Big Data Applications in Healthcare, Big Data and Healthcare: The Healthcare industry has created huge amounts of data historically, which were driven by record keeping ,patient care, compliance and regulatory requirements. Though the Healthcare industry has created huge amounts of data historically, which were driven by record keeping ,patient care, compliance and regulatory requirements. Though major part of data is stored in hard copy form, the current context is of rapid digitization of these abundant amounts of data. These are driven by compulsory requirements and potential of improving healthcare delivery qualities and at the same time reducing costs , this huge amount of data holds up the promise of supporting medical and healthcare functions of wide range , which includes other clinical decision support, population health management and disease surveillance .So here we are going to describe how healthcare data management becomes much more efficient by using big data and the use of big data in healthcare.
Role of Big Data in Healthcare
Going by definition big data in healthcare refers to health data electronic set which are set so large and complex that it is almost impossible to be managed with traditional software or hardware and also can’t be easily managed with common traditional data tools and management methods. Big data in healthcare is not only overwhelming because of its dense volume but also because of the variations of data types and speed at which it must be managed. It includes clinical data from clinical decision support systems, patient data in EPR’s i.e electronic patient records and machine generated sensor data like monitoring social media posts that include twitter feeds on facebook and other platforms, web pages and less patient related information, emergency data care , news feeding and articles in different medical journals.
For Big Data scientist there is vast amount of data among them and a several array of data opportunity. By discovering up the associations and understanding the patterns and several trends within the data, big data analytics has the capability to improve care, save lives and decrease costs. Thus, big data analytics in healthcare take advantage or having the edge of the explosiveness in data to absorb and bring out the key points for creating better informed decisions and as a research category are then referred to as, no shock here, big data analytics in healthcare . When the big data is created and analysed and those associations ,design patterns and trends reveal healthcare providers and several other stakeholders in the existing healthcare delivery systems can produce more clear and meaningful treatments and diagnosis , resulting, one is surely going to expect in greater quality care at lower costs and in better-coming result.
The potential or ability for big data analysis in healthcare for leading to better outcomes persists across different scenarios, for example: by researching patient characteristics and the cost estimation and outcomes of care for identifying the clinically most effective and cost effective treatments and offer analysis and several other tools. Hence influencing behavior of provider applying up advanced analytics to the patient profiles (for instance segmentation and predictive kind of modelling) to proactively identify several individuals who will be benefited from preventive care or lifestyle changes; briefly describing disease profiling in order to identify predictive events and thus supporting prevention initiatives for collecting and publishing data on several medical procedures, thus assisting patients for determining the care protocols that offer the best fit value by identifying, and predicting and also minimizing fraud by implementing up advanced analytic systems for fraud detection and at the same time checking the precision and accuracy and thereby consistency of claims and implementing much nearer to real-time, authorization of claims thus creating new revenue stream flow by aggregating and processing patient clinical records and their claimed data sets for providing data and services
Hence influencing behavior of provider applying up advanced analytics to the patient profiles (for instance segmentation and predictive kind of modelling) to proactively identify several individuals who will be benefited from preventive care or lifestyle changes; briefly describing disease profiling in order to identify predictive events and thus supporting prevention initiatives for collecting and publishing data on several medical procedures, thus assisting patients for determining the care protocols that offer the best fit value by identifying, and predicting and also minimizing fraud by implementing up advanced analytic systems for fraud detection and at the same time checking the precision and accuracy and thereby consistency of claims and implementing much nearer to real-time, authorization of claims thus creating new revenue stream flow by aggregating and processing patient clinical records and their claimed data sets for providing data and services by third parties, just like licensing data for assisting pharmaceutical companies for identifying patients for inclusion in clinical trials. Many payers are developing mobile apps that are helping patients manage their care.
Advantages of Big Data Analytics in Healthcare
By digitizing and combining and using big data to good effect, healthcare organizations consists of range from physician symbol offices and multi-provider groups for large hospital networks and also are accountable organizations stand to realize important benefits. Potential benefits include detecting of diseases at beginning stages when they could be treated with much more effect and ease. By managing particular individual and population health and also detecting health care fraud with more efficient and quick manner . Numerous questions could be addressed with big data analytics. Certain developments or outcomes might be predicted and/or estimated based on large amounts of historical data like length of stay (LOS) ,patients who will choose elective surgery, the patients who likely will not be benefited from surgery, and several other complications. According to McKinsey big data could help in reducing waste and inefficiency of the following three areas:
Clinical operations: Comparative effectiveness research for determining better clinically relevant and cost-effective ways for diagnosing and treating patients.
Research & development: predictive modelling for lower attrition and producing a leaner, faster and more targeted R & D pipeline in several drugs and devices. statistical tools and different algorithms to focus on improving clinical trial designs and patients recruitment for better match treatments to individual patients, thereby reducing trial failures and speeding up new treatments to market; and analyzing clinical trials and records of patients for identifying follow-on indications and discovering the adverse effects before products are going to reach the market.
Public health: analyzing up the disease patterns and tracking disease outbreaks and transmission to improve public health surveillance and quick response. faster development of more precisely targeted vaccines, e.g., choosing annual influenza strains; and, turning large amounts of data into an actionable information that could be used to identify needs, for providing services, and predicting and prevent crises, especially those for the benefit of populations.
In addition, big data analytics in healthcare can contribute to:-
Evidence-based medicines: Combine and analyse different variety of structured and variety of unstructured data-EMRs, financial and operational data, clinical data, and also genomic data for match treatments with outcomes, predicting patients at risk for disease or readmission and provide much more efficient care.
Genomic analytics: Executing gene sequencing more efficiently and in an cost effective way and make genomic analysis as a part of the regular basis medical care decision process and the increasing patient medical records.
Patient profile analytics: Applying advanced analytics to the patient profiles (e.g., segmentation and predictive modelling) for identifying individuals who would benefit from the proactive care or lifestyle changes, , those patients at risk of developing a specific disease (e.g. diabetes) who would get benefit from preventive care.
Other Appications of Big Data
Benefits of big data technology are listed below accordingly with the usage of particular sectors.
Big Data analysis in energy sector
In the energy sector big data is used in predictive modelling to provide support in decision making that has been used for integrating and ingesting huge and abundant amount of data from geospatial data , graphical data and several texts . the technology has been used for interpretation of seismic waves and reservoir characterization.
Big Data management in public sectors
Following sectors are defined under public sector big data management :-
Big Data in education
In the education sector big data is usually used up in higher education like in an Australian University. The University has deployed a management and learning system that tricks among several things whenever a student is online and logging on to the system, the overall time spent by the student on pages and his overall progress in that time .
Big Data application in healthcare
Some hospitals use the data collected by cell phone app and million of doctors to allow them to use evidence based medicine in oppose to several medical tests for the patients who went to the hospital .the university of Forida are using google maps and free public health data for creating visual data that allows faster identification and healthcare analysis information used for tracking spread of chronic disease .
Big data application in government sector
Big data in Transportation
The government uses the big data for congestion control, route planning ,traffic control and intelligent transport systems . for any individual big data could be used for route planning for saving fuel and time and can also be used in travel arrangements in tourism. At the same time large amount of data from location based social networks and higher speed data fro telecom industry have effected the travel behavior and thus is a challenge for government sector .
Big Data Application in banking and security
The industry largely relies on big data for risk analytics including high frequency trading , fraud mitigation etc.
The SEC (Securities Exchange Commission) is utilizing up the big data technology in monitoring the financial market activities. The industry is currently using a natural language processor and network analytics to catch up the illegal trading activities in the current financial markets.
For Big Data Scientists, this industry offers up a vast amount of opportunities. The skill of slicing and dicing the data, making sense out of the ingested data by understanding the patterns and trends in the historical data always had an ever lasting potential for improving healthcare services by reducing the required cost, more facility and saving up more lives. The data can provide the correct picture associated with a particular disease and thereby pertinent steps could be taken up by the industry.
The other potential benefits that big data provides is the detection of diseases beforehand which allows doctors to act treat proactively. This will also help in detecting fraud in healthcare more quickly and efficiently. I believe predictive modelling can be the lead actor in improving the healthcare services. This can be smartly done by predictive the effects of medicines on patients and accordingly prescribing the medicines by studying the previous results. By using big data analytics tools, it has become much more easier to manage the data