This article is the first in a series of three articles on Healthcare IT trends before, during, and after Covid-19. 

When it comes to adopting emerging technologies, Healthcare has always been a laggard among other industries. However, over the last 10 years, the industry has grown to become more digitally mature, relying on the power of innovations that appeared in the tech sphere. In this part, we will go over the technology trends in the Healthcare industry that dominated the industry before the pandemic inflicted the turbulent times. This will allow us to gain an understanding of how Healthcare evolved and what impact the pandemic has had on the industry. 

Healthcare software trends in Telemedicine

The idea of treating patients virtually has been on the radar for a while, as more and more patients become drawn to the concept of healthcare coming to them, not vice versa. In recent years, the concept of Telemedicine has taken a clearer shape and even became the backbone of Healthcare digitization. Several trends within Telemedicine serve as its driving force:

  • Healthcare applications 

Many Healthcare software trends are based on the need for robust remote communication between Healthcare providers and patients. In recent years, Telemedicine software has become very accessible and increasingly capable and includes various integrations, such as Electronic Health Records (EHR) and medical billing software platforms. 

  • Decentralization of Healthcare

This is one of the most impactful changes that has found reflection in pre-Covid Healthcare IT trends. As a part of the decentralization wave, many medical professionals chose to forfeit large hospitals in favor of smaller community-based practices, thus pushing the development of more robust Telemedicine solutions. 

  • Cybersecurity

Digital technologies and cybersecurity go hand in hand. As an industry that deals with sensitive data, Healthcare is extremely vulnerable to cybercrime. Thus, data protection will always be the industry’s number one priority, drawing significant investments into its solidifying. 

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The Internet of Medical Things (IoMT)

IoMT is a convergence of IoT and Telemedicine, which has brought about a variety of wearable devices (also known as wearables) used to monitor the health status in patients with existing conditions. Connected medical devices make up a significant share of IoT, and their number is projected to exceed 20 billion in the near future. 

IoMT (and wearables, in particular), benefit high-risk patients, allowing them to get medical aid on time. They also let people monitor their health constantly instead of undergoing planned check-ups, thus contributing largely to the disease prevention trend. 

Before Covid-19, overcoming challenges in IoMT was a primary focus of Healthcare providers. Developers were tasked with ensuring a solid technical base for providing consistent communication within the ecosystem of IoT devices. Because connected devices generate massive amounts of data, developers need to find a way to ensure proper data handling and eliminate the instances of slow or failed connections, which may cost a patient life. 

Take a look at the connected, AI-powered asthma inhaler that relies on sensors to collect data and monitor a user’s dose, enabling personalized, data-driven respiratory therapy.  

READ ALSO: Why Ukraine is a Safe Choice When it Comes to IoT Development

Cloud trends in Healthcare

As remote clinical care continues to gain traction, so does the need for the clinics to move to more robust solutions. Adopting Cloud platforms is a great solution when it comes to improving remote communication between patients and doctors. One more benefit of the Cloud is reducing costs. When patient records are stored online, there’s no need to build out costly IT infrastructures on-premise. Additionally, medical records are easier to access when they are stored virtually, which means a patient’s location can no longer impede the treatment process. 

Another challenge for the Cloud to overcome is dealing with regulations and policies. Many Healthcare providers find it a stumbling stone that prevents them from embracing the power of Cloud, as the regulations are too stringent to meet. Thus, one of the Cloud trends in Healthcare is to find a solution that would help them achieve a high level of compliance with HIPAA and other data regulation policies. 

For instance, UCLA Health is employing Microsoft Azure cloud services to synthesize vast amounts of clinical and research data to speed medical discoveries and improve patient care. 

READ ALSO: AWS vs MS Azure vs Google Cloud: Feature Overview, Pros and Cons

Big Data in Healthcare

Big Data comes from various sources, including electronic health records, wearable devices, search engines, genetic studies, etc. For example, due to the insights gleaned from Big Data in genetic studies, patients can be aware of potential health risks and react to problems faster, which is the key to predictive Healthcare. 

Big Data can be analyzed and turned into actionable insights by building predictive algorithms, becoming a promising method in the following functions: 

  • lower the rate of medication errors;
  • create illness prevention plans;
  • reduce wait time in hospitals due to understaffing;
  • prevent patients from readmission and improve long-term care. 

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Each of these areas can potentially become the next Big Data trend in Healthcare. Data-driven medicine took a significant place among Pre-covid trends. With the adoption of the Cloud, nearly a trillion gigabytes of medical data are generated annually, which brings us to AI trends in Healthcare.

IBM Watson is an AI system widely used in Healthcare that suggests tailored treatment plans for patients and helps patients manage diabetes daily.  

Artificial Intelligence (AI) trends in Healthcare

Although AI is still in its early stages, machine learning algorithms are already bringing a lot of value to Healthcare, helping healthcare providers diagnose diseases more accurately, create better treatment plans, do medical research and drug discovery, and increase operational efficiency during peak loads in hospitals. On top of that, AI is widely applied in the following spheres: 

  • Image classification. As part of assisted AI trends in Healthcare, image classification systems help doctors conduct high-quality diagnostics over a short period of time. In many instances, the efforts of radiologists and ultrasound specialists go into image classification and their description, while AI can automate this process without bringing humans into the loop. 
  • Predictive analysis. Early treatment can save lives. AI can help identify health risks early on so that they can be addressed immediately. 
  • Epidemic prediction. AI systems allow specialists to control and predict outbreaks. This has already proven effective during the Ebola outbreak in Africa.
  • Personalized medicine. Personalized medicine is Healthcare’s future, and AI is one of its key enablers. According to forecasts, personalized treatment powered by AI will be available for mass use by 2030.

Consider the following Healthcare IT trend: AI has been used in human eye surgery, proving equal or better efficacy than in the traditional manual approach.

Mixed Reality (Augmented Reality + Virtual Reality)

Benefiting both patients and doctors, Mixed Reality is one of the key actors in transforming Healthcare. Environments simulated in Augmented Reality help relieve pains, cope with stress and post-traumatic disorders, and overcome motor deficiencies. It can also be used by doctors as a guide during complex surgeries, procedures, and diagnoses. Another beneficiary of Augmented Reality are medical students who can utilize AR and VR for more immersive research. 

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Virtual reality provides access to activities and visual experiences otherwise unavailable, including planning complicated surgeries, training and skills improvement, motivating patients to exercise, and improving their emotional health. 

As an example of an efficient Mixed Reality application, take a look at how Nanthia Suthana, a neuroscientist, uses virtual reality to help her patients overcome memory loss. Wearing brain implants, patients can dive into virtual reality that simulates the real-world environment so that doctors can record brain signals, decipher brain activities to develop therapies. 


Digitalization often comes with security risks, and Blockchain plays a significant role in safeguarding patients’ digital data as a technology that ensures complete transparency of patient’s data usage. Blockchain also addresses the issues related to accessibility and portability of data, so Healthcare providers can access it (with a patient’s electronic permission), no matter a patient’s location. Additionally, Blockchain allows patients to use a private key that helps them manage who has access to their healthcare record, so when a patient needs to involve a specialist (or several specialists), they can grant them access to their records. 

Consider the case of Blockchain application in Healthcare from Simply Vital Health, a US-based company that develops a decentralized system that allows the exchange of patient data between several clinics using Blockchain technology. 

READ ALSO: Four Questions for Blockchain Development Experts

Healthcare IT trends before Covid-19: Synopsys 

Even without the pandemic factor, remote patient treatment remained the key focus of the industry. In the sphere of Telemedicine, there were several trends at play, including the Internet of Things of Medical devices, wearable devices, and software that enables constant communication between patients and Healthcare providers. 

Another trend we mentioned is the Big Data trend in Healthcare, which is fueled by an increasing number of IoT devices and the need to collect and process data coming from them. Then, there’s an AI trend in Healthcare that focuses on gleaning insights to enable patients to see the big picture of their health and develop personalized treatment plans. 

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Big Data and Cloud often go hand in hand, which led us to Cloud trends in Healthcare, where main task was to find ways of building a sustainable and cost-effective infrastructure, ensuring compliance with regulations and policies. 

Mixed Reality has also found a wide application in Healthcare, providing doctors and medical students with a kind of a “third eye” by putting valuable information into their eyesight during surgeries (or preparing for thereof) and training. 

 Last but not least, the application of Blockchain in Healthcare ensures security, transparency, and immutability when it comes to operating patients’ data. 

In the next article, we will discuss technology trends in the Healthcare industry during the pandemic.

In the healthcare industry, data is the key to innovation. Without doubt, it opens up new opportunities for improved, more informed healthcare. Data scientists and machine learning engineers around the world strive to find ways to collect and process a high volume of healthcare data to further interpret and use it for medical discoveries. In this article, we take a look at several areas where data science can benefit healthcare. But first, let’s address a few obvious questions.

Why is data important in public health?

Machine learning programs help doctors make more informed decisions and prescribe the best treatment possible. For example, patients can use wearable devices, which lets doctors have a full picture of the disease and automatically assess a patient’s condition not only during an appointment but also between visits.

Ultimately, all innovations in healthcare are needed to improve the quality and reduce the cost of medical care. Data science allows the doctor to spend more time with the patient, while the machine analyzes the data set quickly and accurately. Data scientists here take part in the creation of smart systems and are actively engaged in the process of invention. 

Health data science: Who’s ahead of the curve?

Among tech giants, those are Google Health and IBM. The latter has created a line of solutions under the IBM Watson brand, which is now actively used in healthcare. The IBM Watson Health toolbox includes the platforms for oncology, cardiology, radiology, and other areas of medicine. Google Health, on the other hand, was designed to create a repository of health records and data in order to connect doctors, hospitals, and pharmacies directly. 

1. Data Science in Healthcare: Medical image analysis

With the help of data science, medical image interpretation has come to fruition. Leveraging data science in this field opens up countless methods for determining the difference in the modality, the dimension of the images, and the resolution. Those include X-rays, computed tomography, magnetic resonance imaging (MRI), and mammography. Many new tools are underway to provide extremely accurate means of extracting data from the images with the highest quality. The deep learning algorithms offer detailed diagnoses with competent interpretation.

Mathematical methods and machine learning will help to significantly simplify and expedite diagnosis, especially in the early stages. Diagnoses will be more accurate, established in a short time, meaning that there will be more chances to save a patient’s health and life.

How AI improves the quality of image evaluation can be seen in radiology. Every day, doctors use their experience and knowledge to draw the right conclusions from images. With thousands of images that have been processed and marked by a healthcare professional, data scientists can train the neural network to recognize deviations in new images. A neural network model trained on a massive image collection from a database can analyze the picture and conclude if there is a disease.

2. Wearables

The human body generates terabytes of data daily and technology allows us to gather most of it. This way, scientists get access to high volumes of data like heart rate, calories burnt, blood pressure, glucose levels, and more. This information plays a vital role in innovative health monitoring. Machine learning helps analyze heart beating or breathing patterns and can detect changes, predict deviations, and transmit them to hospitals. Wearables are also the key to chronic disease prevention. Smart devices collect and process behavioral data to create and adjust individual health programs. 

3. Data science meaning for drug discovery

Machine learning presents an excellent opportunity for the biochemical and pharmaceutical industries. Traditionally, a single formula undergoes multiple tests before it’s approved. In most cases, the formula is rejected, despite all the time, effort and money invested.

With data science, the process can be shortened and become much more efficient. Machine learning algorithms add steps for the initial screening of each component and can predict success rates based on various biological factors. Instead of opting for laboratory experiments, technology is applied to create simulations and further develop mathematical models for analysis. 

4. Big Data in healthcare: Genetics and genomics

Advancements in genetics and genomics create many ways for personalized treatment. We can analyze the influence of DNA and the reaction of various medications to a person’s health with regard to their biological compounds. 

Data science promotes the integration of various data into the genome. Such an in-depth analysis enables efficient disease research. A good example of using data science in this field is Deep Genomics, a project that builds life-saving genetic therapies. It is making a remarkable contribution to predicting the molecular effects of different genetic and DNA interpretations. This way, researchers can predict how genetic variations will affect the genetic code.

5. Incidental disease findings

Data science brings a lot of value to the undiagnosed disease discovery. Doctors usually consider tests and images in light of the problem the patient came with. A machine can distinguish other diseases and abnormalities, for example, it can identify lung cancer in an image of a fractured rib. Essentially,  machine learning in healthcare is here to help doctors examine a patient for all possible diseases after only one visit. 


6. Data science in MRI

AI capabilities are applied increasingly in magnetic resonance imaging. Evaluating images obtained with this method can be time-consuming. During one study, doctors get dozens of images. To help the doctor analyze this data, scientists interweave AI into MR scanners, which evaluates the quality of images and compares the results with the previous indicators to identify the dynamics of the disease.

7. Data science and pathomorphology

In oncology, an accurate diagnosis can be made in the only way – by analyzing the tissue through a microscope. To help pathologists, data scientists create algorithms for processing cell images, similar to those that recognize people and identify objects in pictures. These are specialized medical decision systems that detect and classify the affected cells, and then inform the doctor about their findings. Besides, the specialist immediately receives additional information like the concentration of cells, the stage of the disease, the characteristics of intracellular processes, and more, which helps immensely in diagnosing.

8. Virtual support for patients

In light of the pandemics, the global health organization aims to make efforts to limit patient visits to the hospital, moving everything to virtual platforms. With Al-Integrated tools, patients can do doctor visits virtually and interact with therapists online through voice and video calls. Many chronic health conditions can be monitored virtually and with a due level of efficiency. Patients can interact with AI chatbots and receive efficient health solutions. 

9. Predictive medicine

Data science is widely applied in predictive analysis. Researchers collect patient data, find correlations, analyze clinical notes, associated symptoms, habits, family disease history to make predictions. In addition, biomedical factors, including clinical variables and genome structure, are used to make the prediction and identify the development of certain diseases. Reducing risk is the ultimate goal of data science in this field.

10. Manage customer data

Dealing with patient data has become much more convenient thanks to data science. The entire paperwork is shifted to digital data. Data scientists can back up the records digitally and use the data for many discoveries and inventions. In this field, optical character recognition and vector machine are the two very helpful techniques. 

Each health organization implements different techniques to make progress in collecting patient records. While some offer online accounts, others distribute electronic cards to record all of the data individually.

Wrap up: Looking for a healthcare data scientist? 

health image recognition

As we can see, data science is disrupting the healthcare industry in many ways. One of the key fields in this discipline is machine learning. We come across machine learning algorithms several times throughout the day. Think navigators, movie and purchase recommendations. To name a few, machine learning is used heavily in security, logistics, risk management, trading, insurance, and much more.

Circling back to healthcare, health-related information offers a foundation for doctors and researchers to use as a basis in their research. It creates a solid foundation for carrying out the evaluation and producing more effective drugs. It fosters better communication between patients and doctors. Finally, it improves the overall quality of health services by providing more in-depth insight into a patient’s health report and their response to a particular drug. That requires a close-knit collaboration between doctors and data scientists. 

Many businesses worldwide have come to realize that data is their biggest asset. That is the reason why data scientists are in high demand nowadays. Pushing the search outside your location, you can find a wealth of data science experts. Our specialists from NCube will help you build a team of skilled data scientists and machine learning specialists. They possess the necessary knowledge and field-relevant experience to solve problems that involve Big Data. Let’s connect?

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