9 ways big data continues to transform healthcare
The 20th century has witnessed unprecedented growth in the healthcare sector, experiencing breakthrough technologies and a transition from middle-age suppositions and methods to modern techniques. With rapid technological advancements, all the way from artificial intelligence to genomics, the healthcare industry has radically transformed into a sophisticated and digitally advanced industry. Internet of Things (IoT) and Big Data are two revolutionary technologies that are substantially altering the healthcare ecosystem.
Owing to these innovative technologies, delivering quality patient care has become far more efficient than it used to be. It has also brought about substantial cost savings and quality improvements. Big Data, in particular, has played a pivotal role in advancing diagnostics and decision support by combining human reasoning and deep learning. Predictive analytics is becoming a gamechanger for the entire healthcare landscape.
Before we enumerate 9 ways that Big Data is transforming healthcare, it remains imperative to outline the entire domain of Big Data for better understanding. Big Data is a technical term that describes the huge volume of data that inundates business processes on a daily basis. This data is both structured and unstructured and is generally analyzed through statistical methods. This Big Data is used for analyzing insights which eventually lead to better decision-making and strategic planning. In the context of the healthcare industry, a large amount of information is generated by default. These include clinical, administrative and financial data, medical information generated by sensors and tracker, and research. The ultimate objective of Big Data remains to improve quality and efficiency and reducing costs.
1. Electronic Health Records (EHR)
The American healthcare industry is already ahead of others in terms of wide-scale EHR implementation . Transforming the entire process of [BH1] maintain patient data, from paper to digital records, EHR has made access to data far easier and faster. Healthcare practices are now able to access patient history simply with a click via a tablet or even with a smartphone. This has enabled them to access past conditions as well as to keep track of ongoing treatment. Moreover, in case the patient switches a physician, the new physician need not go through the extensive and cumbersome process of collecting, and recording their history again. They just need to enter the patient identifiers and the entire medical record can be easily brought in from all the places where patient record exists. This then allows practices to more effectively diagnose patients, reduce medical errors and provide better care. Since EHRs facilitate healthcare convenience, more reliable prescribing is also possible.
2. Data Mining
Deploying EMR Software as the primary source of patient records , physicians are able to perform deep learning algorithms for data mining. Given the digitization of healthcare information, all patient data is stored on a cloud and is shared conveniently between the care team across the medical community for multiple purposes – diagnosis, academic, research, etc. Similarly, the neural networks use the EMR data as input, scan through patterns and trends, and substantially reveal underlying connections as well as future predations. This plays a vital role in early diagnosis and timely interventions. .
3. Predictive Analysis
Research suggests that Big Data largely facilitates predictive analysis. This refers to extracting valuable information from existing data sets to identify similarities and patterns to predict future outcomes. Any trends that are identified help make sense of lab tests and medical images to better evaluate patient concerns. At the same time, any risks can be easily highlighted and warnings issued well in time for effective treatment. For instance, Big Data has been particularly useful in finding a cure for cancer. It has also proven useful in sending off an alarm at the outset of the growth of malign cells to call for action.
4. Health Risk Management
Fitness trackers and sensors have radically transformed health risk management in recent times. Thanks to Big Data, personalized care plans, which are strictly patient-focused, allow better patient care in a much cost-effective manner. Instead of relying on costly techniques of applying standardized tests to sort out patient conditions, health risk management is easier done through fitness trackers to identify risks before they turn into diseases.
While being around for quite some time now, telemedicine has gained the much-needed recognition and momentum in the wake of the COVID-19 pandemic. The state-of-the-art technological innovation has allowed healthcare professional to deliver quality care at their optimal expertise across geographical boundaries. Incorporating sensors, trackers and real-time data flows, telemedicine has equipped physicians and specialties both to attempt remote diagnosis as well as remote treatment, with ease and expertise. This has also significantly expanded the access to health services for many.
6. Hospital Efficiency
Predictive analysis is particularly helpful in anticipating staffing and medication needs for the practices as well as the hospitals. It makes the healthcare professionals well-prepared to face any seasonal outbreaks, by better inventory management and by processing real-time algorithms and data to trigger low-level alerts and warnings to prepare ahead of time. Deploying techniques such as Six Sigma and Lean Methodology, hospitals are able to reduce inventory costs while successfully delivering quality care to their patients.
7. Predicting Cardiovascular Diseases and Mitigating Mortality Rate
Cardiovascular diseases remain the number one cause of deaths in the U.S. according to AHA estimates, an average of 2000 people in the US die every day due to cardiovascular diseases. Big Data has significantly aided in cardiac monitoring, through the use of remote monitoring tools and smartphone apps, eliminating the need for in-hospital visits. Wearable self-adhesive devices allow remote censoring and transmission of valuable data to the practice, which expedites the response in the case of a cardiac emergency.
8. Developing Advanced Surgical Equipment
Apart from accelerating patient dare and providing easy access to patient data, Big Data has largely supported the design and development of advanced surgical equipment. Deploying predictive algorithm and machine learning, it has empowered the healthcare industry to not only effectively handle massive data pools but also simplify the process as it moves along.
9. Reduced Fraud and Enhanced Security
The healthcare industry is prone to 200% more data breaches than any other industry. The reason is simply the fact that personal data is highly valuable and, hence, equally vulnerable. Since any breach of this valuable information can lead to serious repercussions, healthcare organizations use Big Data to prevent security threats through encryption technology, firewalls, anti-virus software, and so forth. Moreover, analytics streamline the processing of insurance claims as well, enabling the patients to secure better returns on their claims.
All in all, Big Data has massive applications in the healthcare industry. It has proven itself in increasing the efficiency of the healthcare professionals while improving the quality of treatment and satisfaction of the patients.
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