In the status quo, big data has made monumental innovations in countless industries, including the healthcare industry. Big data encapsulates large volumes of unstructured and structured data that is gathered from EHRs (Electronic health record), EPRs(Electronic Patient Record), and clinical decision support systems swamps corporations daily. Imperatively, big data is not relevant due to the quantity, but its relevance is determined by further utilization and impact. In the healthcare sector, big data is currently employed for prognostic and analytic reasons, and assists scientists in accomplishing medical breakthroughs for the future.

Healthcare big data encompasses compiling, evaluating, and leveraging vast amounts of sophisticated consumer physical, mental, and clinical data. These statistics are processed by machine comprehending algorithms and data scientists, to make strategic business decisions with calculated risks. Moreover, due to the rise of telehealth and the virtualization of Medicare, big data is producing actionable intuitions.

How is Big Data currently revolutionizing?

 In value-based compensation, big data acts as the establishment of how a healthcare provider is assessed and rewarded for guaranteeing the adequate health of a patient, often on the grounds of biometric data. On the foundation of big data, the government rewards or denies health plans’ performance payment, and these very state-sponsored health programs use the data to document HEDIS (Healthcare Effectiveness Data and Information Set) and STAR (Medicare’s quality rating system) estimates for the government.

Moreover, information accumulated from big data ensures better healthcare and wellness, be it physical or mental health, and be it at physical healthcare centers or via telehealth. While big data allows the medical industry to recognize those at a higher risk of illness, it can prevent ailments before they occur and prevent type 1 and type 2 errors in diagnosis. With device-agnostic systems and The Internet of Things (IoT) devices like Fitbit and the Apple Watch that tracks physical activities and boost healthcare, the information is sent to medical practitioners to regulate the progress of the patient.

Big data is also an extraordinary solution to evaluate existing business models, cost models, and expenditures, for companies such as Redox, Flatiron Health, and Apixio. This allows corporations to reduce their costs, and manage their revenue while meeting their basic criteria of volume, variety, and velocity.

A Glimpse into the Future

In merely three brief years, malaria should come to an end in Zambia. How?

Data analytics has the potential to thoroughly eliminate life-threatening diseases, namely malaria. Even though airborne illnesses are still prevalent throughout Zambia, with 5 million cases documented the previous year, this abundance could be ameliorated to zero by 2021. In collaboration with PATH (Program for Appropriate Technology in Health) and eight technology sponsors, the Zambian government aims to break the cycle of illnesses under a plan, called Visualise No Malaria. The operative data analytics allows the movements of the disease to be trailed and anticipated by healthcare workers. Through visualization of field laborers and weather data in maps, the Medicare operatives can intervene to deter an outbreak, instead of simply treating cases of infection. Moreover, the data can ascertain how life-saving resources should be allocated.

As per the Journal of Big data, a myriad of biomedical and healthcare devices such as genomics, portable biometric detectors, and smartphone applications yield a considerable amount of information. After assessing medicare strategies and methods, the complete capacity of patient-specific medical specialty or personalized medicinal services is underway. From supercomputers to quantum computers, consequential data is elicited from big data in exceptionally decreased durations. The notable apparatus to contribute to the above innovations are first, CRM — integrated customer relationship management systems for producing and monitoring widespread information, and procuring documents and interpreting various issues, second, EHR — electronic health record systems for consolidating and deciphering private patient data and developing deeper insights and lastly, mobile applications — software that operates on mobile equipment to connect clinicians and patients, accumulate and access data in numerous databases.

However, the most substantial asset of big data lies in its limitless possibilities. From evolutions in the medicare sector to medical data organization, drug discovery strategies have been designed for intricate human diseases including cancer and neurodegenerative disorders. Also, big data will enlarge the workforce and augment the channel of healthcare advances, such as the sphere of remote patient management and telemedicine, instead of replacing the skilled workforce. Moreover, this will aid in the prediction of epidemics as per the healthcare status of the population, provision of timely warnings of circumstances surrounding the disease, and facilitate the discovery of peculiar biomarkers and intelligent therapeutic intervention schemes for the exceptional quality of life.

Remote patient monitoring and Big Data

With the surge of the Internet of Medical Things (IoMT), Artificial Intelligence (AI), and Big Data analytics, effective remote patient monitoring (RPM) has flooded the medicare industry. RPM is a type of Ambient Assisted Living (AAL) application. The long-term examination of patients employing the AALs yields big data. Hence, AALs must adopt cloud-based architectures to reserve, function, and assess big data. Constructing smart RPM models adopting cloud-based technologies will protect the lives of patients, especially older adults undergoing chronic diseases.

As per the National Library of Medicine, research was conducted as a literature review employing 49 articles. It was found that big data analytics are extensively advantageous in diagnostic accuracy and patient outcome development, healthcare centers readmission reduction, chronic disease management, and minimization in chronic disease burden, and cost reduction.

For high-risk patients, primarily those with chronic diseases, big data has paved the way for customized care. With the virtualization of all hospital records, healthcare practitioners have awareness of corrective measures that curtail patients’ regular visits and employ telehealth services with device-agnostic systems available in the comfort of their homes.

Moreover, innovative implanted medical apparatus can endlessly regulate, store, and transmit a person’s vital signs, and specific health condition data for long periods. Further avant-garde, intricate, and progressive implanted devices regulate, store, transmit, and sense altering and potentially life-threatening health conditions to perform life-saving therapy to a patient. Within the field of cardiology, revolutionary remote monitoring technology is permitting medicare practitioners near real-time help processing, explicating, and triaging patient needs on the foundation of implantable device data.

The Grim Realities of Big Data

While big data is a progressive future, critics have asserted negative feedback to big data in healthcare pertaining to privacy-related concerns. This assertion is considerably founded on a report promulgated by the Federal Trade Commission, which alleged that based on an examination of approximately 10 fitness applications, 76 third parties received private information produced by these fitness trackers.

Moreover, other barriers include the specialized technical skill required and the security concerns of big data. Due to the multifaceted quality of the files, big data requires a techno-scientific skill set. Healthcare centers IT experts that are accustomed to SQL (Structured Query Language) programming and conventional databases are not equipped for the learning curve. Consequently, hospitals will be required to secure the cooperation and aid of data scientists to operate and control information within a big data environment. Security poses a major threat to the healthcare system. Big data storage is becoming infamously attractive for hackers or advanced persistent threats (APTs), who may sell information to illegal organizations, instigating a national security threat.

Furthermore, many healthcare centers that are located in poor socio-economic areas lack the financial capital to hire data scientists and acquire memory-heavy software tools. These hospitals still operate with relatively outdated computers, and manual database systems, and would consequently, be reluctant to drastically transform their operations.

However, being aware of these challenges, all stakeholders, i.e. the government, big data healthcare companies, the healthcare industry, insurance providers, and patients will be required to take steps and make compromises for a better future. Effective frameworks and affirmations must have to be present to ensure the privacy of the patient’s information, including de-identification preceding to 3rd party access.

The quantity and quality of data will continue to expand as device-agnostic systems, and the Internet of Medical Things (IoMT) attains acclaim. Consistent remote patient monitoring via wearable technology and the IoT will become common as big data becomes more secure and widespread. Additionally, policy changes will be needed to overcome the obstacles to healthcare data analytics. The primary priority should enable medical care institutions to report, handle, and distribute patient data in beneficial methods, by ensuring the interoperability of Electronic Medical Records (EMRs). The state can also indirectly finance and promote the advancement of healthcare data analytics by proceeding to boost payment based on the value of care, through the Medicare plan, promoting alternative payment strategies, and by striving to align quality standards and payment processes with private insurers.

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