Author | Joe Smith
In recent years, the healthcare industry has witnessed a tremendous surge in data volume. This data originates from various sources, encompassing patient health records, electronic devices, genomics, medical imaging and more. Simultaneously, advancements in computational power have enabled the processing of vast amounts of data, leading to the emergence of Big Data analytics and Artificial Intelligence (AI) in healthcare. These technologies offer the medical industry and practitioners the potential to gain invaluable insights into patient care, uncover predictive patterns, develop personalized treatment plans and improve the overall efficiency of the healthcare process. In this blog post, we explore the transformative impact of Big Data and AI on patient care within the healthcare sector.
Big Data and AI in healthcare offer advantages in decision-making and treatment customization. By utilizing extensive medical information, physicians can diagnose and treat patients precisely. AI predicts treatment outcomes and recommends optimal action based on medical history, genetics, lifestyle, and environment. This wealth of data helps identify effective therapies for each patient. Additionally, AI and Big Data enhance efficiency, resource allocation, and patient care. Patients can engage in their own well-being through self-management tools and wearable health devices, enabling personalized healthcare plans. AI and Big Data also improve R&D organizations building new medical devices, manufacturing and development of new therapeutics.
As an example, radiology is a specialty that has the potential of significantly benefiting from the implementation of Big Data analytics and AI. Radiologists analyze medical images to provide accurate diagnoses. AI has the capacity to analyze medical images, detect intricate patterns, and identify subtle anomalies that may elude human observation. Looking at thousands of images daily can be a strain, and AI assistants to highlight key aspects promise to improve the efficiency and quality of the process.
Combining Structured Data and Unstructured Data at Scale
Combining structured and unstructured data provides a comprehensive, multi-faceted analysis that is vital in the field of healthcare. Structured data, such as patient demographics, lab results, or diagnosis codes, is highly organized and easily searchable. On the other hand, unstructured data, which includes doctors’ notes, medical imaging, or social media posts, is less organized but offers rich, contextual information that structured data alone may miss.
The merger of these two data types can result in a much richer understanding of a patient’s health status. For instance, structured data can provide objective, quantifiable details about a patient’s health, such as blood pressure readings or cholesterol levels. In contrast, unstructured data can offer valuable context, such as lifestyle choices or a patient’s subjective experience of their symptoms, which can be gleaned from doctors’ notes or patient self-reports.
Furthermore, integrating structured and unstructured data can enable the creation of more comprehensive patient profiles. These profiles can be used to make more informed and personalized healthcare decisions, enhancing patient care outcomes. Moreover, it opens avenues for predictive analytics and trend analysis, which play a pivotal role in proactive healthcare and disease prevention.
AI and Big Data techniques are instrumental in the integration of structured and unstructured data. These technologies can process vast amounts of information, identify key trends and patterns, and present them in a digestible format for physicians. This ensures a holistic view of the patient’s health, enhancing the precision of diagnosis and the effectiveness of the treatment plan. Thus, combining structured and unstructured data offers a more rounded, insightful analysis, fueling a revolution in patient care.
Challenges Faced by Big Data and AI in Healthcare
While Big Data and AI hold significant promise for revolutionizing patient care, certain key challenges prevent data scientists and IT practitioners from effectively utilizing these technologies in healthcare.
One of the foremost challenges is data privacy and security. Healthcare data is highly sensitive, and its misuse can lead to severe consequences. Stringent regulations, such as HIPAA in the USA, create a complex legal environment that data scientists must navigate. Ensuring privacy, security, and compliance while extracting useful insights from the data is a difficult balancing act.
Secondly, data quality and standardization pose significant obstacles. Healthcare data is heterogeneous, originating from various sources such as electronic health records (EHRs), wearable devices, and genomic sequencing. This diversity makes it hard to aggregate and standardize the data, and poor data quality can lead to inaccurate analyses and predictions.
The third challenge is the lack of technical expertise and infrastructure. Implementing Big Data and AI requires advanced technical skills and robust computational infrastructure. Many healthcare organizations lack the necessary resources, and finding skilled data scientists who understand both AI technologies and the healthcare domain is difficult.
The fourth challenge is the interpretability of AI models. Physicians are less likely to trust AI if they can’t understand how it reached a specific decision. Black-box models of AI, which do not provide clear explanations of how they work, can be a barrier to their adoption in healthcare.
Lastly, the integration of AI and Big Data technologies into existing healthcare workflows can be disruptive. It involves changes in work practices, considerations about interoperability with existing systems, and user acceptability.
New Technologies to help Process Big Data and AI for Healthcare
The need for sophisticated technologies to process and interpret large data sets has become paramount. Particularly in healthcare, where the volume and complexity of data are often overwhelming, technologies such as Graphical Processing Units (GPUs) and AI accelerators are becoming increasingly pivotal in managing, processing, and extracting valuable insights from healthcare data.
GPUs and AI accelerators are now coming online for AI and Big Data processing due to their parallel processing capabilities. They handle complex tasks like training deep learning models and processing large amounts of patient data. Remote GPU technologies allow multiple users to share a single GPU, optimizing hardware utilization and reducing costs. AI accelerators enhance AI algorithms, enabling faster data processing and improved real-time analytics. They expedite the analysis of unstructured data such as radiological images, doctor notes or genomic sequences, leading to improved outcomes.
Workflow schedulers like Argo play a crucial role in managing Big Data workflows, particularly in an AI-driven healthcare environment. They automate and control the execution of tasks, managing dependencies and handling failures. Argo, for example, enables the creation of complex, data-intensive workflows, and provides features such as parallelism, conditional execution, and error handling. With Argo, healthcare organizations can streamline their Big Data processing tasks, making it easier to manage and coordinate AI workloads.
For effective data management in healthcare, Big Data databases like Greenplum Database are essential. Greenplum is a massively parallel processing (MPP) database, capable of ingesting, querying, and analyzing large volumes of data. It’s designed to run on commodity hardware, making it a cost-effective solution for storing and processing healthcare Big Data. By employing advanced data partitioning, Greenplum can distribute data across multiple nodes, ensuring efficient data processing and high performance. It also provides robust data management capabilities, including backup and recovery, data compression, and security features that are crucial for handling sensitive healthcare data.
Learning More About Big Data And AI for Healthcare
Exploring the field of Big Data and AI in healthcare is an enriching journey that involves continuous learning. Numerous resources are available to help you grasp these topics and understand their applications in healthcare.
For a comprehensive understanding of Big Data and AI in healthcare, books like “Artificial Intelligence in Healthcare” by Adam Bohr and Kaveh Memarzadeh and online courses like Coursera’s “AI in Healthcare” can be beneficial. These resources dive into the core concepts, current applications, and future directions of AI and Big Data in healthcare.
Regarding Greenplum Database, the official website is a great place to start. It provides valuable resources including technical documentation, tutorials, and user forums that can help you understand this powerful MPP database system. The Greenplum Database YouTube channel is also a valuable asset.
For learning about Argo, the project’s GitHub page is the best resource. It contains the latest project documentation, codebase, and a vibrant community of developers who contribute to the project and share their expertise.
To enhance your knowledge about GPUs and their role in big data processing and AI, you can refer to resources provided by Nvidia, a leading GPU manufacturer. VMware also has interesting technologies to leverage GPUs efficiently.
Purdue University, a renowned institution, has effectively managed the Covid Pandemic response with the help of advanced Big Data techniques and the powerful Greenplum Database. In a captivating video titled “Purdue University Uses Big Data to Keep Educational Promise,” they showcase their innovative approach towards ensuring a safe and uninterrupted learning environment. Watch this insightful video on YouTube to discover the remarkable ways in which Purdue leverages technology to overcome challenges and fulfill their commitment to education.
In summary, integrating Big Data and AI in healthcare has immense potential for transforming patient care, enabling precision medicine, and improving health outcomes. However, challenges such as data privacy and security, data quality, technical expertise, interpretability of AI models, and integration into existing healthcare protocols exist. To overcome these hurdles, emerging technologies like GPUs, AI accelerators, Argo workflow schedulers, and Greenplum Big Data databases are instrumental in managing complex healthcare data, optimizing hardware utilization, enhancing AI performance, automating task execution, and providing robust data management capabilities. These technologies are pivotal in unlocking the full potential of Big Data in healthcare.