Al-Nahda Research Centre
Health
Machine learning in healthcare has transformative potential, allowing providers to gain insights and predict health outcomes with greater accuracy. At ARC, we use diagnostic and prognostic models to predict patient health using surveys and electronic health records (EHRs). By analyzing EHRs from both primary and secondary care, we can assess disease burden, incidence, prevalence, and treatment impact. Our machine learning models help identify patients at risk, aid in accurate diagnoses, and enable personalized treatment plans tailored to individual needs. Additionally, we streamline operations by automating tasks like image analysis and enhancing telehealth. With expertise in predictive modeling and EHR management, ARC is dedicated to helping healthcare providers optimize outcomes and reduce costs.
Clinical Trials
At ARC, we specialize in clinical trial data analysis, providing comprehensive support for studies that test the safety and effectiveness of new treatments. Our team of skilled analysts handles every stage of the data process, from designing statistically sound studies to managing, analyzing, and visualizing the results. We ensure precise data management, using state-of-the-art software for collection and cleaning, and apply advanced statistical techniques to provide meaningful insights. Our services also include customized data visualization and strict regulatory compliance to meet industry standards. Dedicated to supporting the development of new medical treatments, ARC delivers reliable data analysis to help our clients make informed decisions. Contact us to learn more about our clinical trial data analysis services.
Health Survey
Providing services in analyzing health surveys using statistical methods is a crucial aspect of public health and healthcare research. Health surveys play a pivotal role in understanding the health status, needs, and challenges of populations, making it essential to interpret the data accurately and derive meaningful insights. Statistical analysis is a powerful tool that enables researchers and healthcare professionals to make informed decisions and implement evidence-based interventions.
One of the primary benefits of statistical analysis in health survey services is the ability to identify patterns and trends within the data.
Training
ARC is a leading provider of healthcare survey training, with a strong focus on statistics to build the skills of healthcare professionals. One of its primary offerings is comprehensive training in healthcare survey design, which helps professionals develop surveys that accurately capture key metrics. This training covers question development, survey instrument selection, sampling techniques, and result analysis, enabling participants to create and interpret high-quality surveys.
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ARC also offers survey administration training, which covers practical aspects like choosing survey modes, managing data collection, and ensuring response accuracy and validity. Additionally, ARC provides specialized training in data analysis techniques for healthcare surveys. This training equips participants with skills in statistical software, data visualization, and reporting, empowering them to communicate findings effectively to stakeholders.
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Beyond training, ARC provides consulting for healthcare organizations conducting surveys. Their expert team assists with survey design, sampling plans, data analysis, and actionable recommendations to enhance patient satisfaction, organizational performance, and compliance with regulations. Overall, ARC’s services help healthcare organizations develop reliable surveys and leverage data-driven insights to make informed decisions in an increasingly data-centric industry.
Health Data Science
Health data science is a rapidly-evolving interdisciplinary domain that integrates concepts from statistical, computational, and health sciences. It attracts some of the brightest scientific intellects who utilize the UK's extensive repositories of large-scale data to achieve objectives such as:
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· Advancing the discovery of new diseases and therapies
· Enhancing the assessment of secure, efficient, and affordable treatments
· Developing clinical innovations that improve the NHS and social care
· Increasing the healthy lifespan of individuals with common ailments
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Currently, there is a substantial demand for individuals worldwide to lead a data science revolution in the UK. The following are the stepping stones toward a career in health data science.
Transforming EHR Data into Real-World Outcomes with AI & M
The digitalization of medical records has made electronic health records (EHRs) a valuable source of real-world data (RWD), offering insights that complement traditional study designs and improve generalizability, especially in fields like rheumatology where diseases are rare and slow-developing. EHR data includes a wide range of patient information, which can be analyzed using machine learning (ML) to identify patterns relevant to clinical care. However, ML models require extensive training, testing, and validation, and their findings may be limited by the specific structure of the EHR used. When conducting population studies with EHR data, it's crucial to consider factors like access to care and insurance status to avoid biases. Artificial intelligence has the potential to advance healthcare further, but specialized expertise is essential to fully realize its benefits, and collaboration is key to overcoming current challenges and driving innovation.