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Healthcare is one of the most crucial sectors of data science today, with its growing demands and needs being met by Data Scientists. The collection and analysis of patient data have never been more important in the history of healthcare than it is today. Today’s health care professionals are constantly bombarded with questions, such as Can you please help me fill out this paperwork? What’s the diagnosis?, or When can I go home? There simply aren’t enough hours in the day to answer all these questions without leveraging technology to augment the skills of doctors and healthcare professionals. There are many data science applications in healthcare; let’s look at some of the top ones. As technology continues to progress at an exponential rate, the fields of healthcare and medicine will undergo radical changes in the coming years. Healthcare systems that have been slow to adopt technology up until now will have to catch up quickly or risk being left behind. We are already seeing evidence of this shift, as hospitals around the world have begun adding technology like electronic medical records (EMRs) and electronic health records (EHRs) to their facilities in order to allow doctors and healthcare professionals to provide better care and enable easier data analysis by medical researchers.
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1) Advanced Analytics
1: Which of the following algorithms is most suitable for classification tasks?
Advanced analytics is a practice that encompasses analytical practices applied to extremely large data sets (big data). It uses methods and tools to analyze large volumes of data with speed, accuracy, insight, complexity, and uniqueness. Advanced analytics is used for making real-time decisions based on historical data or modeling future outcomes. As such, it has broad applications across various industries including healthcare, retail, banking and financial services. However, these organizations need skilled individuals who can help create value from immense amounts of structured and unstructured data using advanced analytical methods. This has led to a greater demand for professionals with expert-level expertise in advanced analytics techniques like machine learning. Many companies are turning to analytics software vendors to develop an advanced analytical capability as they strive to compete effectively while enhancing customer satisfaction through better service delivery. According to Gartner, Inc., Advanced analytics involves taking insights gained from data analysis and applying them to business processes. The ability to understand patterns, trends and associations within complex datasets enables business users—not just IT experts—to ask relevant questions about their operations. They can also identify hidden opportunities that were previously difficult or impossible to detect. For example, your company might discover patterns related to customers’ buying habits over time, which could allow you to tailor marketing campaigns more accurately. That’s why it’s important for businesses of all sizes—and those working in every industry—to begin exploring ways they can leverage advanced analytics solutions today so they’re ready when big data arrives.
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2) Biostatistics
A lot of big data technology can’t be effectively utilized unless there is a strong biostatistics team to make sense of it. Specifically, biostatisticians analyze epidemiological data and can help transform medicine by identifying problems such as poor health habits, poor access to quality healthcare, or unsafe environmental conditions (i.e., contaminated drinking water). This area is poised for major growth thanks to new tools such as artificial intelligence and machine learning. Biostatisticians will play an important role in deploying these technologies. If you are interested in combining programming with statistics, then you might want to consider studying maths or science at university and learning how to code on your own time. Many universities now offer free online courses that you can complete from home. In addition, most schools have open-source communities where students share what they’ve learned about specific coding languages. It’s also possible to gain experience through freelancing sites like Upwork or Freelancer, but ensure that you ask references about their experiences working with coders. As more companies adopt data science strategies, positions will open up quickly. Companies like IBM and SAS already employ hundreds of biostatisticians each—and they’re just two examples out of many!
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3) Clinical Decision Support Systems
As you can imagine, there are many clinical decisions that need to be made while a patient is at a hospital. Doctors rely on a large number of data points, including previous test results and patient histories, to make critical decisions about diagnoses and treatment plans. However, large databases are difficult to manage without systems designed specifically for healthcare. Clinical decision support systems (CDSS) are often used by doctors to help find past cases that resemble current ones or display information about possible treatments based on what others have done before. These tools can increase efficiency of care and safety. CDSS are now regularly used in emergency rooms and intensive care units as well as general practice situations such as community health clinics. Their proliferation into daily medical care has transformed health delivery for both patients and providers alike. In fact, an April 2017 study found that use of CDSS was associated with lower mortality rates. With these advantages come drawbacks. Many hospitals are experiencing an influx of data from these systems, which may lead to some information being lost in translation if it isn’t properly analyzed. The amount of sensitive patient information stored in these databases may also lead to privacy concerns if not handled correctly. In addition, different institutions use different software solutions for their CDSSs which means one solution might not fit all practices’ needs and will require customization—and more time spent on training staff members how to use it effectively—to accommodate each unique set-up.
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4) Prescription Drug Discovery
Drugs take years to develop and can cost up to $1.2 billion, so they need to be highly targeted and have a very low probability of side effects. In other words, pharmaceutics companies have a lot at stake (literally) when they move forward with new drugs; that’s why companies are increasingly relying on computer science to identify compounds with desirable drug-like properties. The increased reliance on data science to design new drugs is evident in patent filings. From 2006-2015, there was an 80% increase of information technology patents that involved medicine or healthcare over all other patients during the same time period. Overall, information technology patents represented about 15% of all US patent filings from 2008-to 2013 with pharmaceuticals representing 27%. This represents a massive shift towards using data science to design new drugs. And it makes sense because we now live in an era where most of our medical care relies on scientific advances—from diagnostic equipment to lab tests—and many treatments rely on taking medicines for extended periods of time. It only makes sense that we would use similar methods for designing these medicines as well. In fact, using machine learning algorithms has already shown promising results for discovering potential new medications: machine learning methods were able to predict which molecules were likely candidates for clinical trials with 92% accuracy compared to existing methods which had only 67% accuracy [15]. This means we could dramatically reduce timelines and costs associated with developing new medications while increasing our chances of success!
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5) Electronic Health Records (EHRs)
EHRs are a critical component of healthcare for many reasons. A simple example of their impact: if a doctor prescribes a medicine, but doesn’t record that prescription into an EHR, that medicine might not get delivered to your pharmacy. EHRs have improved medical care—and saved lives—but they’re only as good as their data science applications. Data science can help detect anomalies (like unusual spikes or drops) and deliver evidence-based treatment recommendations. In fact, some hospitals have already seen impressive results from using machine learning algorithms to determine which patients need additional testing. The best part? Most of these algorithms don’t require any human intervention at all! They’re constantly self-improving based on past patient records. That means they won’t just work better than humans…they will also work faster than humans. These improvements are so dramatic that researchers predict by 2020, 50% of doctors’ time will be spent interacting with computers rather than patients. And that’s exactly what we want–it’s our goal to make sure doctors spend more time with people who need them most.
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6) Health Information Exchanges (HIEs)
Some businesses and organizations are replacing their traditional databases with an HIE, which provides an organization a way to share information more easily. Healthcare is becoming increasingly data-driven, and an HIE can help eliminate duplicated records, track important statistics like readmission rates for hospitals, and reduce medical errors. One example of a successful HIE is Utah’s statewide repository that allows doctors to access patient records from every hospital in one place. You might also look into perinatal registries—which allow mothers to give birth at any hospital as long as they agree to register with that hospital—and electronic health records (EHRs). EHRs have enabled healthcare providers to streamline operations by enabling patients’ medical history to be easily accessed by staff. For example, if you go to a new doctor who doesn’t know your full medical history, your doctor will be able to quickly pull up your past lab results on his or her computer. This has been shown to save time and money while improving care quality. EHRs can also improve communication between healthcare professionals by allowing them to communicate through secure messaging platforms instead of relying on phone calls or faxes. For example, if you need medication refills while you’re away from home, your primary care physician could send a message through your EHR rather than calling or faxing it over. This saves time and money because it eliminates unnecessary phone calls and faxes that would otherwise slow down communication between offices.
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7) Population Health Management
Our healthcare system is broken. In fact, it’s really two healthcare systems: one for those who have health insurance, and another for those who don’t. The gap between them is growing larger each year, along with its costs. Population health management (PHM) aims to bridge that gap by providing coordinated care for patients regardless of their ability to pay. It’s about offering preventive services, making use of natural care providers like pharmacists and dieticians, streamlining access to records—and even helping people prevent conditions before they develop. Here are a few ways PHM can help you improve your patient outcomes while cutting costs. This will involve working together on a schedule that works best for both parties and exchanging ideas back and forth until we reach an agreement on what our product should look like, how much time it will take us to make it, how much money we should invest into advertising, etc.
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8) Patient Engagement Platforms
According to one study, patients who were engaged and regularly followed up with by their physician were 20% more likely to follow their treatment plan. That’s a significant improvement over an average retention rate of 62%. With more reliable patient data from such platforms, doctors are able to use that information to adjust treatments and medications on a more individualized basis. As a result, patient satisfaction rates increase and spending is reduced. While some physicians may not be as receptive to social media platforms like Facebook or Twitter for example (patients are encouraged to post about questions they have or symptoms they’re experiencing), engaging platforms offer a structured communication interface that makes it easy for patients to stay in touch with their care providers. The net result? More effective and efficient healthcare. For many doctors, these programs can also serve as marketing tools that drive new patients to their practice. And since users tend to engage more frequently with messages sent through these channels than those received via email, chances are good you’ll reach your target audience faster. All told, technology has helped revolutionize how we approach medical care today—and data science is at its heart. It’s important then for medical professionals to get familiar with these applications so they can best harness them for maximum effectiveness and impact.
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9) Precision Medicine/Genomics
New sequencing technologies and bioinformatics methods have allowed us to sequence the genomes of thousands of people. These genomes contain a treasure trove of information: they provide us with biological insight into everything from risks for genetic diseases to which medications are likely to work best for an individual. This is known as personalized or precision medicine. Using genomic data, researchers can also learn how an individual will respond to certain treatments. For example, some cancer drugs only work on tumors that carry specific mutations. By learning your tumor’s genome, doctors can determine whether you’re eligible for such treatment. It’s no surprise then that precision medicine has become one of healthcare’s hottest trends. But how exactly does it work? And what applications does it have? Let’s take a look at ten ways data science is transforming healthcare. A big part of being a data scientist is analyzing huge amounts of data and turning them into insights. As healthcare becomes more digitized, more medical records get digitized too – resulting in humongous datasets full of critical information about patients. The good news? Now there’s a whole lot more we can do with all that data! Here are just a few examples. That concludes our roundup of the top 10 data science applications in healthcare! We hope you enjoyed reading through these examples and got inspired by some ideas to help improve patient care – we certainly did! If you want to find out more about any particular application, check out our sources below. Happy reading!
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10) Radiology Informatics
IBM’s Watson is making waves as a pioneering tool for oncology research. Though it wasn’t designed specifically to work with medical data, IBM continues to find ways to implement its artificial intelligence technology into cancer care. The goal is to have radiologists and other doctors rely on Watson-based tools so they can focus on patients’ overall health instead of sifting through large amounts of unorganized research data. In fact, The American Society of Clinical Oncology reported that more than 80% of oncologists see real value in AI tools like Watson (PDF). It’s only a matter of time before hospitals around the world use these applications and services to improve patient care—and medical data science could also help doctors predict adverse reactions before they happen . If you’re interested in helping healthcare professionals do their jobs better, then you should consider an education in data science. You’ll learn how to analyze massive amounts of information quickly and accurately; plus, there are plenty of healthcare-specific job opportunities available for graduates with those skills. According to Business Insider Intelligence, demand for people who know how to manage big data will reach $284 billion by 2020. That’s up from $174 billion last year—an increase of almost 50%. When you consider that many companies are still figuring out how best to make use of their own data sets , now is a great time to jump into an industry where demand will continue rising at such a rapid pace. If you are interested to learn new coding skills, the Entri app will help you to acquire them very easily. Entri app is following a structural study plan so that the students can learn very easily. If you don’t have a coding background, it won’t be any problem. You can download the Entri app from the google play store and enroll in your favorite course.
