The integration of these data sources would require developing a new infrastructure where all data providers collaborate with each other. One of the most notable areas where data analytics is making big changes is healthcare. Big data is helping to solve this problem, at least at a few hospitals in Paris. Whether it be vaccines, synthetic insulin or simple antihistamines, medicines produced by the pharmaceutical industry play an important role in the treatment of disease. This application enables doctors to treat these patients well. Proposes and aims to reach the communities where conventional health care providers cannot reach. Big Data and Cancer. But with emerging big data technologies, healthcare organizations are able to consolidate and analyze these digital treasure troves in order to discover trend… Increases the efficiency of the current radiologists. Helping the health insurance companies to provide the best service and making it easy for them to detect any fraud activities. These analyses allowed the researchers to see relevant patterns in admission rates. Choosing the best platform - Linux or Windows is complicated. Generates metrics outcome and flawlessly exposes the specified patterns associated in a pathology. The healthcare industry has undergone a drastic transformation today with the use of technologies such as big data and advanced analytics. Kaiser Permanente is leading the way in the U.S. and could provide a model for the EU to follow. Big data in healthcare is a term used to describe massive volumes of information created by the adoption of digital technologies that collect patients' records and help in managing hospital performance, otherwise too large and complex for traditional technologies. If such a circumstance arises when you need to visit ER for more than 900 times within three years, then how would you feel? One of the most common problem shift managers face is to staff the optimal number of people for any given period of time. Uses big data to enable AI to generate intelligent and perfect diagnosis report for providing better healthcare. Besides, comparing, establishing the relationship between datasets and applying data mining to extract hidden patterns are also required to be able to predict the chance of acute heart attack. Keeps the record of the treatments that one patient has received and consultants can check the history before making a decision. Prevent Frequent ER Visits by Big Data, 12. Automates the delivery process of insulin. This is one of the best big data applications in healthcare. Patients are directly involved in the monitoring of their own health, and incentives from health insurance can push them to lead a healthy lifestyle (e.g. As entities that see a wealth of patients every single day, healthcare institutions can use data analysis to identify individuals that might be likely to harm themselves. Aims to make important data of patients that includes medical history and general information readily available to authorized users like health care organizations, government, and doctors. Big data analytics in healthcare encourages us to dig deep into a data set and extract meaningful learnings. Helps to keep track of a patient’s condition by regulating his/her treatment plans and prevent from deteriorating health condition. Big data analysis in healthcare has the power to assist in new therapy and innovative drug discoveries. We will then look at 18 big data examples in healthcare that already exist and that medical-based institutions can benefit from. Another way to do so comes with new wearables under development, tracking specific health trends, and relaying them to the cloud where physicians can monitor them. Uses clustering a method of data mining to extract the required information from the medical records of AIDS patients. You have probably heard this name as they are operating for more than 40 years now. In fact, healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve the quality of life in general. But first, let’s examine the core concept of big data healthcare analytics. Ditch the Cookbook, Move to Evidence-Based Medicine. These technologies have revealed new possibilities with data-driven insights using disparate sources of information. Institutions and care managers will use sophisticated tools to monitor this massive data stream and react every time the results will be disturbing. Collects data from insurance companies and pharmacies and blends it with data science to generate an accurate prediction. Patients suffering from asthma or blood pressure could benefit from it, and become a bit more independent and reduce unnecessary visits to the doctor. The use of big data in healthcare allows for strategic planning thanks to better insights into people’s motivations. It uses patient data and analyzes it to invent better treatment for curing cancer. It connects the results generated from health devices with other trackable data to eliminate the risk of being potential patients. U.S. has made a major leap with 94% of hospitals adopting EHRs according to this HITECH research, but the EU still lags behind. Too often, there is a significant lack of fluidity in healthcare institutions, with staff distributed in the wrong areas at the wrong time. Improving Health in Low & Middle-income Countries, Top 20 Examples and Applications of Big Data in Healthcare. Medical images are essential for radiologists to identify any diseases or symptoms. Tries to fit complex data collected from many sources. And current incentives are changing as well: many insurance companies are switching from fee-for-service plans (which reward using expensive and sometimes unnecessary treatments and treating large amounts of patients quickly) to plans that prioritize patient outcomes. Almost 60% of healthcare organizations already use big data and nearly all the remaining ones are open to adopting big data initiatives in the future. Here are 10 great data sets to start playing around with & improve your healthcare data analytics chops. Capturing data that is clean, complete, accurate, and formatted correctly for use in multiple systems is an ongoing battle for organizations, many of which aren’t on the winning side of the conflict.In one recent study at an ophthalmology clinic, EHR data ma… Healthcare industry has not been quick enough to adapt to the big data movement compared to other industries. The field is slowly maturing as industry-specific Big Data software and consulting services come to market, but there is still a long way to go before the market … As Tracy Schrider, who coordinates the care management program at Alta Bates Summit Medical Center in Oakland stated in a Kaiser Health News article: “Everybody meant well. Just like other epidemic diseases like malaria, influenza, chikungunya, zika virus; dengue has become one of the world’s most known viruses that are causing many lives every year. This automotive tool of big data in healthcare helps the doctor prescribe medicines for patients within a second. Big data has changed the way we manage, analyze, and leverage data across industries. Enhancing Pharmaceutical R&D with Big Data. This data can also lead to unexpected benefits, such as finding that Desipramine, which is an antidepressant, has the ability to help cure certain types of lung cancer. Moreover, medical data analysis will empower senior staff or operatives to offer the right level of support when needed, improve strategic planning, and make vital staff and personnel management processes as efficient as possible. Medical data is sensitive and can cause severe problems if manipulated. Predictive Analytics in Healthcare. They provide far richer nuance and context about a patient’s medical history, diagnoses, treatment plans, test results, and other details than codes and other reference data—so ubiquitous across healthcare—ev… Analytics, already trending as one of the business intelligence buzzwords in 2019, has the potential to become part of a new strategy. Other examples of data analytics in healthcare share one crucial functionality – real-time alerting. This application tries to develop healthcare by proper nutrition plan using this vital data that is readily available around us. As a result of this, the government can take necessary actions. What advice has already been given to the patient, so that a coherent message to the patient can be maintained by providers. Big Data and Cancer. Generates electronic statistical reports containing demographics, allergy history, medical tests, or health checkups of all the patients. Another real-world application of healthcare big data analytics, our dynamic patient dashboard is a visually-balanced tool designed to enhance service levels as well as treatment accuracy across departments. Now that you understand the importance of health big data, let’s explore 18 real-world applications that demonstrate how an analytical approach can improve processes, enhance patient care, and, ultimately, save lives. Besides, it focuses more on low- and middle-income countries. The recent development of AI, machine learning, image processing, and data mining techniques are also available to find patterns and make representable visuals using Big Data in healthcare.eval(ez_write_tag([[728,90],'ubuntupit_com-medrectangle-3','ezslot_4',623,'0','0'])); The recent development of AI & machine learning techniques is helping data scientists to use the data-centric approach. The term refers to the delivery of remote clinical services using technology. 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Applications for Big Data in Healthcare . Big Cities Health Inventory Data. Blends Big data and healthcare to prevent patients from wasting so much money and make them able to live a longer life. Incompatible data systems. Provides an easy to use platform for all type of users, including doctors, shift managers, nurses, and soon. Likewise, it can help prevent fraud and inaccurate claims in a systemic, repeatable way. Thanks to the considerable benefits and opportunities, it has attracted the momentous attention of all the stakeholders in the healthcare industry. This project is still in the process of development and can bring new light to tackle the problem of other dangerous diseases also.eval(ez_write_tag([[300,250],'ubuntupit_com-large-leaderboard-2','ezslot_6',600,'0','0'])); This is an automotive tool of big data in healthcare that helps the doctor to prescribe medicines for patients within a second. Data driven mindset- Training all institution staff and patient care personnel on how to accurately record data, store and share it. It’s the most widespread application of big data in medicine. Alongside other technologies, Big data is playing an essential role in opening new doors of possibilities. Focused on finding the mechanisms that relate periodontal disease with rheumatoid arthritis. To keep the institution running at optimum capacity, you have to encourage continual learning and development. An HR dashboard, in this case, may help: Though data-driven analytics, it’s possible to predict when you might need staff in particular departments at peak times while distributing skilled personnel to other areas within the institution during quieter periods. Patient confidentiality issues. Successfully detects fraud claims and enables heal insurance companies to provide better returns on the demands of real victims. Big Data has unlocked a new opening in healthcare. Data can be generated from two sources: humans, or sensors. Even now, data-driven analytics facilitates early identification as well as intervention in illnesses while streamlining institutions for swifter, safer, and more accurate patient care. Using years of insurance and pharmacy data, Fuzzy Logix analysts have been able to identify 742 risk factors that predict with a high degree of accuracy whether someone is at risk for abusing opioids. The healthcare industry where patient data has largely remained unstructured is one industry where big opportunities for big data are being discovered. Clearly, we are in need of some smart, data-driven thinking in this area. It can easily detect if anybody is at high risk of suffering from a disease in the future. As people of today’s day and age, we already know it. Many consumers – and hence, potential patients – already have an interest in smart devices that record every step they take, their heart rates, sleeping habits, etc., on a permanent basis. Over the past five years, Big Data, and the data sciences field in general, has been hyped as the "Holy Grail" for the healthcare industry … This application enables shift managers to accurately predict the number of doctors required to serve the patients efficiently. Emphasizes the required number of hospitals or medical services. Makes the data available for the local care providers that are stored in a database to investigate emergency department use, hospital admissions, and preventable readmission rates. Thank you. Combining Big Data with Medical Imaging, 11. Managing Partners: Martin Blumenau, Jakob Rehermann | Trade Register: Berlin-Charlottenburg HRB 144962 B | Tax Identification Number: DE 28 552 2148, News, Insights and Advice for Getting your Data in Shape, BI Blog | Data Visualization & Analytics Blog | datapine. Records are shared via secure information systems and are available for providers from both the public and private sectors. This application is intended to decrease the amount of money for taxpayers and health care organizations. After analyzing the vast data, it uses the result for strategic planning to perform certain activities. Not only identifies the patients who are abusing Opioid but also reports to the health physicians. This is perhaps the biggest technical challenge, as making these data sets able to interface with each other is quite a feat. To be fair, reaching out to people identified as “high risk” and preventing them from developing a drug issue is a delicate undertaking. But advances in security such as encryption technology, firewalls, anti-virus software, etc, answer that need for more security, and the benefits brought largely overtake the risks. Provides tumor samples, recovery rates, and treatment records. In hospitals, Clinical Decision Support (CDS) software analyzes medical data on the spot, providing health practitioners with advice as they make prescriptive decisions. But while this is a very difficult area to tackle, big data uses in healthcare are helping to make a positive change concerning suicide and self-harm. By analyzing the user’s food habit, lifestyle, and prescription records, it can predict if he/she is at risk of any cardiovascular disease. For example, if a patient’s blood pressure increases alarmingly, the system will send an alert in real-time to the doctor who will then take action to reach the patient and administer measures to lower the pressure. “If somebody tortures the data enough (open or not), it will confess anything.” – Paolo Magrassi, former vice president, research director, Gartner. This application focuses on detecting HIV in the early stages. Well, in the previous scheme, healthcare providers had no direct incentive to share patient information with one another, which had made it harder to utilize the power of analytics. Every record is comprised of one modifiable file, which means that doctors can implement changes over time with no paperwork and no danger of data replication. Medical imaging is vital and each year in the US about 600 million imaging procedures are performed. Notifies the related personnel, whether the treatment process should be updated or not after analyzing the result of the data-centric approach. This imbalance of personnel management could mean a particular department is either too overcrowded with staff or lacking staff when it matters most, which can develop risks of lower motivation for work and increases the absenteeism rate. This helped me a lot in my research project and hope it has helped others too. It uses a closed-loop system to know how a user responds to food, exercise, and insulin. Many people have died already as an outcome of arriving at the hospital very late. Patients can avoid waiting in lines and doctors don’t waste time on unnecessary consultations and paperwork. Big data analytics in healthcare has enabled doctors to fight against horrifying diseases like Cancer & AIDS. For instance, bed occupancy rate metrics offer a window of insight into where resources might be required, while tracking canceled or missed appointments will give senior executives the data they need to reduce costly patient no-shows. Examples of Big Data Analytics in Healthcare. This application has identified this problem, found the solution, and become one of the most popular big data applications around the world. It strives to enable governments to face this situation strongly so that it remains in control. The application of big data analytics in healthcare has a lot of positive and also life-saving outcomes. Big data in healthcare can be easily applied as databases containing so many patient records that are available now. Data analytics in healthcare can streamline, innovate, provide security, and save lives. Guards valuable data against going in the wrong hands, from where criminals can use it for creating unpleasant situations. A McKinsey report on big data healthcare states that “The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.”. The sector slowly adopts the new technologies that will push it into the future, helping it to make better-informed decisions, improving operations, etc. Facing the challenge of unpredictable heart attacks is not easy and requires a large dataset. This system lets the ER staff know things like: This is another great example where the application of healthcare analytics is useful and needed. Although EHR is a great idea, many countries still struggle to fully implement them. Collects all the previous reports of biopsies, and doctors can take information before making a decision. Thanks to the widespread adoption of wearables, fitness trackers and healthcare apps, collecting and compiling data for big data analytics has only become easier. Such a holistic view helps top-management identify potential bottlenecks, spot trends, and patterns over time, and in general assess the situation. The last of our healthcare analytics examples centers on working for a brighter, bolder future in the medical industry. It also identifies how environment and humidity can affect and create a suitable condition for Aedes mosquitoes. One study found that big data can help reduce opioid use by 17%. New drug discovery and creation depends on data to assess the viability and effectiveness of treatments. The patients who are suffering from high blood pressure, asthma, migraine, or other severe health problems, doctors can observe their lifestyle and bring changes if important. With the radical power of AI, image, natural language processing, and machine learning, big data is changing the world by providing more dependable service in every aspect of our daily life. It can also calculate the number of bones and predict whether a patient is at risk of fracture or not. Focuses on using the necessary data that patients collect from wearable health-tracking devices such as heart rate, blood pressure, etc. Focuses on reducing the waiting time for patients and extending the quality of health care services. Need of Big data in Healthcare. So medical researchers can find the best treatment trends in the real world. Data science in healthcare is the most valuable asset. Intended to evaluate complex datasets to predict, prevent, manage, and treat heart-related diseases such as heart attacks. EHRs can also trigger warnings and reminders when a patient should get a new lab test or track prescriptions to see if a patient has been following doctors’ orders. Saving time, money, and energy using big data analytics for healthcare is necessary. Wearables will collect patients’ health data continuously and send this data to the cloud. Also, it uses the smartphone’s sensors to accumulate data for predicting and assessing symptoms of nutrition-related diseases. Evaluates data to extract potential information of lifestyle and provides feedback if any change in lifestyle is needed to the sufferers. Analytics expert Bernard Marr writes about the problem in a Forbes article. This application of big data in healthcare tries to present a digital tool that processes data with KDT and ML to generate the result. Healthcare Big Data: Velocity. Many of the promises of Big Data are being felt in the healthcare profession as real-time processing and data analytics is allowing for faster and more comprehensive decision-making and actions on the part of the medical field.. Cancer is a disease that has no specific treatment and caused due to abnormal cell growth. The database is created directly from user interaction with their friends and family. You can see here the most important metrics concerning various aspects: the number of patients that were welcomed in your facility, how long they stayed and where, how much it cost to treat them, and the average waiting time in emergency rooms. Using 10 years of records from the Hospitals and apply Time Analysis techniques to measure the rate of admission into the health care organizations. By Sandra Durcevic in Business Intelligence, Oct 21st 2020. The mosquito Aedes spread dengue. Uses the technique of fuzzy logic to identify the 742 risk factors that can be evaluated to predict whether a patient is abusing opioid. So, there is a need for the development of new infrastructure which can integrate all the data from such sources. The goal of this application is to decrease the frequency of visiting doctors for minor problems by regulating daily activities. Examples of Big Data in Healthcare. Therefore, big data usage in the healthcare sector is still in its infancy. Top 10 Analytics And Business Intelligence Trends For 2021, Utilize The Effectiveness Of Professional Executive Dashboards & Reports, Accelerate Your Business Performance With Modern IT Reports. Clinicians use telemedicine to provide personalized treatment plans and prevent hospitalization or re-admission. The speed at which some applications generate new data can overwhelm a system’s ability to store that data. As the authors of the popular Freakonomics books have argued, financial incentives matter – and incentives that prioritize patients' health over treating large amounts of patients are a good thing. The goal of healthcare online business intelligence is to help doctors make data-driven decisions within seconds and improve patients’ treatment. Optum Labs, a US research collaborative, has collected EHRs of over 30 million patients to create a database for predictive analytics tools that will improve the delivery of care. These technologies raise blood glucose, insulin, blood pressure, diet, and weight data from users. They’ve fully implemented a system called HealthConnect that shares data across all of their facilities and makes it easier to use EHRs. 12 Examples of Big Data In Healthcare That Can Save People. Without a cohesive, engaged workforce, patient care will dwindle, service rates will drop, and mistakes will happen. They can inspire you to adapt and adopt some good ideas. For our first example of big data in healthcare, we will look at one... 2) Electronic Health Records (EHRs). When a patient needs to pay for the same medical test for several times, it causes a waste of money. Much has been written on the benefits of big data for healthcare such as improving patient outcomes, public health surveillance, and healthcare policy decisions. Big data is vast and not easily manageable. This application tries to establish a bridge between the two ends. Cloud technology is one of the successful examples of technology to facilitate data sharing within and between organizations. This is the industry’s attempt to tackle the siloes problems a patient’s data has: everywhere are collected bits and bites of it and archived in hospitals, clinics, surgeries, etc., with the impossibility to communicate properly. Big Data aims to collect data from pre-treatment and pre-diagnosis data to the end-stage. Big data and healthcare are essential for tackling the hospitalization risk for specific patients with chronic diseases. This data is being used in conjunction with data from the CDC in order to develop better treatment plans for asthmatics. In a nutshell, here’s a shortlist of the examples we have gone over in this article. With healthcare data analytics, you can: “Most of the world will make decisions by either guessing or using their gut. It gives confidence and clarity, and it is the way forward. The problem has traditionally been figuring out how to collect all that data and quickly analyze it to produce actionable insights. Besides, It can produce reliable detection of inaccurate claims and saves a lot of money for the insurance companies every year. This new treatment attitude means there is a greater demand for big data analytics in healthcare facilities than ever before, and the rise of SaaS BI tools is also answering that need. This application has solved one of the significant problems in healthcare, which is storing medical images with precise value. If everyone is able to evolve with the changes around them, you will save more lives — and medical data analytics will help you do just that. Data science in healthcare can protect this data and extract many important features to bring revolutionary changes. For example, healthcare and biomedical big data have not yet converged to enhance healthcare data with molecular pathology. Alongside this, the database containing sensitive data can be further used for improving the health care process. This application tries to implement data science in healthcare. In this article, we’re going to address the need for big data in healthcare and hospital big data: why and how can it help? Storing the data into an accessible database is also a part of this application. Check out what BI trends will be on everyone’s lips and keyboards in 2021. It is seen that predictive analytics is taking the healthcare sector to a new level. 10 Examples Of Big Data In Healthcare. As you may know, each patient has their own digital record including allergy information, blood types and so on. Summing up the product of all this work, the data science team developed a web-based user interface that forecasts patient loads and helps in planning resource allocation by utilizing online data visualization that reaches the goal of improving the overall patients' care. This application monitors the trend and notifies if necessary actions should be taken. It was not only bad for the patient, it was also a waste of precious resources for both hospitals.”. A McKinsey report on big data healthcare states that “The integrated system has improved outcomes in cardiovascular disease and achieved an estimated $1 billion in savings from reduced office visits and lab tests.” 3) Real-Time Alerting. Finally, physician decisions are becoming more and more evidence-based, meaning that they rely on large swathes of research and clinical data as opposed to solely their schooling and professional opinion. Prediction of Expected Number of Patient. Transform Diabetes Care using Big Data, 14. Another example is that of Asthmapolis, which has started to use inhalers with GPS-enabled trackers in order to identify asthma trends both on an individual level and looking at larger populations. People’s demographics, age, behavior, medical reports, hospital admissions are also taken into consideration for generating an improved outcome. It enables doctors to compare the provided health care systems to identify the best one and bring out a better outcome. This is a visual innovation that has the power to improve every type of medical institution, big or small. Uses the characteristics of a relational database for predictive analytics tools that will improve the delivery of care. Smart algorithms- Building smart algorithms that will consume the large volume of data, properly analyze it and produce relevant results, which will be used in predicting the righ… As people of today’s day and age, we already know it. As comprehensive datasets are now available, this application tries to exhibit and find the evidence behind this connection. Electronic Health Records (EHRs) Improved Data Security. Real Life Examples… By keeping patients away from hospitals, telemedicine helps to reduce costs and improve the quality of service. What are the obstacles to its adoption? There is still no available vaccine to fight against dengue virus. Wearables are perhaps the most familiar example of such a device. Many people now can wear a fitness device that tracks how many steps they’ve taken, their heartrate, their weight, and how it’s all trending. Some studies have shown that 93% of healthcare organizations have experienced a data breach. Understands the condition of a patient’s health and triggers notification before any devastating situation can occur. Insight of this applicationeval(ez_write_tag([[300,250],'ubuntupit_com-large-mobile-banner-2','ezslot_10',132,'0','0'])); A heart attack is one of the deadliest health problems that cause many lives every year. Too few workers, you can have poor customer service outcomes – which can be fatal for patients in that industry. It also offers medical education for professionals. Naturally, doctors and surgeons are highly skilled in their areas of expertise. Identifies the reasons behind some problems like rapid population growth or the spread of any epidemic diseases. This is one of the best initiatives taken so far that uses big data to find the solution to a serious problem. Even after taking many initiatives, this problem was not solved until this application introduced big data to detect patients who are at high risk.eval(ez_write_tag([[300,250],'ubuntupit_com-banner-1','ezslot_3',199,'0','0'])); This application uses health-related data to inspire people to visit a healthcare organization for treatment. They even go further, saying that it could be possible that radiologists will no longer need to look at the images, but instead analyze the outcomes of the algorithms that will inevitably study and remember more images than they could in a lifetime. The data is aggregated with clinical and diagnostic data, it will make prediction feasible for cancer care. September 04, 2018 - As healthcare organizations develop more sophisticated big data analytics capabilities, they are beginning to move from basic descriptive analytics towards the realm of predictive insights.. Predictive analytics may only be the second of three steps along the journey to analytics maturity, but it actually represents a huge leap forward for many organizations. 1. New BI solutions and tools would also be able to predict, for example, who is at risk of diabetes and thereby be advised to make use of additional screenings or weight management. Big data in healthcare refers to the vast quantities of data—created by the mass adoption of the Internet and digitization of all sorts of information, including health records—too large or complex for traditional technology to make sense of. This woman’s issues were exacerbated by the lack of shared medical records between local emergency rooms, increasing the cost to taxpayers and hospitals, and making it harder for this woman to get good care. Currently, there is no suggested treatment for this disease. It can also help prevent deterioration. 3. Providing health care to a large number of people is a big challenge and a combined effort at both personal and community levels. Finding effective ways using Forest Algorithm to prevent people from taking an overdose of Opioid unconsciously. In a 2018 study from KP and the Mental Health Research Network, a mix of EHR data and a standard depression questionnaire identified individuals who had an enhanced risk of a suicide attempt with great accuracy. Big data has become more influential in healthcare due to three major shifts in the healthcare industry: the vast amount of data available, growing healthcare costs, and a focus on consumerism. As there is no loss of medical data, the rate of predicting high risk or depicting the current condition of the eye is almost accurate. The University of Florida made use of Google Maps and free public health data to prepare heat maps targeted at multiple issues, such as population growth and chronic diseases. This is key in order to make better-informed decisions that will improve the overall operations performance, with the goal of treating patients better and having the right staffing resources. Why does this matter? Keeping patients healthy and avoiding illness and disease stands at the front of any priority list. Besides, this application also has a plan to use the power of data science to improve the treatment process for specific diseases. Uses the influential data generated by Clinical Decision Support software and helps health care providers to decide while generating a prescription. Tries to engage people to improve medical service and use data analytics to identify symptoms. Chronic insomnia and an elevated heart rate can signal a risk for future heart disease for instance. Our fourth example of big data healthcare is tackling a serious problem in the US. Healthcare needs to catch up with other industries that have already moved from standard regression-based methods to more future-oriented like predictive analytics, machine learning, and graph analytics. This is a clearcut example of how analytics in healthcare can improve and save people’s lives. Telemedicine has been present on the market for over 40 years, but only today, with the arrival of online video conferences, smartphones, wireless devices, and wearables, has it been able to come into full bloom. Improved Staff Management. Big Data in healthcare is performing well. Telemedicine also improves the availability of care as patients’ state can be monitored and consulted anywhere and anytime. Signified to replace radiologists by integrating Algorithm. That situation is a reality in Oakland, California, where a woman who suffers from mental illness and substance abuse went to a variety of local hospitals on an almost daily basis. Data science in healthcare can protect this data and extract many important features to bring revolutionary changes. This predictive analysis helps to categorize different cancers and improves cancer treatment.