Artificial intelligence has numerous applications, and of the most popular ones is AI and data science in healthcare. According to Zion Market Research, the global artificial intelligence in the healthcare market reached USD 1.4 billion in 2018. By 2025, this number is expected to turn into USD 17.8 billion. The forecasted difference is more than impressive, and that’s why we have prepared this guide. It will provide you with a better understanding of data science applications in healthcare.
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If you are one of the health tech startups, this information will help you to enter the market successfully. However, even if you have nothing to do with the health tech industry at the moment, this article may inspire you to join the trend. So keep reading to learn more about data science solutions in healthcare!
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Nowadays wearables exist in spades, and some of them can be extremely useful in terms of healthcare. The thing is that they monitor crucial health indicators, such as heart rate, blood pressure, blood glucose level, sleep patterns, and so on. The wearables are connected to smartphones, and the data is stored in the cloud. It can be accessed whenever it is necessary, so a person (or family members) can monitor their health.
In turn, data scientists can analyze the information collected from numerous wearables and develop an efficient analytical model. On the basis of the data, the model will be able to detect variations in the patients’ health. As a result, it can be much easier for doctors to predict potential problems and prescribe an efficient treatment.
Diagnostics using data science in healthcare
Medical errors can cost a lot — over USD 100 billion per year, and that’s only in the United States. What is much worse, up to 80,000 patients die every year (in the USA) because of the wrong diagnosis. However, it is possible to reduce these scary rates. Using data science in healthcare can significantly improve the accuracy of diagnostics.
As a result, more lives are saved, while the budget doesn’t suffer that much. Applying deep learning techniques, data scientists can process loads of medical data. As a result, they can detect symptoms of the illness and get more accurate diagnosis. Moreover, this method should deliver the diagnosis faster than the traditional one, which is crucial in some cases.
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Treatment stage in diagnostics
When the diagnostics are done, comes the treatment stage, and data science is a great tool for simplifying it. Using the data of people with an already confirmed diagnosis, it is possible to provide every new patient with a personalized treatment and care. Here is an example for you — imagine ten different people suffering from pneumonia.
They have the same diagnosis, but data science can help to define the individual condition of each patient, specific characteristics of their diseases, their health records, and so on. In this way, patients get more efficient, customized treatment, and its outcome is more likely to be positive. This may seem to be not that important when talking about the common cold. But when it comes to more serious diseases, artificial intelligence, machine learning, and data science can have an extremely important impact on the results of the treatment.
Unfortunately, even the most effective treatment sometimes can’t guarantee that a patient won’t face recurrence or suffer from pain and diverse complications after leaving the hospital. With the help of data science, experts can predict the potential changes and develop suitable post-treatment program. The data can be collected from wearable devices of patients suffering from the same diseases. Such a strategy also improves the usage of hospital resources. For instance, it is easier for doctors to understand if a particular patient has to stay in a clinic for a few days longer. Thus, they can decide if this person will need a bed and hospital drugs or no.
Data Science helps with Public Healthcare
Public health can be defined as an art of improving the community’s health and preventing diseases by educating society and promoting healthy behavior. Using data science to analyze the data from websites, wearables, and other sources, it is possible to understand the overall health status in a particular region.
Thanks to this knowledge, specialists can develop efficient public health strategies and, therefore, reach their goals. For example, they find out that there are a lot of people having problems with blood pressure. However, only a few of them have a doctor’s appointment, while others prefer to ignore the problem or deal with it on their own. Experts can focus their efforts on a specific issue — to explain the society that even small problems with the pressure can turn into serious ones over time. As a result, the overall health condition in the area will be improved.
Drug discovery is a pretty time-consuming and expensive process. According to the Tufts Center for the Study of Drug Development, it costs USD 2.7 billion to invent a drug and bring it to the market. Sure, this price highly depends on the type of drug and the quantity of failed tests, but it is still very high. Proper use of data science in healthcare can improve the situation. After using data obtained from case studies, tests and treatment results, and creating advanced algorithms, it is possible to simulate the interaction of the drug and human body. This stimulation can show how effective the drug can be and speed up the discovery process.
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Data science in healthcare can also help to ensure that every patient can count on the right staff at the right time. Analyzing the already available data, it is possible to predict the patients’ visits and, therefore, allocate the staff properly.
For instance, the data scientists find out that in a certain area there are a lot of cases of flu in January and February. Knowing that medical organizations can improve their staff management and provide that particular area with more specialists over that period of time. Thanks to this information, they won’t have to look for experts in a hurry while everyone around will be suffering from the flu. Instead, they will be able to develop a detailed plan in advance and be absolutely ready for the epidemic when it starts. As a result, even in spite of numerous flu cases, each patient will get the help of experienced doctors. The same strategy can be used for resource planning (beds, medicines, etc.).
Summing up – Data Science in Healthcare
There are a lot of ways AI, machine learning and data science can be applied in healthcare, and now you know about some of them. However, there is still one benefit of using data science in healthcare we want to mention. With its help, it is possible to reduce healthcare costs. That’s the typical advantage of technical transformation and digitization, but it can still lead to significant results. For instance, thanks to data science healthcare companies and hospitals can improve the schedule of medical equipment maintenance. As a result, expensive breakdowns can be avoided.
Besides, as we already mentioned, data science can help to optimize supply chains and monitor patients’ recovery in order to prevent remissions and save their budget. In this way, data science in healthcare is profitable both for organizations and patients. And if you have any extra questions regarding how to apply it, we are always here to help. Just get in touch with us, and we will get back to you.