A Comparative look amid Centralization and Decentralization of Data and the Process
Originally published by Illumination on Medium
Health information and healthcare, in general, are enriched by unlimited valuable data; that is not a hidden truth anymore. Because under grassroots in nature, it is personal.
When you know a person’s private information, therefore you have the privilege to control that person and manipulate it in any way you desire. It places you at a great advantage, hence the other person at a disadvantage, therefore at your mercy.
Today with overwhelming advances in information technology and science, having access to public data is even further an easy task. Today, the data extractor, or the technical realm called” Data Mining,” can collect patient information freely without needing permission from you, the patient. Of course, the latter does require some strategic alignment that places an entity or a person in a data mining position. Nonetheless, gaining such a situation is permitted under the current sociopolitical environment. That is because even though encryption and strict data privacy laws are on the development path, too many loopholes exist that help the data collector achieve its goal. For example, using machine learning and Artificial Intelligence Algorithms, one can extract medical information from a pool of giant data silos and match it to that of patients identifying information without the need for permission or authentication.
The process of data mining is the initial step, which will primarily yield unrefined raw data with its non-reusable extracts or data that are not useful to humans. That is where data analytics come to play, particularly real-time data analytics (RTDA).
Healthcare is Stepping into a Grey Zone of Uncertainty
For the past decade, Data mining and analytics efforts have been stepping up efforts in many avenues, from collecting as much data as possible in a short period and refining it to the maximum efficiency. This trend has affected almost every industry, on top of all healthcare. For instance, recently, In light of the COVID-19 dilemma, HIMSS digital, the Healthbox has formulated a digital think tank where healthcare pros across the globe can share unsurpassed practices about how efficiently test, triage, and treat patients during the pandemic. As promising as it may sound, nevertheless, it asks for additional due diligence. Because just like the surveillance program to fight “pandemics,” thus too may be a potential tool for the invasion of privacy and abuse by certain entities. And unless algorithm and tactical and we’ll as the strategic alignment of the innovator of such technology becomes transparent, it will be nothing short of blindly accepting risks.
A unique platform commenced by Imperial College of London, enabled by REDASA (REaltime Data Analysis and Synthesis) of Amazon Web Services (AWS), is expected to puddle global data on COVID-19 from over half a million sources in response to an overabundance of information or what World Health Organization called it the infodemic. Latter is about big data, some accurate and some not, that makes it hard for people to treasure dependable sources and reliable guidance when they need it.
The new scheme intends to respond to the ‘infodemic’ by streamlining information with creating a global knowledge platform by Combining artificial intelligence with human expertise. But the two primary questions are- what are they looking for, and what pertains to valid data?
What is Real-Time Data Analytics (RTDA)
Real-time data analytics pertains to the analysis of collected data as soon as it becomes available. Through RTDA, users gain acumens or can decide as soon as the data enters their system. It allows companies to react without pause while seizing timeliness or prevent problems before they occur. By comparison, batch-style analytics may take hours or even days to yield results. Consequently, group analytical applications often return only after the fact insights.
With the ever-increasing size of centralized data silos, The speed of change is hard to interpret. Day in day out, we make thousands of decisions. While doing that, we add to an ever-growing stream of data. Entities diligently tap into the data above. Often they use it for good reasons, such as casting off their products and services and to foresee their customers’ demands. However, other times they take their mission further and monopolize their way to ultimate financial success.
Traditionally, slower data gathering and processing techniques have relegated this practice to one of two compasses. One- business either studied the past, or Two- it theorized regarding the future. But real-time data analytics award institutions to do more and to do it promptly. By interacting with and affecting the market in real-time, RTDA is revolutionizing the contemporary business environment.
The Utility of Real-Time Data Analytics in Medical Practice
Real-Time Data analytics is used across all industries. Its utility is also prominent in healthcare. For instance, it cuts Hospitalizations and Improves Population Health. Using real-time data, researchers targeted population health intervention in two neighborhoods and decreased hospitalizations by 20%. Researchers at Cincinnati Children’s Hospital performed a study on two communities in Hamilton County, Ohio, with high hospitalization incidences among children. Researchers also highlighted the use of collaboration and building aptitudes among partners in the population and throughout healthcare. These partnerships are essential for addressing patients’ social determinants of health and require the industry to rethink its payment models.
Predictive analytics holds most of the traditional medicine and healthcare, whether technology-enabled or not. Most of the latter operate under “predictive analytics” today, driven by physicians’ minds versus software tools. The goal of introducing predictive data analytics to medicine is to widen the training data set outside an individual’s experiences so that individual patients can be better treated. The plethora of data and the overall readiness of tools has catalyzed predictive analytics in healthcare. However, Big data and various algorithm production have reignited interest and excitement around predictive analytics. That has invited many nonmedical entities into the Healthcare sphere, among which not all are purely legitimate. Of the startup companies that received funding before 2014, the majority of them focused on enterprise customers. Analytics and tools alike have been designed to interface and influence the healthcare expert’s workflow, with a growing list of businesses interacting with both the physician and patient. However, few predictive analytics companies are directed toward patients only or personalized.
Keeping the RTDA and the predictive analytics in mind, it is incredibly vital to watch out for potential risks. Hence, it’s essential to establish “True” Real-time Data as a matter of necessity.
The Risks of Real-Time Data Analytics
Businesses across the globe are investing profoundly in the infrastructure and intermediaries necessary to capitalize on big data. While the data industry is reacting to such increasing demand, more robust data analytics systems are also emerging. Large centralized pools of data have also become the cornerstone of many big data strategies when they should not be. Relying totally on data pools for data strategy is out-of-date and can be incurable. While they play a vital role in serving organizations store information, data lakes are the underminer of ‘true’ real-time data analytics. While the latter may be happening in real-time, the data is moreover outdated to be helpful. Businesses that rely on old data for real-time actions are putting themselves at a competing scarcity. In the case of a security attack, a one-second delay then puts an organization behind on detecting and preventing a fraudster from infiltrating its systems.
Large corporations already have access to big data when they should not have access to them in the majority of cases. But based on efficiency, they also want your real-time data flow, which can be analyzed through real-time data analytics and less through prediction analytics. Hence, while providing real-time data for use, the Black Box Artificial Intelligence and Surveillance programs, RTDA is becoming a matter of necessity rather than an option. Utilizing the aforementioned valuable tool, the public information, more so the health information piracy and abuse, seems to become less dependent on the pool of centralized data and more about fishing for dynamic data floating on the internet and servers. This is an excellent strategy by the 3rd party entities to bypass some security obstacles associated with database haven initiatives.
Real-time data is going to play a critical role in every data analytics approach. For the reason that the contemporary computation capabilities and technology advancements are enabling businesses to run analytics, gain insights, and take actions on raw data as events happen. Considering the current trends, thus, more ever, it is vital to take advantage of Blockchain technology.
One Way to Minimizing Medical Data Processing Risks is Constant Mitigation
Some experts in the data analytic arena postulate that the Keys to Success with real-time healthcare data processing are via Understanding the use Cases it is trying to deliver and the timeframe it is required. In addition, Clear metrics and objects are relevant because they help us understand use cases the organization covets to measure.
Real-time streaming of the RTDA is within its immediacy. However, sometimes it is still essential to persist the data by storing a copy of the data, mainly if there is a possibility of future analytical value for that data.
Concomitantly, even though the core of the conversation may seem to be about technology, not all technologies are designed equally or offer equivalent aptitudes. Therefore, choose carefully!
With selecting the right technology, one must ensure that such a technique is adaptable and scalable to the needs of the solution and grow parallel with the business.
In contrast to Realtime Data Analytics, the Near-Realtime option may carry its particular advantages. For instance, a Near-Realtime uses transactional replication whenever possible, helping move updated patient records for use more frequently and efficiently.
The use of Incremental loading of medical data is something experts see as highly valuable during data processing. It’s also noteworthy to understand what data refresh rate is acceptable to the practice. Depending on data size, getting end-to-end data model refresh down to the 5–15-minute range is often possible.
In Near-Realtime situations, one may preferably use a direct query to address those simple up-to-date reporting needs.
Real-Time Data Analytics needs Blockchain.
Blockchain ensures quality, as it focuses on validating data, while data science or big data involves making predictions from large amounts of data. Blockchain provides the management of data no longer in a central panorama. The latter is where all data should be affected together but in a decentralized manner where data may be analyzed right off the edges of individual devices where they belong.
Blockchain combines with other forward-looking technologies, like cloud solutions, Artificial intelligence (AI), and the Internet of Things (IoT). Furthermore, verified data generated via blockchain technology arrives structured and complete, and it is immutable, like we mentioned earlier. Another vital area where blockchain-created data becomes a boost for big data is data integrity since blockchain determines the origin of data through its linked chains.
There are many ways blockchain data help data scientists without jeopardizing the security of private health information
Blockchain Ensures Trust by Maintaining Data Integrity
Data entered on the blockchain are honest because they must have gone through a verification process that guarantees its quality. It also offers transparency since activities and transactions that take place on the blockchain network are trackable.
Blockchain Prevents Malicious Activities and Usage
Because blockchain utilizes an agreement algorithm to verify events, a single unit can’t threaten the data network. A node (or group) in the blockchain that starts to act abnormally can readily be identified and expunged from the system. And Because the system is so distributed, it makes it almost improbable for a single party to produce enough computational power to alter the validation criteria and allow unwanted data in the system. To modify the blockchain rules, most nodes must be pooled together to create an accord, which will not be possible for a single bad actor to accomplish.
Blockchain enables Data Analytics to Make better Predictions (Predictive Analysis)
Like many other types of Data, Blockchain rendered data can be analyzed to reveal valuable insights into habits and inclinations, hence used to predict future upshots. In predictive analysis, data scientists base their decisions on large data sets to determine with high accuracy.
Due to the distributed nature of blockchain and the immense computational potential, data scientists, even in smaller establishments, can commence widespread predictive analysis tasks. Researchers can use the computational power of many computers linked to a blockchain network to analyze societal outcomes on a scale that would not have been otherwise plausible.
Utilizing Blockchain in Real-Time Data Analysis is Scholarly
Organizations that demand real-time data analysis on a large scale can call on a blockchain-enabled system to achieve. With blockchain, health organizations and others can observe changes in data in real-time, making it conceivable to make quick conclusions — whether to block a suspicious transaction or track anomalous activities.
Data Sharing Management
Concerning the data obtained can easily be stored in a decentralized blockchain system. That will reduce data abuse and help project teams not repeat data analysis already carried out by other groups. Most importantly, a blockchain platform can support data scientists monetize their efforts, probably by trading analysis stored on the forum.
One concern is that the blockchain application would be expensive to pursue compared to traditional centralized means. That is because Blocks deal with comparatively small amounts of data at a time compared to the large volumes of data collected per second for big data and other data analysis assignments. The long-term result of the blockchain utility in real-time data analytics is for the best. After all, it’s not necessarily the quantity of data that’s important, as is about what organizations do with the data. Big data is frequently examined for intuitions that lead to healthier decisions and strategic business moves, and such a move may not be the safest for the consumer or patient.
Data analytics has become the key to ambitious corporate interest because of its role in identifying emerging market drifts. In turn, companies can use this information to make quicker and better decisions that help them drive profitability, mainly at the expense of the patients.
Finally,- Blockchain technology brings three essential properties to the healthcare counter: Decentralization, Immutability, and Honesty.