Big Data is a collection of a high volume of data yet growing exponentially over time. The data is big and complex that none of the data management tools can process it efficiently. Big data has changed the way we analyse, maintain, interpret, and support data across industries. Big Data is making big changes across the industries, but one of the most notable sectors is Healthcare.
Healthcare analytics has the ability to reduce treatment costs, predict pandemic outbreaks, circumvent preventable diseases, and improve the overall quality of life.
With more than 80% of the healthcare data being unstructured, it is no surprise that healthcare industry is unable to make use of the data to improve research, treatments, or clinical operations.
Human lifespan is on a rise along with the constant increase in the world population which poses challenges to the existing system of working and delivery. This is where big data analytics can bring significant breakthroughs.
After all, big data is turning out to be the new “choice of poison” for innovators bringing together patients, doctors, health professionals and developers.
While no one doubts the ability of “Big Data” to integrate disparate information and identify patterns, predict outcomes, and move towards a more valuable healthcare; the realization that healthcare is of utmost importance and that a move towards Electronic Health Records (EHR) alone is not enough to ensure quality care, have given it the much-needed impetus.
No wonder, stakeholders have been hoping that big data innovations will result in a pool of change in the way healthcare systems work especially decision-making processes, diagnosis, interaction with patients and the final delivery.
Not only does big data provides the ability to improve quality of life, increase preventive care, enhance patient-to-patient interaction but it also helps improve selectivity in clinical trials, prescribing medicines according to the gene and providing actionable insights to improve various management aspects (like that of discharge and admissions, staff management, performance reporting, quality assurance, etc.) of a hospital.
Big data in healthcare is a term used to describe huge volumes of information created by the adoption of digital technologies. This collects patients' records and benefits in treating the patient.
The application of big data analytics in healthcare has a lot of positive and also life-saving outcomes. In essence, big-style data refers to the vast quantities of information created by the digitization of everything that gets consolidated and analysed by specific technologies. Applied to healthcare, it will use health data of a population (or of a particular individual) and potentially help prevent epidemics, cure disease, cut down costs, etc.
In this guide, we will bring you the latest big data trends in healthcare industry.
With the advent of big data, healthcare professionals have begun to see an advantage in sharing the data with each other and structuring it.
You may ask, why?
One of the major reasons is that more and more healthcare professionals are now paid based on the outcomes and not the service they provide. Hence, they value information related to the patient which helps them decide which treatment suits best and gives optimal outcome.
Not only will it result in quality patient care and reduction of expenses, but it has the potential to significantly improve reporting, data management, processing claims, increasing support for the payments and automation of processes.
The efforts of doctors, insurers, hospitals, and other relevant actors could be easily streamlined to bring together a patient-centric system which focuses on personalized care, reasonable bills, and transparency.
Beside this, physicians are moving towards evidence-based treatment which means they have started relying on data (research and clinical data) much more than before. Hence, the need of data analytics is increasing day by day.
IoT and big data has the ability to revolutionize healthcare.
It refers to smart and interconnect devices which can gather relevant data and transfer it to the relevant person. However, this “relevant data” is mostly unstructured thereby increasing the demand for Hadoop or Kafka platforms to handle the data in a required manner.
While you have ample devices, which can be used by patients to monitor conditions like diabetes, insulin levels, and blood pressure but you cannot avoid visiting the hospitals. IoT devices have the capacity to turn these frequent visits into phone calls, short visits or even boost the concept of telemedicine.
Let us see a few examples to understand IoT better.
Roche has acquired rights to a glucose monitoring system which uses a sensor below the skin of the patient. This sensor communicates with a transmitter which in turn sends glucose levels to an app on patients’ phone.
In 2016, Roche launched a Bluetooth enabled coagulation system which could tell the patient how quickly their blood clots.
Another area of IoT usage, is adherence. Sensor based technology help doctors to oversee their patients’ adherence to prescriptions.
A McKinsey report states, “After more than 20 years of steady increases, health care expenses now represent 17.6 percent of GDP —nearly $600 billion more than the expected benchmark for a nation of the United States’ size and wealth.
Healthcare is a $2.7 trillion industry in the U.S. alone, and it is estimated that one-third is lost due to different forms of waste, mismanagement, and abuse. It is no different in a lot of other countries.
But what does it tend to include?
Redundant testing, prescribing meds and devices which are expensive and no better than the cheaper ones available are a few examples.
The ability to store humungous amounts of patient data and to be able to use machine learning algorithms to identify patterns and developing rules to flag certain items which helps detecting discrepancies has resulted in a reduction of waste and frauds.
For example, it can detect if the hospitals over utilized their services or if the patient receives service from different hospitals at the same time etc.
Clubbed with AI, it can also help in identifying new fraudulent patterns and develop new “rules”.
Big data can also help in managing day to day functions of the hospital. These four hospitals have been using data to predict admission rates. The data scientists made use of the hospital admission records to carry on a time series analysis to predict patterns and staff accordingly.
Broadening uses of EHR and knowledge of its potential (also, enactment of HITECH in the USA) has provided adequate incentives to adopt EHR and encourage sharing the data.
Other countries can take a cue from Kaiser Permanente which is a leading healthcare company in the US. They have implemented a system which shares EHRs amongst all the facilities.
A McKinsey report on big data in healthcare states that “The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.”
Predictive analysis uses technology and machine learning algorithms to analyse data (mostly, medical history and latest medical research) to predict outcomes for patients. After all, the machine can analyse many more factors than doctors by adding additional features.
Hence, it can be effectively used for early diagnosis and help reduce mortality rates from problems like heart failure. The earlier a condition is diagnosed, the better it gets treated (or prevented).
For example, Asthampolis has started to use GPS enabled inhalers. It allows them to identify trends in asthma patients and develop better treatment plans.
Not only that, but predictive analysis in genome also allows a doctor to identify at-risk patients and insist on lifestyle changes to avoid health issues in future.
On a much higher level, analysis and monitoring of health data of the entire population can help in identifying outbreaks of disease or an epidemic.
There is no doubt big data is the answer to a lot of worries in the healthcare sector. Not only can it improve the delivery mechanisms, but it can also help improve the management and other associated factors.
Providers and developers need to work together on their big data analytics competencies to gain maximum value.
The main objective of the healthcare sector is to provide the best available facilities to their patients. Sometimes treatments or medical attention gets expensive, many health insurance companies or other organizations are making use of technology to provide value-based and data-driven incentives, which will eventually help cut down costs. The main features of patient-oriented healthcare are improving the quality of the healthcare service so that the desired outcomes are obtained, reducing the cost of seeking healthcare, and providing support to reformed paying structures. By focusing primarily on the patient, their needs, and history, the doctors, hospitals, organizations, and insurance companies, etc. need to work a way to not only efficiently record data but also provide the right diagnosis and treatment while ensuring that the prices are justified and not too high.
With many improvements in advanced technology and the healthcare sector, doctors are using advanced technology to monitor the health situation. Patients are constantly connected to the device which monitors critical signs such as heart rate, blood sugar level, etc., and sends an alert to the doctors or nurses immediately if there is any change in condition. Even if the patients are not admitted to the hospital, they wear or carry devices that come with sensors and monitor health. For instance, those with diabetes or asthma can wear such devices, which will ensure that the right medication is being administered at the right time. Such devices also provide a comprehensive idea of the patient’s medical condition.
At the starting of 2020, the digital transformation in health systems was well underway, and the pandemic emphasized the need for centralized and efficient data management to study big data. Suddenly, data collection and reporting efforts hurried during the pandemic, and even small businesses looking to migrate their data over the cloud to securely store and coordinate data. There are many cloud providers where big data can be stored and some of the health-specific clouds are Microsoft, Amazon Cloud, and Google.
Analyzing the patient data will lead to improvements in the population health. It is believed that critical decisions affecting the treatment should be solely based on the real-time reliable data. To bring balance to healthcare, transparency in inpatient data is very important.
New regulations that promote interoperability and exchange of electronic health information will impact health IT development.
In 2020, the Centers for Medicare and Medicaid Services and the Office of the National Coordinator for Health IT issued rules implementing interoperability, information blocking, and patient access provisions of the 21st Century Cures Act to facilitate the access and exchange of electronic health information.
New requirements that take effect in 2021 include:
Data sharing and AI collaboration agreements are likely to increase as companies seek to efficiently extract useful insights and inferences from larger and more diverse data sets.
Because rights in data are not uniformly governed by statute, contractual provisions are critical to addressing novel issues in the rights of collaborators. Participants in data pooling initiatives need to consider what data will be shared, participants’ rights to use the compiled data sets, and the rights retained by each contributor in such data and arising inferences outside of the agreements.
Critically, AI platform providers and owners of proprietary data sets will need to address whether the AI platform is permitted to retain any learning derived from the data set that may be used for other customers, including potential competitors of the data set owner.
V2Soft will help customers deal with the challenges of Big Data and help them resolve complex event processing and predictive analytics. V2Soft offers a complete Big Data analytics infrastructure with data mining, SEO (Search Engine Optimization), SMO (Social Media Optimization), data warehousing and business intelligence capabilities so you can share access more securely, speed decision making in real time, and leverage insight for competitive advantage.
V2Soft has built a robust set of customized Big Data services and these include the following:
V2Soft technical teams are not only responsible for developing the Big Data application, but also streamline the entire end-to-end process and generate analytical output. This entire process of designing, building solutions, determining the appropriate file format for storage, processing the stored data helps in presenting the results to the end-user in an easy-to-digest form.