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St-(Eal)-Health ST-(EAL)-HEALTH ~Fortifying Big Data ​ Big Data In Healthcare The Healthcare system has evolved once with technology, trying to improve the quality of living and save human lives. Big data is nowadays one of the most important domains of future technology and has gained the attention of the healthcare system. Big Data refers to the abundant health data amassed from numerous sources including Electronic Health Records (EHRs), medical imaging, genomic sequencing, payor records, pharmaceutical research, wearables, and medical devices, to name a few. With its diversity in format, type, and context, it is difficult to merge big healthcare data into conventional databases, making it enormously challenging to process, and hard for industry leaders to harness its significant promise to transform the industry. We live in an age where data is used to drive business decisions. Every company has access to massive amounts of data about their customers, but successful businesses are able to turn that data into actionable intelligence to develop better and more optimized business processes. The healthcare industry is no different. In fact, the growth rate of healthcare data is projected to be greater than that of the total global data set. At 153 exabytes back in 2013, the healthcare industry is expected to generate 2,314 exabytes of data by 2020, a 48% annual growth rate. Now Big Data has both pros and cons which are as follows: Pros ❖ Consistency of Care is perhaps the easiest use of patient information, allowing different doctors, nurses, and other staff to view patient histories to ensure they are delivering consistent care, or are able to modify treatment to generate more positive outcomes if initial treatment plans aren’t having the desired effect. On a broader scale, the aggregation of patient data can help identify best practices for specific conditions, driving standardization of treatment and consistency of care as well as results. ❖ Personalized Medicine where a constant flow of data from sensors monitoring and recording a variety of vital statistics can help providers tailor care based on individual patient needs, circumstances, and results. Connected healthcare devices deliver data that can be used to create more effective treatment plans while recognizing patterns or elevated conditions sooner, allowing faster recognition of changes in condition and adjustment of treatments. ❖ Increased Efficiency: While a large part of data and analytics can directly ​ impact patient care, healthcare systems also have an opportunity to use data to increase their own operational efficiency, which also impacts patients. By understanding how staff and equipment are being used, in conjunction with patient information, systems can identify opportunities for operational improvements, including automation, better use of existing resources, and ways to leverage new capabilities like connected healthcare systems to deliver better outcomes to more patients. This includes automated data collection, rather than manual measurement and recording of information into records. ❖ Increased Awareness: Simply having data available allows physicians to ​ measure conditions and outcomes on a more regular basis. But, it doesn’t have to place an additional burden on doctors. Rather, connected healthcare enables measurement and data collection remotely – whether that’s automated thanks to wearable and connected devices and apps, or through manual entry into patient portals or apps – and the sharing of information between doctors and patients. Cons ❖ Data Classification: Big data is a massive, less structured and ​ heterogeneous. As such, there is a need to identify and classify the data so that it can be used effectively. However, it is laborious to search for specific data in big data. The big data also required to be contextualized or pooled together so that it will become more relevant to specific individuals or groups. ❖ Data Modeling: Although big data is excellent for modeling and ​ simulation, there is a need to identify, structure and pool the proper relevant data so that it can be used to model the problems, which later can be used for intervention. Without properly structured data, it is challenging to analyze and visualize the output and to extract specific information or data. ❖ Cloud Storage: Cloud storage can be used to upload data or having the ​ whole system designed in the cloud. Thus, the cloud will need to have sufficient space for the storage and sufficient speed for data upload at the same time. The storage apart involving word documentations, should also able to store graphic types such as X-ray, CT or MRI. The system should also be able to generate graphics presentations from the available data so that clinicians are able to visualize and understand quickly and take prompt decisions. ❖ Data Accommodation: One simplified big data system is required to ​ accommodate all the data and it has to be compatible and simplified. This is to ensure that the users are able to retrieve the information without any hassle. It is a difficult task to get all the relevant systems to link to each other. ❖ Security: Now the first and foremost issue that arises with the Big Data concept is its security. Since the big data contained the subject’s personal information and their health history, it is important for the database to be protected from hacking, cyber theft, and phishing, where the stolen data can be sold for a huge sum. Apart from the health information and personal information from the health system which can be hacked or stolen, other big data in other commercial organizations such as telecommunications companies (telcos), banks or financial institutions are also vulnerable without the knowledge of the clients. Now, as security issues of healthcare data arises, here comes the concept of Data Breaching. ​ A medical data breach is a data breach of health information and could include either the personal health information of any individual's electronic health record or medical billing information from their health insurance. Between 2009 and 2018 there have been 2,546 healthcare data breaches involving more than 500 records. Those breaches have resulted in the theft/exposure of 189,945,874 healthcare records. That equates to more than 59% of the population of the United States. Healthcare data breaches are now being reported at a rate of more than once per day. Data breach incidents in India higher than the global average “Healthcare data are attractive to cyber-criminals because they contain financial and personal data, can be used for blackmail, and most valuable, are ideal for fraudulent billing”. The rapid digitization of the healthcare industry has led to a huge increase in the number of ransomware, malware and targeted attacks, which puts confidential patient data like personal details, medical history and financial information at risk. The healthcare systems are emerging as an attractive industry for hackers to target with each stolen medical record fetching from anywhere US$50 up to US$ 20,000, according to industry estimates. Security Breach is higher in India because they have been spending their budget either in the wrong places or were more focussed only at the endpoints. "Around 52 percent of Indian respondents reported a data breach last year, way above the global average of around 36 percent. A full three quarters (75 percent) of respondents in India reported data breach at some time in the past, compared with just 67 percent globally." Some real-life incidents of Data Breach in Healthcare Sector Of India are as follows: ● A technical error led to the records of 12.5 million pregnant women being publically accessible earlier this year, as well as information about practitioners. It took more than three weeks for the data to be erased after the breach was first identified. Fortunately, there were no reports at the time of the data being misused. A past incident in Maharashtra saw more than 35,000 patient records compromised due to a security breach. ● In April 2018, it was found that Andhra Pradesh government websites were leaking Aadhaar numbers of women, their reproductive history from pregnancy to delivery, whether they had had an abortion, and so on. It also tracked the infants’ early years and vaccinations. ● In June 2018, a public website run by the Andhra Pradesh government tracked state-run ambulances in real-time, allowing anyone with an internet connection to monitor the movement of these vehicles and obtain sensitive information about the patient — such as the pick-up point, why the ambulance was called, and the hospital to which the patient was taken. ● The same month, an unsecured Andhra Pradesh government website exposed the names and numbers of every person who purchased medicines, including those who bought Suhagra (a medicine for erectile dysfunction) from government-run stores. A dashboard on the Anna Sanjivini website allowed anyone with an Internet connection to access details including the names and phone numbers of every person who bought medicines from every single such store. Norms And Policies The Reserve Bank of India (RBI) in April this year mandates that all data generated by the payment systems in India is to be stored in India. The Ministry of Health and Welfare has published the draft legislation called Digital Information Security in Healthcare Act (DISHA), to safeguard ​ e-health records and patients’ privacy. Thus, all these new rules/policies/regulations (collectively referred to as “the Data Protection Framework”) indicate a very strong direction that the Government wishes to undertake on data localization, which helps in enforcing data protection, secure nation’s security and protect its citizen’s data, better control on transmission of data outside the country and more. DISHA’S main purpose, as per the preamble is to (i) establish NeHA, State eHealth Authorities (“SeHA”) and Health Information Exchanges; (ii) standardize and regulate the process related to collection, storing, transmission and use of digital health data; (iii) and to ensure reliability, data privacy, confidentiality and security of digital health data”.
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