Applications of Big Data in Healthcare

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.

big data analytics in healthcare, healthcare data

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

 

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