Big Data Changing the Healthcare Industry

Gloify > Big Data Changing the Healthcare Industry

The healthcare industry operates 24/7 looking for ways to improve patient care and identify innovative opportunities, all while generating petabytes of data. Healthcare professionals are constantly developing breakthroughs in diagnosis, medicine, and patient care based on information from health records, studies about patients, and medical devices.

 

It must seem almost impossible to leverage this volume of big data while maintaining quality and managing the sheer volume. Organizations that fail to make the most of big data are potentially failing patients; as a result, they are losing out on substantial revenue. Organizations can begin taking advantage of the digital revolution by understanding how big data is transforming the healthcare industry and managing a reliable database.

Role of Big Data in healthcare

In healthcare, big data refers to extremely large datasets compiled from a variety of sources. Medical device manufacturers, insurance companies, physicians, hospitals, and pharmaceutical companies, among others, can provide the data.

It is nearly impossible to manage these large data sets with traditional hardware or commonly used data management methods because they are so massive, complex, and varied. Implementing big data infrastructure with cloud-based solutions is possible in a scalable environment. Integration and analysis of siloed data sets from different sources, environments, and formats have a profound impact on the healthcare industry.

 

The impact of big data on healthcare

Analyzing and understanding actionable data is critical for advanced patient care, innovative new treatments, and medication creation. Providers, caregivers, and administrators can use data-derived insights to improve the quality of care.

Making evidence-based decisions with big data analytics will increase efficiency, define best practices, and lower costs.

  • Improved tracking of patients 

Patients benefit from Remote Patient Monitoring (RPM) by being monitored outside a typical clinical setting, improving their care dramatically.

These systems need to be able to properly acquire, verify, and organize data. Data management techniques can reduce hospital readmissions, improve care at home, and more. There are multiple ways you can use data for future research and development, only how user create and manage the data.

  •  More accurate diagnosis 

Correct and timely disease identification is one of the biggest challenges in diagnostics. A number of tools are possible with big data technology, including predictive analytics, early detection, and improved care. By mining and analyzing large data sets, the cause of illnesses can be identified and reduced.

  • A more effective approach to overcoming substance abuse

In the fight against opioid abuse, big data is leading the way. Analytical and machine learning techniques can be used to identify patients at high risk of overdosing on opioids.

Big data is used to detect medication errors and to signal adverse drug reactions, thus helping to reverse medication events. Researchers are able to identify risk factors associated with opioid abuse by analyzing petabytes of pharmaceutical and insurance data.

  • Improved treatment development

New treatments and medicines are being developed through data-driven medical and pharmacological research. Big data analysis is essential if we are to observe variations in the human genome and identify key correlations within large sets of data patterns. By analyzing data from big data, machine learning can determine the correct treatment for an individual’s DNA. Also, this process can be used for non-genetic diseases.

  • Fraud reduction

Data breaches are a common occurrence in the healthcare industry due to the large amount of patient information it has access to. Data within its possession can be extremely valuable and personal, which necessitates proper security and advanced technology.

Data management tools enable healthcare organizations to identify threats and errors more quickly and efficiently. Cyberattacks and other suspicious behaviors, such as inaccurate claims, can be detected by comprehensive data management solutions.

  • More efficient medical imaging

The storage of medical images is expensive, while their examination requires a high level of skills. Healthcare analytics based on big data is changing how images are assessed today.

By identifying specific patterns and offering a precise numeric output, big data helps physicians make more accurate diagnoses. Algorithms based on machine learning are capable of rapidly analyzing vast numbers of medical images, saving both time and money.

  •  Better healthcare staff scheduling models

A machine learning analysis uncovers patterns in visit and admissions rates to resolve key inefficiencies. Predictive solutions that are based on big data are capable of predicting hospital visits and admissions.

Other than reducing the waiting times and improving customer service, big data solutions reduce labor costs also.

  •  Reduced costs

Health care organizations and patients are at risk of costly and inefficient errors when there is no proper data tracking and management. Big data has the potential to enable a host of potential cost-saving opportunities for the healthcare industry.

Using healthcare data analytics, businesses will be able to uncover usage patterns, offer supply chain analysis, monitor performance, and make better strategic decisions. Quantifying the benefits of big data analysis revealed an 8% increase in revenue and a 10% reduction in costs for organizations.

  • Efficient electronic health records access

Due to the multitude of demographic, historical, and medical information stored on electronic health records, integration of digital health records is essential. As a result of interdepartmental patient alignment, big data has been a key driver of minimizing paperwork and duplication, as well as reducing office visits and lab tests.

As a result of big data, records are more easily accessible within secure information systems, in both the public and private sectors. Additional to this, patients and doctors are now notified immediately of key information, such as prescription tracking, via warnings and reminders.

Healthcare’s big data “ocean” ecosystem

In the healthcare and pharmaceutical industry, advanced analytics made possible by big data technology are crucial for the transformation of healthcare and pharmaceutical operations. There is a tremendous amount of data being produced by the healthcare industry, data that can be used to improve healthcare solutions as well as revitalize the healthcare ecosystem.

Data lakes are gaining popularity because they provide easier access to diverse data types than traditional data warehouses. The cost of cloud data lakes is decreasing and they are becoming more reliable. This means organizations now have access to scalable, cost-effective, and web-based solutions.

In the healthcare industry, data lakes have become veritable data seas because of the quantity and diversity of patient data. The best examples of data repositories are already being built by many enterprises:

The Royal Philips company has helped healthcare professionals gain access to massive amounts of information by aggregating data from more than 390 million medical records.

As part of its Big Data to Knowledge (BD2K) initiative, the National Institute of Health aims to empower researchers and clinicians to better serve their patients, reduce costs, and build information for disease prevention and cure.

Scientists and clinicians can access pharmacological data through Open PHACTS, making it easier for them to make informed decisions about complex pharmacology.

Healthcare’s big data challenges

There are common challenges across any data-driven industry in tracking, storing, accessing, and analyzing data. Because of the large amount of sensitive data generated every day, the health care sector is well suited to using big data. These unified databases make data processing faster and more fluid by performing data quality checks.

In the healthcare industry, the amount of available data is increasing at an unstoppable rate. As data sources increase and datasets become more complex and large, volume increases. To cope with the significant amount of information, organizations must implement solutions that don’t slow down key processes, such as electronic health records access or provider communication.

Whether or not big data can produce any real and meaningful returns on investment determines its value. The size and complexity of healthcare data makes deriving value from analytics particularly challenging. For example, a revenue loss chart, identifying specific patient populations, or reporting on performance all involve specific use cases. Patients could face the risk of life-threatening complications due to poor data quality in this industry.

For the Future of Healthcare

With the right strategy and big data, it may be possible to predict who will leave a particular plan and at what time. The integration and implementation of medical data across heterogeneous platforms are challenging for data scientists. By using bioinformatics, health informatics, and analytics, personalized and more effective treatment can be developed. From the management of medical data to the development of new drug discovery programs for complex problems such as cancer and neurodegenerative disorders, a great deal has been accomplished in the health care sector since the rise of big data several years ago. A number of people argue that big data will reinforce the existing pipeline of healthcare advancements rather than replace skilled manpower, subject knowledge experts, and intellectuals. As the health care market continues to grow from a broader volume base to one of individual customization, one can clearly see this trend. Technologists and professionals should therefore be aware of this evolving situation. A predictive system will be a major component of big data analytics within the next year. Prediction of future health outcomes from data that already exists would be an example of this. Likewise, it can also be expected that structured information about certain geography would result in population health information. All in all, big data will simplify healthcare by facilitating predictions of epidemics (in relation to population health), providing early warnings of disease conditions, and helping to discover new biomarkers, and help us develop intelligent therapeutic intervention strategies to improve quality of life.

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