Strengthening to Improve Global Health Outcomes: A Review of Guidelines and Potential Roles

2017 ◽  
Vol 37 (3-4) ◽  
pp. 139-149 ◽  
Author(s):  
Emma Sacks ◽  
Robert C. Swanson ◽  
Jean J. Schensul ◽  
Anna Gleave ◽  
Katharine D. Shelley ◽  
...  
Keyword(s):  
Global Health ◽  
Health Outcomes ◽  
Health Systems ◽  
Community Involvement ◽  
Health Systems Strengthening

scholarly journals Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?

2018 ◽  
Vol 3 (4) ◽  
pp. e000798 ◽  
Author(s):  
Brian Wahl ◽  
Aline Cossy-Gantner ◽  
Stefan Germann ◽  
Nina R Schwalbe
Keyword(s):  
Artificial Intelligence ◽  
Patient Safety ◽  
Global Health ◽  
Health Outcomes ◽  
Health System ◽  
Healthcare Services ◽  
Full Potential ◽  
High Profile ◽  
Resource Poor ◽  
Improve Health

The field of artificial intelligence (AI) has evolved considerably in the last 60 years. While there are now many AI applications that have been deployed in high-income country contexts, use in resource-poor settings remains relatively nascent. With a few notable exceptions, there are limited examples of AI being used in such settings. However, there are signs that this is changing. Several high-profile meetings have been convened in recent years to discuss the development and deployment of AI applications to reduce poverty and deliver a broad range of critical public services. We provide a general overview of AI and how it can be used to improve health outcomes in resource-poor settings. We also describe some of the current ethical debates around patient safety and privacy. Despite current challenges, AI holds tremendous promise for transforming the provision of healthcare services in resource-poor settings. Many health system hurdles in such settings could be overcome with the use of AI and other complementary emerging technologies. Further research and investments in the development of AI tools tailored to resource-poor settings will accelerate realising of the full potential of AI for improving global health.

scholarly journals 3165 Diseased and Healthy Gastrointestinal Tissue Data Mining requires an Engaged Transdisciplinary team

2019 ◽  
Vol 3 (s1) ◽  
pp. 131-132
Author(s):  
Sana Syed ◽  
Marium Naveed Khan ◽  
Alexis Catalano ◽  
Christopher Moskaluk ◽  
Jason Papin ◽  
...  
Keyword(s):  
Global Health ◽  
Data Science ◽  
Team Member ◽  
Medical Graduate ◽  
Science Students ◽  
Team Leaders ◽  
Research Project ◽  
Team Members ◽  
The Past ◽  
New Methods

OBJECTIVES/SPECIFIC AIMS: To establish an effective team of researchers working towards developing and validating prognostic models employing use of image analyses and other numerical metadata to better understand pediatric undernutrition, and to learn how different approaches can be brought together collaboratively and efficiently. METHODS/STUDY POPULATION: Over the past 18 months we have established a transdisciplinary team spanning three countries and the Schools of Medicine, Engineering, Data Science and Global Health. We first identified two team leaders specifically a pediatric physician scientist (SS) and a data scientist/engineer (DB). The leaders worked together to recruit team members, with the understanding that different ideas are encouraged and will be used collaboratively to tackle the problem of pediatric undernutrition. The final data analytic and interpretative core team consisted of four data science students, two PhD students, an undergraduate biology major, a recent medical graduate, and a PhD research scientist. Additional collaborative members included faculty from Biomedical Engineering, the School of Medicine (Pediatrics and Pathology) along with international Global Health faculty from Pakistan and Zambia. We learned early on that it was important to understand what each of the member’s motivation for contributing to the project was along with aligning that motivation with the overall goals of the team. This made us help prioritize team member tasks and streamline ideas. We also incorporated a mechanism of weekly (monthly/bimonthly for global partners) meetings with informal oral presentations which consisted of each member’s current progress, thoughts and concerns, and next experimental goals. This method enabled team leaders to have a 3600 mechanism of feedback. Overall, we assessed the effectiveness of our team by two mechanisms: 1) ongoing team member feedback, including team leaders, and 2) progress of the research project. RESULTS/ANTICIPATED RESULTS: Our feedback has shown that on initial development of the team there was hesitance in communication due to the background diversity of our various member along with different cultural/social expectations. We used ice-breaking methods such as dedicated time for brief introductions, career directions, and life goals for each team member. We subsequently found that with the exception of one, all other team members noted our working environment professional and conducive to productivity. We also learnt from our method of ongoing constant feedback that at times, due to the complexity of different disciplines, some information was lost due to the difference in educational backgrounds. We have now employed new methods to relay information more effectively, with the use of not just sharing literature but also by explaining the content. The progress of our research project has varied over the past 4-6 months. There was a steep learning curve for almost every member, for example all the data science students had never studied anything related to medicine during their education, including minimal if none exposure to the ethics of medical research. Conversely, team members with medical/biology backgrounds had minimal prior exposure to computational modeling, computer engineering and the verbage of communicating mathematical algorithms. While this may have slowed our progress we learned that by asking questions and engaging every member it was easier to delegate tasks effectively. Once our team reached an overall understanding of each member’s goals there was a steady progress in the project, with new results and new methods of analysis being tested every week. DISCUSSION/SIGNIFICANCE OF IMPACT: We expect that our on-going collaboration will result in the development of new and novel modalities to understand and diagnose pediatric undernutrition, and can be used as a model to tackle several other problems. As with many team science projects, credit and authorship are challenges that we are outlining creative strategies for as suggested by International Committee of Medical Journal Editors (ICMJE) and other literature.

scholarly journals The power of language-concordant care: a call to action for medical schools

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Rose L. Molina ◽  
Jennifer Kasper
Keyword(s):  
Global Health ◽  
Health Outcomes ◽  
Health Equity ◽  
Limited English Proficiency ◽  
English Proficiency ◽  
Medical Schools ◽  
Dental Students ◽  
Language Concordance ◽  
The World ◽  
The U.S

Abstract We live in a world of incredible linguistic diversity; nearly 7000 languages are spoken globally and at least 350 are spoken in the United States. Language-concordant care enhances trust between patients and physicians, optimizes health outcomes, and advances health equity for diverse populations. However, historical and contemporary trauma have impaired trust between communities of color, including immigrants with limited English proficiency, and physicians in the U.S. Threats to informed consent among patients with limited English proficiency persist today. Language concordance has been shown to improve care and serves as a window to broader social determinants of health that disproportionately yield worse health outcomes among patients with limited English proficiency. Language concordance is also relevant for medical students engaged in health care around the world. Global health experiences among medical and dental students have quadrupled in the last 30 years. Yet, language proficiency and skills to address cultural aspects of clinical care, research and education are lacking in pre-departure trainings. We call on medical schools to increase opportunities for medical language courses and integrate them into the curriculum with evidence-based teaching strategies, content about health equity, and standardized language assessments. The languages offered should reflect the needs of the patient population both where the medical school is located and where the school is engaged globally. Key content areas should include how to conduct a history and physical exam; relevant health inequities that commonly affect patients who speak different languages; cultural sensitivity and humility, particularly around beliefs and practices that affect health and wellbeing; and how to work in language-discordant encounters with interpreters and other modalities. Rigorous language assessment is necessary to ensure equity in communication before allowing students or physicians to use their language skills in clinical encounters. Lastly, global health activities in medical schools should assess for language needs and competency prior to departure. By professionalizing language competency in medical schools, we can improve patients’ trust in individual physicians and the profession as a whole; improve patient safety and health outcomes; and advance health equity for those we care for and collaborate with in the U.S. and around the world.

scholarly journals Application of Epidemiological Geographic Information System: An Open-Source Spatial Analysis Tool Based on the OMOP Common Data Model

2020 ◽  
Vol 17 (21) ◽  
pp. 7824
Author(s):  
Jaehyeong Cho ◽  
Seng Chan You ◽  
Seongwon Lee ◽  
DongSu Park ◽  
Bumhee Park ◽  
...  
Keyword(s):  
United States ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Analysis ◽  
Health Outcomes ◽  
Open Source ◽  
Geographical Distribution ◽  
The United States ◽  
Geographic Information ◽  
Common Data Model

Background: Spatial epidemiology is used to evaluate geographical variations and disparities in health outcomes; however, constructing geographic statistical models requires a labor-intensive process that limits the overall utility. We developed an open-source software for spatial epidemiological analysis and demonstrated its applicability and quality. Methods: Based on standardized geocode and observational health data, the Application of Epidemiological Geographic Information System (AEGIS) provides two spatial analysis methods: disease mapping and detecting clustered medical conditions and outcomes. The AEGIS assesses the geographical distribution of incidences and health outcomes in Korea and the United States, specifically incidence of cancers and their mortality rates, endemic malarial areas, and heart diseases (only the United States). Results: The AEGIS-generated spatial distribution of incident cancer in Korea was consistent with previous reports. The incidence of liver cancer in women with the highest Moran’s I (0.44; p < 0.001) was 17.4 (10.3–26.9). The malarial endemic cluster was identified in Paju-si, Korea (p < 0.001). When the AEGIS was applied to the database of the United States, a heart disease cluster was appropriately identified (p < 0.001). Conclusions: As an open-source, cross-country, spatial analytics solution, AEGIS may globally assess the differences in geographical distribution of health outcomes through the use of standardized geocode and observational health databases.

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