While healthcare costs have been constantly rising, the quality of care provided to patients in the United States has not seen considerable improvements. Recently, several researchers have conducted studies that showed that by incorporating the current healthcare technologies, they can reduce mortality rates, healthcare costs, and medical complications at various hospitals. Due to the rapid advancements in data sensing and acquisition technologies, hospitals and healthcare institutions have started collecting vast amounts of healthcare data about their patients. Data analytics training institutes follow strict guidelines while allotting data analytics course fees for students.
In 2009, the US government enacted the Health Information Technology for Economic and Clinical Health Act (HITECH) which includes an incentive program (around $27 billion) for the adoption and meaningful use of Electronic Health Records (EHRs). The recent advances in information technology have led to an increasing ease in the ability to collect various forms of healthcare data. In this digital world, data has become an integral part of healthcare. A recent report on Big Data suggests that the overall potential of healthcare data will be around $300 billion. This article will justify data analytics course fees by focusing on the health sector and portraying a clear image of data analytics’ involvement in the betterment process.
Domain Challenges:
From a researcher and practitioner perspective, a major challenge in healthcare is its interdisciplinary nature. The field of healthcare has often seen advances coming from diverse disciplines such as databases, data mining, information retrieval, medical researchers, and healthcare practitioners. While this interdisciplinary nature adds to the richness of the field, it also adds to the challenges of making significant advances. Computer scientists are usually not trained in domain-specific medical concepts, whereas medical practitioners and researchers also have limited exposure to the mathematical and statistical background required in the data analytics area.
Another major challenge that exists in the healthcare domain is the “data privacy gap” between medical researchers and computer scientists. Healthcare data is very sensitive because it can reveal compromising information about individuals. Several laws in various countries, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States, explicitly forbid the release of medical information about individuals for any purpose, unless safeguards are used to preserve privacy. Medical researchers have natural access to healthcare data because their research is often paired with actual medical practice. On the other hand, acquiring data is not quite as simple for computer scientists without proper collaboration with a medical practitioner. Even then, there are barriers to the acquisition of data.
Many of these challenges can be avoided if accepted protocols, privacy technologies, and safeguards are in place. Data analytics course fees assure to receive quality education & training in data analytics theologies & tools to leverage data in real life.
Healthcare Data Sources and Basic Analytics
In this section, the various data sources and their impact on analytical algorithms will be discussed to help students understand the expensive nature of data analytics course fees. The heterogeneity of the sources for medical data mining is rather broad, and this creates the need for a wide variety of techniques drawn from different domains of data analytics.
● Electronic Health Records
Electronic health records (EHRs) contain a digitized version of a patient’s medical history. It encompasses a full range of data relevant to a patient’s care such as demographics, problems, medications, physician’s observations, vital signs, medical history, laboratory data, radiology reports, progress notes, and billing data. Many EHRs go beyond a patient’s medical or treatment history and may contain additional broader perspectives of a patient’s care. An important property of EHRs is that they provide an effective and efficient way for healthcare providers and organizations to share.
● Biomedical Image Analysis
Medical imaging plays an important role in modern-day healthcare due to its immense capability to provide high-quality images of anatomical structures in human beings. Effectively analyzing such images can be useful for clinicians and medical researchers since it can aid disease monitoring, treatment planning, and prognosis. The most popular imaging modalities used to acquire a biomedical image are magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (U/S). Being able to look inside the body without hurting the patient and being able to view the human organs has tremendous implications for human health. Such capabilities allow the physicians to better understand the cause of an illness or other adverse conditions without cutting open the patient.
● Biomedical Signal Analysis
Biomedical Signal Analysis consists of measuring signals from biological sources, the origin of which lies in various physiological processes. Examples of such signals include the electroneurogram (ENG), electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), electrogastrogram (EGG), phonocardiogram (PCG), and so on. The analysis of these signals is vital in diagnosing the pathological conditions and in deciding an appropriate care pathway. The measurement of physiological signals gives some form of quantitative or relative assessment of the state of the human body. These signals are acquired from various kinds of sensors and transducers either invasively or non-invasively.
● Genomic Data Analysis
A significant number of diseases are genetic, but the nature of the causality between the genetic markers and the diseases has not been fully established. For example, diabetes is well known to be a genetic disease; however, the full set of genetic markers that make an individual prone to diabetes is unknown. In some other cases, such as the blindness caused by Stargardt disease, the relevant genes are known but all the possible mutations have not been exhaustively isolated. A broader understanding of the relationships between various genetic markers, mutations, and disease conditions has significant potential to assist the development of various gene therapies to cure these conditions.
One will be mostly interested in understanding what kind of health-related questions can be addressed through in-silico analysis of the genomic data through typical data-driven studies. Moreover, translating genetic discoveries into personalized medicine practice is a highly non-trivial task with a lot of unresolved challenges. For example, the genomic landscapes in complex diseases such as cancers are overwhelmingly complicated, revealing a high order of heterogeneity among different individuals. Solving these issues will be a major piece of the puzzle and it will bring the concept of personalized medicine much closer to reality.