Big data refers to massive amounts of information collected through the digitization of everything. The information that’s collected from diverse sources will be consolidated and analyzed by technologies to make a drastic change in healthcare. Healthcare analytics has the power to reduce costs, provide better patient outcomes, detect and suggest methods to avoid preventable diseases and thereby improve quality of life. All these have led to better levels of treatment strategies, leading to an increase in the average human lifespan.
All this is made possible due to the large quantity of clinical data, and how this data is analyzed to detect trends, patterns and associations. Both statistical and quantitative analysis of the data is done, and this helps healthcare systems come up with predictive and explanatory models. They are able to make fact-based decisions and not just treatments based on assumptions.
Interestingly, there are three stages to data analysis, that’s applicable to all domains. They are Descriptive, Predictive and Prescriptive. In this article, we will examine all the three stages in detail, and how it’s combined with AI and blockchain to provide better healthcare for patients.
Descriptive analysis is all about what has happened. That’s what it is all about - description of the data that’s collected. For example, how many patients were admitted in hospital last year as victims of motor vehicle accidents? How many fatalities occurred in the accidents, how many of them suffered an infection and so on.
Through descriptive analysis you can quantify the data based on events and then convert them to human readable format. This way a series of information in various formats from various sources are converted to actionable insights that would be very useful in drawing conclusions.
This is an elementary form of reporting, so it talks about events that have already occurred. For example, how many are afflicted with diabetes every year, how many of them are living with it, what are benchmark outcomes and other aspects of diabetic care. Even though it is very basic, the reporting capabilities of the data still elude the grasp of many organizations. This data must extracted through various extraction methods and that might add to the infrastructural costs of the organization. However, unless there is a proper method to extract this information, it would be forever frozen in incomprehensible form - in a series of numbers that do not make sense. This is just the first step in making use of data, and health centers have to make use of this first step in big data analytics.
Predictive analysis is all about predictions - what is likely to happen. The data is there, now what is going to happen? How will the data help to draw conclusions. This is the scenario faced by healthcare sector - you have the data in hand, now what are you going to make of it. Curiously enough, only very few healthcare providers have reached the second stage - the stage of Predictive analysis.
They have to use this data to reduce costs, reach meaningful conclusions on how to avoid epidemics, what steps should be taken to control chronic diseases, how to avoid adverse events that might have to be controlled and so on. Having a bunch of data in hand is just the beginning of the game, you must be able to make a meaningful pattern out of it. For example, if you have a list of people who were readmitted with heart attacks, you can probably study their medication, lifestyle and other factors, and predict whether newly admitted heart attack patients will also have a recurrence. Through predictive analysis, you can observe the writing on the wall and make decisions that help you clinically.
EHR or Electronic Health Records that you see in the hospitals is just the beginning. You need proper infrastructure to make proper insights from this data, because just having EHRs won’t do. For example, having integrated medical devices that give the clinicians updated information about the patient’s vital signs may be greatly helpful in preventing a recurrence of a past event. Similarly, hospitals can install clinical support systems that generate alerts and alarms that could be critical warnings of a patient’s conditions.
But these alerts have to be genuine so they don’t affect the routine of the clinicians and helpful in providing accurate diagnosis on the patient’s condition. Many healthcare providers are trying their best to secure the infrastructure that contribute to predictive analysis, and have tried their best to procure the technologies that can combat the roadblocks in achieving the promise delivered by predictive analysis.
This is the final stage of healthcare big data analytics. In the previous stages, we learnt about collecting the data, drawing insights from it and making predictions based on the presented data. Here in the final phase, the discussion is not just on predicting an event, but having the capability to do something about it. Suppose, a locality suddenly has people getting admitted in hospitals for a curious case of infection. Through predictive analysis, the installed software program will not just flag this anomaly from the data collected from various hospitals, but will also mark potential victims (people who are likely to come down with the same infection) and even give details on the caregivers so they can do something to prevent spreading the disease themselves.
This proves that prescriptive analysis doesn’t just predict what’s about to happen, it can help the healthcare organization on the different actions to be taken to avoid the circumstances, and even suggest the best possible course of action. For this, the healthcare organizations should have state of the art integrated data analytics infrastructure to make this possible. But the sad part is that, only a smattering of healthcare organizations will be able to afford this in a large scale manner.
However, the importance of prescriptive analysis is that it is the future of healthcare, and with the help of Internet of Things, the decision making capabilities have taken an enormous leap forward. Internet of Things makes it possible to look at the algorithms in a totally different way and it is a major help too because the way you can manipulate these algorithms have also undergone a massive change. Right from a consumer choosing something from the grocery shop to making crucial stock market decisions, the change has been revolutionary. So imagine how it can influence the healthcare industry.
Prescriptive analysis can really break the way traditional way of diagnosis and treatments have been going on. They can bring about a totally different attitude to patient care.
A machine can be converted into possessing Artificial Intelligence (AI) when it is able to perceive the surrounding environment and collect information that would help achieve a particular goal. Through AI, information would be gathered and then applied to provide intelligent solutions. The applications of AI are far and wide, and especially very significant in healthcare, finance, economics and energy consumption.
Artificial Intelligence in healthcare makes it practically possible to make decisions by observing the patterns and providing better patient care. It has the ability to stimulate human behavior. Machine learning is a core AI concept and consists of computer systems that are modeled on the human brain, After collecting algorithms from the existing data, there is multilevel analysis that makes it possible for a device to stimulate and expand in the way a brain does. This is done through deep learning that recognizes patterns and gives suggestions through AI to systems to provide predictive and prescriptive analytics.
Big data analytics can simplify both structured and unstructured data, so it goes through patient records, health plans and other details of the patient to provide diagnosis and treatments. For example, imagine a situation wherein a suspected cancer patient enters a hospital for tests. The AI in the device springs immediately into action analyzing the patient completely, including all the cells. It can observe the pattern and immediately spot the anomaly. This way the doctors would quickly be able to distinguish between the cancerous cells and the non-cancerous ones.
Blockchain is a kind of distributed ledger or decentralized ledger that contains unchangeable digitally recorded data packaged as blocks. As there are a number of decentralized database architecture, the data recorded in them is contingent on the simultaneous working of a number of resources rather than a single authority. These resources are participating members of a community, each with a ledger copy of its own.
The members compare notes and decide which would be the most recent one. Once a valid transaction is recorded, it will be recorded on the blockchain, with a permanent timestamp. This timestamp cannot be altered or changed. When analyzing other centralized technologies, you can safely say that blockchain technology is safe, transparent and fast. This is a crucial feature because in healthcare, accuracy and transparency are very important features.
The most immediate applicability of blockchain is in the field of life sciences, mainly clinical trials. This is because it moves away from the error-prone, document driven approach to a flexible and more reliable process. The technology has made its presence felt everywhere - including artificial intelligence (AI)-based diagnostics, interoperability, big data analytics, data security, data sharing, patient engagement, health information exchange (HIE), fighting counterfeit drugs, R&D diagnostics, and so on.
There are three main components to a blockchain. They are: distributed network, shared ledger and digital transactions. In distributed network, the data structure will be spread across multiple computer devices that could be in any location or region.
Shared ledger is a logical component and once the node application is running, you can check the content for that ecosystem in the respective ledger. The viewing is as per the rules of that particular ecosystem. Cryptography plays a very important role in blockchain. It is what provides security to the innumerable transactions, authenticity and immutability of data.
Being a distributed ledger of transactions, there are blocks which are linked together in blockchain. Hence you can say that this technology is the future of digital transactions. Details about the patient and his/her health status can be shared with other systems for further research or targeted marketing.
An example: Studying the behavior of a new disease in order to reach a decision on what kind of treatment would be favorable. Using blockchain, the data will be shared amongst multiple research organizations and they work on finding a cure for the rare disease. This patient’s privacy and other details will be protected.
The impact of big data analytics in healthcare is staggering. The amount of data remains in both structured and unstructured formats unless some pattern is drawn from it. Big data analytics simplifies this data format, especially in patient records, treatment plans, imaging data, insurance information, etc. Once a pattern is drawn from the data, it is shared among clinicians and medical research companies for various purposes, including research, diagnosis and treatment.
It is Artificial Intelligence that helps in drawing the patterns in all the data that’s coming in. Apart from aiding in more efficient operations and insights into the patient, his lifestyle and health, AI can also help in leveraging the analytics for making strategic decisions. This is done by analyzing the algorithms and making highly complex decisions. So when a patient goes into a hospital with a life threatening disease, it is the computer that performs the diagnosis and suggests possible forms of treatments, even duration of hospital stay. The diagnosis will be accurate because the computer analyzes loads of data, mostly making use of Google’s massive computing resources.
Hanover, the AI technology developed by Microsoft uses artificial super intelligence for mining data in the healthcare sector. Through this technology, it is possible for the devices to memorize medical research papers to find possible treatments for various patients. It will analyze the patient’s personal information by going through their medical history, corroborate it with the information from medical research papers and suggest effective treatments.
Blockchain technology plays a major role in determining and assuring the safety and transparency of drugs sold by pharmaceutical companies. It ensures that the data is recorded as it happens, and this is then shared analogously to provide better patient outcomes.
According to WHO estimates, about one million people die every year from counterfeit medicines.Through this technology, pharmaceutical companies like Big Pharma ensure that there will be no counterfeit drugs by providing each drug with a unique serial number. Once any drug with a fake number crosses the system, it will be instantly flagged. This can also ensure that drugs are not switched, point in shipment, poorly made, etc.
Tracking of the drugs is done through serialization unique identifiers in this manner - National Drug Code + Unique Serial Numbers. Under the DSCSA legislation, each pack of tablet, vial, syringe etc. has an individual unique serial number to identify the drug, where and when it was made. Blockchain helped in full traceability of drugs for Big Pharma.
The flowchart shows how structured and unstructured data flows through the various entities in the healthcare system, and how meaningful insights are created from all the collected data though Artificial Intelligence and Blockchain technology.
Blockchain helps in creating a system that creates and manages blocks of content known as ledgers, where the information analysis is secure and automated. All the health related data will be recorded and analyzed securely so medical experts and healthcare providers and payers can get accurate updates. This is taken one step further by integrating AI algorithms into the blockchain. AI began to think and learn like the physician to understand the health trends and patterns. It collects the data from all the sources (the patient himself/herself, the radiologist through MRI scans and images in the form of unstructured data).
Data is the most crucial aspect in every industry, and in the healthcare sector, you cannot stress its importance in mere words. With proliferating volumes of data in the form of patient histories, EHRs and clinical trials, IT departments are looking forward to using the technologies of today to treat the diseases and health challenges that may strike tomorrow.
AI is an approach by which machines make use of data analytics to identify patterns that help in making crucial decisions, including relying on real time insights to prevent patient hospitalizations, determine the best course of treatment and streamline care delivery.
Similarly, Blockchain has evolved to be the norm in the healthcare sector. It’s value in the pharmaceutical supply chain, de-identified patient record transmission and medical billing is still being tried out. Together with AI, Blockchain can really make operations fast, safe, flexible and transparent. In the synergy of both these technologies, there is so much to explore, and yet, we have only reached the tip of the iceberg.
Looking to integrate AI, blockchain and big data analytics in your healthcare system? We can help you!
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