Demographic Policies and Human Capital Challenges

Muftah, Hend Al

doi:10.1057/978-1-137-46639-6_10

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…A more experienced team may have approached aspects of this project differently and collected more demographic data. Second, demographic diversity, representing multiple languages and levels of health education awareness, challenged the development of patient education material 55. Third, the project relied on secondary data; hence some data were missing.…”

Section: Discussionmentioning

confidence: 99%

Quality improvement tools to manage emergency callbacks from patients with diabetes in a prehospital setting

et al. 2023

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Diabetes is rising at an alarming rate, as 1 in 10 adults worldwide now lives with the disease. In Qatar, a middle eastern Arab country, diabetes prevalence is equally concerning and is predicted to increase from 17% to 24% among individuals aged 45 and 54 years by 2050. While most healthcare strategies focus on preventative and improvement of in-hospital care of patients with diabetes, a notable paucity exists concerning diabetes in the prehospital setting should ideally be provided. This quality improvement study was conducted in a middle eastern ambulance service and aimed to reduce ambulance callbacks of patients with diabetes-related emergencies after refusing transport to the hospital at the first time. We used iterative four-stage problem-solving models. It focused on the education and training of both paramedics and patients. The study showed that while it was possible to reduce the rate of ambulance callbacks of patients with diabetes, this was short-lived and numbers increased again. The study demonstrated that improvements could be effective. Hence, changes that impacted policy, systems of care and ambulance protocols directed at managing and caring for patients with diabetes-related prehospital emergencies may be required to reify them.

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Section: Discussionmentioning

confidence: 99%

Quality improvement tools to manage emergency callbacks from patients with diabetes in a prehospital setting

et al. 2023

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“…Qatari law, for instance, caps naturalization at 50 cases per year (Babar 2014). Ever-expanding non-citizen populations are a source of public discontent in most Gulf countries, as citizens must compete with white-collar expatriates for high-salary professional positions and view expatriates as dissipating government resources at the expense of nationals (Mitchell and Gengler 2019;Al Muftah 2016). This makes nationality a highly impermeable and highly salient ethnic category.…”

Section: Group Conflict and Nationality-of-interviewer Effects In Thementioning

confidence: 99%

Citizenship and Surveys: Group Conflict and Nationality-of-Interviewer Effects in Arab Public Opinion Data

Gengler

Wittrock

2019

Polit Behav

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More research than ever before uses public opinion data to investigate society and politics in the Middle East and North Africa (MENA). Ethnic identities are widely theorized to mediate many of the political attitudes and behaviors that MENA surveys commonly seek to measure, but, to date, no research has systematically investigated how the observable ethnic category(s) of the interviewer may influence participation and answers given in Middle East surveys. Here we measure the impact of one highly salient and outwardly observable ascriptive attribute of interviewersnationality-using data from an original survey experiment conducted in the Arab Gulf state of Qatar. Applying the total survey error (TSE) framework and utilizing an innovative nonparametric matching technique, we estimate treatment effects on both nonresponse error and measurement error. We find that Qatari nationals are more likely to begin and finish a survey, and respond to questions, when interviewed by a fellow national. Qataris also edit their answers to sensitive questions relating to the unequal status of citizens and noncitizens, reporting views that are more exclusionary and less positive toward out-group members, when the interviewer is a conational. The findings have direct implications for consumers and producers of a growing number of surveys conducted inside and outside the Arab world, where migration and conflict have made respondent-interviewer mismatches along national and other ethnic dimensions more salient and more common.

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“…Other studies revealed that ML could lead to cost savings in healthcare by reducing unnecessary interventions aligning with the goal of value improvement, indicating that ML can contribute to cost reduction and quality enhancement in healthcare services. Qatar's multinational population makes it a uniquely challenging environment for the emergency care service [4]. Hamad Medical Corporation Ambulance Service (HMCAS) is the leading provider in this setting and continually evolves its healthcare infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques

Farhat,

Makhlouf,

Gangaram

et al. 2024

PLoS ONE

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Background The global evolution of pre-hospital care systems faces dynamic challenges, particularly in multinational settings. Machine learning (ML) techniques enable the exploration of deeply embedded data patterns for improved patient care and resource optimisation. This study’s objective was to accurately predict cases that necessitated transportation versus those that did not, using ML techniques, thereby facilitating efficient resource allocation. Methods ML algorithms were utilised to predict patient transport decisions in a Middle Eastern national pre-hospital emergency medical care provider. A comprehensive dataset comprising 93,712 emergency calls from the 999-call centre was analysed using R programming language. Demographic and clinical variables were incorporated to enhance predictive accuracy. Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) algorithms were trained and validated. Results All the trained algorithm models, particularly XGBoost (Accuracy = 83.1%), correctly predicted patients’ transportation decisions. Further, they indicated statistically significant patterns that could be leveraged for targeted resource deployment. Moreover, the specificity rates were high; 97.96% in RF and 95.39% in XGBoost, minimising the incidence of incorrectly identified “Transported” cases (False Positive). Conclusion The study identified the transformative potential of ML algorithms in enhancing the quality of pre-hospital care in Qatar. The high predictive accuracy of the employed models suggested actionable avenues for day and time-specific resource planning and patient triaging, thereby having potential to contribute to pre-hospital quality, safety, and value improvement. These findings pave the way for more nuanced, data-driven quality improvement interventions with significant implications for future operational strategies.

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Demographic Policies and Human Capital Challenges

Cited by 8 publications

References 16 publications

Quality improvement tools to manage emergency callbacks from patients with diabetes in a prehospital setting

Quality improvement tools to manage emergency callbacks from patients with diabetes in a prehospital setting

Citizenship and Surveys: Group Conflict and Nationality-of-Interviewer Effects in Arab Public Opinion Data

Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques

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