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Healthcare Big Data Analysis and Future Prospects

Introduction

It is impossible for traditional methods of data processing to handle the vast amounts of clinical information that must be gathered and analyzed using big data. The information gathered can be used to support clinical decision-making based on solid evidence (Dash et al., 2019). The electronic medical record (EMR) is an example of big data in healthcare because it is capable of reliably and securely handling large sets of enormous amounts of data. Big data can help healthcare professionals identify specific groups of patients or medical conditions, which could have a positive impact on patient care (Dash et al., 2019). In hospitals, for example, electronic medical records (EMRs) are used to identify patients at high risk of readmission and prevent them from being readmitted (Dash et al., 2019). In order to mitigate the risk, interventional plans can be developed quickly using big data patterns and trends to identify the affected population as soon as possible.

Healthcare as a Big Data Repository

In the healthcare industry, big data includes everything from healthcare payer and provider records to genomics experiments and other data gleaned from the Internet of Things smart web (IoT). Electronic health records (EHRs) were not widely adopted in the United States until 2009. This type of healthcare data is increasingly being managed and utilized by means of computer technology (Dash et al., 2019). More and more emphasis is being placed on the creation of biomedical and health monitoring systems that can send alerts and share patient health data with the appropriate health care providers, as well as the associated software. Medical professionals can use the data generated by these gadgets to provide patients with real-time clinical or medical care (Dash et al., 2019). The use of healthcare big data shows promise in terms of improving health outcomes and controlling costs.

Types and Sources of Big Data in Healthcare

Large amounts of data created by hospitals, including medical imaging and clinical information, fall under the umbrella of big data in medicine and clinics. Patients and doctors have a strong connection to this type of data. The hospital’s information resources generate a lot of big data in the medical field. Among these are computer tomography, surgical procedures, physical examinations, magnetic resource imaging, radiography, patient information and treatment (Dash et al., 2019). It is the physiological data of users that is the primary focus of public health big data. Information like this is frequently gathered through the use of portable devices such as vital signs monitoring devices, electrocardiogram monitoring devices, health record-keeping devices, contagion detection devices, and fitness tracking devices (Dash et al., 2019). Human body data sets, molecular biology samples, biological samples, clinical trials and medical research, gene sequences and laboratory tests make up the bulk of medical experiment big data.

Importance of Big data in Healthcare

Recency bias may be reduced by using big data. Recency bias occurs when recent events are given more weight than those that occurred in the past as a way to better the current situation (Dash et al., 2019). However, it can lead to bad decisions. Incorporating real-time data into large datasets is also possible. An employer’s mistakes or problems can be spotted immediately, and operational difficulties can be overcome. In addition, the services are able to provide comprehensive information on the patients and, at the same time, administer medical assistance without delay. Healthcare professionals are able to lessen risk and triumph over this challenge by using data from large databases (Dash et al., 2019). In addition, thorough information can help detect healthcare fraud, particularly in the area of insurance claims.

Internet of Things (IoT)

Healthcare and big biomedical data have not yet converged to enhance healthcare data with the molecular disease. Convergence in the field of predictive biology can shed light on a variety of different mechanisms of action or other aspects. A person’s health status can be determined by combining biomolecular and clinical data. Clinical data can be obtained from the “internet of things” (IoT) (IoT) (Dash et al., 2019). Using these chips or sensors to collect and analyze data could reveal valuable insights into how we live our lives, how we use energy, how we get around, and how we care for our health. In fact, the Internet of Things (IoT) is becoming increasingly popular in the healthcare industry (Dash et al., 2019). Patients’ health is being monitored by IoT devices, which generate a steady stream of data, making this technology a major contributor to healthcare’s big data.

Nature of the big data in healthcare

EHRs can enable advanced analytics and improve clinical decision-making by delivering huge data. However, a considerable amount of this data is now unstructured in nature. Unstructured data is information that does not comply with a pre-defined model or organizational system (Dash et al., 2019). The rationale for this choice may simply be that we can capture information in a multiplicity of formats. Another argument for preferring an unstructured format is that often the structured input methods can fall short for gathering data of complicated kinds (Dash et al., 2019). In the healthcare industry, it could materialize in terms of better management, care and low-cost therapies. In order to attain these aims, we need to handle and analyze the large data in a methodical manner.

Management and analysis of big data

Big data is the vast amounts of a variety of data generated at a rapid rate. The data acquired from diverse sources is largely essential for optimizing consumer services rather than customer consumption (Dash et al., 2019). The data generated using the sensors can be made available on a storage cloud using pre-installed software tools produced by analytic tool makers. Upon deployment, it would boost the efficiency of gathering, storing, processing, and visualizing of big data from healthcare. The Hadoop approach involves putting massive volumes of data into the memory of even the most capable computing clusters (Dash et al., 2019). Apache Spark is another open-source alternative to Hadoop. It is a single-engine for distributed data processing that contains higher-level libraries for enabling SQL queries (Spark SQL), streaming data (Spark Streaming), machine learning (MLlib) and graph processing (GraphX) (GraphX).

Machine learning for information extraction, data analysis and predictions

Healthcare providers have scarcely been able to transform such data into electronic health records (EHRs). Patients’ histories from pre-EHR era notes are being digitized with the goal of supplementing the standardizing process by converting static images into machine-readable text. Optical character recognition (OCR) software, for example, recognizes the handwriting as well as computer fonts and allows for push digitalization (Dash et al., 2019). Such unstructured and structured healthcare datasets contain a wealth of untapped data that can be mined using powerful AI programs to derive vital, actionable insights in the context of patient care. Using proper ML methods, healthcare professionals examine such data for specific abnormalities (Dash et al., 2019). From such unstructured input, machine learning may extract organized information. Emerging machine learning and artificial intelligence (AI) strategies are assisting in the refinement of the healthcare industry’s data processing capabilities.

Challenges Associated with Healthcare Big Data

One of the main issues is storing massive amounts of data, yet many businesses are comfortable with data storage on their own premises. Controlling security, access, and uptime are just a few of the benefits. To achieve high levels of correctness and integrity, the cleaning procedure can be done manually or automatically using logic rules. Machine-learning algorithms are used in more complex and precise technologies to save time and money while also preventing bad data from derailing big data projects (Dash et al., 2019). According to some research, patient data reporting into EMRs or EHRs is not totally accurate yet, owing to poor EHR utility, complex workflows, and a misunderstanding of why big data is so crucial to capture efficiently (Dash et al., 2019). Because of the numerous security breaches, hackings, phishing attempts, and ransomware incidents, healthcare businesses have made data protection a top concern.

Conclusion and Future Prospects

One of the main issues is storing massive amounts of data, yet many businesses are comfortable with data storage on their own premises. Controlling security, access, and uptime are just a few of the benefits. To achieve high levels of correctness and integrity, the cleaning procedure can be done manually or automatically using logic rules. Machine-learning algorithms are used in more complex and precise technologies to save time and money while also preventing bad data from derailing big data projects (Dash et al., 2019). According to some research, patient data reporting into EMRs or EHRs is not totally accurate yet, owing to poor EHR utility, complex workflows, and a misunderstanding of why big data is so crucial to capture efficiently (Dash et al., 2019). Because of the numerous security breaches, hackings, phishing attempts, and ransomware incidents, healthcare businesses have made data protection a top concern.

References

Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 1-25. Web.

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ApeGrade. (2022, December 26). Healthcare Big Data Analysis and Future Prospects. Retrieved from https://apegrade.com/healthcare-big-data-analysis-and-future-prospects/

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"Healthcare Big Data Analysis and Future Prospects." ApeGrade, 26 Dec. 2022, apegrade.com/healthcare-big-data-analysis-and-future-prospects/.

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ApeGrade. "Healthcare Big Data Analysis and Future Prospects." December 26, 2022. https://apegrade.com/healthcare-big-data-analysis-and-future-prospects/.

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ApeGrade. 2022. "Healthcare Big Data Analysis and Future Prospects." December 26, 2022. https://apegrade.com/healthcare-big-data-analysis-and-future-prospects/.

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